Evaluation Approaches of Value at Risk for Tehran Stock Exchange

Document Type : Research Paper

Authors

1 Ph.D. Candidate in Faculty of Economics, University of Tehran, Iran

2 Professor, Faculty of Economics, University of Tehran, Iran

3 Associated Professor, Faculty of Management, University of Tehran, Iran

Abstract

The purpose of this study is estimation of daily Value at Risk (VaR) for total index of Tehran Stock Exchange using parametric, nonparametric and semi-parametric approaches. Conditional and unconditional coverage backtesting are used for evaluating the accuracy of calculated VaR and also to compare the performance of mentioned approaches. In most cases, based on backtesting statistics Results, accuracy of calculated VaR is approved for historical, Monte Carlo and Volatility-Weighted historical simulation methods. It is also approved for GARCH type of volatility models under normal distribution and Riskmetrics model under student-t distribution. On the other hand, it is observed that parametric approach measures VaR value more than non-parametric and semi-parametric approaches. This result indicates that GARCH type of volatility models under student-t distribution overestimate magnitude of value at risk. Finally, four volatility models of parametric approach including NARCH, NAGARCH and APGARCH under normal distribution and Riskmetrics under student-t distribution are selected best methods to measure accurate value of VaR.

Keywords

References

1. Abad, Pilar, Benito, Sonia and Carmen Lopez (2013). A comprehensive review of Value at Risk methodologies, The Spanish Review of Financial Economics, 22, 1-18.
2. Alouin Chaker, Samir Mabrouk (2010). “Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models”, Energy Policy 38 (2010) 2326–2339.
3. Baumol, W.J. (1963). An expected gain confidence limit criterion for portfolio selection. Management Science, 10, 174–82.
4. Basel Committee on Banking Supervision (1996). Amendment to the Capital Accord to Incorporate Market Risks.
5. Barone-Adesi, G., Giannopoulos, K. (2001).Non-parametric VaR techniques. Myths and realities. Economic Notes by Banca Monte dei Paschi di Siena, SpA. 30, 167–181.
6. Barone-Adesi, G., Giannopoulos, K., Vosper, L., (1999). VaR without correlations for nonlinear portfolios. Journal of Futures Markets 19, 583–602.
7. Barone-Adesi, G., Giannopoulos, K., Vosper, L. (2002). Backtesting derivative portfolios with filtered historical simulation (FHS). European Financial Management 8, 31–58.
8. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, pp. 307-327.
9. Boudoukh J, M Richardson and R Whitelaw, 1998, “The Best of Both Worlds”, Risk , 11, May, pp64-67.
10. Chen, C.W.S., Gerlach, R., Lin, E.M.H. (2008). Volatility forecasting using threshold heteroskedastic models of the intra-day range. Computational Statistics and Data Analysis, 52, 2990–3010.
11. Chen, C., So, M., Lin, E. (2009). Volatility forecasting with double Markov switching GARCH models. Journal of Forecasting 28, 681–697.
12. Chena Qi and Rongda Chen (2013). “Method of Value-at-Risk and empirical research for Shanghai stock market” Procedia Computer Science 17(2013) 671 – 677.
13. Christoffersen, P. (1998). Evaluating interval forecasting. International Economic Review 39, 841–862.
14. Christofferssen, P., Pelletier, P. (2004). Backtesting Value-at-Risk: A Duration Based Approach. Journal of Empirical Finance, 2, 2004, 84-108.
15. David Enocksson & Joakim Skoog (2011). Evaluating VaR with the ARCH/GARCH Family. Bachelor Thesis, Uppsala Univercity.
16. Ding, Z., C.W.J. Granger and R.F. Engle (1993). “A Long Memory Property of Stock Market Returns and a New Model,” Journal of Empirical Finance, 1, 83-106.
17. Engle, R.F. (1982). “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation,” Econometrica, 50, 987-1008.
18. Engle, R.F. (1990). “Discussion: Stock Market Volatility and the Crash of ‘87,” Review of Financial Studies, 3, 103-106.
19. Engle, R.F. and T. Bollerslev (1986). “Modeling the Persistence of Conditional Variances,” Econometric Reviews, 5, 1-50.
20. Engle, R.F. and V.K. Ng (1993). “Measuring and Testing the Impact of News on Volatility,” Journal of Finance, 48, 1749-1778.
21. Gerlach, R., Chen, C., Chan, N., (2011). Bayesian time-varying quantile forecasting for value-at-risk in financial markets. Journal of Business & Economic Statistics 29, 481–492.
22. Glosten, L.R., R. Jagannathan and D. Runkle (1993). “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks,” Journal of Finance, 48, 1779-1801.
23. Gupta, R. (2008). Assessing the Value at Risk (VAR) for BSE Index consisting of 30 stocks by using various parametric, nonparametric and semi -parametric models for estimating Value -at -Risk (VAR). University of Nottingham.
24. Higgins, M.L. and A.K. Bera (1992). “A Class of Nonlinear ARCH Models,” International Economic Review, 33, 137-158.
25. Hull, J., White, A., (1998). Incorporating volatility updating into the historical simulation method for value-at-risk. Journal of Risk 1, 5-19.
26. Nelson, D.B. (1991), “Conditional Heteroskedasticity in Asset Returns: A New Approach,” Econometrica, 59, 347-370.
27. J.P. Morgan, Riskmetrics (1996). Technical Document, 4th ed., J.P. Morgan, New York.
28. Kupiec, P. (1995). Techniques for verifying the accuracy of risk measurement models. Journal of Derivatives 2, 73-84.
29. Mabrouk, Samir and Samir Saadi (2012). “Parametric Value at Risk Analysis: Evidence From Stock Indices”, The Quarterly Review of Economics and Finance52, pp 305-321.
30. Mabrouk, S. & Aloui, C. (2010). One-day-ahead value-at-risk estimations with dual long-memory models: Evidence from the Tunisian stock market. International Journal of Financial Services Management, 4, 77-94.
31. W.S. Chen Cathy, Gerlach Richard, B.K. Hwang Bruce and Michael McAleer (2012). “Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range”, International Journal of Forecasting 28 557–574.
32. Wu, P.T., Shieh, S.J. (2007). Value-at-risk analysis for long-term interest rate futures: fat-tail and long memory in return innovations. Journal of Empirical Finance 14, 248–259.
33. Tang, T.L., Shieh, S.J. (2006). Long memory in stock index future markets: a value-at risk approach. Physica A, 437-448.
34. So, M., Yu, P. (2006). Empirical analysis of GARCH models in value at risk estimation. Journal of International Financial Markets, Institutions & Money 16, 180-197.
35 White, H. (2000). A reality check for data snooping. Econometrica 68, 1097-1126.
36. William H. Panning (1999). the strategic uses of value at risk: long term capital management for property/casualty insurers, North American Actuarial Journal, Vol.3, Issue 2.

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