View Articals 01.05.2017 12:52


Vol. 24(2), April 2017


Applying three VaR (value at risk) approaches in measuring market risk of stock portfolio: The case study of VN-30 stocks basket in HOSE
Nguyen Quang Thinh & Vo Thi Quy
(Xem: 62 | Tải về: 0)
KeywordsValue at risk Market risk Stock portfolio Variance-covariance Historical simulation Monte Carlo simulation
Page90-113
QuoteNguyen Quang Thinh & Vo Thi Quy (2017), "Applying three VaR (value at risk) approaches in measuring market risk of stock portfolio: The case study of VN-30 stocks basket in HOSE", Tạp chí Phát triển Kinh tế 24(2), 90-113.
AbstractThis study examines and applies the three statistical value at risk models including variance-covariance, historical simulation, and Monte Carlo simulation in measuring market risk of VN-30 portfolio of Ho Chi Minh stock exchange (HOSE) in Vietnam stock market and some back-testing techniques in assessing the validity of the VaR performance in the timeframe of January 30, 2012–February 26, 2016. The models are constructed from two volatility methods of stock price: SMA and EWMA throughout the five chosen confi-dence level: 90%, 93%, 95%, 97.5%, and 99%. The findings of the study show that the differences among the results of three models are not significant. Additionally, three VaR (Value at Risk) models have generally the similar accepted range assessed in both types of back-tests at all confidence levels considered and at the 97.5% con-fidence level. They can work best to achieve the highest validity level of results in satisfying both conditional and unconditional back-tests. The Monte Carlo Simulation (MCS) has been considered the most appropriate method to apply in the context of VN-30 port-folio due to its flexibility in distribution simulation. Recommenda-tions for further research and investigations are provided according-ly.
References

Alexander, C. (2008). Market risk analysis (Vol. 1 : Quantitative Methods in Finance). John Wiley & Sons, England.

Allen, L., Boudoukh, J., & Sanders, A. (2004). Understanding market, credit, and operational risk: The value at risk approach. Blackwell Publishing, United States.

Anderson, R., Sweeney, J., & Williams, A. (2011). Statistics for business and economics(11th Ed.). South-Western Cengage Learning, United States.

Angelovska, J. (2013). Managing market risk with VaR (Value at Risk). Journal of Management, 18(2), 81–96.

Best, P. (1998). Implementing value at risk. John Wiley & Sons, England.

Bohdalova, M. (2007). A comparison of value at risk methods for measurement of the financial risk.Working Paper, Faculty of Management, Comenius University, Bratislava, Slovakia.

Campbell, S. (2005). A review of backtesting and backtesting procedure. Finance and Economics Discussion Series. Divisions of Research & Statistics and Monetary Affairs, Federal Reserve Board, Washington, DC.

Cassidy, C., & Gizycki, M. (1997). Measuring traded market risk: Value-at-risk and backtesting techniques.Research Discussion Paper. Bank Supervision Department.

Christofferssen, P. (1998). Evaluating Interval forecasts. International Economic Review, 39, 841–862.

Christofferssen, P., & Pelletier, P. (2004). Backtesting value-at-risk: A duration based approach. Journal of Empirical Finance,2, 84–108.

Corkalo, S. (2011). Comparison of value at risk approaches on a stock portfolio. Croatian Operational Research Review, 2.

Dowd, K. (1998). Beyond value at risk, the new science of risk management. John Wiley & Sons, England.

Dowd, K. (2002). An introduction to market risk measurement. John Wiley & Sons, England.

Dowd, K. (2005). Measuring market risk (2nd Ed.). John Wiley & Sons, England.

Duda, M., & Schmidt, H. (2009). Evaluation of various approaches to value at risk: Empirical check. Master Thesis. Lund University, Sweden.

Finger, C. (2005). Back to backtesting. Research Monthly. RiskMetrics Group.

Frain, J., & Meegan, C. (1996). Market risk: An introduction to the concept & analytics of value-at-risk, Technical Paper. Economic Analysis Research & Publications Department, Ireland.

Haas, M. (2001). New methods in backtesting. Financial Engineering. Research Center Caesar, Bonn.

Hendricks, D. (1996). Evaluation of value-at-risk models using historical data. Economic Policy Review, 2(1).

Holá, A. (2012). Mathematical models of value at risk. Department of Mathematics, University of West Bohemia, Pilsen.

Jorion, P. (2001). Value at risk:  The new benchmark for managing financial risk (2nd Ed.). McGraw–Hill, United States.

Katsenga, G. Z. (2013). Value at risk (var) backtesting: Evidence from a South African market portfolio. University of Witwatersrand Business School.

Linsmeier, T. J., & Pearson, N. D. (1996). Risk measurement: An introduction to value at risk. Working Paper. University of Illinois at Urbana Champaign.

Lupinski, M. (2013). Comparison of alternative approaches to VaR evaluation. Working Paper. University of Warsaw and Narodowy Bank Polski, Warszawa.

Nieppola, O. (2009). Backtesting value-at-risk models. Helsinki School of Economics.

Saita, F. (2007). Value at risk and bank capital management: Risk adjusted performance, capital management and capital allocation decision making. Academic Press Advanced Finance Series.

Sanders, A., & Cornett, M. M. (2008). Financial institution management: A risk management approach (6th Ed.). McGraw-Hill, United States.

van den Goorbergh, R., & Vlaar, P. (1999). Value-at-risk analysis of stock returns: Historical simulation, variance techniques or tail index estimation? DNB Staff Report, Amsterdam.

Wiener, Z. (1999). Introduction to VaR (Value-at-Risk), risk management and regulation in banking. Kluwer Academic Publishers, Boston. 

     
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