Forecasting the Direction of Daily Changes in the India VIX Index Using Machine Learning

Akhilesh Prasad () and Priti Bakhshi
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Akhilesh Prasad: IFMR Graduate School of Business, Krea University, Sri City 517646, Andhra Pradesh, India
Priti Bakhshi: S. P. Jain School of Global Management, Mumbai 400070, Maharashtra, India

JRFM, 2022, vol. 15, issue 12, 1-26

Abstract: Movements in the India VIX are an important gauge of how the market’s risk perception shifts from day to day. This research attempts to forecast movements one day ahead of the India VIX using logistic regression and 11 ensemble learning classifiers. The period of study is from April 2009 to March 2021. To achieve the stated task, classifiers were trained and validated with 90% of the given sample, considering two-fold time-series cross-validation for hyper-tuning. Optimised models were then predicted on an unseen test dataset, representing 10% of the given sample. The results showed that optimal models performed well, and their accuracy scores were similar, with minor variations ranging from 63.33% to 67.67%. The stacking classifier achieved the highest accuracy. Furthermore, CatBoost, Light Gradient Boosted Machine (LightGBM), Extreme Gradient Boosting (XGBoost), voting, stacking, bagging and Random Forest classifiers are the best models with statistically similar performances. Among them, CatBoost, LightGBM, XGBoost and Random Forest classifiers can be recommended for forecasting day-to-day movements of the India VIX because of their inherently optimised structure. This finding is very useful for anticipating risk in the Indian stock market.

Keywords: ensemble learning; implied volatility; India VIX; fear index; stock market risk (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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