Critical Analysis of the New Basel Minimum Capital Requirements for Market Risk
By J. Orgeldinger. 2017
129 citations recorded by the source corpus.
In its October 2013’s consultative paper for a revised market risk framework (FRTB), and subsequent versions published thereafter, the Basel Committee suggests new ways of dealing with market risk in banks’ trading and banking books. The Basel Committee estimates that the new rules will result in an approximate median capital increase of 22% and a weighted average capital increase of 40% [1], compared with the current framework. Key changes can be found in the internal model approach, in the standard rules and in the scope/approval process. Among the significant changes that are being introduced by the FRTB is a stricter separation of the trading book and banking book. Regardless of whether they use standardised or internal models, banks will need to review their portfolios to determine if existing classifications of instruments and desks as trading book or banking book are still applicable or whether a revision of desk structure is needed. In this article, we analyse the theoretical foundations of the internal model approach (IMA), which are the stressed expected shortfall, liquidity adjustments, default & migration risk and non-modellable risk factors. We thoroughly investigate the criticisms for Internal Risk Model (IMA) and the introduction of a standardised floor, the sensitivity based approach (SBA) with Delta, Vega and Curvature, shock scenarios and the aggregation with asymmetric correlation and reflection of basis/default risk.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/f7e8c76c3be58ec11f97878a22fc5111fd9e1167
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Citations in the corpus
- Forecasting risk with Markov-switching GARCH models:A large-scale performance study (2018, unknown): “The new standard for capital requirements for market risk (Basel Committee on Banking Supervision, 2016) calls for backtesting at both the individual desk level and the aggregate level.”
- Machine Learning in Banking Risk Management: A Literature Review (2019, background): “To capture tail risk better, the revised framework also saw a shift in the measure of risk under stress from the Value-at-Risk (VaR) to Expected Shortfall (ES) (Basel Committee on Banking Supervision 2006).”