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Volatility Forecast Incorporating Investors’ Sentiment and its Application in Options Trading Strategies: A Behavioural Finance Approach at Nifty 50 Index

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  • Kelvin Mutum

Abstract

The present study was to examine whether the performance of options trading strategies can be improved if volatility forecasting incorporating investors’ sentiment was incorporated in the decision-making process at the Indian options market. The study adopted the multiple-factor model to build the Indian volatility forecasting model. The benchmark forecasting model (BMF) includes absolute daily returns (|RA|), daily high–low range (HLR) and daily realized volatility (RV). The proxies of investors’ sentiment considered in the study were India volatility index (IVIX), advance decline ratio (ADR), put-call open interest (PCOI) and their changes. The results of the causality and regression test indicate that investors’ sentiment and their changes should be included in the forecasting model. Mean absolute percentage error (MAPE) indicates that 15-day holding period shows the minimum error. Straddle strategies were simulated 15 days ahead before the options maturity date base on the direction of the forecast for different volatility forecasting models. The simulation result shows that the options trading performance might be improved if volatility forecasting incorporating investor sentiment, particularly IVIX, was incorporated in the decision-making process at the Indian options market. From the behavioural finance point of view, the study bridges the gap between options trading, volatility forecasting and information content of investors’ sentiment at the Indian financial market.

Suggested Citation

  • Kelvin Mutum, 2020. "Volatility Forecast Incorporating Investors’ Sentiment and its Application in Options Trading Strategies: A Behavioural Finance Approach at Nifty 50 Index," Vision, , vol. 24(2), pages 217-227, June.
    • Handle: RePEc:sae:vision:v:24:y:2020:i:2:p:217-227
    DOI: 10.1177/0972262920914117
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    1. Kumari, Jyoti & Mahakud, Jitendra, 2015. "Does investor sentiment predict the asset volatility? Evidence from emerging stock market India," Journal of Behavioral and Experimental Finance, Elsevier, vol. 8(C), pages 25-39.
    2. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    3. Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
    4. Stephen J. Taylor, 1994. "Modeling Stochastic Volatility: A Review And Comparative Study," Mathematical Finance, Wiley Blackwell, vol. 4(2), pages 183-204, April.
    5. Her-Jiun Sheu & Yu-Chen Wei, 2011. "Options Trading Based on the Forecasting of Volatility Direction with the Incorporation of Investor Sentiment," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(2), pages 31-47, March.
    6. Athanasia Gavala & Nikolay Gospodinov & Deming Jiang, 2006. "Forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 381-400.
    7. Breitung, Jörg & Hafner, Christian M., 2016. "A simple model for now-casting volatility series," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1247-1255.
    8. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    9. Lopez, Jose A, 2001. "Evaluating the Predictive Accuracy of Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(2), pages 87-109, March.
    10. Divya Aggarwal, 2017. "Exploring relation between Indian market sentiments and stock market returns," Asian Journal of Empirical Research, Asian Economic and Social Society, vol. 7(7), pages 147-159, July.
    11. Ahmed Naeem & Sarfraz Mudassira, 2018. "Stock Market Volatility Measure Using Non-Traditional Tool Case of Germany," Economics and Business, Sciendo, vol. 32(1), pages 126-135, July.
    12. Hun Y. Park & R. Stephen Sears, 1985. "Estimating stock index futures volatility through the prices of their options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 5(2), pages 223-237, June.
    13. Saurabh Kumar & Sarveshwar Kumar Inani, 2017. "Intraday price discovery in Indian stock index futures market: new evidence from neural network approach," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 6(1), pages 12-29.
    14. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    15. Divya Aggarwal, 2017. "Exploring Relation between Indian Market Sentiments and Stock Market Returns," Asian Journal of Empirical Research, Asian Economic and Social Society, vol. 7(7), pages 147-159.
    16. Sarveshwar Kumar Inani, 2017. "Price discovery in Indian stock index futures market: new evidence based on intraday data," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 14(1), pages 23-43.
    17. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    18. Pramod Kumar Naik & Puja Padhi, 2016. "Investor sentiment, stock market returns and volatility: evidence from National Stock Exchange of India," International Journal of Management Practice, Inderscience Enterprises Ltd, vol. 9(3), pages 213-237.
    19. Blair, Bevan J. & Poon, Ser-Huang & Taylor, Stephen J., 2001. "Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 5-26, November.
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