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Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach

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  • Muhammad Sheraz

    (Department of Mathematical Sciences, Institute of Business Administration, The School of Mathematics and Computer Science, Karachi 75270, Pakistan)

  • Imran Nasir

    (Department of Mathematical Sciences, Institute of Business Administration, The School of Mathematics and Computer Science, Karachi 75270, Pakistan)

Abstract

The volatility analysis of stock returns data is paramount in financial studies. We investigate the dynamics of volatility and randomness of the Pakistan Stock Exchange (PSX-100) and obtain insights into the behavior of investors during and before the coronavirus disease (COVID-19 pandemic). The paper aims to present the volatility estimations and quantification of the randomness of PSX-100. The methodology includes two approaches: (i) the implementation of EGARCH, GJR-GARCH, and TGARCH models to estimate the volatilities; and (ii) analysis of randomness in volatilities series, return series, and PSX-100 closing prices for pre-pandemic and pandemic period by using Shannon’s, Tsallis, approximate and sample entropies. Volatility modeling suggests the existence of the leverage effect in both the underlying periods of study. The results obtained using GARCH modeling reveal that the stock market volatility has increased during the pandemic period. However, information-theoretic results based on Shannon and Tsallis entropies do not suggest notable variation in the estimated volatilities series and closing prices. We have examined regularity and randomness based on the approximate entropy and sample entropy. We have noticed both entropies are extremely sensitive to choices of the parameters.

Suggested Citation

  • Muhammad Sheraz & Imran Nasir, 2021. "Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach," Risks, MDPI, vol. 9(5), pages 1-20, May.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:5:p:89-:d:550572
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