Open Access Open Access  Restricted Access Subscription or Fee Access
Total views : 1654

Comparison of VaR Methods : The Case of Indian Equities

Affiliations

  • Research Scholar, Department of Financial Studies, Arts Faculty Building, Benito Juarez Marg, University of Delhi, Delhi - 110 021, India
  • Research Scholar, Faculty of Management Studies, University of Delhi, Delhi - 110 007, India

Abstract


Different approaches to calculate VaR are based on different assumptions. This study dealt with a comparative evaluation of four Value-at-Risk models namely, historical VaR, normal VaR, GARCH (1,1) VaR, and volatility weighted historical simulation (VWHS) VaR in terms of their prediction accuracy for an active portfolio of Indian equities. Daily NAVs of 34 Indian equity growth mutual fund schemes for a period of 10 years were used to calculate 95% VaR and backtest the results using Kupiec's POF test for all four VaR models. To identify the better performing VaR methods accurately, the analysis was performed in two phases : pre-crisis analysis and post crisis analysis. We concluded that there was a significant (insignificant) difference in performance of different VaR models if market conditions during VaR calculation and VaR backtesting periods were in contrast (congruence) to each other. The study found VWHS to be a better methodology for measuring VaR of an active portfolio of Indian equity stocks in both phases of the analysis. The results are relevant for traders & retail and institutional investors who hold stocks of Indian companies in their portfolio and need to calculate VaR as a measure of market risk for their positions.

Keywords

Backtesting, Historical Var, Kupiec's POF Test, GARCH (1,1) VaR, Volatility Weighted Historical Simulation VaR, Normal VaR, Value At Risk

C52, C53, C14, C15, G32

Paper Submission Date : February 28, 2017 ; Paper sent back for Revision : November 4, 2017 ; Paper Acceptance Date : December 15, 2017.


Full Text:

 |  (PDF views: 1)

References


  • Abad, P., & Benito, S. (2013). A detailed comparison of value at risk estimates. Mathematics and Computers in Simulation, 94, 258 - 276. doi : http://dx.doi.org/10.1016/j.matcom.2012.05.011

  • Abad, P., Benito, S., & López, C. (2014). A comprehensive review of value at risk methodologies. The Spanish Review of Financial Economics, 12(1), 15 - 32. DOI : http://dx.doi.org/10.1016/j.srfe.2013.06.001

  • Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885 - 905. DOI: http://dx.doi.org/10.2307/2527343

  • Bali, T. G., Mo, H., & Tang, Y. (2008). The role of autoregressive conditional skewness and kurtosis in the estimation of conditional VaR. Journal of Banking & Finance, 32(2), 269-282. DOI: http://dx.doi.org/10.1016/j.jbankfin.2007.03.009

  • Bao, Y., Lee, T., & Saltoglu, B. (2006). Evaluating predictive performance of value-at-risk models in emerging markets: A reality check. Journal of Forecasting, 25(2), 101-128. DOI: http://dx.doi.org/10.1002/for.977

  • Bhat, A. P. (2015). A test of alternative value-at-risk models during volatile periods. Indian Journal of Finance, 9 (8), 19 - 33. doi : http://dx.doi.org/10.17010/ijf/2015/v9i8/74560

  • Brooks, C., & Persand, G. (2002). Model choice and value-at-risk performance. Financial Analysts Journal, 58 (5), 87-97. DOI : http://dx.doi.org/10.2469/faj.v58.n5.2471

  • Campbell, S. D. (2007). A review of backtesting and backtesting procedures. Journal of Risk, 9 (2), 1-17. DOI : http://dx.doi.org/10.21314/jor.2007.146

  • Chou, H., & Wang, D. K. (2013). Estimation of tail-related value-at-risk measures: Range-based extreme value approach. Quantitative Finance, 14(2), 293-304. DOI : http://dx.doi.org/10.1080/14697688.2013.819113

  • Christoffersen, P. (1998). Evaluating interval forecasts. International Economic Review, 39 (4), 841- 862. DOI : http://dx.doi.org/10.2307/2527341

  • Christoffersen, P. F., & Diebold, F. X. (2000). How relevant is volatility forecasting for financial risk management ? Review of Economics and Statistics, 82 (1), 12-22. DOI : http://dx.doi.org/10.1162/003465300558597

  • Christoffersen, P., & Pelletier, D. (2004). Backtesting value-at-risk: A duration-based approach. Journal of Financial Econometrics, 2(1), 84 - 108. DOI : http://dx.doi.org/10.1093/jjfinec/nbh004

  • Consigli, G. (2002). Tail estimation and mean - VaR portfolio selection in markets subject to financial instability. Journal of Banking & Finance, 26 (7), 1355 - 1382. DOI : http://dx.doi.org/10.1016/s0378-4266(02)00267-4

  • Dan..elsson, J. (2002). The emperor has no clothes: Limits to risk modelling. Journal of Banking & Finance, 26 (7), 1273-1296. DOI : http://dx.doi.org/10.1016/s0378-4266(02)00263-7

  • Dani.elsson, J. (2011). Financial risk forecasting (1st ed.). Chichester : John Wiley.

  • Danielsson, J., & De Vries, C. (2000). Value-at-risk and extreme returns. Annales D'Economie Et De Statistique, 60, 239 - 270. doi:10.2307/20076262

  • 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.

  • Dowd, K. (2006). Measuring market risk (1st ed.). Chichester : John Wiley & Sons.

  • Ergen, I. (2014). Two-step methods in VaR prediction and the importance of fat tails. Quantitative Finance, 15(6), 1013 - 1030. DOI : http://dx.doi.org/10.1080/14697688.2014.942230

  • Ergun, A. T., & Jun, J. (2010). Time-varying higher-order conditional moments and forecasting intraday VaR and expected shortfall. The Quarterly Review of Economics and Finance, 50 (3), 264 - 272. DOI : http://dx.doi.org/10.1016/j.qref.2010.03.003

  • Gencay, R., & Selcuk, F. (2004). Extreme value theory and value-at-risk: Relative performance in emerging markets. International Journal of Forecasting, 20(2), 287-303. DOI : http://dx.doi.org/10.1016/j.ijforecast.2003.09.005

  • Giannopoulos, K., & Tunaru, R. (2005). Coherent risk measures under filtered historical simulation. Journal of Banking & Finance, 29 (4), 979 - 996. DOI : http://dx.doi.org/10.1016/j.jbankfin.2004.08.009

  • Gonzalez-Rivera, G., Lee, T., & Mishra, S. (2004). Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood. International Journal of Forecasting, 20 (4), 629 - 645. DOI : http://dx.doi.org/10.1016/j.ijforecast.2003.10.003

  • Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH(1,1) ? Journal of Applied Econometrics, 20 (7), 873 - 889. DOI : http://dx.doi.org/10.1002/jae.800

  • Holton, G. (2003). Value-at-risk (1st ed.). Amsterdam: Academic Press.

  • Huang, A. Y. (2015). Value at risk estimation by threshold stochastic volatility model. Applied Economics, 47(45), 4884 - 4900. DOI : http://dx.doi.org/10.1080/00036846.2015.1037439

  • Hull, J., & White, A. (1998). Incorporating volatility updating into the historical simulation method for value-at-risk. The Journal of Risk, 1(1), 5-19. DOI : http://dx.doi.org/10.21314/jor.1998.001

  • Javed, F., & Mantalos, P. (2013). GARCH-type models and performance of information criteria. Communications in Statistics - Simulation and Computation, 42 (8), 1917-1933.

  • Jorion, P. (2001). Financial risk manager handbook 2001-2002 (1st ed.). New York : John Wiley & Sons.

  • Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models. The Journal of Derivatives, 3 (2), 73 - 84. DOI : http://dx.doi.org/10.3905/jod.1995.407942

  • Linsmeier, T. J., & Pearson, N. D. (1996). Risk measurement : An introduction to value at risk. Working Paper, 6153(July), 1 - 44. Retrieved from http://www.casact.net/education/specsem/99frmgt/pearson2.pdf

  • Lopez, J. A. (1998). Methods for evaluating value-at-risk estimates. DOI : http://dx.doi.org/10.2139/ssrn.1029673

  • Malhotra, R. (2014). Analysis of pure weather portfolios using parametric, non-parametric, and conditional VaR in relation to bank's risk capital. Indian Journal of Finance, 8(5), 19 - 26. doi: http://dx.doi.org/10.17010/ijf/2014/v8i5/71915

  • Mancini, L., & Trojani, F. (2011). Robust value at risk prediction. Journal of Financial Econometrics, 9 (2), 281 - 313. DOI : http://dx.doi.org/10.1093/jjfinec/nbq035

  • Nath, G., & Reddy, Y. (2003). Value at risk: Issues and implementation in Forex market in India. DOI : http://dx.doi.org/10.2139/ssrn.474141

  • Ñíguez, T. (2008). Volatility and VaR forecasting in the Madrid Stock Exchange. Spanish Economic Review, 10 (3), 169-196. DOI : http://dx.doi.org/10.1007/s10108-007-9030-6

  • Nozari, M., Raei, S. M., Jahangiri, P., & Bahramgiri, M. (2010). A comparison of heavy-tailed VaR estimates and filtered historical simulation : Evidence from emerging markets. International Review of Business Research Papers, 6 (4), 347 - 359.

  • Polanski, A., & Stoja, E. (2009). Incorporating higher moments into value-at-risk forecasting. Journal of Forecasting, 29 (6), 523 - 535. DOI : http://dx.doi.org/10.1002/for.1155

  • Sarma, M., Thomas, S., & Shah, A. (2003). Selection of value-at-risk models. Journal of Forecasting, 22 (4), 337- 358. DOI : http://dx.doi.org/10.1002/for.868

  • Tripathi, V., & Gupta, S. (2008). Estimating the accuracy of value-at-risk (VAR) in measuring risk in equity investment in India. ICFAI Journal of Applied Finance, 14(7), 15 - 40.


Refbacks

  • There are currently no refbacks.