Computing value-at-risk and expected shortfall in operational risk

Operational Risk has become more popular in the past fifteen years. The Basel committee realized its importance and banks have to allocate more capital charge, yet this is still not enough. With these new rules, banks have put in place new procedures to compute their risk measures and allocate enough capital charge to avoid bankruptcy. The Basel committee under Basel II has proposed different approaches to compute risk measures for Operational Risk, namely the Basic Indicator Approach, the Advanced Measurement Approach and the Standardized Approach. In our research, we will study the case of Loss Distribution Approach, which has been discussed before, and will contribute to the field by using a heavy-tailed distributed severity: g-and-h distributed. Then, we will analyze and test some methods to compute the value-at-risk( VaR) and conditional value-at-risk or expected shortfall (CVaR).

Computing value-at-risk and expected shortfall in operational risk

Operational Risk has become more popular in the past fifteen years. The Basel committee realized its importance and banks have to allocate more capital charge, yet this is still not enough. With these new rules, banks have put in place new procedures to compute their risk measures and allocate enough capital charge to avoid bankruptcy. The Basel committee under Basel II has proposed different approaches to compute risk measures for Operational Risk, namely the Basic Indicator Approach, the Advanced Measurement Approach and the Standardized Approach. In our research, we will study the case of Loss Distribution Approach, which has been discussed before, and will contribute to the field by using a heavy-tailed distributed severity: g-and-h distributed. Then, we will analyze and test some methods to compute the value-at-risk( VaR) and conditional value-at-risk or expected shortfall (CVaR).

Basel III FRTB: data pooling innovation to lower capital charges

Background Anticipated to overhaul the structure of market risk teams, IT teams, and trading desks within banks by 2023, Basel III's Fundamental Review of the Trading Book requirements will also increase capital charges banks will incur globally. The case study focuses on describing what is needed with regards to the risk factor eligibility test (RFET) as well as for implementing a data pool to lower capital charges. By establishing a consortium of banks per region to implement a data pooling solution, participants can prove a wider breadth of modellable risk factors per asset class and use the Internal Models Approach (IMA) of valuing risk to lower capital charge requirements significantly. Case description First, a description on the historical context surrounding the Fundamental Review of the Trading Book rules and the business requirements needed to comply with the risk factor eligibility test is made. Then an examination is conducted on the innovative data pooling initiative implemented by CanDeal, TickSmith Corp., and the 6 largest Canadian banks to lower capital charge requirements under the Fundamental Review of the Trading Book. Discussion and evaluation A description is made on what types of data, expertise, and technology is needed to calculate for risk factor modellability. It is up to each firm to decide if the benefits to using the Internal Models Approach to lower capital charges outweighs implementation and running costs of the underlying data platform. Implementing a data pool for each region comes with challenges that include anti-competition law that may block the initiative, varied benefits to each competitive participant, and data security concerns. Conclusion It is evident that the data pool innovation provides benefits to lowering capital charges as the Canadian banks have seen an increase of modellability by several factors using the sample bond asset class. While each firm must still determine internally if the benefits outweighs the technological costs they will incur, it is clear that regulators are pushing for increased data retention and scrutiny.

The Effect of Risk-Based Capital on Investment Returns of Insurance Companies in Kenya

This paper focuses on analyzing the effect of risk-based capital on investment returns of insurance companies in Kenya. The study population comprised of 63 insurance companies licensed by Insurance Regulatory Authority (IRA). A longitudinal (panel) design was used to describe the association amongst variables on the study duration. Moreover, secondary data was collected from the insurance companies’ annual returns submitted to IRA for five-year duration (2014-2018), which yielded adequate data points for each insurance company deeming it viable. Risk-based capital was determined by the standard formulae as per the risk-based supervision model. It was a composition of operational risk charge, market risk charge, insurance risk charge, credit risk capital charge, and an adjustment which considered the lossabsorbing capacity of technical provisions and deferred taxes. Investment returns in insurance companies was calculated using the investment income ratio. Test of normality, linearity, multicollinearity, and independence were conducted and were found suitable for linear regression to be conducted. Linear regression was used to evaluate the nature of the relationship between the variables based on the hypothesis in the study and at a significance level of 5%. Coefficient of determination ( ) was derived to show how the model fits the data. The study findings revealed a positive and significant relationship between risk-based capital and investment returns, thus allowing investment portfolio managers in the insurance industry to justify their investments in high risk areas that may attract a high capital charge.

Part III Investment Banking, 16 Credit Value Adjustment

The chapter discusses credit value adjustment (CVA) under Basel 2.5 and Basel 3. CVA is an adjustment to the fair value (or price) of derivative instruments to account for counterparty credit risk (CCR). Thus, CVA is commonly viewed as the price of CCR. The purpose of the CVA capital charge is to capitalize the risk of future changes in CVA. For most exposures, at any given time the market credit spread on the relevant counterparty is good proxy for the CVA applicable to the exposure, but the regulatory calculations involved reflect a number of factors as well as this particular input.

Operational Risk Management and the Case of Albania

Starting 1999, when operational risk was instroduced for the first time as part of pillar 1 minimum regulatory capital charge, supervisors and the banking industry recognized the importance of such risk in evaluating the risk profiles of financial institutions. The increasing use of automated technology, the growth of e-commerce and the expansion of activity ect. , create increased operational risk, and expose the institution to possible losses. Such risk has been introduced in the regulatory framework of Bank of Albania in 2011 and since then, there have been positive developments in the consideration of this risk from the supervisory point of view. The regulation included qualitative criteria for the identification and monitoring of operational risk, whereas the quantitative measurement of capital charge for operational risk has been introduced through the implementation of capital adequacy ratio regulation based in Basel 2.

Analyzing and Comparing Basel III Sensitivity Based Approach for the Interest Rate Risk in the Trading Book

A bank’s capital charge computation is a widely discussed topic with new approaches emerging continuously. Each bank computes this figure using internal methodologies in order to reflect its capital adequacy; however, a more homogeneous model is recommended by the Basel committee to enable judging the situation of these financial institutions and relating different banks among each other.In this paper, we compare different numerical and econometric models to the Sensitivity Based Approach (SBA) implemented by the Basel Committee on Banking Supervision (BCBS) under Basel III in its December 2014 (rev. March 2015) publication in order to compute the capital charge in the trading book. We study the influence of having several currencies and maturities within the portfolio and try to define the time horizon and confidence level implied by Basel’s III approach through an application on bonds portfolios.By implementing several approaches, we are able to find equivalent VaRs to the one computed by the SBA on a pre-defined confidence level (97.5%). However, the time horizon differs according to the chosen methodology and ranges from 1 month up to 1 year.

Comparison of market risk models with respect to suggested changes of Basel Accord

The market risk capital charge of financial institutions has been mostly calculated by internal models based on integrated Value at Risk (VaR) approach, since the introduction of the Amendment to Basel Accord in 1996. The internal models should fulfil several quantitative and qualitative criteria. Besides others, it is the so called backtesting procedure, which was one of the main reasons why the alternative approach to market risk estimation — conditional Value at Risk or Expected Shortfall (ES) — were not applicable for the purpose of capital charge calculation. However, it is supposed that this approach will be incorporated into Basel III. In this paper we provide an extensive simulation study using various sets of market data to show potential impact of ES on capital requirements.