Implied Volatility V/s Realized Volatility: A Forecasting Dimension for Indian Markets

Prof. Karam Pal Narwal; Prof. Ved Pal Sheera; Prof. Ruhee Mittal

doi:https://doi.org/10.51768/dbr.v17i2.172201606

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Implied Volatility V/s Realized Volatility: A Forecasting Dimension for Indian Markets

Karam Pal Narwal, Ved Pal Sheera, Ruhee Mittal

Vol 17 , Issue 2 , July - December 2016 | Pages: 75-85 | Research Paper

https://doi.org/10.51768/dbr.v17i2.172201606

Published Online: November 30, 2016

Author Details ( * ) denotes Corresponding author

1. * Karam Pal Narwal, Professor, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Haryana, India (karampalsingh@yahoo.com)

2. Ved Pal Sheera, Professor, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Haryana, India

3. Ruhee Mittal, Assistant Professor, Rukmini Devi Institute of Advanced Studies, New Delhi, Delhi, India

Purpose: The aim of the present study is to examine the forecasting efficiency of implied volatility index of India in predicting the future stock market volatility. Therefore, the forecasting efficacy of implied volatility index is compared with intra high-low price range volatility in providing volatility forecasts for S&P CNX Nifty 50 index.

Design/Methodology/Approach: The generalized autoregressive conditional heteroskedasticity model (GJR-GARCH) is used for the Indian markets as this model captures the asymmetric effect of good news and bad news on conditional volatility. The GJR-GARCH model is augmented with implied volatility and high-low price range volatility.This model is used to compare the forecasting efficiency of implied volatility index with the realised volatility represented by high-low range price volatility, to find out which is a better measure of forecasting the future stock market volatility. For measuring the forecasting performance of IVIX on various forecasting horizons (1-, 5, 10-, and 20-days), the test for in-sample and out-of-sample data is done.

Findings: The results of in-sample regression show that both implied and high-low volatility contains significant information about the conditional volatility. On the other hand, the overall ranking given to the different models on the basis of out-of-sample forecasting evaluations show that the GJR-GARCH model with IVIX consistently performs better than other models, over various forecast horizons. This shows that IVIX is able to provide incremental information about future volatility forecasts and is a better measure of predicting future volatility than the high-low range volatility.

Research Limitations: The major limitation of the study is the data period. Further, more countries can be included in this research to compare the predictive abilities of volatility indices of international markets.

Practical Implications: The major implication of this study is for the investors who can use implied volatility indices for forecasting the future volatility, thus can be used as a market timing tool.

Originality/Value: In this study a novel approach is developed for examining the predictive ability of Indian implied volatility index. To the best of authors knowledge no research of this kind has been conducted using Indian stock markets has been carried out.

Keywords

Indian VIX, S&P CNX Nifty, GJR-GARCH Model, Indian Stock Markets.

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