Indian Journal of Finance

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Stochastic Volatility Model for Indian Security Indices: VaR Estimation and Backtesting


Affiliations
1 Assistant Professor, Department of CSE/IT, Netaji Subhash Engineering College, Kolkata, India
2 Professor, School of Education Technology, Jadavpur University, Kolkata, India
3 Professor, Department of Electrical Engineering, Jadavpur University, Kolkata, India

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Value-at-Risk (VaR) estimation through volatility analysis is a regulatory requirement. Many asset management companies and bourses tend to use EWMA and GARCH based techniques towards this. This paper compares the predictive power of Stochastic Volatility Model (SVM) and Kalman Filter (KF) based approach vis-à-vis EWMA and GARCH based approaches with data from Indian security indices. A Quasi-Maximum Likelihood (QML) based on KF is used for estimation of parameters for the underlying state space SVM. It is found that, with a representative data set, VaR backtesting result from the SVM significantly outperforms the traditionally recommended EWMA based techniques.
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  • Stochastic Volatility Model for Indian Security Indices: VaR Estimation and Backtesting

Abstract Views: 185  |  PDF Views: 0

Authors

Atanu Das
Assistant Professor, Department of CSE/IT, Netaji Subhash Engineering College, Kolkata, India
Pramatha Nath Basu
Professor, School of Education Technology, Jadavpur University, Kolkata, India
Tapan Kumar Ghosal
Professor, Department of Electrical Engineering, Jadavpur University, Kolkata, India

Abstract


Value-at-Risk (VaR) estimation through volatility analysis is a regulatory requirement. Many asset management companies and bourses tend to use EWMA and GARCH based techniques towards this. This paper compares the predictive power of Stochastic Volatility Model (SVM) and Kalman Filter (KF) based approach vis-à-vis EWMA and GARCH based approaches with data from Indian security indices. A Quasi-Maximum Likelihood (QML) based on KF is used for estimation of parameters for the underlying state space SVM. It is found that, with a representative data set, VaR backtesting result from the SVM significantly outperforms the traditionally recommended EWMA based techniques.