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We investigate the dynamic volatility connectedness of regional stocks, gold, Bitcoin, oil, and uncertainty index related to infectious diseases for the period from January 2014 to June 2022. We investigate the connectivity during Ebola & MERS periods, the normal period, the COVID-19 period and the full sample period. We find that the regional stock indices of the US, Europe, Africa and Latin America are net volatility transmitters whereas regional indices of Asia Pacific, Middle East and North Africa, and other assets like gold, oil and Bitcoin are net volatility recipients throughout the sample periods. By employing the TVP-VAR-based dynamic connectedness approach, we find the temporal evolution of system-wide total connectedness and pair-wise connectedness of financial assets to exhibit higher intensity of volatility spillover during the COVID-19 pandemic as compared to other sub-sample periods. We further observe, based on quantile connectedness approach, that the degree of dynamic connectedness is strong and significant across all the quantile spectrums only during the COVID-19 period. We observe that the safe haven characteristics of assets like gold, oil and Bitcoin diminish during the COVID-19 period due to strong dynamic connectedness with regional stock indices. Our findings have implications for policymakers, investors and portfolio managers in better risk management during periods of health epidemics and pandemics. [ABSTRACT FROM AUTHOR]

We investigate the dynamic volatility connectedness of geopolitical risk, stocks, bonds, bitcoin, gold, and oil from January 2018 to April 2022 in this study. We look at connectivity during the Pre-COVID, COVID, and Russian-Ukraine war subsamples. During the COVID-19 and Russian-Ukraine war periods, we find that conventional, Islamic, and sustainable stock indices are net volatility transmitters, whereas gold, US bonds, GPR, oil, and bitcoin are net volatility receivers. During the Russian-Ukraine war, the commodity index (DJCI) shifted from being a net recipient of volatility to a net transmitter of volatility. Furthermore, we discover that bilateral intercorrelations are strong within stock indices (DJWI, DJIM, and DJSI) but weak across all other financial assets. Our study has important implications for policymakers, regulators, investors, and financial market participants who want to improve their existing strategies for avoiding financial losses. [ABSTRACT FROM AUTHOR]

This research investigates the effectiveness of various machine learning models, including Random Forest, Neural Networks, Adaboost, Discriminant Analysis, Logit Model, Support Vectors, and Kernel Factory. The study aims to forecast fluctuations in the ASEAN-5 stock index prices within an eleven-year period. The study provides useful information about how well machine learning techniques can predict changes in the stock market, with potential implications for both academic researchers and market participants. The findings imply that Adaboost consistently outperforms all others in predicting price changes accurately. This shows that machine learning algorithms are capable of accurately forecasting the movement of the ASEAN-5 stock index values. This study contributes to the growing body of research on the use of machine learning techniques in finance and provides investors with information to make informed decisions about investments in the ASEAN-5 region, ultimately leading to increased returns and improved portfolio performance. [ABSTRACT FROM AUTHOR]

In this paper, we study the power of moment‐based normality tests which include Jarque Bera (JB) test and D′Agostino and Pearson (DP) omnibus tests. Power comparison were obtained via Monte Carlo simulation of sample data generated from four alternative distributions like Uniform, Logistic, Student t and Gamma distribution. Our simulation results show that for Uniform distribution, DP test has better power compared to JB test. For Logistic, Student t and Gamma distributions, we find JB normality test to be powerful compared to DP test. We further apply the moment‐based normality tests empirically on the Indian stock market indices (NSE Nifty 50 and BSE Sensex) for different frequencies (daily, weekly, monthly and quarterly) during the period from 2010 to 2020. We find that daily returns of Indian stock indices are non‐normal whereas weekly, monthly and quarterly returns are normally distributed. [ABSTRACT FROM AUTHOR]

This study investigates the dynamic volatility connectivity of important environmental, social, and governance (ESG) stock indexes from May 2010 to March 2021. The empirical research is focused on five major S&P ESG stock indexes from the US, Latin America, Europe, the Middle East and Africa, and Asia Pacific regions. The study reveals that ESG stock indexes in the Middle East Africa, and Latin America are net shock transmitters, whereas the United States and Asia Pacific are net volatility receivers. Furthermore, the study finds that bilateral intercorrelations are higher among US, Latin America, and Europe region group pairs and weaker in relation to Middle East Africa and Asia Pacific region group pairs, indicating the presence of contagion within developed and/or emerging regions, which has relevance for portfolio and risk management. [ABSTRACT FROM AUTHOR]

Shaik and Maheswaran (2018) proposed the Expected Lifetime Range (ELR) ratio based on high minus low price (range) series to detect mean reversion. This paper extends this test statistic and proposes a set of test statistics—Expected Lifetime Surplus (ELSp) and Expected Lifetime Shortfall (ELSf) ratios—which are based on high and low price series respectively. These test statistics help in precisely detecting whether the source of market inefficiency is the high or the low price series. The paper empirically tests the three test statistics on the BRICS stock market indices. The sample period is divided into pre and post subprime crisis data sets. It is found that the BRICS stock market indices show mean reversion from 2001 to 2018. While in the pre subprime crisis period the indices followed random walk, after the subprime crisis the BRICS stock markets show mean reversion behavior. The source of this mean reversion are the high prices which have become more predictable after the subprime crisis. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the power of various normality tests against alternative distributions using Monte Carlo simulation experiments. We use seven different normality tests classified as moments tests, correlation and regression tests, and empirical distribution functional tests against six symmetric and four asymmetric alternative distributions. We also perform the rank analysis for the power of the normality tests. Furthermore, we conduct an empirical analysis of five emerging Asian stock indices (India, Indonesia, Malaysia, Singapore, and Taiwan) to understand whether the returns follow a normal distribution or not during the period from January 2000 to January 2020. We find that emerging Asian stock index returns do not follow normal distribution irrespective of the different frequencies of the data. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, declared on March 11, 2020 by the World Health Organisation (WHO), has had a severe economic and financial impact on every economy around the world. This paper aims to analyze the short-term impact of COVID-19 on global financial stock market indices. We study the impact of six different WHO announcements regarding COVID-19 on five different sectors (Pharma, Healthcare, Information Technology, Hotel & Airline) based on the indices of three different economies (World, Developed and Emerging economy). We also study the movement of stock prices and volume of nine different global stock market indices (classified as developed & emerging) based on the number of new cases and deaths due to COVID-19. The study's findings suggest that there is a significant effect of COVID-19 on global financial stock markets. However, the effect is varied for developed and emerging economies. [ABSTRACT FROM AUTHOR]

We use the Expected Lifetime Range (ELR) Ratio proposed in Shaik and Maheswaran (2018) to find evidence of mean reversion in the BRICS stock markets indices. We divide the sample period into pre-crisis, crisis, and post-subprime crisis data sets. We find that the BRICS stock market indices show mean reversion from 2001 to 2018. While before the subprime crisis, the indices followed a random walk, after the subprime crisis, the BRICS stock markets show mean reversion behavior and have become more predictable. We also conduct the Lo and Mackinlay variance Ratio test and find that the Expected Lifetime Range Ratio is better at detecting the presence of mean reversion. [ABSTRACT FROM AUTHOR]

In this paper, we come up with an alternate theoretical proof for the independence and unbiased property of extreme value robust volatility estimator with respect to the standard robust volatility estimator. We show that the robust volatility ratio is unbiased both in the population and in the finite samples. We empirically test the robust volatility ratio on nine global stock indices from America, Asia Pacific and EMEA markets for the period from January 1996 to June 2017 based on daily open, high, low and close prices to understand the volatility behavior of stock returns over a period of time. Our results show that robust volatility ratio for different k-month periods is significantly less than 1 for all the global stock indices, thus finding the clear evidence of random walk behavior. This is possibly the first study based on robust volatility ratio to understand the volatility behavior of global stock indices. [ABSTRACT FROM AUTHOR]

We use the expected lifetime range (ELR) ratio based on the extreme values of asset prices to detect the presence of mean reversion in stock returns. We find that the actual cross-sectional average of the ELR ratio is significantly less than its bootstrap means, thereby indicating a considerable amount of mean reversion. We argue that ELR ratio is more conclusive in detecting mean reversion when compared to the traditional Lo and MacKinlay variance ratio variance ratio. On the empirical side, we find that mean reversion is a robust feature among the constituents of India's BSE SENSEX stock index. [ABSTRACT FROM AUTHOR]