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.
Highlights
Risk is an important element when evaluating the effectiveness of business operations
The results evidentially prove that parametric model using normal distribution with generalized autoregressive conditionally heteroskedastic (GARCH) [1,1] fits best for estimating value at risk
The results show that extreme value theory (EVT) performs better than the parametric models as it focuses on the fat tail risk as well
Summary
Risk is an important element when evaluating the effectiveness of business operations. A Risk management plan can help a firm identify future losses, operational inefficiencies, reduce uncertainty and provide a healthier bottom line. Due to increased global competition, increasing regulations, financial engineering leading to development of complicated securitisation and derivative product, risk management is gaining huge importance. One of the most important steps in risk management is risk measurement. Risk is measured using some common tools such as standard deviation, beta, value at risk and conditional value at risk or sophisticated risk models can be developed for better results.
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