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Articles

Performance of Value at Risk models in the midst of the global financial crisis in selected CEE emerging capital markets

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Pages 132-166 | Received 14 Nov 2012, Accepted 06 Feb 2015, Published online: 07 Apr 2015

References

  • Alexander, C. O., & Leigh, C. T. (1997). On the covariance models used in value at risk models. Journal of Derivatives, 4, 50–62.10.3905/jod.1997.407974
  • Andjelic, G., Djokovic, V., & Radisic, S. (2010). Application of VaR in emerging markets: A case of selected central and eastern European countries. African Journal of Business Management, 4, 3666–3680.
  • Bao, Y., Lee, T.-H., & Saltoglu, B. (2006). Evaluating predictive performance of value-at-risk models in emerging markets: A reality check. Journal of Forecasting, 25, 101–128.10.1002/(ISSN)1099-131X
  • Barone-Adesi, G., & Giannopoulos, K. (2001). Non-parametric VaR, technics, myths and realities. Review of Banking, Finance and Monetary Economics, 30, 167–181.
  • Barone-Adesi, G., Giannopoulos, K., & Vosper, L. (1999). VaR without correlations for portfolios of derivative securities. Journal of Futures Markets, 19, 583–602.10.1002/(ISSN)1096-9934
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327.10.1016/0304-4076(86)90063-1
  • Bollerslev, T., Engle, R., & Nelson, D. (1994). ARCH models. In R. Engle & D. McFadden (Eds.), Handbook of econometrics (Vol. 4, pp. 2959–3038). Amsterdam: North Holland.
  • Boudoukh, J., Richardson, M., & Whitelaw, R. F. (1998). The best of both worlds: A hybrid approach to calculating value at risk. Risk, 11, 64–67.
  • Campbell, S. (2005). A review of backtesting and backtesting procedure. Finance and Economics Discussion Series. Washington DC: Division of Research & Statistics and Monetary Affairs, Federal Reserve Board.
  • Christoffersen, P., Hahn, J., & Inoue, A. (2001). Testing and comparing value-at-risk measures. (Paper 2001s-03). Montreal: CIRANO.
  • Christoffersen, P. (1998). Evaluating interval forecasts. International Economic Review, 39, 841–862.10.2307/2527341
  • Danielsson, J., & De Vries, C. (1997). Value at risk and extreme returns ( FMG Discussion Paper No. 273). London: Financial Markets Group, London School of Economics.
  • Degiannakis, S. (2004). Volatility forecasting: Evidence from a fractional integrated asymmetric power ARCH skewed-t model. Applied Financial Economics, 14, 1333–1342.10.1080/0960310042000285794
  • Degiannakis, S., Floros, C., & Livada, A. (2012). Evaluating value-at-risk models before and after the financial crisis of 2008: International evidence. Managerial Finance, 38, 436–452.10.1108/03074351211207563
  • Ding, Z., Granger, C. W., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1, 83–106.10.1016/0927-5398(93)90006-D
  • Duffie, D., & Pan, J. (1997). An overview of value at risk. The Journal of Derivatives, 5, 7–49.10.3905/jod.1997.407971
  • Guermat, C., & Harris, D. F. (2002). Forecasting value at risk allowing for time variation in the variance and kurtosis of portfolio returns. International Journal of Forecasting, 18, 409–419.10.1016/S0169-2070(01)00122-4
  • Hull, J., & White, A. (1998). Incorporating volatility updating into the historical simulation method for value at risk. Journal of Risk, 1, 5–19.
  • Jorion, P. (2001). Value at risk, The new benchmark for managing financial risk (2nd ed.). New York, NY: McGraw Hill.
  • Kavussanos, M. G., Dimitrakopoulos, D., & Spyrou, S. I. (2010). Value at risk models for volatile emerging markets equity portfolios. The Quarterly Review of Economics and Finance, 50, 515–526.
  • Kupiec, P. (1995). Techniques for verifying the accuracy of risk management models. Journal of Derivatives, 3, 73–84.10.3905/jod.1995.407942
  • Linsmeier, T. J., & Pearson, N. D. (2000, March/April). Value at risk. Financial Analysts Journal, 56, 47–67.10.2469/faj.v56.n2.2343
  • Meera, S. (2012). The historical simulation method for value-at-risk: A research based evaluation of the industry favourite ( Working Paper). Mumbai: Indian Institute of Management.
  • Mladenovic, Z., & Mladenovic, P. (2006). Estimation of the value-at-risk parameter: Econometric analysis and the extreme value theory approach. Economic Annals, 51, 32–73.10.2298/EKA0671032M
  • Mutu, S., Balogh, P., & Moldovan, D. (2011). The efficiency of value at risk models on central and eastern European stock markets. International Journal of Mathematics and Computers in Simulation, 5, 110–117.
  • Neftci, S. D. (2004). Principles of financial engineering. London: Elsevier Academic Press.
  • Nelson, D. B. (1991). Conditional heteroscedasticity in asset return: A new approach. Econometrica, 52, 347–370.10.2307/2938260
  • Nieppola, O. (2009). Backtesting value-at-risk models. Helsinki: Helsinki School of Economics.
  • Nikolic-Djoric, E., & Djoric, D. (2011). Dynamic value at risk estimation for BELEX15. Metodoloski zvezki, 8, 79–98.
  • So, M. K. (2000). Long-term memory in stock market volatility. Applied Financial Economics, 10, 519–524.10.1080/096031000416398
  • Su, E., & Knowles, W. T. (2006). Asian Pacific stock market volatility modeling and Value at Risk analysis. Emerging Markets Finance and Trade, 42, 18–62.
  • Thupayagale, P. (2010). Evaluation of GARCH-based models in value-at-risk estimation: Evidence from emerging equity markets. Investment Analysts Journal, 72, 13–29.
  • Wong, C. S., Cheng, Y. W., & Wong, Y. P. (2002). Market risk management of banks: Implications from the accuracy of VaR forecast. Journal of Forecasting, 22, 22–33.
  • Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18, 931–955.10.1016/0165-1889(94)90039-6
  • Zivkovic, S. (2007). Testing popular VaR models in EU new member and candidate states. Journal of Economics and Business, 25, 325–346.