Effectiveness of copula-extreme value theory in estimating value-at-risk: empirical evidence from Asian emerging markets

Effectiveness of copula-extreme value theory in estimating value-at-risk: empirical evidence from... A traditional Monte Carlo simulation using linear correlations induces estimation bias in measuring portfolio value-at-risk (VaR), due to the well-documented existence of fat-tail, skewness, truncations, and non-linear relations in return distributions. In this paper, we consider the above issues in modeling VaR and evaluate the effectiveness of using copula-extreme-value-based semiparametric approaches. To assess portfolio risk in six Asian markets, we incorporate a combination of extreme value theory (EVT) and various copulas to build joint distributions of returns. A backtesting analysis using a Monte Carlo VaR simulation suggests that the Clayton copula-EVT evinces the best performance regardless of the shapes of the return distributions, and that in general the copulas with the EVT provide better estimations of VaRs than the copulas with conventionally employed empirical distributions. These findings still hold in conditional-coverage-based backtesting. These findings indicate the economic significance of incorporating the down-side shock in risk management. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

Effectiveness of copula-extreme value theory in estimating value-at-risk: empirical evidence from Asian emerging markets

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Publisher
Springer Journals
Copyright
Copyright © 2011 by Springer Science+Business Media, LLC
Subject
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
ISSN
0924-865X
eISSN
1573-7179
D.O.I.
10.1007/s11156-011-0261-0
Publisher site
See Article on Publisher Site

Abstract

A traditional Monte Carlo simulation using linear correlations induces estimation bias in measuring portfolio value-at-risk (VaR), due to the well-documented existence of fat-tail, skewness, truncations, and non-linear relations in return distributions. In this paper, we consider the above issues in modeling VaR and evaluate the effectiveness of using copula-extreme-value-based semiparametric approaches. To assess portfolio risk in six Asian markets, we incorporate a combination of extreme value theory (EVT) and various copulas to build joint distributions of returns. A backtesting analysis using a Monte Carlo VaR simulation suggests that the Clayton copula-EVT evinces the best performance regardless of the shapes of the return distributions, and that in general the copulas with the EVT provide better estimations of VaRs than the copulas with conventionally employed empirical distributions. These findings still hold in conditional-coverage-based backtesting. These findings indicate the economic significance of incorporating the down-side shock in risk management.

Journal

Review of Quantitative Finance and AccountingSpringer Journals

Published: Nov 1, 2011

References

  • Common stochastic trends among Asian currencies: evidence for Japan, Asians, and the Asian Tigers
    Aggarwal, R; Mougoue, M
  • Asymmetric correlations of equity portfolios
    Ang, A; Chen, J
  • A robust VaR model under different time periods and weighting schemes
    Angelidis, T; Benos, A; Degiannakis, S
  • Evaluating effects of excess kurtosis on VaR estimates: evidence for international stock indices
    Baixauli, JS; Alvarez, S
  • A generalized extreme value approach to financial risk measurement
    Bali, T

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