¹¹ For example, each instrument that is an option or that includes an option (eg an embedded option such as convertibility or rate dependent prepayment and that is subject to the capital requirements for market risk). A non-exhaustive list of example instruments with optionality includes: calls, puts, caps, floors, swaptions, barrier options and exotic options.
¹² An instrument with a prepayment option is a debt instrument which grants the debtor the right to repay part of or the entire principal amount before the contractual maturity without having to compensate for any foregone interest. The debtor can exercise this option with a financial gain to obtain funding over the remaining maturity of the instrument at a lower rate in other ways in the market.

Sensitivities-based method: definition of sensitivities
7.15	Sensitivities for each risk class must be expressed in the reporting currency of the bank.
7.16	For each risk factor defined in [7.8] to [7.14], sensitivities are calculated as the change in the market value of the instrument as a result of applying a specified shift to each risk factor, assuming all the other relevant risk factors are held at the current level as defined in [7.17] to [7.38].
As per [7.17], a bank may make use of alternative formulations of sensitivities based on pricing models that the bank’s independent risk control unit uses to report market risks or actual profits and losses to senior management. In doing so, the bank is to demonstrate to SAMA that the alternative formulations of sensitivities yield results very close to the prescribed formulations.
Requirements on instrument price or pricing models for sensitivity calculation
7.17	In calculating the risk capital requirement under the sensitivities-based method in [7], the bank must determine each delta and vega sensitivity and curvature scenario based on instrument prices or pricing models that an independent risk control unit within a bank uses to report market risks or actual profits and losses to senior management.
[7.17] states that banks must determine each delta sensitivity, vega sensitivity and curvature scenario based on instrument prices or pricing models that an independent risk control unit within a bank uses to report market risks or actual profits and losses to senior management. Banks should use zero rate or market rate sensitivities consistent with the pricing models referenced in that paragraph.
7.18	A key assumption of the standardised approach for market risk is that a bank’s pricing models used in actual profit and loss reporting provide an appropriate basis for the determination of regulatory capital requirements for all market risks. To ensure such adequacy, banks must at a minimum establish a framework for Prudent Valuation Guidance set out in Basel Framework .
Sensitivity definitions for delta risk
7.19	Delta GIRR: the sensitivity is defined as the PV01. PV01 is measured by changing the interest rate r at tenor t (rt) of the risk-free yield curve in a given currency by 1 basis point (ie 0.0001 in absolute terms) and dividing the resulting change in the market value of the instrument (Vi) by 0.0001 (ie 0.01%) as follows, where:
	(1)	rt is the risk-free yield curve at tenor t;
	(2)	cst is the credit spread curve at tenor t; and
	(3)	Vi is the market value of the instrument i as a function of the risk-free interest rate curve and credit spread curve:
7.20	Delta CSR non-securitisation, securitisation (non-CTP) and securitisation (CTP): the sensitivity is defined as CS01. The CS01 (sensitivity) of an instrument i is measured by changing a credit spread cs at tenor t (cst) by 1 basis point (ie 0.0001 in absolute terms) and dividing the resulting change in the market value of the instrument (Vi) by 0.0001 (ie 0.01%) as follows:
In cases where the bank does not have counterparty-specific money market curves, the bank can proxy PV01 to CS01
7.21	Delta equity spot: the sensitivity is measured by changing the equity spot price by 1 percentage point (ie 0.01 in relative terms) and dividing the resulting change in the market value of the instrument (Vi) by 0.01 (ie 1%) as follows, where:
	(1)	k is a given equity;
	(2)	EQk is the market value of equity k; and
	(3)	Vi is the market value of instrument i as a function of the price of equity k.
7.22	Delta equity repo rates: the sensitivity is measured by applying a parallel shift to the equity repo rate term structure by 1 basis point (ie 0.0001 in absolute terms) and dividing the resulting change in the market value of the instrument Vi by 0.0001 (ie 0.01%) as follows, where:
	(1)	k is a given equity;
	(2)	RTSk is the repo term structure of equity k; and
	(3)	Vi is the market value of instrument i as a function of the repo term structure of equity k.
7.23	Delta commodity: the sensitivity is measured by changing the commodity spot price by 1 percentage point (ie 0.01 in relative terms) and dividing the resulting change in the market value of the instrument Vi by 0.01 (ie 1%) as follows, where:
	(1)	k is a given commodity;
	(2)	CTYk is the market value of commodity k; and
	(3)	Vi is the market value of instrument i as a function of the spot price of commodity k:
7.24	Delta FX: the sensitivity is measured by changing the exchange rate by 1 percentage point (ie 0.01 in relative terms) and dividing the resulting change in the market value of the instrument Vi by 0.01 (ie 1%), where:
	(1)	k is a given currency;
	(2)	FXk is the exchange rate between a given currency and a bank’s reporting currency or base currency, where the FX spot rate is the current market price of one unit of another currency expressed in the units of the bank’s reporting currency or base currency; and
	(3)	Vi is the market value of instrument i as a function of the exchange rate k:
Sensitivity definitions for vega risk
7.25	The option-level vega risk sensitivity to a given risk factor18 is measured by multiplying vega by the implied volatility of the option as follows, where:
	(1)	vega, ∂vi/∂σi, is defined as the change in the market value of the option Vi as a result of a small amount of change to the implied volatility σi, and
	(2)	the instrument’s vega and implied volatility used in the calculation of vega sensitivities must be sourced from pricing models used by the independent risk control unit of the bank.
7.26	The following sets out how to derive vega risk sensitivities in specific cases:
	(1)	Options that do not have a maturity, are assigned to the longest prescribed maturity tenor, and these options are also assigned to the RRAO.
	(2)	Options that do not have a strike or barrier and options that have multiple strikes or barriers, are mapped to strikes and maturity used internally to price the option, and these options are also assigned to the RRAO.
	(3)	CTP securitisation tranches that do not have an implied volatility, are not subject to vega risk capital requirement. Such instruments may not, however, be exempt from delta and curvature risk capital requirements.
Under the sensitivities-based method and In the case where options do not have a specified maturity (eg cancellable swaps), the bank must assign those options to the longest prescribed maturity tenor for vega risk sensitivities and also assign such options to the RRAO.
In the case of the bank viewing the optionality of the cancellable swap as a swaption, the bank must assign the swaption to the longest prescribed maturity tenor for vega risk sensitivities (as it does not have a specified maturity) and derive the residual maturity of the underlying of the option accordingly.

¹³ The assignment of risk factors to the specified tenors should be performed by linear interpolation or a method that is most consistent with the pricing functions used by the independent risk control function of a bank to report market risks or P&L to senior management.
¹⁴ Cross-currency basis are basis added to a yield curve in order to evaluate a swap for which the two legs are paid in two different currencies. They are in particular used by market participants to price cross-currency interest rate swaps paying a fixed or a floating leg in one currency, receiving a fixed or a floating leg in a second currency, and including an exchange of the notional in the two currencies at the start date and at the end date of the swap.
¹⁵ For example, an option with a forward starting cap, lasting 12 months, consists of four consecutive caplets on USD three month Libor. There are four (independent) options, with option expiry dates in 12, 15, 18 and 21 months. These options are all on underlying USD three-month Libor; the underlying always matures three months after the option expiry date (its residual maturity being three months). Therefore, the implied volatilities for a regular forward starting cap, which would start in one year and last for 12 months should be defined along the following two dimensions: (i) the maturity of the option’s individual components (caplets) – 12, 15, 18 and 21 months; and (ii) the residual maturity of the underlying of the option – three months.
¹⁶ For example, a contract that can be delivered in five ports can be considered having the same delivery location as another contract if and only if it can be delivered in the same five ports. However, it cannot be considered having the same delivery location as another contract that can be delivered in only four (or less) of those five ports.
¹⁷ For example, for an FX forward referencing USD/JPY, the relevant risk factors for a CAD- reporting bank to consider are the exchange rates USD/CAD and JPY/CAD. If that CAD- reporting bank calculates FX risk relative to a USD base currency, it would consider separate deltas for the exchange rate JPY/USD risk and CAD/USD FX translation risk and then translate the resulting capital requirement to CAD at the USD/CAD spot exchange rate.
¹⁸ As specified in the vega risk factor definitions in [7.8] to [7.14], the implied volatility of the option must be mapped to one or more maturity tenors.
¹⁹ Since vega (, ∂v/∂σi) of an instrument is multiplied by its implied volatility ( ), the vega risk sensitivity for that instrument will be the same under the log-normal assumption and the normal assumption. As a consequence, banks may use a log-normal or normal assumption for GIRR and CSR (in recognition of the trade-offs between constrained specification and computational burden for a standardised approach). For the other risk classes, banks must only use a log-normal assumption (in recognition that this is aligned with common practices across jurisdictions).
²⁰ In other words, a bank can initially not apply a look-through approach, and later decide to apply it. However once applied (for a certain type of instrument referencing a particular index), the bank will require SAMA approval to revert to a “no look-through” approach.
²¹ As specified in the vega risk factor definitions in [7.8] to [7.14], the implied volatility of an option must be mapped to one or more maturity tenors.

Delta CSR non-securitisations buckets, risk weights and correlations
7.51	For delta CSR non-securitisations, buckets are set along two dimensions – credit quality and sector – as set out in Table 3. The CSR non-securitisation sensitivities or risk exposures should first be assigned to a bucket defined before calculating weighted sensitivities by applying a risk weight.
Buckets for delta CSR non-securitisations Table 3 Bucket number Credit quality Sector 1 Investment grade (IG) Sovereigns including central banks, multilateral development banks 2 Local government, government-backed non-financials, education, public administration 3 Financials including government-backed financials 4 Basic materials, energy, industrials, agriculture, manufacturing, mining and quarrying 5 Consumer goods and services, transportation and storage, administrative and support service activities 6 Technology, telecommunications 7 Health care, utilities, professional and technical activities 8 Covered bonds25 9 High yield (HY) & non-rated (NR) Sovereigns including central banks, multilateral development banks 10 Local government, government-backed non-financials, education, public administration 11 Financials including government-backed financials 12 Basic materials, energy, industrials, agriculture, manufacturing, mining and quarrying 13 Consumer goods and services, transportation and storage, administrative and support service activities 14 Technology, telecommunications 15 Health care, utilities, professional and technical activities 16 Other sector26   17 IG indices   18 HY indices
Consistent with the treatment of external ratings under SAMA Minimum Capital Requirements for Credit Risk paragraphs 8.10 and 8.12, if there are two ratings which map into different risk weights, the higher risk weight should be applied. If there are three or more ratings with different risk weights, the ratings corresponding to the two lowest risk weights should be referred to and the higher of those two risk weights will be applied.
Consistent with the treatment where there are no external ratings, banks may, subject to SAMA approval:
-	For the purpose of assigning delta CSR non-securitisation risk weights, map the internal rating to an external rating, and assign a risk weight corresponding to either “investment grade” or “high yield” in [7.51];
-	For the purpose of assigning default risk weights under the DRC requirement, map the internal rating to an external rating, and assign a risk weight corresponding to one of the seven external ratings in the table included [8.24]; or
-	Apply the risk weights specified in [7.51] and [8.24] for unrated/non-rated categories.
7.52	To assign a risk exposure to a sector, banks must rely on a classification that is commonly used in the market for grouping issuers by industry sector.
	(1)	The bank must assign each issuer to one and only one of the sector buckets in the table under [7.51].
	(2)	Risk positions from any issuer that a bank cannot assign to a sector in this fashion must be assigned to the other sector (ie bucket 16).
7.53	For calculating weighted sensitivities, the risk weights for buckets 1 to 18 are set out in Table 4. Risk weights are the same for all tenors (ie 0.5 years, 1 year, 3 years, 5 years, 10 years) within each bucket: Risk weights for buckets for delta CSR non-securitisations
Risk weights for buckets for delta CSR non-securitisations Table 4 Bucket number Risk weight 1 0.5% 2 1.0% 3 5.0% 4 3.0% 5 3.0% 6 2.0% 7 1.5% 8 2.5%27 9 2.0% 10 4.0% 11 12.0% 12 7.0% 13 8.5% 14 5.5% 15 5.0% 16 12.0% 17 1.5% 18 5.0%
7.54	For buckets 1 to 15, for aggregating delta CSR non-securitisations risk positions within a bucket, the correlation parameter ρkl between two weighted sensitivities WSk and WSɭ within the same bucket, is set as follows, where:
	(1)	ρkl (name) is equal to 1 where the two names of sensitivities k and ∫ are identical, and 35% otherwise;
	(2)	ρkl (tenor) is equal to 1 if the two tenors of the sensitivities k and ∫ are identical, and to 65% otherwise; and
	(3)	ρkl (basis) is equal to 1 if the two sensitivities are related to same curves, and 99.90% otherwise.
Bond and CDS credit spreads are considered distinct risk factors under [7.9](1), and ρkl(basis) referenced in [7.54] and [7.55] is meant to capture only the bond-CDS basis.
7.55	For buckets 17 and 18, for aggregating delta CSR non-securitisations risk positions within a bucket, the correlation parameter ρkl between two weighted sensitivities WSk and WSi  within the same bucket is set as follows, where:
	(1)	ρk (name) is equal to 1 where the two names of sensitivities k and ∫ are identical, and 80% otherwise;
	(2)	ρk (tenor) is equal to 1 if the two tenors of the sensitivities k and ∫ are identical, and to 65% otherwise; and
	(3)	ρkl (basis) is equal to 1 if the two sensitivities are related to same curves, and 99.90%.
7.56	The correlations above do not apply to the other sector bucket (ie bucket 16).
	(1)	The aggregation of delta CSR non-securitisation risk positions within the other sector bucket (ie bucket 16) would be equal to the simple sum of the absolute values of the net weighted sensitivities allocated to this bucket. The same method applies to the aggregation of vega risk positions.
	(2)	The aggregation of curvature CSR non-securitisation risk positions within the other sector bucket (ie bucket 16) would be calculated by the formula below.
7.57	For aggregating delta CSR non-securitisation risk positions across buckets 1 to 16, the correlation parameter γbc is set as follows, where:
	(1)	γbc(rating) is equal to 50% where the two buckets b and c are both in buckets 1 to 15 and have a different rating category (either IG or HY/NR). γbc(rating) is equal to 1 otherwise; and
	(2)	γbc(sector) is equal to 1 if the two buckets belong to the same sector, and to the specified numbers in Table 5 otherwise.
Values of  γbc(sector) where the buckets do not belong to the same sector Table 5 Bucket 1/9 2/10 3/11 4/12 5/13 6/14 7/15 8 16 17 18 1/9   75% 10% 20% 25% 20% 15% 10% 0% 45% 45% 2/10     5% 15% 20% 15% 10% 10% 0% 45% 45% 3/11       5% 15% 20% 5% 20% 0% 45% 45% 4/12         20% 25% 5% 5% 0% 45% 45% 5/13           25% 5% 15% 0% 45% 45% 6/14             5% 20% 0% 45% 45% 7/15               5% 0% 45% 45% 8                 0% 45% 45% 16                   0% 0% 17                     75% 18

Delta CSR securitisation (CTP) buckets, risk weights and correlations
7.58	Sensitivities to CSR arising from the CTP and its hedges are treated as a separate risk class as set out in 7.1]. The buckets, risk weights and correlations for the CSR securitisations (CTP) apply as follows:
	(1)	The same bucket structure and correlation structure apply to the CSR securitisations (CTP) as those for the CSR non-securitisation framework as set out in [7.51] to [7.57] with an exception of index buckets (ie buckets 17 and 18).
	(2)	The risk weights and correlation parameters of the delta CSR nonsecuritisations are modified to reflect longer liquidity horizons and larger basis risk as specified in [7.59] to [7.61].
7.59	For calculating weighted sensitivities, the risk weights for buckets 1 to 16 are set out in Table 6. Risk weights are the same for all tenors (ie 0.5 years, 1 year, 3 years, 5 years, 10 years) within each bucket:
Risk weights for sensitivities to CSR arising from the CTP Table 6 Bucket number Risk weight 1 4.0% 2 4.0% 3 8.0% 4 5.0% 5 4.0% 6 3.0% 7 2.0% 8 6.0% 9 13.0% 10 13.0% 11 16.0% 12 10.0% 13 12.0% 14 12.0% 15 12.0% 16 13.0%
7.60	For aggregating delta CSR securitisations (CTP) risk positions within a bucket, the delta risk correlation ρkl is derived the same way as in [7.54] and [7.55], except that the correlation parameter applying when the sensitivities are not related to same curves, ρkl (basis), is modified.
	(1)	ρkl (basis) is now equal to 1 if the two sensitivities are related to same curves, and 99.00% otherwise.
	(2)	The identical correlation parameters for ρkl(name) and ρkl(tenor) to CSR non-securitisation as set out in [7.54] and [7.55] apply.
7.61	For aggregating delta CSR securitisations (CTP) risk positions across buckets, the correlation parameters for γbc are identical to CSR non-securitisation as set out in [7.57].
Delta CSR securitisation (non-CTP) buckets, risk weights and correlations
7.62	For delta CSR securitisations not in the CTP, buckets are set along two dimensions– credit quality and sector – as set out in Table 7. The delta CSR securitisation (non-CTP) sensitivities or risk exposures must first be assigned to a bucket before calculating weighted sensitivities by applying a risk weight.
Buckets for delta CSR securitisations (non-CTP) Table 7 Bucket number Credit quality Sector 1 Senior investment grade (IG) RMBS – Prime 2 RMBS – Mid-prime 3 RMBS – Sub-prime 4 CMBS 5 Asset-backed securities (ABS) – Student loans 6 ABS – Credit cards 7 ABS – Auto 8 Collateralised loan obligation (CLO) non-CTP 9 Non-senior IG RMBS – Prime 10 RMBS – Mid-prime 11 RMBS – Sub-prime 12 Commercial mortgage-backed securities (CMBS) 13 ABS – Student loans 14 ABS – Credit cards 15 ABS – Auto 16 CLO non-CTP 17 High yield & non-rated RMBS – Prime 18 RMBS – Mid-prime 19 RMBS – Sub-prime 20 CMBS 21 ABS – Student loans 22 ABS – Credit cards 23 ABS – Auto 24 CLO non-CTP 25 Other Sector29
7.63	To assign a risk exposure to a sector, banks must rely on a classification that is commonly used in the market for grouping tranches by type.
	(1)	The bank must assign each tranche to one of the sector buckets in above Table 7.
	(2)	Risk positions from any tranche that a bank cannot assign to a sector in this fashion must be assigned to the other sector (ie bucket 25).
7.64	For calculating weighted sensitivities, the risk weights for buckets 1 to 8 (senior IG) are set out in Table 8:
Risk weights for buckets 1 to 8 for delta CSR securitisations (non-CTP) Table 8 Bucket number Risk weight (in percentage points) 1 0.9% 2 1.5% 3 2.0% 4 2.0% 5 0.8% 6 1.2% 7 1.2% 8 1.4%
7.65	The risk weights for buckets 9 to 16 (non-senior investment grade) are then equal to the corresponding risk weights for buckets 1 to 8 scaled up by a multiplication by 1.25. For instance, the risk weight for bucket 9 is equal to 1.25 × 0.9% = 1.125%.
7.66	The risk weights for buckets 17 to 24 (high yield and non-rated) are then equal to the corresponding risk weights for buckets 1 to 8 scaled up by a multiplication by 1.75. For instance, the risk weight for bucket 17 is equal to 1.75 × 0.9% = 1.575%.
7.67	The risk weight for bucket 25 is set at 3.5%.
7.68	For aggregating delta CSR securitisations (non-CTP) risk positions within a bucket, the correlation parameter ρkl between two sensitivities WSk and WSl within the same bucket, is set as follows, where:
	(1)	ρkl (tranche) is equal to 1 where the two names of sensitivities k and l are within the same bucket and related to the same securitisation tranche (more than 80% overlap in notional terms), and 40% otherwise;
	(2)	ρkl (tenor) is equal to 1 if the two tenors of the sensitivities k and l are identical, and to 80% otherwise; and
	(3)	ρkl (basis) is equal to 1 if the two sensitivities are related to same curves, and 99.90% otherwise.
[7.68] includes ρkl (tranche) , which equals 1 where the two sensitivities within the same bucket are related to the same securitisation tranche, or 40% otherwise. There is no issuer factor. This mean a two sensitivities relating to the same issuer but different tranches require 40% correlation. There is no granularity for issuers in the delta CSR securitisation part as set out in [7.10]. Where two tranches have exactly the same issuer, same tenor and same basis, but different tranches (ie different credit quality), the correlation must be 40%.
7.69	The correlations above do not apply to the other sector bucket (ie bucket 25).
	(1)	The aggregation of delta CSR securitisations (non-CTP) risk positions within the other sector bucket would be equal to the simple sum of the absolute values of the net weighted sensitivities allocated to this bucket. The same method applies to the aggregation of vega risk position.
	(2)	The aggregation of curvature CSR risk positions within the other sector bucket (ie bucket 16) would be calculated by the formula below.
7.70	For aggregating delta CSR securitisations (non-CTP) risk positions across buckets 1 to 24, the correlation parameter γbc is set as 0%.
7.71	For aggregating delta CSR securitisations (non-CTP) risk positions between the other sector bucket (ie bucket 25) and buckets 1 to 24, the correlation parameter γbc is set at 1. Bucket level capital requirements will be simply summed up to the overall risk class level capital requirements, with no diversification or hedging effects recognised with any bucket.

Equity risk buckets, risk weights and correlations
7.72	For delta equity risk, buckets are set along three dimensions – market capitalisation, economy and sector – as set out in Table 9. The equity risk sensitivities or exposures must first be assigned to a bucket before calculating weighted sensitivities by applying a risk weight.
Buckets for delta sensitivities to equity risk Table 9 Bucket number Market cap Economy Sector 1 Large Emerging market economy Consumer goods and services, transportation and storage, administrative and support service activities, healthcare, utilities 2   Telecommunications, industrials 3 Basic materials, energy, agriculture, manufacturing, mining and quarrying 4 Financials including government-backed financials, real estate activities, technology 5   Advanced economy Consumer goods and services, transportation and storage, administrative and support service activities, healthcare, utilities 6   Telecommunications, industrials 7 Basic materials, energy, agriculture, manufacturing, mining and quarrying 8 Financials including government-backed financials, real estate activities, technology 9 Small Emerging market economy All sectors described under bucket numbers 1, 2, 3 and 4 10 Advanced economy All sectors described under bucket numbers 5, 6, 7 and 8 11 Other sector30 12 Large market cap, advanced economy equity indices (non-sector specific) 13 Other equity indices (non-sector specific)
7.73	Market capitalisation (market cap) is defined as the sum of the market capitalisations based on the market value of the total outstanding shares issued by the same listed legal entity or a group of legal entities across all stock markets globally, where the total outstanding shares issued by the group of legal entities refer to cases where the listed entity is a parent company of a group of legal entities. Under no circumstances should the sum of the market capitalisations of multiple related listed entities be used to determine whether a listed entity is “large market cap” or “small market cap”.
7.74	Large market cap is defined as a market capitalisation equal to or greater than USD 2 billion and small market cap is defined as a market capitalisation of less than USD 2 billion.
7.75	The advanced economies are Canada, the United States, Mexico, the euro area, the non-euro area western European countries (the United Kingdom, Norway, Sweden, Denmark and Switzerland), Japan, Oceania (Australia and New Zealand), Singapore and Hong Kong SAR.
An equity issuer must be allocated to a particular bucket according to the most material country or region in which the issuer operates. As stated in [7.76]: “For multinational multisector equity issuers, the allocation to a particular bucket must be done according to the most material region and sector in which the issuer operates.
7.76	To assign a risk exposure to a sector, banks must rely on a classification that is commonly used in the market for grouping issuers by industry sector.
	(1)	The bank must assign each issuer to one of the sector buckets in the table under [7.72] and it must assign all issuers from the same industry to the same sector.
	(2)	Risk positions from any issuer that a bank cannot assign to a sector in this fashion must be assigned to the other sector (ie bucket 11).
	(3)	For multinational multi-sector equity issuers, the allocation to a particular bucket must be done according to the most material region and sector in which the issuer operates.
7.77	For calculating weighted sensitivities, the risk weights for the sensitivities to each of equity spot price and equity repo rates for buckets 1 to 13 are set out in Table 10:
Risk weights for buckets 1 to 13 for sensitivities to equity risk   Table 10 Bucket number Risk weight for equity spot price Risk weight for equity repo rate 1 55% 0.55% 2 60% 0.60% 3 45% 0.45% 4 55% 0.55% 5 30% 0.30% 6 35% 0.35% 7 40% 0.40% 8 50% 0.50% 9 70% 0.70% 10 50% 0.50% 11 70% 0.70% 12 15% 0.15% 13 25% 0.25%
7.78	For aggregating delta equity risk positions within a bucket, the correlation parameter ρkl between two sensitivities WSk and WSl within the same bucket is set at as follows
	(1)	The correlation parameter ρkl is set at 99.90%, where:
		(a)	one is a sensitivity to an equity spot price and the other a sensitivity to an equity repo rates; and
		(b)	both are related to the same equity issuer name.
	(2)	The correlation parameter ρkl is set out in (a) to (d) below, where both sensitivities are to equity spot price, and where:
		(a)	15% between two sensitivities within the same bucket that fall under large market cap, emerging market economy (bucket number 1, 2, 3 or 4).
		(b)	25% between two sensitivities within the same bucket that fall under large market cap, advanced economy (bucket number 5, 6, 7 or 8).
		(c)	7.5% between two sensitivities within the same bucket that fall under small market cap, emerging market economy (bucket number 9).
		(d)	12.5% between two sensitivities within the same bucket that fall under small market cap, advanced economy (bucket number 10).
		(e)	80% between two sensitivities within the same bucket that fall under either index bucket (bucket number 12 or 13)
	(3)	The same correlation parameter ρkl as set out in above (2)(a) to (d) apply, where both sensitivities are to equity repo rates.
	(4)	The correlation parameter ρkl is set as each parameter specified in above (2)(a) to (d) multiplied by 99.90%, where:
		(a)	One is a sensitivity to an equity spot price and the other a sensitivity to an equity repo rate; and
		(b)	Each sensitivity is related to a different equity issuer name.
7.79	The correlations set out above do not apply to the other sector bucket (ie bucket 11).
	(1)	The aggregation of equity risk positions within the other sector bucket capital requirement would be equal to the simple sum of the absolute values of the net weighted sensitivities allocated to this bucket. The same method applies to the aggregation of vega risk positions.
	(2)	The aggregation of curvature equity risk positions within the other sector bucket (ie bucket 11) would be calculated by the formula:
7.80	For aggregating delta equity risk positions across buckets 1 to 13, the correlation parameter γbc is set at:
	(1)	15% if bucket b and bucket c fall within bucket numbers 1 to 10;
	(2)	0% if either of bucket b and bucket c is bucket 11;
	(3)	75% if bucket b and bucket c are bucket numbers 12 and 13 (i.e. one is bucket 12, one is bucket 13); and
	(4)	45% otherwise.

Commodity risk buckets, risk weights and correlations
7.81	For delta commodity risk, 11 buckets that group commodities by common characteristics are set out in Table 11.
7.82	For calculating weighted sensitivities, the risk weights for each bucket are set out in Table 11:
Delta commodity buckets and risk weights Table 11 Bucket number Commodity bucket Examples of commodities allocated to each commodity bucket (non-exhaustive) Risk weight 1 Energy - solid combustibles Coal, charcoal, wood pellets, uranium 30% 2 Energy - liquid combustibles Light-sweet crude oil; heavy crude oil; West Texas Intermediate (WTI) crude; Brent crude; etc (ie various types of crude oil) Bioethanol; biodiesel; etc (ie various biofuels) Propane; ethane; gasoline; methanol; butane; etc (ie various petrochemicals) Jet fuel; kerosene; gasoil; fuel oil; naphtha; heating oil; diesel etc (ie various refined fuels) 35% 3 Energy - electricity and carbon trading Spot electricity; day-ahead electricity; peak electricity; off-peak electricity (ie various electricity types) Certified emissions reductions; in-delivery month EU allowance; Regional Greenhouse Gas Initiative CO2 allowance; renewable energy certificates; etc (ie various carbon trading emissions) 60% 4 Freight Capesize; Panamax; Handysize; Supramax (ie various types of dry-bulk route) Suezmax; Aframax; very large crude carriers (ie various liquid-bulk/gas shipping route) 80% 5 Metals — non-precious Aluminium; copper; lead; nickel; tin; zinc (ie various base metals) Steel billet; steel wire; steel coil; steel scrap; steel rebar; iron ore; tungsten; vanadium; titanium; tantalum (ie steel raw materials) Cobalt; manganese; molybdenum (ie various minor metals) 40% 6 Gaseous combustibles Natural gas; liquefied natural gas 45% 7 Precious metals (including gold) Gold; silver; platinum; palladium 20% 8 Grains and oilseed Corn; wheat; soybean seed; soybean oil; soybean meal; oats; palm oil; canola; barley; rapeseed seed; rapeseed oil; rapeseed meal; red bean; sorghum; coconut oil; olive oil; peanut oil; sunflower oil; rice 35% 9 Livestock and dairy Live cattle; feeder cattle; hog; poultry; lamb; fish; shrimp; milk; whey; eggs; butter; cheese 25% 10 Softs and other agriculturals Cocoa; arabica coffee; robusta coffee; tea; citrus juice; orange juice; potatoes; sugar; cotton; wool; lumber;pulp; rubber 35% 11 Other commodity Potash; fertilizer; phosphate rocks (ie various industrial materials) Rare earths; terephthalic acid; flat glass 50%
7.83	For the purpose of aggregating commodity risk positions within a bucket using a correlation parameter, the correlation parameter ρkl between two sensitivities WSk and WSl within the same bucket, is set as follows, where:
	(1)	ρkl(cty) is equal to 1 where the two commodities of sensitivities k and l are identical, and to the intra-bucket correlations in Table 12 otherwise, where, any two commodities are considered distinct commodities if in the market two contracts are considered distinct when the only difference between each other is the underlying commodity to be delivered. For example, WTI and Brent in bucket 2 (ie energy – liquid combustibles) would typically be treated as distinct commodities;
	(2)	ρkl(tenor) is equal to 1 if the two tenors of the sensitivities k and l are identical, and to 99.00% otherwise; and
	(3)	ρkl(basis) is equal to 1 if the two sensitivities are identical in the delivery location of a commodity, and 99.90% otherwise.
Values of ρkl(cty)  for intra-bucket correlations Table 12 Bucket number Commodity bucket Correlation ρkl(cty) 1 Energy - Solid combustibles 55% 2 Energy - Liquid combustibles 95% 3 Energy - Electricity and carbon trading 40% 4 Freight 80% 5 Metals - non-precious 60% 6 Gaseous combustibles 65% 7 Precious metals (including gold) 55% 8 Grains and oilseed 45% 9 Livestock and dairy 15% 10 Softs and other agriculturals 40% 11 Other commodity 15%
Instruments with a spread as their underlying are considered sensitive to different risk factors. In the example cited, the swap will be sensitive to both WTI and Brent, each of which require a capital charge at the risk factor level (ie delta of WTI and delta of Brent). The correlation to aggregate capital charges is specified in [7.83].
7.84	For determining whether the commodity correlation parameter (ρkl(cty)) as set out in Table 12 in [7.83](1)(a) should apply, this paragraph provides non-exhaustive examples of further definitions of distinct commodities as follows:
	(1)	For bucket 3 (energy – electricity and carbon trading):
		(a)	Each time interval (i) at which the electricity can be delivered and (ii) that is specified in a contract that is made on a financial market is considered a distinct electricity commodity (eg peak and off-peak).
		(b)	Electricity produced in a specific region (eg Electricity NE, Electricity SE or Electricity North) is considered a distinct electricity commodity.
	(2)	For bucket 4 (freight):
		(a)	Each combination of freight type and route is considered a distinct commodity.
		(b)	Each week at which a good has to be delivered is considered a distinct commodity.
7.85	For aggregating delta commodity risk positions across buckets, the correlation parameter ybc is set as follows:
	(1)	20% if bucket b and bucket c fall within bucket numbers 1 to 10; and
	(2)	0% if either bucket b or bucket c is bucket number 11.

²² Specified currencies by the Basel Committee are: EUR, USD, GBP, AUD, JPY, SEK, CAD as well as the domestic reporting currency of a bank.
²³ The delta GIRR correlation parameters (ρ_kl) set out in Table 2 is determined by ma , where Tk (respectivelyTl) is the tenor that relates to WSk (respectively WSl); and θ is set at 3%. For example, the correlation between a sensitivity to the one-year tenor of the Eonia swap curve and the a sensitivity to the five-year tenor of the Eonia swap curve in the same currency is max = 88.69%
²⁴ For example, the correlation between a sensitivity to the one-year tenor of the Eonia swap curve and a sensitivity to the five-year tenor of the three-month Euribor swap curve in the same currency is (88.69%) . (0.999) = 88.60%.
²⁵ Covered bonds must meet the definition provided by Large Exposure Rules for Banks issued via SAMA circular No. 1651 / 67 dated 09/01/1441.
²⁶ Credit quality is not a differentiating consideration for this bucket.
²⁷ For covered bonds that are rated AA- or higher, the applicable risk weight may at the discretion of the bank be 1.5%..
²⁸ For example, a sensitivity to the five-year Apple bond curve and a sensitivity to the 10- year Google CDS curve would be 35% . .65% . 99.90% = 22.73%.
²⁹ Credit quality is not a differentiating consideration for this bucket.
³⁰ Market capitalisation or economy (ie advanced or emerging market) is not a differentiating consideration for this bucket.
³¹ For example, the correlation between the sensitivity to Brent, one-year tenor, for delivery in Le Havre and the sensitivity to WTI, five-year tenor, for delivery in Oklahoma is 95% - 99.00% - 99.90% = 93.96%.
³² Specified currency pairs are: SAR/USD, USD/EUR, USD/JPY, USD/GBP, USD/AUD, USD/CAD, USD/CHF, USD/MXN, USD/CNY, USD/NZD, USD/RUB, USD/HKD, USD/SGD, USD/TRY, USD/KRW, USD/SEK, USD/ZAR, USD/INR, USD/NOK, USD/BRL.
³³ example, EUR/AUD is not among the selected currency pairs specified by the Basel Committee, but is a first-order cross of USD/EUR and USD/AUD.

7.90	[7.91] to [7.95] set out buckets, risk weights and correlation parameters to calculate vega risk capital requirement as set out in [7.4].
7.91	The same bucket definitions for each risk class are used for vega risk as for delta risk.
7.92	For calculating weighted sensitivities for vega risk, the risk of market illiquidity is incorporated into the determination of vega risk, by assigning different liquidity horizons for each risk class as set out in Table 13. The risk weight for each risk class34 is also set out in Table 13.
Regulatory liquidity horizon, LHrisk class and risk weights per risk class Table 13 Risk class LHrisk class Risk weights GIRR 60 100% CSR non-securitisations 120 100% CSR securitisations (CTP) 120 100% CSR securitisations (non-CTP) 120 100% Equity (large cap and indices) 20 77.78% Equity (small cap and other sector) 60 100% Commodity 120 100% FX 40 100%
7.93	For aggregating vega GIRR risk positions within a bucket, the correlation parameter ρkl is set as follows, where:
	(1)	pkl (option maturity) is equal to ? , where:
		(a)	α is set at 1%;
		(b)	Tk (respectively Tl) is the maturity of the option from which the vega sensitivity VRk (VRl) is derived, expressed as a number of years; and
	(2)	pkl(underlyins maturity) is equal to ? , where:
		(a)	α is set at 1%; and
		(b)	Tku (respectively Tlu) is the maturity of the underlying of the option from which the sensitivity VRk (VRl) is derived, expressed as a number of years after the maturity of the option.
7.95	For aggregating vega risk positions across different buckets within a risk class (GIRR and non- GIRR), the same correlation parameters for γbc, as specified for delta correlations for each risk class in [7.39] to [7.89] are to be used for the aggregation of vega risk (eg γbc = 50% is to be used for the aggregation of vega risk sensitivities across different GIRR buckets).

³⁴ risk weight for a given vega risk factor k (RW_k) is determined by RW_k = min , where RW∂ is set at 55%; and LH_{risk class} is specified per risk class in Table 13.