MEASURING RISK MODELS' EFFICIENCY: THE CASE FOR THE MALAYSIA MARKET
Keywords:
Value-at-risk, Efficiency test, Mean relative scaled biasedAbstract
The intention of this paper is to determine the most efficient risk model which can be implemented in diverse business sectors of an economy. The methodology involved using Value-at-Risk (VaR) technique with the integration of GARCH-based representation on three selected non-financial sectors in Malaysia. Using time-series data from 1993 until 2010, the efficiency test namely the Mean Relative Scaled Bias (MRSB) is then conducted. The evidence showed that the VaR forecast integrated with t-distribution GARCH has better capabilities to track movements in true risk exposures thus suggesting it as the most efficient model within specific assumptions and constraints.
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References
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Johansson, F., Seiler, M., & Tjarnberg, M. (1999). Measuring downside portfolio risk. Journal of Portfolio Management, 26(1), 96–107.
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Lin, C, H., & Chien, C.C.C. (2006) Incorporating a GED model into the historical simulation for Value-at-risk. Review of Pacific Basin Financial Markets and Policy, 9(2), 257–274.
Lin, C. H., Chien, C. C. C., & Chen, S. W. (2005). A general revised historical simulation method for portfolio VaR. The Journal of Alternative Investments(Fall), 87–103.
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Pederzoli, C. (2006) Stochastic volatility and GARCH: A comparison based on UK stock data. The European Journal of Finance, 12(1), 41–59.
Rahl, L., & Lee, C. (2000). Reflections on Risk Management. Business Economics, (April), 20‒26.
Urbani, P. (2004). All about Value at Risk. Finance Week, (Summer), 12‒13.
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Baharumshah, Z., & Almasaied, S. W. (2005). Financial Intermediation, Investment and Economic Growth: Evidence from Malaysia and other ASEAN countries. Paper presented at the Workshop on Finance and Banking, Universiti Kebangsaan Malaysia, on 11 August, 2005.
Basle Committee on Banking Supervision. (1994). Risk Management Guidelines for Derivatives. Basle: Bank for International Settlements.
Brachinger, H. W. (2002). Measurement of risk. Optimization and Operations Research Design,1119‒1137.
Brooks, C., & Persand, G. (2002). Model choice and value-at-risk performance. Financial Analysts Journal(September/October), 87‒97.
Butler, C. (1999). Mastering Value at Risk: A step-by-step guide to understanding and applying
VaR. Great Britain: Financial Times Management.
Caporin, M. (2003). The trade off between complexity and efficiency of VaR measures: A comparison of RiskMetrics and GARCH-type models. Retrieved September 1, 2005, Website: http://www.gloriamundi.org.
Chiu, C. L., Lee, M. C., & Hung, J. C. (2005). Estimation of VaR under jump dynamics and asymmetric information. Applied Financial Economics, 15, 1095‒1106.
Dowd, K. (2005). Measuring Market Risk. West Sussex, England: John Wiley & Sons.
Dunis, C. L., & Chen, Y. X. (2005). Alternative volatility models for risk management and trading: Application to the EUR/USD and USD/JPY rates. Derivatives Use, Trading & Regulation, 11(2), 126‒156.
Engel, J., & Gizycki, M. (1999). Conservatism, accuracy and efficiency: Comparing value-atrisk models. Retrieved September 1, 2005, Website: http://www.gloriamundi.org.
Fong, H. G., & Vasicek, O. A. (1997). A multimedia framework for risk analysis Financial Analysts Journal, 53(4), 51‒57.
Gastineau, G. L. (1993). The essentials of financial risk management. Financial Analysts Journal, 49(5), 17‒21.
Hendricks, D. (1996). Evaluation of value-at-risk models using historical data. Federal Reserve Bank. Economic Policy Review, 2(April), 39–70.
Holton, G. (2003). Value-at-Risk: Theory and Practice. U.S.A: Elsevier.
Ibrahim, I. (1994). Pelaburan dalam Pasaran Sekuriti, Hadapan dan Opsyen. Kuala Lumpur:
Dewan Bahasa dan Pustaka.
Ibrahim, Y., & Mazlan, A. R. (2006). The adoption of residual dividend policy in Malaysia. Paper presented at the Malaysia Finance Association, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, on 8-9 May, 2006.
JP Morgan. (1996). RiskMetrics Technical Document.
Johansson, F., Seiler, M., & Tjarnberg, M. (1999). Measuring downside portfolio risk. Journal of Portfolio Management, 26(1), 96–107.
Jorion, P. (1997). In defense of VaR. Derivatives Strategy. 20–23.
Jorion, P. (2006). Value at Risk: The New Benchmark for Controlling Market Risk. Third Edition. Chicago: Irwin.
Lin, C, H., & Chien, C.C.C. (2006) Incorporating a GED model into the historical simulation for Value-at-risk. Review of Pacific Basin Financial Markets and Policy, 9(2), 257–274.
Lin, C. H., Chien, C. C. C., & Chen, S. W. (2005). A general revised historical simulation method for portfolio VaR. The Journal of Alternative Investments(Fall), 87–103.
Mohamed, A. R. (2005). Would Student’s t-GARCH Improve VaR Estimates. University of Jyvaskyla. Retrieved on August 2, 2006 from http://www.gloriamundi.org.
Nath, G. C., & Reddy, Y. V. (2003). Value at Risk: Issues and implementation in Forex marketin India. Retrieved on August 2, 2006 fromebsite: http://www.gloriamundi.org.
Pederzoli, C. (2006) Stochastic volatility and GARCH: A comparison based on UK stock data. The European Journal of Finance, 12(1), 41–59.
Rahl, L., & Lee, C. (2000). Reflections on Risk Management. Business Economics, (April), 20‒26.
Urbani, P. (2004). All about Value at Risk. Finance Week, (Summer), 12‒13.
Venkatesh, P. C. (2003). Value-at-Risk for Corporate Bond Portfolios. Journal of Fixed Income.
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Published
2019-03-06
How to Cite
binti Ahmad Baharul Ulum, Z. K., Bin Ahmad, H. I., & Binti Salamudin, N. (2019). MEASURING RISK MODELS’ EFFICIENCY: THE CASE FOR THE MALAYSIA MARKET. Journal of Contemporary Issues and Thought, 3, 114–126. Retrieved from https://ojs.upsi.edu.my/index.php/JCIT/article/view/985
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