Indian Journal of Finance

Open Access Open Access  Restricted Access Subscription Access

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

   Subscribe/Renew Journal


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.
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 192

PDF Views: 0




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

Abstract Views: 192  |  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.