Reducing Basel III Capital Requirements with Dynamic Conditional Correlation and Monte Carlo Simulation

12 Pages Posted: 18 Jan 2016

See all articles by Manuel Kleinknecht

Manuel Kleinknecht

University of Essex

Wing Lon Ng

Bounded Rationality Advancement in Computational Intelligence Laboratory (BRACIL)

Date Written: January 17, 2016

Abstract

Value-at-Risk (VaR) and Conditional-Value-at-Risk (CVaR) are popular risk measure in portfolio optimisation and market regulations. However, so far little research has been done on how these risk measures reduce the Basel III market risk capital requirements. This paper analyses the efficiency of empirical, parametric and simulation based VaR and CVaR optimised portfolios on the regulatory capital requirements. Furthermore, we show how the Population-Based Incremental Learning algorithm can be used to solve the constraint optimisation problems. We find that the parametric and empirical distribution assumption generate similar results and neither of them clearly outperforms the other. Our results indicate that portfolios optimised with a multivariate Dynamic Conditional Correlation simulation approach reduce the capital requirements by about 11%.

Keywords: Basel III, DCC Simulations, PBIL

Suggested Citation

Kleinknecht, Manuel and Ng, Wing Lon, Reducing Basel III Capital Requirements with Dynamic Conditional Correlation and Monte Carlo Simulation (January 17, 2016). Available at SSRN: https://ssrn.com/abstract=2717051 or http://dx.doi.org/10.2139/ssrn.2717051

Manuel Kleinknecht (Contact Author)

University of Essex ( email )

Wivenhoe Park
Colchester, CO4 3SQ
United Kingdom

Wing Lon Ng

Bounded Rationality Advancement in Computational Intelligence Laboratory (BRACIL) ( email )

United Kingdom

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