Measuring the Effectiveness of VaR in Indian Stock Market
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2018 by IJETT Journal|
|Year of Publication : 2018|
|Authors : N. Sai Pranav, Satya Sai Mudigonda, R.Prabhakara Rao
|DOI : 10.14445/22315381/IJETT-V64P210|
MLA Style: N. Sai Pranav, Satya Sai Mudigonda, R.Prabhakara Rao "Measuring the Effectiveness of VaR in Indian Stock Market" International Journal of Engineering Trends and Technology 64.1 (2018): 53-61.
APA Style:N. Sai Pranav, Satya Sai Mudigonda, R.Prabhakara Rao (2018). Measuring the Effectiveness of VaR in Indian Stock Market. International Journal of Engineering Trends and Technology, 64(1), 53-61.
Given the growing need for managing financial risks, risk prediction which is critical for the success of any business has now gained more importance, especially in the financial markets. The financial managers, the actuaries, the stock brokers and the regulatory authority such as SEBI have one common goal is to reduce risk of their investments. To understand and mitigate these risks several approaches were suggested one among them is Value at Risk method which answers the question of “What is the most I will lose if I invest in particular security or an asset?” This study has been taken up to estimate the risk involved in the Indian Stock Market by taking the daily data from 1st April 2007 to 31st March 2017. This study employs various Value at Risk methods such as Variance-Covariance Method, Monte Carlo Simulation using Brownian Motion, Filtered Historical Simulation, Generalised extreme value method, t Copula, Exponential Weighted Moving Average, GARCH and Hybrid models. To assess the risk in NIFTY 50, we have selected the sectoral indices of Nifty Bank, Nifty IT, Nifty Private Bank, Nifty FMCG and Nifty Financial Services. We have calculated the risk for all these sectorial indices at 95% and 99% confidence level by using aforementioned methods. After evaluating these models, it has been observed that the hybrid method with GJR GARCH–EVT-t Copula model, performed better when compared to other methods considered in this study. The Empirical results clearly validates that the maximum loss and gain of GJR GARCH-EVT-t Copula based approach which outperforms traditional VaR.
 Alexander , & Leigh. (1997). On the Covariance Models Used in Value at Risk Models. Journal of Derivatives, 4 , 50-62.
 Allen, M. (1994). Building a Role Model. Risk, 7 , 73-80.
 Andjelic, G., Djakovic, V., & Radisic, S. (2010). Application of VaR in Emerging Markets: A Case of Selected Central and Eastern European Countries. Africal Journal of Business Management, 4 (17), 3666-3680.
 Angelidis, T., Benos, A., & Stavros , A. (2004). The Use of GARCH Models in VaR Estimation 1(1). Statistical Methodology, 105-128.
 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.
 Beder, T. (1995). VAR: Seductive but dangerous. Financial Analysts Journal, 51(5), 12-24.
 Benakovic, & Posedel. (2010). Do Macroeconomic Factors Matter for Stock Returns ? Evidence from Estimation a Multifactor Model on the Croatian Market. Business Systems Research, 1(1), 1-50.
 Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307-327.
 Bohdalova, M. (2007). A comparison of Value–at–Risk methods for measurement.
 Bu?evska. (2013). An Empirical Evaluation of GARCH Models in Value-at-Risk Estimation: Evidence from the Macedonian Stock Exchange. Business Systems Research, 4((1), 49-64.
 Burns, P. (2002). The Quality of Value at Risk Via Univariate GARCH. SSRN, 19.
 Celcuz, R. G. (2003). High volatility, thick tails and extreme value theory in value-at-risk estimation. Insurance: Mathematics and Economics, 33(2), 337-356.
 Cerovi? Milena, J., Vujoš, & Božovi?Saša, L. (2015). A Comparitive Analysis of Value at Risk Measurement on Emerging Stock Markets:Case of Montenegro. Business Systems Research, 6(1), 36-55.
 Chena, Q., & Chenb, R. (2013). Method of Value-at-Risk and Empirical Research for Shanghai Stock Market. Procedia Computer Science, 17, 671-677.
 Christoffersen, P., Hahn, J., & Inoue, A. (2001). Testing and comparing Value-at-Risk measures. Journal of Empirical Finance 8, 325–342.
 Da Silva, A., Beatriz, V., & De Melo Mendes, B. (2003). Value-at-Risk and Extreme Returns in Asian Stock Markets. International Journal of Business, 8(1), 17- 40.
 Diebold, F., Schuerman, T., & Stroughair, J. (1998). Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management.Working Paper 98-10, The Wharton School, University of Pennsylvania.
 Emberechts, P., Lindskog, R., & Mcneil, A. (2001). Modelling Dependence with Copulas and Applications to Risk Management. Handbook of heavy tailed distributions in finance, 329-384.
 Engle, R. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of UK Inflation. Econometrica 50(4), 987-1008.
 Gilli , M., & këllezi, E. (2006). An Application of Extreme Value Theory for Measuring Financial Risk. Computational Economics, 27(2-3), 207-228.
 Gondje-Dacka, I.-M., & Yang, Z. (2014). Modelling risk of foreign exchange portifolio based on Garch-Evt-Copula and filtered historical simulation approaches . TheEmpirical Econometrics and Quantitative Economics Letters, 3(2), 33-46.
 Guermat, C., & Harris, R. (2002). Robust Conditional Variance Estimation and Value at Risk. Journal of Risk 4, 25-41.
 Harmantzis, F., Miao , L., & Chien, Y. (2006). Empirical Study of Value-at-Risk and Expected Shortfall Models with Heavy Tails . The Journal of Risk Finance, 7(2), 117–35.
 Harvey, D. (2011). Does Value at Risk provide an accurate and reliable measure of risk exposure, as a stand - alone risk management tool for a financial institution in periods of economic uncertainty? Ireland: The National College of Ireland.
 Helder , P. P., & Hotta, L. K. (2006). Using Conditional Copula to Estimate Value at Risk. Journal of Data Science, 4, 93-115.
 Huang, S. C. (2011). Applying Garch-Evt-Copula Models for Portfolio Value-at-Risk on G7 Currency Markets. International Research Journal of Fiannce and Economics (74).
 Jamshidian, F., & Zhu, Y. (1996). Scenario Simulation: Theory and methodology. Finance Stochast, 1(1), 43-67.
 Lauridsen, S. (2000). Estimation of Value at Risk by Extreme Value Methods. Extremes, 3(2), 107-144.
 Lee, T.-H. Y., & Saltoglu, B. (2002). Assessing the Risk Forecasts for Japanese Stock Market. Japan and the World Economy, 14(1), 63-85.
 Longin, F. M. (2000). From value at risk to stress testing:The Extreme Value approach. Journal of Banking & Finance, 24(7), 1097-1130.
 Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. The Journal of Business, 36(4), 394-419.
 Manganelli, Simone, & Engle, R. F. (2001). Value at Risk Models in Finance ECB Working Paper No. 75. Retrieved from SSRN:https://ssrn.com/abstract=356220
 Mendes, B. V., & Carvalhal, A. (2003). Value-at-Risk and Extreme Returns in Asian Stock Markets. International Journal of Business, 8(1).
 Nozari, M., Raei, S. M., Jahangiri, P., & Baharamgiri, M. (2010). A Comparision of Heavy-tailed VaR Estimates and Filtered Historical Simulation. International Review of Business, 6(4), 347-359.
 Raghavan, R. R., Rao, R. P., & Gupta, K. S. (2017). Evaluation of Value at Risk in Eme4rging Markets. International Journal of Financial Management, 7(1), 10-19.
 Samanta, G., & Thakur , S. (2006). On Estimating Value at Risk Using Tail Index: Application to Indian stock market. ICFAI Journal of Applied Finance 12(6).
 Sarma, M. (2003). Selection of Value-at-Risk models. Journal of Forecasting, 22(4), 337-358.
 Selcuk, Gencay, R., & Fatuk. (2004). Extreme Value Theory and Value at Risk Relative Performance in Emerging Markets. International Journal of Forecasting, 20(2), 287-303.
 Skiadopoulos, S, G., Lambadiaris, Greg, Louiza, Zoulis, & Yiannis. (2003). VAR: History or Simulation? SSRN, 16(9), 122-127.
 Staudt, A., FCAS, & MAAA. (2010). Tail Risk, Systemic Risk and Copulas. Semantic Scholar.
 Su, J. (2015). Value at Risk Estimates of the Stock Indices in Developed and Emerging Marketrs Including the Spillover Effects of Currency Market. Economic Modelling, 46, 204-224.
 Tripathi, V., & Aggarwal, S. (2007). 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.
 V. B., & Bai, G. I. (2013). Etimating Value at Risk iin financial assets;a Parametric and non parametric approach. International Journal Of Mathemetical Archive, 4(4).
 Xiao, Z., & Koenker, R. (2009). Conditional Quantile Estimation for Garch models. Journal of the American Statistical Association.
 Yang, Z., & Gondge-Dacka, I. (2014). Modelling Risk of Foreign Exchange Portfolio based on Garch-Evt-Copula and Filtered Historical Simulation Approaches. The Empirical Econometrics and Quantitative Economics Letters, 3 (2), 33-46.
 Yi, Y., Feng, X., & Huang, Z. (2014). Estimation of Extreme Value-at-Risk: An EVT Approach for Quantile GARCH Model. Elsevier, 124(3), 378-381.
 Zangari. (1996). An Improved Methodology for Measuring VaR. RiskMetrics Monitor, 7-25.
 Zhang, H., Guo, J., & Zhou, L. (2015). Study on Financial Market Risk Measurement Based on GJR GARCH and FHS. Science Journal of Applied Mathematics and Statistics, 70-74.
 Zikovic, S. (2007). Measuring Market Risk in EU New Member States. Retrieved from http://www.hnb.hr/dub-konf/13-konferencija/zivkovic.pdf.
VaR, GARCH, EVT, Copula