¹ The bilateral risk of loss in this example arises because the bank receives, i.e. takes possession of, the collateral as part of the transaction. By contrast, collateralized loans where the collateral is not exchanged prior to default, do not give rise to a bilateral risk of loss; for example a corporate or retail loan secured on a property of the borrower where the bank may only take possession of the property when the borrower defaults does not give rise to counterparty credit risk.
² The counterparty credit risk rules capture the risk of loss to the bank from the default of the derivative counterparty. The risk of gains or losses on the changing market value of the derivative is captured by the market risk framework. The market risk framework captures the risk that the bank will suffer a loss as a result of market movements in underlying risk factors referenced by the derivative (e.g. interest rates for an interest rate swap); however, it also captures the risk of losses that can result from the derivative declining in value due to a deterioration in the creditworthiness of the derivative counterparty. The latter risk is the credit valuation adjustment risk set out in Chapter 11 of this Framework.

³ The terms “exposure” and “EAD” are used interchangeable in the counterparty credit risk chapters of the credit risk standard. This reflects the fact that the amounts calculated under the counterparty credit risk rules must typically be used as either the “exposure” within the standardized approach to credit risk, or the EAD within the internal ratings-based (IRB) approach to credit risk, as described in 5.12.
⁴ SAMA circulars would be Circular No.: 341000015689, which I will be referencing in CCR Framework. Section A: Final Guidance Document

⁵ See chapter 12 and Chapter 13 of this framework for illustrative examples of the application of the SA-CCR to sample portfolios
⁶ The EAD can be set to zero only for sold options that are outside netting and margin agreements.

⁷ The netting contract must not contain any clause which, in the event of default of a counterparty, permits a non-defaulting counterparty to make limited payments only, or no payments at all, to the estate of the defaulting party, even if the defaulting party is a net creditor.
⁸ The haircut applicable in the replacement cost calculation for unmargined trades should follow the formula in paragraphs 62 of chapter 9 of the Minimum Capital Requirements for Credit Risk. In applying the formula, banks must use the maturity of the longest transaction in the netting set as the value for N , capped at 250 days, in order to R scale haircuts for unmargined trades, which is capped at 100%.
⁹ As set out in 6.4, netting sets that include a one-way margin agreement in favor of the bank’s counterparty (i.e. the bank posts, but does not receive variation margin) are treated as unmargined for the purposes of SA-CCR. For such netting sets, C also includes, with a negative sign, the variation margin amount posted by the bank to the counterparty.
¹⁰ See chapter 12 and Chapter 13 of this framework for illustrative examples of the effect of standard margin agreements on the SA-CCR formulation.
¹¹ For example, the 1992 (Multicurrency-Cross Border) Master Agreement and the 2002 Master Agreement published by the International Swaps & Derivatives Association, Inc. (ISDA Master Agreement). The ISDA Master Agreement includes the ISDA Credit Support Annexes: the 1994 Credit Support Annex (Security Interest – New York Law), or, as applicable, the 1995 Credit Support Annex (Transfer – English Law) and the 1995 Credit Support Deed (Security Interest – English Law).
¹² For example, in the ISDA Master Agreement, the term “Credit Support Amount”, or the overall amount of collateral that must be delivered between the parties, is defined as the greater of the Secured Party’s Exposure plus the aggregate of all Independent Amounts applicable to the Pledgor minus all Independent Amounts applicable to the Secured Party, minus the Pledgor’s Threshold and zero.

6.23.	As a general principle, over-collateralization should reduce capital requirements for counterparty credit risk. In fact, many banks hold excess collateral (i.e. collateral greater than the net market value of the derivatives contracts) precisely to offset potential increases in exposure represented by the add-on. As discussed in 6.12 and 6.20, collateral may reduce the replacement cost component of the exposure under the SA-CCR. The PFE component also reflects the risk-reducing property of excess collateral.
6.24.	Banks should apply a multiplier to the PFE component that decreases as excess collateral increases, without reaching zero (the multiplier is floored at 5% of the PFE add-on). When the collateral held is less than the net market value of the derivative contracts (“under-collateralization”), the current replacement cost is positive and the multiplier is equal to one (i.e. the PFE component is equal to the full value of the aggregate add-on). Where the collateral held is greater than the net market value of the derivative contracts (“over-collateralization”), the current replacement cost is zero and the multiplier is less than one (i.e. the PFE component is less than the full value of the aggregate add-on).
6.25.	This multiplier will also be activated when the current value of the derivative transactions is negative. This is because out-of-the-money transactions do not currently represent an exposure and have less chance to go in-the-money. The formula for the multiplier is as follows, where:
	(1)	exp(…) is the exponential function
	(2)	Floor is 5%
	(3)	V is the value of the derivative transactions in the netting set
	(4)	C is the haircut value of net collateral held
	Aggregate add-on and asset classes
6.26.	To calculate the aggregate add-on, banks must calculate add-ons for each asset class within the netting set. The SA-CCR uses the following five asset classes:
	(1)	Interest rate derivatives
	(2)	Foreign exchange derivatives
	(3)	Credit derivatives
	(4)	Equity derivatives.
	(5)	Commodity derivatives
6.27.	Diversification benefits across asset classes are not recognized. Instead, the respective add-ons for each asset class are simply aggregated using the following formula (where the sum is across the asset classes):
	Allocation of derivative transactions to one or more asset classes
6.28.	The designation of a derivative transaction to an asset class is to be made on the basis of its primary risk driver. Most derivative transactions have one primary risk driver, defined by its reference underlying instrument (e.g. an interest rate curve for an interest rate swap, a reference entity for a credit default swap, a foreign exchange rate for a foreign exchange (FX) call option, etc.). When this primary risk driver is clearly identifiable, the transaction will fall into one of the asset classes described above.
6.29.	For more complex trades that may have more than one risk driver (e.g. multi-asset or hybrid derivatives), banks must take sensitivities and volatility of the underlying into account for determining the primary risk driver
6.30.	SAMA may also require more complex trades to be allocated to more than one asset class, resulting in the same position being included in multiple classes. In this case, for each asset class to which the position is allocated, banks must determine appropriately the sign and delta adjustment of the relevant risk driver (the role of delta adjustments in SA-CCR is outlined further in 6.32 below).
	General steps for calculating the PFE add-on for each asset class
6.31.	For each transaction, the primary risk factor or factors need to be determined and attributed to one or more of the five asset classes: interest rate, foreign exchange, credit, equity or commodity. The add-on for each asset class is calculated using asset-class-specific formulas.13
6.32.	Although the formulas for the asset class add-ons vary between asset classes, they all use the following general steps:
	(6)	The effective notional (D) must be calculated for each derivative (i.e. each individual trade) in the netting set. The effective notional is a measure of the sensitivity of the trade to movements in underlying risk factors (i.e. interest rates, exchange rates, credit spreads, equity prices and commodity prices). The effective notional is calculated as the product of the following parameters (i.e. D = d * MF * δ):
		(a)	The adjusted notional (d). The adjusted notional is a measure of the size of the trade. For derivatives in the foreign exchange asset class this is simply the notional value of the foreign currency leg of the derivative contract, converted to the Saudi Riyal (SAR). For derivatives in the equity and commodity asset classes, it is simply the current price of the relevant share or unit of commodity multiplied by the number of shares /units that the derivative references. For derivatives in the interest rate and credit asset classes, the notional amount is adjusted by a measure of the duration of the instrument to account for the fact that the value of instruments with longer durations are more sensitive to movements in underlying risk factors (i.e. interest rates and credit spreads).
		(b)	The maturity factor (MF). The maturity factor is a parameter that takes account of the time period over which the potential future exposure is calculated. The calculation of the maturity factor varies depending on whether the netting set is margined or unmargined.
		(c)	The supervisory delta (δ). The supervisory delta is used to ensure that the effective notional take into account the direction of the trade, i.e. whether the trade is long or short, by having a positive or negative sign. It is also takes into account whether the trade has a non-linear relationship with the underlying risk factor (which is the case for options and collateralized debt obligation tranches).
	(7)	A supervisory factor (SF) is identified for each individual trade in the netting set. The supervisory factor is the supervisory specified change in value of the underlying risk factor on which the potential future exposure calculation is based, which has been calibrated to take into account the volatility of underlying risk factors.
	(8)	The trades within each asset class are separated into supervisory specified hedging sets. The purpose of the hedging sets is to group together trades within the netting set where long and short positions should be permitted to offset each other in the calculation of potential future exposure.
	(9)	Aggregation formulas are applied to aggregate the effective notionals and supervisory factors across all trades within each hedging set and finally at the asset-class level to give the asset class level add-on. The method of aggregation varies between asset classes and for credit, equity and commodity derivatives it also involves the application of supervisory correlation parameters to capture diversification of trades and basis risk.
		Time period parameters: Mi, Ei, Si and Ti
6.33.	There are four time period parameters that are used in the SA-CCR (all expressed in years):
	(1)	For all asset classes, the maturity Mi of a contract is the time period (starting today) until the latest day when the contract may still be active. This time period appears in the maturity factor defined in 6.51 to 6.56 that scales down the adjusted notionals for unmargined trades for all asset classes. If a derivative contract has another derivative contract as its underlying (for example, a swaption) and may be physically exercised into the underlying contract (i.e. a bank would assume a position in the underlying contract in the event of exercise), then maturity of the contract is the time period until the final settlement date of the underlying derivative contract.
	(2)	For interest rate and credit derivatives, Siis the period of time (starting today) until start of the time period referenced by an interest rate or credit contract. If the derivative references the value of another interest rate or credit instrument (e.g. swaption or bond option), the time period must be determined on the basis of the underlying instrument. Si appears in the definition of supervisory duration defined in 6.36.
	(3)	For interest rate and credit derivatives, Ei is the period of time (starting today) until the end of the time period referenced by an interest rate or credit contract. If the derivative references the value of another interest rate or credit instrument (e.g. swaption or bond option), the time period must be determined on the basis of the underlying instrument. Ei appears in the definition of supervisory duration defined in 6.36. In addition, Ei is used for allocating derivatives in the interest rate asset class to maturity buckets, which are used in the calculation of the asset class add-on (see 6.60(3)).
	(4)	For options in all asset classes, Ti is the time period (starting today) until the latest contractual exercise date as referenced by the contract. This period shall be used for the determination of the option’s supervisory delta in 6.40 to 6.43.
6.34.	Table 1 includes example transactions and provides each transaction’s related maturity Mi, start date Si and end date Ei. In addition, the option delta in 6.40 to 6.43 depends on the latest contractual exercise date Ti (not separately shown in the table).
	Table 1: Example transactions and related (maturity Mi, start date Si  and end date Ei) Instrument Mi Si  Ei Interest rate or credit default swap maturing in 10 years 10 years 0 10 years 10-year interest rate swap, forward starting in 5 years 15 years 5 years 15 years Forward rate agreement for time period starting in 6 months and ending in 12 months 1 year 0.5 year 1 years Cash-settled European swaption referencing 5-year interest rate swap with exercise date in 6 months 0.5 year 0.5 year 5.5 year Physically-settled European swaption referencing 5-year interest rate swap with exercise date in 6 months 5.5 years 0.5 year 5.5 years 10-year Bermudan swaption with annual exercise dates 10 years 1 year 10 years Interest rate cap or floor specified for semiannual interest rate with maturity 5 years 5 years 0 5 years Option on a bond maturing in 5 years with the latest exercise date in 1 year 1 year 1 year 5 years 3-month Eurodollar futures that matures in 1 year 1 year 1 year 1.25 years Futures on 20-year treasury bond that matures in 2 years 2 years 2 years 22 years 6-month option on 2-year futures on 20-year treasury bond 2 years 2 years 22 years
	Trade-level adjusted notional (for trade i): di
6.35.	The adjusted notionals are defined at the trade level and take into account both the size of a position and its maturity dependency, if any.
6.36.	For interest rate and credit derivatives, the trade-level adjusted notional is the product of the trade notional amount, converted to the Saudi Riyal (SAR), and the supervisory duration SD, which is given by the formula below (i.e.di = notional * SDi). The calculated value of SDi is floored at ten business days.14 If the start date has occurred (e.g. an ongoing interest rate swap), Si must be set to zero.
6.37.	For foreign exchange derivatives, the adjusted notional is defined as the notional of the foreign currency leg of the contract, converted to the Saudi Riyal (SAR). If both legs of a foreign exchange derivative are denominated in currencies other than the Saudi Riyal (SAR), the notional amount of each leg is converted to the Saudi Riyal (SAR) and the leg with the larger Saudi Riyal (SAR) value is the adjusted notional amount.
6.38.	For equity and commodity derivatives, the adjusted notional is defined as the product of the current price of one unit of the stock or commodity (e.g. a share of equity or barrel of oil) and the number of units referenced by the trade.
6.39.	In many cases the trade notional amount is stated clearly and fixed until maturity. When this is not the case, banks must use the following rules to determine the trade notional amount.
	(1)	Where the notional is a formula of market values, the bank must enter the current market values to determine the trade notional amount.
	(2)	For all interest rate and credit derivatives with variable notional amounts specified in the contract (such as amortizing and accreting swaps), banks must use the average notional over the remaining life of the derivative as the trade notional amount. The average should be calculated as “time weighted”. The averaging described in this paragraph does not cover transactions where the notional varies due to price changes (typically, FX, equity and commodity derivatives).
	(3)	Leveraged swaps must be converted to the notional of the equivalent unleveraged swap, that is, where all rates in a swap are multiplied by a factor, the stated notional must be multiplied by the factor on the interest rates to determine the trade notional amount.
	(4)	For a derivative contract with multiple exchanges of principal, the notional is multiplied by the number of exchanges of principal in the derivative contract to determine the trade notional amount.
	(5)	For a derivative contract that is structured such that on specified dates any outstanding exposure is settled and the terms are reset so that the fair value of the contract is zero, the remaining maturity equals the time until the next reset date.

		Supervisory delta adjustment
6.40.	The supervisory delta adjustment (δi) parameters are also defined at the trade i level and are applied to the adjusted notional amounts to reflect the direction of the transaction and its non-linearity.15
6.41.	The delta adjustments for all instruments that are not options and are not collateralized debt obligation (CDO) tranches are as set out in the table below:16
	δi Long in the primary risk factor Short in the primary risk factor Instruments that are not options or CDO tranches +1 -1
6.42.	The delta adjustments for options are set out in the table below, where:
	(1)	The following are parameters that banks must determine appropriately:
		(a)	Pi: Underlying price (spot, forward, average, etc.)
		(b)	Ki: Strike price
		(c)	Ti: Latest contractual exercise date of the option
	(2)	The supervisory volatility σi an option is specified on the basis of supervisory factor applicable to the trade (see Table 2 in 6.75).
	(3)	The symbol Φ represents the standard normal cumulative distribution function.
	δi Bought Sold Call Option Put Option
Delta (δ) Bought Sold Call Option Put Option
6.43.	The delta adjustments for CDO tranches17 are set out in the table below, where the following are parameters that banks must determine appropriately:
	(1)	Ai: Attachment point of the CDO tranche
	(2)	Di: Detachment point of the CDO tranche
	δi Purchased (long protection) Sold (Short protection) CDO tranche s

	Maturity factors
6.51.	The minimum time risk horizon for an unmargined transaction is the lesser of one year and the remaining maturity of the derivative contract, floored at ten business days.22 Therefore, the calculation of the effective notional for an unmargined transaction includes the following maturity factor, where Mi is the remaining maturity of transaction i, floored at 10 business days:
6.52.	The maturity parameter (Mi) is expressed in years but is subject to a floor of 10 business days. Banks should use standard market convention to convert business days into years, and vice versa. For example, 250 business days in a year, which results in a floor of 10/250 years for Mi.
6.53.	For margined transactions, the maturity factor is calculated using the margin period of risk (MPOR), subject to specified floors. That is, banks must first estimate the margin period of risk (as defined in 4.17) for each of their netting sets. They must then use the higher of their estimated margin period of risk and the relevant floor in the calculation of the maturity factor (6.55). The floors for the margin period of risk are as follows:
	(1)	Ten business days for non-centrally-cleared transactions subject to daily margin agreements.
	(2)	The sum of nine business days plus the re-margining period for non-centrally cleared transactions that are not subject daily margin agreements.
	(3)	The relevant floors for centrally cleared transactions are prescribed in the capital requirements for bank exposures to central counterparties (see in Chapter 8 of this framework).
6.54.	The following are exceptions to the floors on the minimum margin period of risk set out in 6.53 above:
	(1)	For netting sets consisting of more than 5000 transactions that are not with a central counterparty the floor on the margin period of risk is 20 business days.
	(2)	For netting sets containing one or more trades involving either illiquid collateral, or an OTC derivative that cannot be easily replaced, the floor on the margin period of risk is 20 business days. For these purposes, "Illiquid collateral" and "OTC derivatives that cannot be easily replaced" must be determined in the context of stressed market conditions and will be characterized by the absence of continuously active markets where a counterparty would, within two or fewer days, obtain multiple price quotations that would not move the market or represent a price reflecting a market discount (in the case of collateral) or premium (in the case of an OTC derivative). Examples of situations where trades are deemed illiquid for this purpose include, but are not limited to, trades that are not marked daily and trades that are subject to specific accounting treatment for valuation purposes (e.g. OTC derivatives transactions referencing securities whose fair value is determined by models with inputs that are not observed in the market).
	(3)	If a bank has experienced more than two margin call disputes on a particular netting set over the previous two quarters that have lasted longer than the applicable margin period of risk (before consideration of this provision), then the bank must reflect this history appropriately by doubling the applicable supervisory floor on the margin period of risk for that netting set for the subsequent two quarters.
	(4)	In the case of non-centrally cleared derivatives that are subject to the requirements under Margin requirements, 6.55(3) applies only to variation margin call disputes.
6.55.	The calculation of the effective notional for a margined transaction includes the following maturity factor, where MPORi is the margin period of risk appropriate for the margin agreement containing the transaction i (subject to the floors set out in 6.53 and 6.54 above).
6.56.	The margin period of risk (MPORi) is often expressed in days, but the calculation of the maturity factor for margined netting sets references 1 year in the denominator. Banks should use standard market convention to convert business days into years, and vice versa. For example, 1 year can be converted into 250 business days in the denominator of the MF formula if MPOR is expressed in business days. Alternatively, the MPOR expressed in business days can be converted into years by dividing it by 250.

	Supervisory correlation parameters
6.57.	The supervisory correlation parameters (ρi) only apply to the PFE add-on calculation for equity, credit and commodity derivatives, and are set out in Table 2 under 6.75. For these asset classes, the supervisory correlation parameters are derived from a single-factor model and specify the weight between systematic and idiosyncratic components. This weight determines the degree of offset between individual trades, recognizing that imperfect hedges provide some, but not perfect, offset. Supervisory correlation parameters do not apply to interest rate and foreign exchange derivatives.
	Asset class level add-ons
6.58.	As set out in 6.27, the aggregate add-on for a netting set (AddOnaggregate) is calculated as the sum of the add-ons calculated for each asset class within the netting set. The sections that follow set out the calculation of the add-on for each asset class.
	Add-on for interest rate derivatives
6.59.	The calculation of the add-on for the interest rate derivative asset class captures the risk of interest rate derivatives of different maturities being imperfectly correlated. It does this by allocating trades to maturity buckets, in which full offsetting of long and short positions is permitted, and by using an aggregation formula that only permits limited offsetting between maturity buckets. This allocation of derivatives to maturity buckets and the process of aggregation (steps 3 to 5 below) are only used in the interest rate derivative asset class.
6.60.	The add-on for the interest rate derivative asset class (AddOnIR) within a netting set is calculated using the following steps:
	(1)	Step 1: Calculate the effective notional for each trade in the netting set that is in the interest rate derivative asset class. This is calculated as the product of the following three terms:
		(i)	the adjusted notional of the trade (d);
		(ii)	the supervisory delta adjustment of the trade (S); and
		(iii)	the maturity factor (MF). That is, for each trade i, the effective notional Di is calculated as Di= di* MFi* δi, where each term is as defined in 6.35 to 6.56.
	(2)	Step 2: Allocate the trades in the interest rate derivative [including inflation derivatives] asset class to hedging sets. In the interest rate derivative asset class the hedging sets consist of all the derivatives that reference the same currency.
	(3)	Step 3: Within each hedging set allocate each of the trades to the following three maturity buckets: less than one year (bucket 1), between one and five years (bucket 2) and more than five years (bucket 3).
	(4)	Step 4: Calculate the effective notional of each maturity bucket by adding together all the trade level effective notionals calculated in step 1 of the trades within the maturity bucket. Let DB1, DB1 and DB1 be the effective notionals of buckets 1, 2 and 3 respectively.
	(5)	Step 5: Calculate the effective notional of the hedging set (ENHS ) by using either of the two following aggregation formulas (the latter is to be used if the bank chooses not to recognize offsets between long and short positions across maturity buckets):
	(6)	Step 6: Calculate the hedging set level add-on (AddOnHS) by multiplying the effective notional of the hedging set (ENHS) by the prescribed supervisory factor (SFHS). The prescribed supervisory factor in the interest rate asset class is set at 0.5%, which means that AddOnHS= ENHS * 0.005.
	(7)	Step 7: Calculate the asset class level add-on (AddOnIR) by adding together all of the hedging set level add-ons calculated in step 6:
	Add-on for foreign exchange derivatives
6.61.	The steps to calculate the add-on for the foreign exchange derivative asset class are similar to the steps for the interest rate derivative asset class, except that there is no allocation of trades to maturity buckets (which means that there is full offsetting of long and short positions within the hedging sets of the foreign exchange derivative asset class).
6.62.	The add-on for the foreign exchange derivative asset class (AddOnFX) within a netting set is calculated using the following steps:
	(1)	Step 1: Calculate the effective notional for each trade in the netting set that is in the foreign exchange derivative asset class. This is calculated as the product of the following three terms: (i) the adjusted notional of the trade (d); (ii) the supervisory delta adjustment of the trade (δ); and (iii) the maturity factor (MF). That is, for each trade i, the effective notional Di is calculated as Di = di* MFi * δi, where each term is as defined in 6.35 to 6.56.
	(2)	Step 2: Allocate the trades in the foreign exchange derivative asset class to hedging sets. In the foreign exchange derivative asset class the hedging sets consist of all the derivatives that reference the same currency pair.
	(3)	Step 3: Calculate the effective notional of each hedging set (ENHS) by adding together the trade level effective notionals calculated in step 1.
	(4)	Step 4: Calculate the hedging set level add-on (AddOnHS) by multiplying the HS absolute value of the effective notional of the hedging set (ENHS) by the HS prescribed supervisory factor (SFHS). The prescribed supervisory factor in the HS foreign exchange derivative asset class is set at 4%, which means that AddOnHS= |ENHS| * 0.04.
	(5)	Step 5: Calculate the asset class level add-on (AddOnFX) by adding together all of the hedging set level add-ons calculated in step 5:
	Add-on for credit derivatives
6.63.	The calculation of the add-on for the credit derivative asset class only gives full recognition of the offsetting of long and short positions for derivatives that reference the same entity (e.g. the same corporate issuer of bonds). Partial offsetting is recognized between derivatives that reference different entities in step 4 below. The formula used in step 4 is explained further in 6.65 to 6.67.
6.64.	The add-on for the credit derivative asset class (AddOnCredlt ) within a netting set is calculated using the following steps:
	(1)	Step 1: Calculate the effective notional for each trade in the netting set that is in the credit derivative asset class. This is calculated as the product of the following three terms:
		(i)	the adjusted notional of the trade (d);
		(ii)	the supervisory delta adjustment of the trade (δ); and
		(iii)	the maturity factor (MF). That is, for each trade i, the effective notional Di is calculated as Di= di* MFi * δi, where each term is as defined in 6.35 to 6.56.
	(2)	Step 2: Calculate the combined effective notional for all derivatives that reference the same entity. Each separate credit index that is referenced by derivatives in the credit derivative asset class should be treated as a separate entity. The combined effective notional of the entity (ENentity) is calculated by adding together the trade level effective notionals calculated in step 1 that reference that entity.
	(3)	Step 3: Calculate the add-on for each entity (AddOnentity) by multiplying the entity combined effective notional for that entity calculated in step 2 by the supervisory factor that is specified for that entity (SFentity). The supervisory entity factors vary according to the credit rating of the entity in the case of single name derivatives, and whether the index is considered investment grade or non-investment grade in the case of derivatives that reference an index. The supervisory factors are set out in Table 2 in 6.75.
	(4)	Step 4: Calculate the asset class level add-on (AddOnCredlt) by using the formula that follows. In the formula the summations are across all entities referenced by the derivatives, AddOnentity is the add-on amount calculated entity in step 3 for each entity referenced by the derivatives and ρ is the entity supervisory prescribed correlation factor corresponding to the entity. As set out in Table 2 in 6.75, the correlation factor is 50% for single entities and 80% for indices.
6.65.	The formula to recognize partial offsetting in 6.64(4) above, is a single-factor model, which divides the risk of the credit derivative asset class into a systematic component and an idiosyncratic component. The entity-level add-ons are allowed to offset each other fully in the systematic component; whereas, there is no offsetting benefit in the idiosyncratic component. These two components are weighted by a correlation factor which determines the degree of offsetting / hedging benefit within the credit derivatives asset class. The higher the correlation factor, the higher the importance of the systematic component, hence the higher the degree of offsetting benefits.
6.66.	It should be noted that a higher or lower correlation does not necessarily mean a higher or lower capital requirement. For portfolios consisting of long and short credit positions, a high correlation factor would reduce the charge. For portfolios consisting exclusively of long positions (or short positions), a higher correlation factor would increase the charge. If most of the risk consists of systematic risk, then individual reference entities would be highly correlated and long and short positions should offset each other. If, however, most of the risk is idiosyncratic to a reference entity, then individual long and short positions would not be effective hedges for each other.
6.67.	The use of a single hedging set for credit derivatives implies that credit derivatives from different industries and regions are equally able to offset the systematic component of an exposure, although they would not be able to offset the idiosyncratic portion. This approach recognizes that meaningful distinctions between industries and/or regions are complex and difficult to analyze for global conglomerates.
	Add-on for equity derivatives
6.68.	The calculation of the add-on for the equity derivative asset class is very similar to the calculation of the add-on for the credit derivative asset class. It only gives full recognition of the offsetting of long and short positions for derivatives that reference the same entity (e.g. the same corporate issuer of shares). Partial offsetting is recognized between derivatives that reference different entities in step 4 below.
6.69.	The add-on for the equity derivative asset class (AddOnEquity) within a netting set is calculated using the following steps:
	(1)	Step 1: Calculate the effective notional for each trade in the netting set that is in the equity derivative asset class. This is calculated as the product of the following three terms:
		(i)	the adjusted notional of the trade (d);
		(ii)	the supervisory delta adjustment of the trade (δ); and
		(iii)	the maturity factor (MF). That is, for each trade i, the effective notional Di is calculated as Di= di* MFi* δi, where each term is as defined in 6.35 to 6.56.
	(2)	Step 2: Calculate the combined effective notional for all derivatives that reference the same entity. Each separate equity index that is referenced by derivatives in the equity derivative asset class should be treated as a separate entity. The combined effective notional of the entity (ENentity) is calculated entity by adding together the trade level effective notionals calculated in step 1 that reference that entity.
	(3)	Step 3: Calculate the add-on for each entity (AddOnentity) by multiplying the entity combined effective notional for that entity calculated in step 2 by the supervisory factor that is specified for that entity (SFentity). The supervisory entity factors are set out in Table 2 in 6.75 and vary according to whether the entity is a single name (SFentity = 32%) or an index (SFentity = 20%).
	(4)	Step 4: Calculate the asset class level add-on (AddOnEquity) by using the formula that follows. In the formula the summations are across all entities referenced by the derivatives, AddOnentity is the add-on amount calculated entity in step 3 for each entity referenced by the derivatives and ρentity is the entity supervisory prescribed correlation factor corresponding to the entity. As set out in Table 2 in 6.75, the correlation factor is 50% for single entities and 80% for indices.
6.70.	The supervisory factors for equity derivatives were calibrated based on estimates of the market volatility of equity indices, with the application of a conservative beta factor23 to translate this estimate into an estimate of individual volatilities.
6.71.	Banks are not permitted to make any modelling assumptions in the calculation of the PFE add-ons, including estimating individual volatilities or taking publicly available estimates of beta. This is a pragmatic approach to ensure a consistent implementation across jurisdictions but also to keep the add-on calculation relatively simple and prudent. Therefore, bank must only use the two values of supervisory factors that are defined for equity derivatives, one for single entities and one for indices.

	Add-on for commodity derivatives
6.72.	The calculation of the add-on for the commodity derivative asset class is similar to the calculation of the add-on for the credit and equity derivative asset classes. It recognizes the full offsetting of long and short positions for derivatives that reference the same type of underlying commodity. It also allows partial offsetting between derivatives that reference different types of commodity, however, this partial offsetting is only permitted within each of the four hedging sets of the commodity derivative asset class, where the different commodity types are more likely to demonstrate some stable, meaningful joint dynamics. Offsetting between hedging sets is not recognized (e.g., a forward contract on crude oil cannot hedge a forward contract on corn).
6.73.	The add-on for the commodity derivative asset class (AddOnCommodlty) within a netting set is calculated using the following steps:
	(1)	Step 1: Calculate the effective notional for each trade in the netting set that is in the commodity derivative asset class. This is calculated as the product of the following three terms:
		(i)	the adjusted notional of the trade (d);
		(ii)	the supervisory delta adjustment of the trade (δ); and
		(iii)	the maturity factor (MF). That is, for each trade i, the effective notional Di is calculated as Di— di * MFi * δi, where each term is as defined in 6.35 to 6.56.
	(2)	Step 2: Allocate the trades in commodity derivative asset class to hedging sets. In the commodity derivative asset class there are four hedging sets consisting of derivatives that reference: energy, metals, agriculture and other commodities.
	(3)	Step 3: Calculate the combined effective notional for all derivatives with each hedging set that reference the same commodity type (e.g. all derivative that reference copper within the metals hedging set). The combined effective notional of the commodity type (ENComTyoe) is calculated by adding ComType together the trade level effective notionals calculated in step 1 that reference that commodity type.
	(4)	Step 4: Calculate the add-on for each commodity type (AddOnComType) within each hedging set by multiplying the combined effective notional for that commodity calculated in step 3 by the supervisory factor that is specified for that commodity type (SFComType). The supervisory factors are ComType set out in Table 2 in 6.75 and are set at 40% for electricity derivatives and 18% for derivatives that reference all other types of commodities.
	(5)	Step 5: Calculate the add-on for each of the four commodity hedging sets (AddOnHS) by using the formula that follows. In the formula the summations are across all commodity types within the hedging set, AddOnComType is the add-on amount ComType calculated in step 4 for each commodity type and ρComTyoe is the supervisory ComType prescribed correlation factor corresponding to the commodity type. As set out in Table 2 in 6.75, the correlation factor is set at 40% for all commodity types.
	(6)	Step 6: Calculate the asset class level add-on (AddOnCommodlty) by adding together all of the hedging set level add-ons calculated in step 5:
6.74.	Regarding the calculation steps above, defining individual commodity types is operationally difficult. In fact, it is impossible to fully specify all relevant distinctions between commodity types so that all basis risk is captured. For example crude oil could be a commodity type within the energy hedging set, but in certain cases this definition could omit a substantial basis risk between different types of crude oil (West Texas Intermediate, Brent, Saudi Light, etc.) Also, the four commodity type hedging sets have been defined without regard to characteristics such as location and quality. For example, the energy hedging set contains commodity types such as crude oil, electricity, natural gas and coal. SAMA may require banks to use more refined definitions of commodities when they are significantly exposed to the basis risk of different products within those commodity types.
	Supervisory specified parameters
6.75.	Table 2 includes the supervisory factors, correlations and supervisory option volatility add-ons for each asset class and subclass.
	Table 2: Summary table of supervisory parameters Asset Class Subclass Supervisory factor Correlation Supervisory option volatility Interest rate   0.50% N/A 50% Foreign exchange   4.0% N/A 15% Credit, Single Name AAA 0.38% 50% 100% AA 0.38% 50% 100% A 0.42% 50% 100% BBB 0.54% 50% 100% BB 1.06% 50% 100% B 1,6% 50% 100% CCC 6.0% 50% 100% Credit, Index IG 0.38% 80% 80% SG 1.06% 80% 80% Equity, Single Name   32% 50% 120% Equity, Index   20% 80% 75% Commodity Electricity 40% 40% 150% Oil/Gas 18% 40% 70% Metals 18% 40% 70% Agricultural 18% 40% 70% Other 18% 40% 70%
6.76.	For a hedging set consisting of basis transactions, the supervisory factor applicable to its relevant asset class must be multiplied by one-half. For a hedging set consisting of volatility transactions, the supervisory factor applicable to its relevant asset class must be multiplied by a factor of five.
	Treatment of multiple margin agreements and multiple netting sets
6.77.	If multiple margin agreements apply to a single netting set, the netting set must be divided into sub-netting sets that align with their respective margin agreement. This treatment applies to both RC and PFE components.
6.78.	If a single margin agreement applies to several netting sets, special treatment is necessary because it is problematic to allocate the common collateral to individual netting sets. The replacement cost at any given time is determined by the sum of two terms. The first term is equal to the unmargined current exposure of the bank to the counterparty aggregated across all netting sets within the margin agreement reduced by the positive current net collateral (i.e. collateral is subtracted only when the bank is a net holder of collateral). The second term is non-zero only when the bank is a net poster of collateral: it is equal to the current net posted collateral (if there is any) reduced by the unmargined current exposure of the counterparty to the bank aggregated across all netting sets within the margin agreement. Net collateral available to the bank should include both VM and NICA. Mathematically, RC for the entire margin agreement is calculated as follows, where:
	(1)	where the summation NS ϵ MA is across the netting sets covered by the margin agreement (hence the notation)
	(2)	V is the current mark-to-market value of the netting set NS and CMA is the cash equivalent value of all currently available collateral under the margin agreement
6.79.	Where a single margin agreement applies to several netting sets as described in 6.78 above, collateral will be exchanged based on mark-to-market values that are netted across all transactions covered under the margin agreement, irrespective of netting sets. That is, collateral exchanged on a net basis may not be sufficient to cover PFE. In this situation, therefore, the PFE add-on must be calculated according to the unmargined methodology. Netting set-level PFEs are then aggregated using the following formula, where is the addon for the netting set NS calculated according to the unmargined requirements:
	Treatment of collateral taken outside of netting sets
6.80.	Eligible collateral which is taken outside a netting set, but is available to a bank to offset losses due to counterparty default on one netting set only, should be treated as an independent collateral amount associated with the netting set and used within the calculation of replacement cost under 6.12 when the netting set is unmargined and under 6.20 when the netting set is margined. Eligible collateral which is taken outside a netting set, and is available to a bank to offset losses due to counterparty default on more than one netting set, should be treated as collateral taken under a margin agreement applicable to multiple netting sets, in which case the treatment under 6.78 and 6.79 applies. If eligible collateral is available to offset losses on non-derivatives exposures as well as exposures determined using the SA-CCR, only that portion of the collateral assigned to the derivatives may be used to reduce the derivatives exposure.

¹³ The formulas for calculating the asset class add-ons represent stylized Effective EPE calculations under the assumption that all trades in the asset class have zero current mark-to-market value (i.e. they are at-the-money).
¹⁴ Note there is a distinction between the time period of the underlying transaction and the remaining maturity of the derivative contract. For example, a European interest rate swaption with expiry of 1 year and the term of the underlying swap of 5 years has S = 1 year and E = 6 i years.
¹⁵ Whenever appropriate, the forward (rather than spot) value of the underlying in the supervisory delta adjustments formula should be used in order to account for the risk-free rate as well as for possible cash flows prior to the option expiry (such as dividends).
¹⁶ “Long in the primary risk factor” means that the market value of the instrument increases when the value of the primary risk factor increases. “Short in the primary risk factor” means that the market value of the instrument decreases when the value of the primary risk factor increases.
¹⁷ First-to-default, second-to-default and subsequent-to-default credit derivative transactions should be treated as CDO tranches under SACCR. For an nth-to-default transaction on a pool of m reference names, banks must use an attachment point of A=(n–1)/m and a detachment point of D=n/m in order to calculate the supervisory delta formula set out 6.43.
¹⁸ Each factor has been calibrated to result in an add-on that reflects the Effective EPE of a single at- the-money linear trade of unit notional and one-year maturity. This includes the estimate of realized volatilities assumed by supervisors for each underlying asset class.
¹⁹ Derivatives with two floating legs that are denominated in different currencies (such as cross-currency swaps) are not subject to this treatment; rather, they should be treated as non-basis foreign exchange contracts.
²⁰ Within this hedging set, long and short positions are determined with respect to the basis.
²¹ For equity and commodity volatility transactions, the underlying volatility or variance referenced by the transaction should replace the unit price and contractual notional should replace the number of units.
²² For example, remaining maturity for a one-month option on a 10-year Treasury bond is the one-month to expiration date of the derivative contract. However, the end date of the transaction is the 10- year remaining maturity on the Treasury bond.
²³ The beta of an individual equity measures the volatility of the stock relative to a broad market index. A value of beta greater than one means the individual equity is more volatile than the index. The greater the beta is, the more volatile the stock. The beta is calculated by running a linear regression of the stock on the broad index.

7.6.	CCR exposure or EAD is measured at the level of the netting set as defined in Chapter 4 of this framework and 7.61 to 7.71 of this framework. A qualifying internal model for measuring counterparty credit exposure must specify the forecasting distribution for changes in the market value of the netting set attributable to changes in market variables, such as interest rates, foreign exchange rates, etc. The model then computes the bank’s CCR exposure for the netting set at each future date given the changes in the market variables. For margined counterparties, the model may also capture future collateral movements. Banks may include eligible financial collateral as defined in 9.37 of the Minimum Capital Requirements for Credit Risk and 9.2 of this framework in their forecasting distributions for changes in the market value of the netting set, if the quantitative, qualitative and data requirements for internal models method are met for the collateral.
7.7.	Banks that use the internal models method must calculate credit RWA as the higher of two amounts, one based on current parameter estimates and one based on stressed parameter estimates. Specifically, to determine the default risk capital requirement for counterparty credit risk, banks must use the greater of the portfolio-level capital requirement (not including the credit valuation adjustment, or CVA, charge in Chapter 11 of this Framework) based on Effective expected positive exposure (EPE) using current market data and the portfolio level capital requirement based on Effective EPE using a stress calibration.24 The stress calibration should be a single consistent stress calibration for the whole portfolio of counterparties. The greater of Effective EPE using current market data and the stress calibration should not be applied on a counterparty by counterparty basis, but on a total portfolio level.
7.8.	To the extent that a bank recognizes collateral in EAD via current exposure, a bank would not be permitted to recognize the benefits in its estimates of loss given-default (LGD). As a result, the bank would be required to use an LGD of an otherwise similar uncollateralized facility. In other words, the bank would be required to use an LGD that does not include collateral that is already included in EAD.
7.9.	Under the internal models method, the bank need not employ a single model. Although the following text describes an internal model as a simulation model, no particular form of model is required. Analytical models are acceptable so long as they are subject to supervisory review, meet all of the requirements set forth in this section and are applied to all material exposures subject to a CCR-related capital requirement as noted above, with the exception of long settlement transactions, which are treated separately, and with the exception of those exposures that are immaterial in size and risk.
7.10.	Expected exposure or peak exposure measures should be calculated based on a distribution of exposures that accounts for the possible non-normality of the distribution of exposures, including the existence of leptokurtosis (“fat tails”), where appropriate.
7.11.	When using an internal model, exposure amount or EAD is calculated as the product of alpha times Effective EPE, as specified below (except for counterparties that have been identified as having explicit specific wrong way risk – see 7.48):
	EAD = α × EffectiveEPE (Equation 1)
7.12.	Effective EPE is computed by estimating expected exposure (EEt) as the average t exposure at future date t, where the average is taken across possible future values of relevant market risk factors, such as interest rates, foreign exchange rates, etc. The internal model estimates EE at a series of future dates t1, t2, t3 ...25 Specifically, “Effective EE” is computed recursively using the following formula, where the current date is denoted as t0 and Effective EEt0 equals current exposure:
	EffectiveEEtk = max(EffectiveEEtk-1, EEtk (Equation 2)
7.13.	In this regard, “Effective EPE” is the average Effective EE during the first year of future exposure. If all contracts in the netting set mature before one year, EPE is the average of expected exposure until all contracts in the netting set mature. Effective EPE is computed as a weighted average of Effective EE, using the following formula where the weights Δtk= tk - tk-1 allows for the case when future exposure is calculated at dates that are not equally spaced over time:
7.14.	Alpha (α) is set equal to 1.4.
7.15.	SAMA may require a higher alpha based on a bank’s CCR exposures. Factors that may require a higher alpha include the low granularity of counterparties; particularly high exposures to general wrong-way risk; particularly high correlation of market values across counterparties; and other institution specific characteristics of CCR exposures.

²⁴ Effective expected positive exposure (EPE) using current market data to be compared with Effective EPE using a stress calibration on annual basis during ICAAP
²⁵ In theory, the expectations should be taken with respect to the actual probability distribution of future exposure and not the risk-neutral one. Supervisors recognize that practical considerations may make it more feasible to use the risk-neutral one. As a result, supervisors will not mandate which kind of forecasting distribution to employ.

7.20.	If the original maturity of the longest-dated contract contained in the set is greater than one year, the formula for effective maturity (M) in 12.42 of the Minimum Capital Requirements for Credit Risk is replaced with formula that follows, where dfK is the risk-free discount factor for future time period tK and the remaining symbols are defined above. Similar to the treatment under corporate exposures, M has a cap of five years.26
7.21.	For netting sets in which all contracts have an original maturity of less than one year, the formula for effective maturity (M) i in 12.42 of the Minimum Capital Requirements for Credit Risk is unchanged and a floor of one year applies, with the exception of short-term exposures as described in paragraphs in 12.45 to 12.48 of the Minimum Capital Requirements for Credit Risk.

²⁶ Conceptually, M equals the effective credit duration of the counterparty exposure. A bank that uses an internal model to calculate a one-sided credit valuation adjustment (CVA) can use the effective credit duration estimated by such a model in place of the above formula with prior approval of SAMA.

²⁷ This section draws heavily on the Counterparty Risk Management Policy Group's paper, Improving Counterparty Risk Management Practices (June 1999).
²⁸ Note that the recoveries may also be possible on the underlying instrument beneath such swap. The capital requirements for such underlying exposure are to be calculated without reduction for the swap which introduces wrong way risk. Generally this means that such underlying exposure will receive the risk weight and capital treatment associated with an unsecured transaction (i.e. assuming such underlying exposure is an unsecured credit exposure).
²⁹ See Chapter 10.1 to Chapter 10.4 of the Minimum Capital Requirements for Market Risk.

³⁰ For contributions to prepaid default funds covering settlement-risk only products, the applicable risk weight is 0%.
³¹ For this purpose, the treatment in 8.12 would also apply.

	Clearing member exposures to clients
8.12	The clearing member will always capitalize its exposure (including potential credit valuation adjustment, or CVA, risk exposure) to clients as bilateral trades, irrespective of whether the clearing member guarantees the trade or acts as an intermediary between the client and the CCP. However, to recognize the shorter close-out period for cleared client transactions, clearing members can capitalize the exposure to their clients applying a margin period of risk of at least five days in IMM or SA-CCR. The reduced exposure at default (EAD) should also be used for the calculation of the CVA capital requirement.
8.13	If a clearing member collects collateral from a client for client cleared trades and this collateral is passed on to the CCP, the clearing member may recognize this collateral for both the CCP-clearing member leg and the clearing member-client leg of the client-cleared trade. Therefore, initial margin posted by clients to their clearing member mitigates the exposure the clearing member has against these clients. The same treatment applies, in an analogous fashion, to multi-level client structures (between a higher-level client and a lower-level client).
	Client exposures
8.14	Subject to the two conditions set out in 8.15 below being met, the treatment set out in 8.7 to 8.11 (i.e. the treatment of clearing member exposures to CCPs) also applies to the following:
	(1)	A bank's exposure to a clearing member where:
		(a)	the bank is a client of the clearing member; and
		(b)	the transactions arise as a result of the clearing member acting as a financial intermediary (i.e. the clearing member completes an offsetting transaction with a CCP).
	(2)	A bank's exposure to a CCP resulting from a transaction with the CCP where:
		(a)	the bank is a client of a clearing member; and
		(b)	the clearing member guarantees the performance the bank's exposure to the CCP.
	(3)	Exposures of lower-level clients to higher-level clients in a multi-level client structure, provided that for all client levels in-between the two conditions in 8.15 below are met.
8.15	The two conditions referenced in 8.14 above are:
	(1)	The offsetting transactions are identified by the CCP as client transactions and collateral to support them is held by the CCP and/or the clearing member, as applicable, under arrangements that prevent any losses to the client due to: (a) the default or insolvency of the clearing member; (b) the default or insolvency of the clearing member's other clients; and (c) the joint default or insolvency of the clearing member and any of its other clients. Regarding the condition set out in this paragraph:
		(a)	Upon the insolvency of the clearing member, there must be no legal impediment (other than the need to obtain a court order to which the client is entitled) to the transfer of the collateral belonging to clients of a defaulting clearing member to the CCP, to one or more other surviving clearing members or to the client or the client's nominee. SAMA should be consulted to determine whether this is achieved based on particular facts and SAMA will consult and communicate with other supervisors.
		(b)	The client must have conducted a sufficient legal review (and undertake such further review as necessary to ensure continuing enforceability) and have a well founded basis to conclude that, in the event of legal challenge, the relevant courts and administrative authorities would find that such arrangements mentioned above would be legal, valid, binding and enforceable under the relevant laws of the relevant jurisdiction(s).
	(2)	Relevant laws, regulation, rules, contractual, or administrative arrangements provide that the offsetting transactions with the defaulted or insolvent clearing member are highly likely to continue to be indirectly transacted through the CCP, or by the CCP, if the clearing member defaults or becomes insolvent. In such circumstances, the client positions and collateral with the CCP will be transferred at market value unless the client requests to close out the position at market value. Regarding the condition set out in this paragraph, if there is a clear precedent for transactions being ported at a CCP and industry intent for this practice to continue, then these factors must be considered when assessing if trades are highly likely to be ported. The fact that CCP documentation does not prohibit client trades from being ported is not sufficient to say they are highly likely to be ported.
8.16	Where a client is not protected from losses in the case that the clearing member and another client of the clearing member jointly default or become jointly insolvent, but all other conditions in the preceding paragraph are met, a risk weight of 4% will apply to the client's exposure to the clearing member, or to the higher-level client, respectively.
8.17	Where the bank is a client of the clearing member and the requirements in 8.14 to 8.16 above are not met, the bank will capitalize its exposure (including potential CVA risk exposure) to the clearing member as a bilateral trade.
	Treatment of posted collateral
8.18	In all cases, any assets or collateral posted must, from the perspective of the bank posting such collateral, receive the risk weights that otherwise applies to such assets or collateral under the capital adequacy framework, regardless of the fact that such assets have been posted as collateral. That is, collateral posted must receive the banking book or trading book treatment it would receive if it had not been posted to the CCP.
8.19	In addition to the requirements of 8.18 above, the posted assets or collateral are subject to the counterparty credit risk requirements, regardless of whether they are in the banking or trading book. This includes the increase in the counterparty credit risk exposure due to the application of haircuts. The counterparty credit risk requirements arise where assets or collateral of a clearing member or client are posted with a CCP or a clearing member and are not held in a bankruptcy remote manner. In such cases, the bank posting such assets or collateral must recognize credit risk based upon the assets or collateral being exposed to risk of loss based on the creditworthiness of the entity holding such assets or collateral, as described further below.
8.20	Where such collateral is included in the definition of trade exposures (see Chapter 3of this framework) and the entity holding the collateral is the CCP, the following risk weights apply where the assets or collateral is not held on a bankruptcy- remote basis:
	(1)	For banks that are clearing members a risk weight of 2% applies.
	(2)	For banks that are clients of clearing members:
		(a)	a 2% risk weight applies if the conditions established in 8.14 and 8.15 are met; or
		(b)	a 4% risk weight applies if the conditions in 8.16 are met.
8.21	Where such collateral is included in the definition of trade exposures (see Chapter 3 of this framework), there is no capital requirement for counterparty credit risk exposure (i.e. the related risk weight or EAD is equal to zero) if the collateral is: (a) held by a custodian; and (b) bankruptcy remote from the CCP. Regarding this paragraph:
	(1)	All forms of collateral are included, such as: cash, securities, other pledged assets, and excess initial or variation margin, also called overcollateralization.
	(2)	The word “custodian” may include a trustee, agent, pledgee, secured creditor or any other person that holds property in a way that does not give such person a beneficial interest in such property and will not result in such property being subject to legally-enforceable claims by such persons creditors, or to a court- ordered stay of the return of such property, if such person becomes insolvent or bankrupt.
8.22	The relevant risk weight of the CCP will apply to assets or collateral posted by a bank that do not meet the definition of trade exposures (for example treating the exposure as a financial institution under standardized approach or internal ratings-based approach to credit risk).
8.23	Regarding the calculation of the exposure, or EAD, where banks use the SA-CCR to calculate exposures, collateral posted which is not held in a bankruptcy remote manner must be accounted for in the net independent collateral amount term in accordance with 6.17 to 6.21. For banks using IMM models, the alpha multiplier must be applied to the exposure on posted collateral.
	Default fund exposures
8.24	Where a default fund is shared between products or types of business with settlement risk only (e.g. equities and bonds) and products or types of business which give rise to counterparty credit risk i.e. OTC derivatives, exchange-traded derivatives, SFTs or long settlement transactions, all of the default fund contributions will receive the risk weight determined according to the formula and methodology set forth below, without apportioning to different classes or types of business or products. However, where the default fund contributions from clearing members are segregated by product types and only accessible for specific product types, the capital requirements for those default fund exposures determined according to the formulae and methodology set forth below must be calculated for each specific product giving rise to counterparty credit risk. In case the CCP's prefunded own resources are shared among product types, the CCP will have to allocate those funds to each of the calculations, in proportion to the respective product specific EAD.
8.25	Whenever a bank is required to capitalize for exposures arising from default fund contributions to a QCCP, clearing member banks will apply the following approach.
8.26	Clearing member banks will apply a risk weight to their default fund contributions determined according to a risk sensitive formula that considers
	(i)	the size and quality of a qualifying CCP's financial resources,
	(ii)	the counterparty credit risk exposures of such CCP, and
	(iii)	the application of such financial resources via the CCP's loss-bearing waterfall, in the case of one or more clearing member defaults. The clearing member bank's risk sensitive capital requirement for its default fund contribution (KCMi) must be calculated using the formulae and methodology set forth below.
8.27	The clearing member bank's risk-sensitive capital requirement for its default fund contribution (KCMi) is calculated in two steps:
	(1)	Calculate the hypothetical capital requirement of the CCP due to its counterparty credit risk exposures to all of its clearing members and their clients.
	(2)	Calculate the capital requirement for the clearing member bank.
	Hypothetical capital requirement of the CCP
8.28	The first step in calculating the clearing member bank's capital requirement for its default fund contribution (KCMi) is to calculate the hypothetical capital requirement of the CCP (KCMi) due to its counterparty credit risk exposures to all of its clearing members and their clients. KCCP is a hypothetical capital requirement for a CCP, calculated on a consistent basis for the sole purpose of determining the capitalization of clearing member default fund contributions; it does not represent the actual capital requirements for a CCP which may be determined by a CCP and its supervisor.
8.29	K is calculated using the following formula, where: CCP
	(1)	RW is a risk weight of 20%33
	(2)	capital ratio is 8%
	(3)	CM is the clearing member
	(4)	EAD is the exposure amount of the CCP to clearing member ‘i', relating to I the valuation at the end of the regulatory reporting date before the margin called on the final margin call of that day is exchanged. The exposure includes both:
		(a)	the clearing member's own transactions and client transactions guaranteed by the clearing member; and
		(b)	all values of collateral held by the CCP (including the clearing member's prefunded default fund contribution) against the transactions in (a).
	(5)	The sum is over all clearing member accounts.
8.30	Where clearing members provide client clearing services, and client transactions and collateral are held in separate (individual or omnibus) sub-accounts to the clearing member's proprietary business, each such client sub-account should enter the sum in 8.29 above separately, i.e. the member EAD in the formula above is then the sum of the client sub-account EADs and any house sub-account EAD. This will ensure that client collateral cannot be used to offset the CCP's exposures to clearing members' proprietary activity in the calculation of KCCP. If any of these sub-accounts contains both derivatives and SFTs, the EAD of that sub-account is the sum of the derivative EAD and the SFT EAD.
8.31	In the case that collateral is held against an account containing both SFTs and derivatives, the prefunded initial margin provided by the member or client must be allocated to the SFT and derivatives exposures in proportion to the respective product-specific EADs, calculated according to:
	(1)	Chapter 9.67 to 9.71 of the Minimum Capital Requirements for Credit Risk; and
	(2)	SA-CCR (see Chapter 6 of this framework) for derivatives, without including the effects of collateral.
8.32	If the default fund contributions of the member (DFi) are not split with regard to i client and house sub-accounts, they must be allocated per sub-account according to the respective fraction the initial margin of that sub-account has in relation to the total initial margin posted by or for the account of the clearing member.
8.33	For derivatives, EADi is calculated as the bilateral trade exposure the CCP has i against the clearing member using the SA-CCR. In applying the SA-CCR:
	(1)	A MPOR of 10 business days must be used to calculate the CCP's potential future exposure to its clearing members on derivatives transactions (the 20 day floor on the MPOR for netting sets with more than 5000 trades does not apply).
	(2)	All collateral held by a CCP to which that CCP has a legal claim in the event of the default of the member or client, including default fund contributions of that member (DFi), is used to offset the CCP's exposure to that member or i client, through inclusion in the PFE multiplier in accordance with 6.23 to 6.25.
8.34	For SFTs, EADi is equal to max(EBRMi- IMi- DFi;0), where:
	(1)	EBRMi denotes the exposure value to clearing member ‘i' before risk mitigation under 9.68 to 9.72 of the Minimum Capital Requirements for Credit Risk; where, for the purposes of this calculation, variation margin that has been exchanged (before the margin called on the final margin call of that day) enters into the mark-to-market value of the transactions.
	(2)	IMi; is the initial margin collateral posted by the clearing member with the CCP.
	(3)	DFi is the prefunded default fund contribution by the clearing member that will be applied upon such clearing member's default, either along with or immediately following such member's initial margin, to reduce the CCP loss.
8.35	As regards the calculation in this first step (i.e. 8.28 to 8.34):
	(1)	Any haircuts to be applied for SFTs must be the standard supervisory haircuts set out in 9.44 of the Minimum Capital Requirements for Credit Risk.
	(2)	The holding periods for SFT calculations in 9.60 to 9.63 of the Minimum Capital Requirements for Credit Risk.
	(3)	The netting sets that are applicable to regulated clearing members are the same as those referred to in 8.10 and 8.11. For all other clearing members, they need to follow the netting rules as laid out by the CCP based upon notification of each of its clearing members. SAMA may demand more granular netting sets than laid out by the CCP.

	Capital requirement for each clearing member
8.36.	The second step in calculating the clearing member bank's capital requirement for its default fund contribution (KCMi) is to apply the following formula,34 where:
	(1)	KCMi is the capital requirement on the default fund contribution of clearing member bank i
	(2)	DFCMPref is the total prefunded default fund contributions from clearing members
	(3)	DRCCP is the CCP's prefunded own resources (e.g. contributed capital, retained earnings, etc.), which are contributed to the default waterfall, where these are junior or pari passu to prefunded member contributions
	(4)	DFiprefis the prefunded default fund contributions provided by clearing member bank i
8.37.	The CCP, bank, CCP supervisor or other body with access to the required data, must make a calculation of KCCP, DFCMpref, DFCCP, in such a way to permit the supervisor of the CCP to oversee those calculations, and it must share sufficient information of the calculation results to permit each clearing member to calculate their capital requirement for the default fund and for SAMA to review and confirm such calculations.
8.38.	KCCP must be calculated on a quarterly basis at a minimum; although SAMA may require more frequent calculations in case of material changes (such as the CCP clearing a new product). The CCP, bank, CCP supervisor or other body that did the calculations must make available to SAMA the sufficient aggregate information about the composition of the CCP's exposures to clearing members and information provided to the clearing member for the purposes of the calculation of KCCP, DFCMpref, DFCCP. Such information must be provided no less frequently than the SAMA would require for monitoring the risk of the clearing member.
8.39.	KCCP and KCMi must be recalculated at least quarterly, and should also be recalculated when there are material changes to the number or exposure of cleared transactions or material changes to the financial resources of the CCP.

³² Where the firm’s internal model permission does not specifically cover centrally cleared products, the IMM scope would have to be extended to cover these products (even where the non-centrally cleared versions are included in the permission). Usually, national supervisors have a well-defined model approval/change process by which IMM firms can extend the products covered within their IMM scope. The introduction of a centrally cleared version of a product within the existing IMM scope must be considered as part of such a model change process, as opposed to a natural extension.
³³ The 20% risk weight is a minimum requirement. As with other parts of the capital adequacy framework, the national supervisor of a bank may increase the risk weight. An increase in such risk weight would be appropriate if, for example, the clearing members in a CCP are not highly rated. Any such increase in risk weight is to be communicated by the affected banks to the person completing this calculation.
³⁴ The formula puts a floor on the default fund exposure risk weight of 2%.

³⁶ Financial Stability Board, Strengthening oversight and regulation of shadow banking, Policy Framework for addressing shadow banking risks in securities lending and repos, 29 August 2013, fsb.org/wpcontent/uploads/r_130829b.

10.6	These are the haircut floors for SFTs referred to above (herein referred to as “in-scope SFTs”), expressed as percentages:
	Residual maturity of collateral Haircut Level Corporate and other issuers Securitized products ≤ 1 year debt securities, and floating rate notes 0.5% 1% >1year, ≤ 5 years debt securities 1.5% 4% >5years, ≤ 10 years debt securities 3% 6% >10 years debt securities 4% 7% Main index equities 6% Other assets within the scope of the framework 10%
10.7.	In-scope SFTs which do not meet the haircut floors must be treated as unsecured loans to the counterparties.
10.8.	To determine whether the treatment in 10.7 applies to an in-scope SFT (or a netting set of SFTs in the case of portfolio-level haircuts), we must compare the collateral haircut H (real or calculated as per the rules below) and a haircut floor f (from 10.6 above or calculated as per the below rules).
	Single in-scope SFTs
10.9.	For a single in-scope SFT not included in a netting set, the values of H and f are computed as:
	(1)	For a single cash-lent-for-collateral SFT, H and f are known since H is simply defined by the amount of collateral received and f is given in 10.6.37 For the purposes of this calculation, collateral that is called by either counterparty can be treated collateral received from the moment that it is called (i.e. the treatment is independent of the settlement period).
	(2)	For a single collateral-for-collateral SFT, lending collateral A and receiving collateral B, the H is still be defined by the amount of collateral received but the effective floor of the transaction must integrate the floor of the two types of collateral and can be computed using the following formula, which will be compared to the effective haircut of the transaction, i.e. (CB/CA)-1.38

	Netting set of SFTs
10.10.	For a netting set of SFTs an effective "portfolio" floor of the transaction must be computed using the following formula,39 where:
	(1)	ES is the net position in each security (or cash) s that is net lent;
	(2)	Ct the net position that is net borrowed; and
	(3)	fs and ft are the haircut floors for the securities that are net lent and net s t borrowed respectively.
10.11.	For a netting of SFTs, the portfolio does not breach the floor where:
10.12.	If the portfolio haircut does breach the floor, then the netting set of SFTs is subject to the treatment in 10.7. This treatment should be applied to all trades for which the security received appears in the table in 10.6 and for which, within the netting set, the bank is also a net receiver in that security. For the purposes of this calculation, collateral that is called by either counterparty can be treated collateral received from the moment that it is called (i.e. the treatment is independent of the settlement period).
10.13.	The following portfolio of trades gives an example of how this methodology works (it shows a portfolio that does not breach the floor):

fportfolio	-0.00023
	0

Minimum Capital Requirements for Credit Valuation Adjustment (CVA)
 

³⁷ For example, consider an in-scope SFT where 100 cash is lent against 101 of a corporate debt security with a 12-year maturity, H is 1% [(101- 100)/100] and f is 4% (per 10.6). Therefore, the SFT in question would be subject to the treatment in 10.7.
³⁸ For example, consider an in-scope SFT where 102 of a corporate debt security with a 10-year maturity is exchanged against 104 of equity, the effective haircut H of the transaction is 104/102 - 1 = 1.96% which has to be compared with the effective floor f of 1.06/1.03 - 1 =2.91%. Therefore, the SFT in question would be subject to the treatment in 10.7.
³⁹ The formula calculates a weighted average floor of the portfolio.

⁴⁰ Note that this is in contrast to the application of the market risk approaches set out in Chapter 3 of the Minimum Capital Requirements for Market Risk, where banks do not need SAMA approval to use the standardized approach.

11.13.	The BA-CVA calculations may be performed either via the reduced version or the full version. A bank under the BA-CVA approach can choose whether to implement the full version or the reduced version at its discretion. However, all banks using the BA-CVA must calculate the reduced version of BA-CVA capital requirements as the reduced BA-CVA is also part of the full BA-CVA capital calculations as a conservative means to limit hedging recognition.
	(1)	The full version recognizes counterparty spread hedges and is intended for banks that hedge CVA risk.
	(2)	The reduced version eliminates the element of hedging recognition from the full version. The reduced version is designed to simplify BA-CVA implementation for less sophisticated banks that do not hedge CVA.
	Reduced version of the BA-CVA (hedges are not recognized)
11.14.	The capital requirement for CVA risk under the reduced version of the BA-CVA (DSBA-CVA × Kreduced, where the discount scalar DSBA-CVA = 0.65) is calculated as follows (where the summations are taken over all counterparties that are within scope of the CVA charge), where:
	(1)	SCVAC is the CVA capital requirement that counterparty c would receive if considered on a stand-alone basis (referred to as “stand-alone CVA capital” below). See 11.15 for its calculation;
	(2)	ρ= 50%. It is supervisory correlation parameter. Its square, ρ2 = 25% represents the correlation between credit spreads of any two counterparties.41 In the formula below, the effect of p is to recognize the fact that the CVA risk to which a bank is exposed is less than the sum of the CVA risk for each counterparty, given that the credit spreads of counterparties are typically not perfectly correlated; and
	(3)	The first term under the square root in the formula below aggregates the systematic components of CVA risk, and the second term under the square root aggregates the idiosyncratic components of CVA risk.
11.15.	The stand-alone CVA capital requirements for counterparty c that are used in the formula in 11.14 (SCVAc) is calculated as follows (where the summation is across all netting sets with the counterparty), where:
	(1)	RWc is the risk weight for counterparty c that reflects the volatility of its credit spread. These risk weights are based on a combination of sector and credit quality of the counterparty as prescribed in 11.16.
	(2)	MNS is the effective maturity for the netting set NS. For banks that have SAMA’s approval to use IMM, NNS is calculated as per 7.20 and 7.21 of this framework, with the exception that the five year cap in 7.20 is not applied. For banks that do not have SAMA’s approval to use IMM, MNS is calculated according to chapter 12.46 to 12.54 of the Minimum Capital Requirements for Credit Risk, with the exception that the five-year cap in chapter 12.46 of the Minimum Capital Requirements for Credit Risk is not applied.
	(3)	EADNS is the exposure at default (EAD) of the netting set NS, calculated in the same way as the bank calculates it for minimum capital requirements for CCR.
	(4)	DFNS is a supervisory discount factor. It is 1 for banks using the IMM to calculate EAD, and is for banks not using IMM.42
	(5)	∝ = 1.4.43
11.16.	The supervisory risk weights (RWc) are given in Table 1. Credit quality is specified as either investment grade (IG), high yield (HY), or not rated (NR). Where there are no external ratings or where external ratings are not recognized within a jurisdiction, banks may, subject to SAMA's approval, map the internal rating to an external rating and assign a risk weight corresponding to either IG or HY. Otherwise, the risk weights corresponding to NR is to be applied.
	Table 1: Supervisory risk weights, RWc Sector of counterparty Credit quality of counterparty IG HY and NR Sovereigns including central banks, multilateral development banks 0.5% 2.0% Local government, government-backed nonfinancials, education and public administration 1.0% 4.0% Financials including government-backed financials 5.0% 12.0% Basic materials, energy, industrials, agriculture, manufacturing, mining and quarrying 3.0% 7.0% Consumer goods and services, transportation and storage, administrative and support service activities 3.0% 8.5% Technology, telecommunications 2.0% 5.5% Health care, utilities, professional and technical activities 1.5% 5.0% Other sector 5.0% 12.0%

	Full version of the BA-CVA (hedges are recognized)
11.17.	As set out in 11.13(1) the full version of the BA-CVA recognizes the effect of counterparty credit spread hedges. Only transactions used for the purpose of mitigating the counterparty credit spread component of CVA risk, and managed as such, can be eligible hedges.
11.18.	Only single-name credit default swaps (CDS), single-name contingent CDS and index CDS can be eligible CVA hedges.
11.19.	Eligible single-name credit instruments must:
	(1)	reference the counterparty directly; or
	(2)	reference an entity legally related to the counterparty; where legally related refers to cases where the reference name and the counterparty are either a parent and its subsidiary or two subsidiaries of a common parent; or
	(3)	reference an entity that belongs to the same sector and region as the counterparty.
11.20.	Banks that intend to use the full version of BA-CVA must calculate the reduced version (Kreduced) as well. Under the full version, capital requirement for CVA risk DSBA-CVA × Kfull is calculated as follows, where DSBA-CVA = 0.65, and β= 0.25 is the SAMA supervisory parameter that is used to provide a floor that limits the extent to which hedging can reduce the capital requirements for CVA risk:
Kfull = β ∙ Kreduced + (1 - β) ∙ Khedged
11.21.	The part of capital requirements that recognizes eligible hedges (Khedged) is calculated formulas follows (where the summations are taken over all counterparties c that are within scope of the CVA charge), where:
	(1)	Both the stand-alone CVA capital (SCVAc) and the correlation parameter (ρ) are defined in exactly the same way as for the reduced form calculation BA-CVA.
	(2)	SNHc is a quantity that gives recognition to the reduction in CVA risk of the counterparty c arising from the bank's use of single-name hedges of credit spread risk. See 11.23 for its calculation.
	(3)	IH is a quantity that gives recognition to the reduction in CVA risk across all counterparties arising from the bank's use of index hedges. See 11.24 for its calculation.
	(4)	HMAc is a quantity characterizing hedging misalignment, which is designed to limit the extent to which indirect hedges can reduce capital requirements given that they will not fully offset movements in a counterparty's credit spread. That is, with indirect hedges present Khedged cannot reach zero. See 11.25 for its calculation.
11.22.	The formula for Khedged in 11.21 comprises three main terms as below:
	(1)	The first term (ρ • ∑c(SCVAc - SNHc) - IH)2 aggregates the systematic components of CVA risk arising from the bank's counterparties, the single name hedges and the index hedges.
	(2)	The second term (1- ρ2) • ∑c(SCVAc - SNHc)2 aggregates the idiosyncratic components of CVA risk arising from the bank's counterparties and the single-name hedges.
	(3)	The third term ∑cHMAc aggregates the components of indirect hedges that are not aligned with counterparties' credit spreads.
11.23.	The quantity SNHc is calculated as follows (where the summation is across all single name hedges h that the bank has taken out to hedge the CVA risk of counterparty c), where:
	(1)	rhc is the supervisory prescribed correlation between the credit spread of counterparty c and the credit spread of a single-name hedge h of counterparty c. The value of rhc is set out the table 2 of 11.26. It is set at 100% if the hedge directly reference the counterparty c, and set at lower values if it does not.
	(2)	MhSN is the remaining maturity of single-name hedge h.
	(3)	BhSN is the notional of single-name hedge h. For single-name contingent credit default swaps (CDS), the notional is determined by the current market value of the reference portfolio or instrument.
	(4)	DFhSN is the supervisory discount factor calculated as .
	(5)	RWh is the supervisory risk weight of single-name hedge h that reflects the volatility of the credit spread of the reference name of the hedging instrument. These risk weights are based on a combination of sector and credit quality of the reference name of the hedging instrument as prescribed in Table 1 of 11.16.
11.24.	The quantity IH is calculated as follows (where the summation is across all index hedges i that the bank has taken out to hedge CVA risk), where:
	(1)	Miind is the remaining maturity of index hedge i.
	(2)	Biind is the notional of the index hedge i.
	(3)	DFiind is the supervisory discount factor calculated as
	(4)	RWi is the supervisory risk weight of the index hedge i. RWi is taken from the Table 1 of 11.16 based on the sector and credit quality of the index constituents and adjusted as follows:
		(a)	For an index where all index constituents belong to the same sector and are of the same credit quality, the relevant value in the Table 1 of 11.16 is multiplied by 0.7 to account for diversification of idiosyncratic risk within the index.
		(b)	For an index spanning multiple sectors or with a mixture of investment grade constituents and other constituents, the name-weighted average of the risk weights from the Table 1 of 11.16 should be calculated and then multiplied by 0.7.
11.25.	The quantity HMAc is calculated as follows(where the summation is across all single name hedges h that have been taken out to hedge the CVA risk of counterparty c), where rhc, MhSN, BhSN, DFhSN and RWh have the same definitions as set out in 11.23.
11.26.	The supervisory prescribed correlations rhc between the credit spread of counterparty c and the credit spread of its single-name hedge h are set in Table 2 as follows:
	Table 2: Correlations between credit spread of counterparty and single-name hedge Single-name hedge h of counterparty c Value of rhc references counterparty c directly 100% has legal relation with counterparty c 80% shares sector and region with counterparty c 50%

⁴¹ One of the basic assumptions underlying the BA-CVA is that systematic credit spread risk is driven by a single factor. Under this assumption, ρ can be interpreted as the correlation between the credit spread of a counterparty and the single credit spread systematic factor.
⁴² DF is SAMA discount factor averaged over time between today and the netting set's effective maturity date. The interest rate used for discounting is set at 5%, hence 0.05 in the formula. The product of EAD and effective maturity in the BA-CVA formula is a proxy for the area under the discounted expected exposure profile of the netting set. The IMM definition of effective maturity already includes this discount factor, hence DF is set to 1 for IMM banks. Outside IMM, netting set effective maturity is defined as an average of actual trade maturities. This definition lacks discounting, so SAMA discount factor is added to compensate for this.
⁴³ ∝ is the multiplier used to convert Effective Expected Positive Exposure (EEPE) to EAD in both SACCR and IMM. Its role in the calculation, therefore, is to convert the EAD of the netting set (EAD_NS) back to EEPE.

11.42.	The SA-CVA capital requirements are calculated as the sum of the capital requirements for delta and vega risks calculated for the entire CVA portfolio (including eligible hedges).
11.43.	The capital requirements for delta risk are calculated as the simple sum of delta capital requirements calculated independently for the following six risk classes:
	(1)	interest rate risk;
	(2)	foreign exchange (FX) risk;
	(3)	counterparty credit spread risk;
	(4)	reference credit spread risk (i.e. credit spreads that drive the CVA exposure component);
	(5)	equity risk; and
	(6)	commodity risk.
11.44.	If an instrument is deemed as an eligible hedge for credit spread delta risk, it must be assigned in its entirety (see 11.37 of this framework) either to the counterparty credit spread or to the reference credit spread risk class. Instruments must not be split between the two risk classes.
11.45.	The capital requirements for vega risk are calculated as the simple sum of vega capital requirements calculated independently for the following five risk classes. There is no vega capital requirements for counterparty credit spread risk.
	(1)	interest rate risk; (IR);
	(2)	FX risk;
	(3)	reference credit spread risk;
	(4)	equity risk; and
	(5)	commodity risk
11.46.	Delta and vega capital requirements are calculated in the same manner using the same procedures set out in 11.47 to 11.53 of this framework.
11.47.	For each risk class, (i) the sensitivity of the aggregate CVA, skCVA, and (ii) the sensitivity of the market value of all eligible hedging instruments in the CVA portfolio, skHdg, to each risk factor k in the risk class are calculated. The sensitivities are defined as the ratio of the change of the value in question (i.e. (i) aggregate CVA or (ii) market value of all CVA hedges) caused by a small change of the risk factor’s current value to the size of the change. Specific definitions for each risk class are set out in 11.54 to 11.77of this framework. These definitions include specific values of changes or shifts in risk factors. However, a bank may use smaller values of risk factor shifts if doing so is consistent with internal risk management calculations. A bank may use AAD and similar computational techniques to calculate CVA sensitivities under the SA-CVA if doing so is consistent with the bank’s internal risk management calculations and the relevant validation standards described in the SA-CVA framework.
11.48.	CVA sensitivities for vega risk are always material and must be calculated regardless of whether or not the portfolio includes options. When CVA sensitivities for vega risk are calculated, the volatility shift must apply to both types of volatilities that appear in exposure models:
	(1)	volatilities used for generating risk factor paths; and
	(2)	volatilities used for pricing options.
11.49.	If a hedging instrument is an index, its sensitivities to all risk factors upon which the value of the index depends must be calculated. The index sensitivity to risk factor k must be calculated by applying the shift of risk factor k to all index constituents that depend on this risk factor and recalculating the changed value of the index. For example, to calculate delta sensitivity of S&P500 to large financial companies, a bank must apply the relevant shift to equity prices of all large financial companies that are constituents of S&P500 and re-compute the index.
11.50.	For the following risk classes, a bank may choose to introduce a set of additional risk factors that directly correspond to qualified credit and equity indices. For delta risks, a credit or equity index is qualified if it satisfies liquidity and diversification conditions specified in Chapter 7.31 of the Minimum Capital Requirements for Market Risk; for vega risks, any credit or equity index is qualified. Under this option, a bank must calculate sensitivities of CVA and the eligible CVA hedges to the qualified index risk factors in addition to sensitivities to the non-index risk factors. Under this option, for a covered transaction or an eligible hedging instrument whose underlying is a qualified index, its contribution to sensitivities to the index constituents is replaced with its contribution to a single sensitivity to the underlying index. For example, for a portfolio consisting only of equity derivatives referencing only qualified equity indices, no calculation of CVA sensitivities to non-index equity risk factors is necessary. If more than 75% of constituents of a qualified index (taking into account the weightings of the constituents) are mapped to the same sector, the entire index must be mapped to that sector and treated as a single-name sensitivity in that bucket. In all other cases, the sensitivity must be mapped to the applicable index bucket.
	(1)	counterparty credit spread risk;
	(2)	reference credit spread risk; and
	(3)	equity risk.
11.51.	The weighted sensitivities WSkCVA and WSkHdg for each risk factor k are calculated by multiplying the net sensitivities SkCVA and SkHdg, respectively, by the corresponding risk weight RWk (the risk weights applicable to each risk class are specified in 11.54 to 11.77 of this framework).
WSkCVA = RWkskCVA
WSkHdg = RWkskHdg
11.52.	The net weighted sensitivity of the CVA portfolio Sk to risk factor k is obtained by44:
11.53.	For each risk class, the net sensitivities are aggregated as follows:
	(1)	The weighted sensitivities must be aggregated into a capital requirement Kb within each bucket b (the buckets and correlation parameters ρKl applicable to each risk class are specified in 11.54 to 11.77 of this framework), where R is the hedging disallowance parameter, set at 0.01, that prevents the possibility of recognizing perfect hedging of CVA risk.
	(2)	Bucket-level capital requirements must then be aggregated across buckets within each risk class (the correlation parameters γbc applicable to each risk class are specified in 11.54 to 11.77 of this framework). Note that this equation differs from the corresponding aggregation equation for market risk capital requirements in Chapter 7.4 of the Minimum Capital Requirements for Market Risk, including the multiplier mCVA.
	(3)	In calculating K in above (2), S is defined as the sum of the weighted b sensitivities WS for all risk factors k within bucket b, floored by -K and k b capped by K, and the S is defined in the same way for all risk factors k in b c bucket c:
	Interest rates buckets, risk factors, sensitivities, risk weights and correlations
11.54.	For interest rate delta and vega risks, buckets must be set per individual currencies.
11.55.	For interest rate delta and vega risks, cross-bucket correlation γbc is set at 0.5 for all currency pairs.
11.56.	The interest rate delta risk factors for a bank’s reporting currency and for the following currencies USD, EUR, GBP, AUD, CAD, SEK or JPY:
	(1)	The interest rate delta risk factors are the absolute changes of the inflation rate and of the risk-free yields for the following five tenors: 1 year, 2 years, 5 years, 10 years and 30 years.
	(2)	The sensitivities to the abovementioned risk-free yields are measured by changing the risk-free yield for a given tenor for all curves in a given currency by 1 basis point (0.0001 in absolute terms) and dividing the resulting change in the aggregate CVA (or the value of CVA hedges) by 0.0001. The sensitivity to the inflation rate is obtained by changing the inflation rate by 1 basis point (0.0001 in absolute terms) and dividing the resulting change in the aggregate CVA (or the value of CVA hedges) by 0.0001.
	(3)	The risk weights RWk are set as follows:
		Table 3: Risk weight for interest rate risk (specified currencies) Risk factor 1 year 2 years 5 years 10 years 30 years Inflation Risk weight 1.11% 0.93% 0.74% 0.74% 0.74% 1.11%
	(4)	The correlations between pairs of risk factors ρkl are set as follows:
		Table 4: Correlations for interest rate risk factors (specified currencies)   1 year 2 years 5 years 10 years 30 years Inflation 1 year 100% 91% 72% 55% 31% 40% 2 years   100% 87% 72% 45% 40% 5 years     100% 91% 68% 40% 10 years       100% 83% 40% 30 years         100% 40% Inflation           100%
11.57.	The interest rate delta risk factors for other currencies not specified in 11.56 of this framework:
	(1)	The interest rate risk factors are the absolute change of the inflation rate and the parallel shift of the entire risk-free yield curve for a given currency.
	(2)	The sensitivity to the yield curve is measured by applying a parallel shift to all risk-free yield curves in a given currency by 1 basis point (0.0001 in absolute terms) and dividing the resulting change in the aggregate CVA (or the value of CVA hedges) by 0.0001. The sensitivity to the inflation rate is obtained by changing the inflation rate by 1 basis point (0.0001 in absolute terms) and dividing the resulting change in the aggregate CVA (or the value of CVA hedges) by 0.0001.
	(3)	The risk weights for both the risk-free yield curve and the inflation rate RWk are set at 1.85%.
	(4)	The correlations between the risk-free yield curve and the inflation rate ρKl are set at 40%.
11.58.	The interest rate vega risk factors for all currencies:
	(1)	The interest rate vega risk factors are a simultaneous relative change of all volatilities for the inflation rate and a simultaneous relative change of all interest rate volatilities for a given currency.
	(2)	The sensitivity to (i) the interest rate volatilities or (ii) inflation rate volatilities is measured by respectively applying a simultaneous shift to (i) all interest rate volatilities or (ii) inflation rate volatilities by 1% relative to their current values and dividing the resulting change in the aggregate CVA (or the value of CVA hedges) by 0.01.
	(3)	The risk weights for both the interest rate volatilities and the inflation rate volatilities RWk are set to 100%.
	(4)	Correlations between the interest rate volatilities and the inflation rate volatilities ρKl are set at 40%.

⁴⁴ Note that the formula in 11.52 is set out under the convention that the CVA is positive as specified in 11.32 (1). It intends to recognize the risk reducing effect of hedging. For example, when hedging the counterparty credit spread component of CVA risk for a specific counterparty by buying credit protection on the counterparty: if the counterparty’s credit spread widens, the CVA (expressed as a positive value) increases resulting in the positive CVA sensitivity to the counterparty credit spread. At the same time, as the value of the hedge from the bank’s perspective increases as well (as credit protection becomes more valuable), the sensitivity of the hedge is also positive. The positive weighted sensitivities of the CVA and its hedge offset each other using the formula with the minus sign. If CVA loss had been expressed as a negative value, the minus sign in 11.52 would have been replaced by a plus sign.
⁴⁵ For example, if a SAR-reporting bank holds an instrument that references the USD-GBP exchange rate, the bank must measure CVA sensitivity both to the SAR-GBP exchange rate and to the SAR- USD exchange rate.