A Test of Alternative Value-at-Risk Models During Volatile Periods
DOI:
https://doi.org/10.17010/ijf/2015/v9i8/74560Keywords:
Value at Risk
, Arch Effects, Long Memory, Foreign CurrencyG10
, G11, G32Paper Submission Date
, February 18, 2015, Paper sent back for Revision, May 15, Paper Acceptance Date, July 6, 2015Abstract
This paper compared the performance of alternative models for estimating Value at Risk (VaR) of four different currencies against the Indian rupee. I examined whether incorporating a volatility estimate capturing the ARCH effects in the normal linear VaR model yielded a better estimate of market risk than the traditional models based on historical simulation and historical moving average volatility. I tested the effectiveness of different VaR models during the volatile period of June-September 2013 and found that VaR models based on an estimate of time-varying volatility performed better than traditional models during turbulent times.Downloads
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References
Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 31 (3), 307-327.
Campbell, S.D. (2006). A review of backtesting and backtesting procedures. Journal of Risk, 9 (2), 1-17.
Culp, C., Mensink, R., & Neves, A. (1998). Value at risk for asset managers. Derivatives Quarterly, 5 (2), 21-33.
Das, A., Basu, P. N., & Ghoshal, T. K. (2009). Stochastic volatility model for Indian security indices: VaR estimation and backtesting. Indian Journal of Finance, 3 (9), 43 - 47.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica ,50(4), 987-1007. DOI: 10.2307/1912773
Fuss, R., Adams, Z., & Kaiser, D. G. (2010). The predictive power of value-at-risk models in commodity futures markets. Journal of Asset Management, 11 (4), 261-285.
Giot, P., & Laurent S. (2003). Value-at-Risk for long and short trading positions. Journal of Applied Econometrics, 18 (6), 641-663. DOI: 10.1002/jae.710
Glosten, L. R., Jagannathan R., & Runkle, D. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48 (5), 1779 - 1801. DOI: 10.2307/2329067
Hendricks, D. (1996). Evaluation of value-at-risk models using historical data. FRBNY Economic Policy Review 2 (1), 39-70.
Hull, J., & White, A. (1998). Incorporating volatility updating into the historical simulation method for value at risk. Journal of Risk, 1 (1), 5-19.
Kupiec, P. (1995). Techniques for verifying the accuracy of risk management models. The Journal of Derivatives, 3 (2), 73-84. DOI: 10.3905/jod.1995.407942
McMillan, D. G., & Speight, A. E. H. (2007). Value-at-Risk in emerging equity markets: Comparative evidence for symmetric, asymmetric, and long-Memory GARCH models. International Review of Finance, 7(1-2), 1-19. DOI: 10.1111/j.1468-2443.2007.00065.x
Mokni, K., Mighri, Z., & Mansouri, F.(2009). On the effect of subprime crisis on value-at-risk estimation: GARCH family models approach. International Journal of Economics and Finance, 1 (2), 88-104. DOI: 10.5539/ijef.v1n2p88
Obi, P., & Sil, S. (2013). VaR and time-varying volatility: A comparative study of three international portfolios. Managerial Finance, 39 (7), 625-640. DOI : http://dx.doi.org/10.1108/03074351311323428
Rabemananjara, R., & Zakoian, J. M. (1993). Threshold ARCH models and asymmetries in volatility. Journal of Applied Econometrics, 8 (1), 31-49. DOI: 10.1002/jae.3950080104
Reserve Bank of India. (Various Years). Database on Indian economy : Exchange rate. Retrieved from http://dbie.rbi.org.in/DBIE/dbie.rbi?site=home
Virdi, N.K. (2011). A review of backtesting methods for evaluating value-at-risk. International Review of Business Research Papers, 7 (4), 14-24.
West, K. D., & Cho, D. (1995). The predictive ability of several models of exchange rate volatility. Journal of Econometrics, 69 (2), 367-391.
