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

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.

Volatility in International Stock Markets: An Empirical Study during COVID-19

Predicting volatility is a must in the finance domain. Estimations of volatility, along with the central tendency, permit us to evaluate the chances of getting a particular result. Financial analysts are frequently challenged with the assignment of diversifying assets in order to form efficient portfolios with a higher risk to reward ratio. The objective of this research is to analyze the influence of COVID-19 on the return and volatility of the stock market indices of the top 10 countries based on GDP using a widely applied econometric model—generalized autoregressive conditional heteroscedasticity (GARCH). For this purpose, the daily returns of market indices from January 2019 to June 2020 were taken into consideration. The results reveal daily negative mean returns for all market indices during the COVID period (January 2020 to June 2020). Though the second quarter of the COVID period reflects a bounce back for all market indices with altered strengths, the volatility remains higher than in normal periods, signaling a bearish tendency in the market. The COVID variable, as an exogenous variance regressor in GARCH modeling, is found to be positive and significant for all market indices. Furthermore, the results confirmed the mean-reverting process for all market indices.

Dynamic Spillovers between Gulf Cooperation Council’s Stocks, VIX, Oil and Gold Volatility Indices

This paper analyzes the conditional correlations between the stock market returns of countries that are members of the Gulf Cooperation Council (GCC). The innovative aspects of the paper consist of focusing on three volatility indices: the oil (OVX), gold (GVZ), and S&P500 (VIX) markets (considered in log-difference). We use weekly data and resort to DCC-GARCH modeling. The novelty of the paper consists in revealing that: (i) GCC stock market returns are negatively correlated with each of the volatility measures, and the correlations are stronger during crisis periods; (ii) GCC stock returns are mostly correlated with oil shocks; and (iii) Saudi Arabia and Qatar are the most responsive to all shocks among the GCC countries, while Bahrain correlates weakly to shocks in oil, gold, and VIX. The most striking results feature extra sensitivity of Saudi Arabia and Qatar in terms of volatility indices, which should be the foremost concern of policymakers and banking analysts.