Regulatory guidance on Counterparty Credit Risk

The Basel Committee on Banking Supervision (BCBS)  defines Counterparty Credit Risk (CCR)  as the risk that the counterparty to a transaction could default before the final settlement of the transaction’s cash flows. CCR arise from OTC derivatives and securities financing transactions.

Stricter rules to compute counterparty credit risk's capital requirement comes out after the last global financial crisis when regulators realized that CCR was a significant source of loses. According to the BCBS,  downgrade losses exceeded actual losses by far.

CCR and CVA timeline:

(2010)  The Basel Committee issued in December 2010 the Basel III rules text which contains the current CVA standard. Source:

(2011)  The Basel Committee completed in June 2011 its review and finalized the Basel III capital treatment for counterparty credit risk in bilateral trades. They BCBS commented, "The review resulted in a minor modification of the credit valuation adjustment (CVA), which is the risk of loss caused by changes in the credit spread of a counterparty due to changes in its credit quality (also referred to as the market value of counterparty credit risk)."  Source:

(2014) The BCBS issued the final standard on The standardized approach for measuring counterparty credit risk exposures includes a comprehensive, non-modeled approach for measuring counterparty credit risk associated with OTC derivatives, exchange-traded derivatives, and long settlement transactions. The new standardized approach (SA-CCR) replaces both the Current Exposure Method (CEM) and the Standardised Method (SM) in the capital adequacy framework. Besides, the Internal Model Method (IMM) shortcut method will be eliminated from the framework once the SA-CCR takes effect, which is scheduled for 1 January 2017. Source:

(2015) The BCBS issued a consultative document with the proposed revision of the CVA framework.Source:

Shortcomings in the current framework:

Capturing all CVA risks and better recognition of CVA hedges:

The existing framework does not cover the exposure component of CVA risk, and, consequently, does not recognize the hedges that banks put in place to target the exposure component of CVA variability.

Alignment with industry practices for accounting purposes.

Since  IMM framework does not require model calibration to market-implied parameters (e.g., the drifts and volatilities for the diffusion processes of the risk factors do not need to be risk-neutral, they can be estimated from historical data).

Based on these considerations, the Basel Committee is proposing an approach to a new regulatory capital treatment of CVA that is based to some extent on accounting CVA. A new regulatory CVA would be calculated via the exposure models that banks also use to calculate their accounting (or front office) CVA.

Alignment with proposed revisions to the market risk framework.

CVA is a fair value adjustment to the price of a fair-valued instrument. Therefore, the capital charge that relates to it should be closely linked to the capital charge for market risk. The revised Basel framework for market risk under the FRTB relies on fair value sensitivities to market risk factors. As a consequence, the CVA capital requirements should be as consistent as possible with those set down under the FRTB.

Therefore,  the CVA risk capital regulation is still under revision.



Applying Mean Variance Optimization to Bonds

When I've assessed financial models; sometimes, I've realized that Analysts tend to make mistakes because they treat bonds as stocks to simplify the computation of models widely known such as Value at Risk and Markowitz/Black Litterman.

Unlike stocks, bonds have a maturity, periodic coupon payments, interest rate risk and different default risk profile. As a result, the inputs parameters such as E(r) and V must be estimated differently. Correlations bring another problem to the analysis. Therefore, applying MV optimizations without taking into account those differences can lead to wrong results.

 CHANDRASEKHAR's thesis (2009) address this topic, he analyzes the construction of bond fund portfolios or ETF as a simplified implementation of a portfolio optimization and mentions the use of term structure model based portfolio optimization as an alternative approach.Term structure

Caldeira, Moura y Santos (2014) implemented the MV approach to bond portfolio optimization based on dynamic factor models for the term structure of the interest rates proposed by Diebold and Li (2006). The factor model for the term structure allows to obtain the vector of expected yields and its covariance matrix then the bond return distribution is computed.

Basis Risk - ALM

Basis risk is a type of interest rate risk. In the context of ALM, even when apparently the gap is zero in a bucket, there is still probably basis risk when the bank's assets and liabilities are priced on different indices. 
For instance:
Assets priced on  6-Month Prime lending rate based loan funded by Liabilities 6-Month LIBOR based borrowing.

If the indices with the same repricing period do not move in the same way, there will be basis risk.

Basis Risk

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