Research ArticleOpen AccessGoogle Scholar indexed
Value at Risk Models in Indian Markets: A Predictive Ability Evaluation Study
Faculty of Management, NMIMS University, Mumbai, India
NMIMS University, Mumbai, India
- 1 Faculty of Management, NMIMS University, Mumbai, India
- 2 NMIMS University, Mumbai, India
Theoretical Economics Letters·Volume 09 (2019)·Pages 2824–2838·Published 22 November 2019·DOI10.4236/tel.2019.98177
Copy link · social · email
Abstract
Value at risk (VaR) is a method of measuring the potential loss in portfolio value for a given distribution of historical returns over a given time period. Measurement of risk therefore becomes essential for a corporate decision. This study attempts to rank the overall predictive ability of select value at risk models in estimating market risks of Indian financial markets. This study estimates the respective predictive ability by employing numerical and graphical measures. The findings plug the gaps in the literature and estimate the best method to be used in the industry. The results evidentially prove that parametric model using normal distribution with GARCH (1,1) fits best for estimating value at risk.
KeywordsValue-at-RiskMonte Carlo SimulationImplied VolatilityHistorical Based Approach
- Kuester, K. (2006) Value-at-Risk Prediction: A Comparison of Alternative Strategies. Journal of Financial Econometrics, 4, 53-89. https://doi.org/10.1093/jjfinec/nbj002
- Skoglund, J., Erdman, D. and Chen, W. (2010) The Performance of Value-at-Risk Models during the Crisis. The Journal of Risk Model Validation, 4, 3-21. https://doi.org/10.21314/JRMV.2010.052
- Chen, L., Peng, J., Zhang, B. and Rosyida, I. (2017) Diversified Models for Portfolio Selection Based on Uncertain Semivariance. International Journal of Systems Science, 48, 637-648. https://doi.org/10.1080/00207721.2016.1206985
- Zhang, B., Peng, J. and Li, S. (2015) Uncertain Programming Models for Portfolio Selection with Uncertain Returns. International Journal of Systems Science, 46, 2510-2519. https://doi.org/10.1080/00207721.2013.871366
- Koutsoyiannis, A. (2006) A Short-Run Pricing Model for a Speculative Asset, Tested with Data from the Gold Bullion Market. Applied Economics, 15, 563-581. https://doi.org/10.1080/00036848300000037
- Gupta, S., Choudhary, H. and Agarwal, D.R. (2018) An Empirical Analysis of Market Efficiency and Price Discovery in Indian Commodity Market. Global Business Review, 19, 771-789. https://doi.org/10.1177/0972150917713882
- Gencay, R. and Selcuk, F. (2004) Extreme Value Theory and Value-at-Risk: Relative Performance in Emerging Markets. International Journal of Forecasting, 20, 287-303. https://doi.org/10.1016/j.ijforecast.2003.09.005
- Zhang, Z. and Pan, H. (2006) Forecasting Financial Volatility: Evidence from Chinese Stock Market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.903937
- Ozun, A. and Yilmazer, S. (2010) Filtered Extreme-Value Theory for Value-at-Risk Estimation: Evidence from Turkey. The Journal of Risk Finance, 11, 164-179. https://doi.org/10.1108/15265941011025189
- Timotheos, A. (2010) Idiosyncratic Risk in Emerging Markets. Financial Review, 45, 1053-1078. https://doi.org/10.1111/j.1540-6288.2010.00285.x
- Lehar, A., Scheicher, M. and Schittenkopf, C. (2002) GARCH vs. Stochastic Volatility: Option Pricing and Risk Management. Journal of Banking & Finance, 26, 323-345. https://doi.org/10.1016/S0378-4266(01)00225-4
- Walsh, D.M. and Tsou, G.Y.-G. (1998) Forecasting Index Volatility: Sampling Interval and Non-Trading Effects. Applied Financial Economics, 8, 477-485. https://doi.org/10.1080/096031098332772