Open Access Open Access  Restricted Access Subscription Access

Comparison of VaR Methods : The Case of Indian Equities


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

   Subscribe/Renew Journal


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.

User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 200

PDF Views: 0




  • Comparison of VaR Methods : The Case of Indian Equities

Abstract Views: 200  |  PDF Views: 0

Authors

Prateek Bedi
Research Scholar, Department of Financial Studies, Arts Faculty Building, Benito Juarez Marg, University of Delhi, Delhi - 110 021, India
Devesh Shankar
Research Scholar, Faculty of Management Studies, University of Delhi, Delhi - 110 007, India
Shalini Agnihotri
Research Scholar, Faculty of Management Studies, University of Delhi, Delhi - 110 007, India
Jappanjyot Kaur Kalra
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.




DOI: https://doi.org/10.17010/ijf%2F2018%2Fv12i1%2F120739