Measuring of Conditional Value at Risk Portfolio Using Copula

Authors

  • Krisada Khruachalee Kasetsart University
  • Winai Bodhisuwan Kasetsart University

Keywords:

Conditional Value-at-Risk, Multivariate Distribution Function, Copula, Tourism & Hospitality

Abstract

The uncertainty of return on investment is a major concern for the vast majority of investors. Under normal market conditions, a portfolio's risk exposure over a specific time frame with a predetermined confidence level can be measured. Since a portfolio’s return is rarely characterized by the assumption of a multivariate normal distribution, the use of normality Value-at-Risk (VaR) plays a crucial role in risk mitigation, but can generate an undesirable measure of risk exposure for portfolio investment. With a dynamic tool in modeling multivariate distribution regardless of the assumption of joint normality, applying a copula is a practical alternative choice for extracting a cumulative joint distribution for a portfolio’s return. The applications in this work are illustrated by the portfolios of the four largest and the four smallest market capitalization stocks in the tourism and hospitality sector. It was found that the portfolio returns of the large and small market capitalization portfolios were characterized by logistic and Student’s t distributions respectively. Consequently, the VaR and conditional VaR based on the Gaussian copula, could be used to characterize and estimated the distributions of the respective portfolio returns according to the logistic and Student’s t distributions. The conditional VaR of the large and small market capitalization portfolios calculated from the copula method provides a slightly higher level of risk than the conditional VaR and the VaR with the assumption of a multivariate normal distribution. Moreover, the small market capitalization portfolio provides slightly higher values of VaR and CVaR than the large market capitalization portfolio for all assumptions of VaR. Therefore, the use of conditional VaR based on the Gaussian copula is more reasonable for investors who conservatively manage their investment portfolios. However, managing the investment portfolio based on a conservative level does not completely imply the performance of portfolio management. On the other hand, an accurate value of VaR, directly estimated from the actual distribution of a portfolio’s returns, provides a vital means of assessing better portfolio management. Due to being sensitively volatile to several surrounding factors within the hospitality and tourism sector, implementing a conservative investment strategy is more suitable.

References

Allen, D., Powell, R., and Singh, A. (2012a). Beyond Reasonable Doubt: Multiple Tail Risk Measures Applied to European Industries, Applied Economic Letters, 19, 671-676.

Allen, D., Boffey, R., Kramadibrata, A., Powell, R., and Singh, A. (2012b). Thumbs Up to Parametric Measures of Relative VaR and CVaR in Indonesian Sectors, International Journal of Business Studies, 20(1), 27-42.

Ang, A. and Chen, J. (2002). Asymmetric Correlations of Equity Portfolios, Journal of Financial Economics, 63, 443-494.

Artzner, P., Delbaen, D., Eber, J., and Heath, D. (1997). Thinking Coherently, Risk, 10, 68-71.

Artzner, P., Delbaen, D., Eber, J., and Heath, D. (1999). Coherent Measures of Risk, Mathematical Finance, 9(3), 203-228.

Balcilar, M., Demirer, R., and Hammoudeh, S. (2015). Global Risk Exposures and Industry Diversification with Shariah-Compliant Equity Sectors, Pacific-Basin Finance Journal, 35(Part B), 499-520.

Bouye, E., Durrleman, V., Nikeghbali, A., Riboulet, G. and Roncalli, T. (2000). Copulas for Finance, A Reading Guide and Some Applications. Working Paper, Financial Econometrics Research Centre, City University, London.

Cherubini, U. and Luciano, E. (2001). Value at Risk Trade-off and Capital Allocation with Copulas. Economic Notes, 30, 235-256.

Daniel, J., (2016). Credit risk lecture notes. MSc Mathematical Finance Lectures, Module 8, April 2016.

Dargiri, M., Shamasabadi, H., Thim, C., Rasiah, D., and Sayedy, B. (2013). Value-at-Risk and Conditional Value-at-Risk Assessment and Accuracy Compliance in Dynamic of Malaysian Industries, Journal of Applied Sciences, 13(7), 974-983.

Delignette-Muller, M. L., Cornu, M., and AFSSA-STEC-Study-Group (2008). Quantitative Risk Assessment for Escherichia coli O157:H7 in Frozen Ground Beef Patties Consumed by Young Children in French Households. International Journal of Food Microbiology, 128(1, SI):158–164.

Embrechts, P., Mcneil, A. and Straumann, D. (2002). Correlation and Dependence in Risk Management: Properties and Pitfalls. In Risk Management Value at Risk and Beyond, Cambridge University Press, 2002.

Embrechts, P., Hoing, A. and Juri, A. (2003). Using Copulae to Bound the Value-at-Risk for Functions of Dependent Risks, Finance and Stochastics, 7, 145-167.

Fortin, I. and Kuzmics, C. (2002). Tail Dependence in Stock Return Pairs. International Journal of Intelligent Systems in Accounting, Finance & Management, 11, 89-107.

Johnson, N. L. and Kotz, S. (1972). Distributions in Statistics: Continuous Multivariate Distributions, John Wiley and Sons, New York.

Jorion, P. (2002). How Informative Are Value-at-Risk Disclosures? The Accounting Review, 77(4), 911-931.

Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk, 3rd ed, New York: McGraw-Hill.

Li, David X., (1999). On default correlation: A copula function approach. Social Science Research Network Working Paper Series, December.

Longin, F. and Solnik, B. (2001). Extreme Correlation of International Equity Markets. Journal of Finance, 10, 649-676.

Markowitz, H. (1952). Portfolio Selection, The Journal of Finance, 7(1), 77-91.

Marshall, C., and Siegel, M. (1997). Value-at-Risk: Implementing a Risk Measurement Standard. Journal of Derivatives, 4, 91-110.

Nelsen, R. B., Introduction to Copulas, New York: Springer Verlag, 1999.

R CORE TEAM., (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Rockafellar, R., and Uryasev, S. (2002). Conditional Value-at-Risk for General Loss Distributions. Journal of Banking and Finance, 26, 1443-1471.

Salvadori, G., De Michele, C., Kottegoda, N. T., and Rosso, R. (2007). Extreme in Nature: An Approach Using Copulas, New York: Springer.

Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0.

Yamai, Y. and Yoshiba, T. (2005). Value-at-Risk versus Expected Shortfall: A Practical Perspective. Journal of Banking & Finance, 29(4), 997-1015, 2005

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Published

2021-07-30