ABSTRACT
PurposeThis study examined the impact of war/conflict-related news on the Russian and Ukrainian stock markets in the build-up and beginning of the war that sparked in the year 2022.Design/methodology/approachIn order to examine the impact of war-related news on stock returns, data were gathered from the United States (US) and Russian stock indices, oil price and volatile index (VIX) from Yahoo.finance;Ukrainian stock values from pfts.ua website and daily related news retrieved from nexis.com were analysed. The data were gathered from January 1, 2022 to February 24, 2022. Seeming unrealated regressions (SUR) and exponential generalised autoregressive conditional heteroscedastic (EGARCH) models were carried out to determine the formulated correlations. This study controlled the oil price, US stock returns, Chicago Board Options Exchange (CBOE) VIX and difference in stock returns of Russia and Ukraine.FindingsThe results are presented two-fold: first, war-related news between the two countries enhanced volatility and caused a significant decline in the stock market indices for both countries. Second, the Russian stock market faced a steeper decline in the build-up and the actual beginning of the war than the Ukrainian stock market. Notably, the Russian markets feared the adverse economic consequences that stemmed from the sanctions the US and the Western world imposed.Research limitations/implicationsAs this study was based on early evidence, future studies with a longer window may provide better insights. This present study is restricted to the stock returns of the countries directly involved in the build-up towards war. Studies focusing on the impact of other asset classes, currencies, commodities and global stock markets might offer holistic insights.Practical implicationsThe study outcomes suggest that global portfolio investors should stay away from stock markets of the war-raged countries and equity markets in general, but instead look for safe-haven assets.Originality/valueThe paper evaluates stock markets' performance during the pre-war period, considering the context of this historical war between the neighbours. It is important to understand this issue as this war is subject to sanctions by the US and leads to a global supply chain crisis.
ABSTRACT
This study explores the impact of the COVID-19 media coverage index (MCI) on the return and volatility connectedness of five MSCI Climate Changes Indices (the USA, Emerging Markets (EMU), Japan, Europe, and the Asia Pacific). The sample period was from 11 March 2020 to 19 January 2022, divided into sub-samples based on four waves of the COVID-19 pandemic. Thus, we use the time-varying parameter vector autoregression (TVP-VAR) model besides the frequency-dependent connectedness network approach. The key findings are as follows. First, the results demonstrate that the MCI is a net receiver of shocks in all waves, and the highest level of connectedness occurs in the first wave. The findings concerning volatility are similar, with the majority of MSCI Climate Change Indices being net transmitters, potentially indicating the severity of the pandemic. Second, estimating the short-, medium-, and long-term return network connectedness indicates the dominance of strong-term connectedness suggesting the spread of shocks within a week. Our results are robust by replacing MCI with Panic Index (PI). These results have implications for investors and policymakers.
ABSTRACT
Purpose>The purpose of this study is to investigate the dynamic return volatility connectedness among S&P, Dow Jones (DJ) sustainability indices and their conventional counterparts.Design/methodology/approach>This study uses time-series daily data for 10 S&P and DJ indices over the period of December 1, 2012 to December 8, 2021. The authors divide the data into three periods;over the whole sample, pre and during the Covid-19 pandemic. The study adopts the connectedness approach developed by Diebold and Yilmaz (2014).Findings>The results reveal a high degree of connectedness between S&P and DJ indices and their relative sustainability indices over the whole sample, pre and during the Covid-19 pandemic, indicating that the sustainability indices converge toward their conventional peers. The results further show that the conventional S&P500, S&P Euro 50 and DJWI are the main transmitters of shocks, whereas the S&P400, S&P500 and S&P50 sustainability indices are the main receivers of shocks.Originality/value>The study provides novel insights in terms of shock transmission of S&P and DJ sustainability indices and their conventional counterparts, where there is a lack of investigation of the connectedness between indices in this field.Practical implications>The study has significant implications for investors and portfolio managers to devise portfolio strategies to minimize risk and trace the cause, the direction and the magnitude of risk transmission among different indices. Also, the results help policymakers to manage diverse types of risks associated with S&P and DJ indices. Finally, faith-based and ethical investors would be able to predict the pairwise spillover connectedness between these indices.
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This paper investigates the asymmetric, time, and frequency-based volatility spillovers in global IT industries. To this end, we introduce a new Wavelet-Time Varying Parameter-VAR (W-TVP-VAR) approach to compute connectedness combined with the asymmetrical connectedness of (Barndorff-Nielsen et al., 2010) and (Baruník et al., 2016, 2017) at different frequencies. Daily stock prices of the IT sector in thirteen countries representing the top technologically advanced countries ranging from January 15, 2016, until June 24, 2022, are used. The empirical results show that the aggregate volatility is slowly transmitted across markets with an effect lasting more than twenty days. The result also supports the presence of asymmetrical transmission as downside spillovers dominate upside spillovers, regardless of the frequency. Furthermore, the time-varying spillover shows the dominance of downside spillovers in various crisis periods, especially during the pandemic. The time and frequency-based spillover indicate that the overall spillover increased during the recent COVID-19 pandemic crisis period, which is mostly driven by the short-term, suggesting that panic decisions and herd behavior result in extreme connectedness. These findings are helpful to participants and policymakers.
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The COVID-19 pandemic has inflicted the global economy and caused substantial financial losses. The energy sector was heavily affected and resulted in energy prices massively tumbling. The Russian invasion of Ukraine has fueled the energy maker more volatile. In such uncertain contexts, an Early Warning System (EWS) would efficiently contribute to stabilizing market swings. It will leverage the ability to control operating costs and pave the way for smooth economic recovery. Within this framework, we deploy Machine Learning (ML) models to forecast energy equity prices by employing uncertainty indices as a proxy for predicting energy market volatility. We empirically examine the comparative effectiveness of prevalent ML models and conventional approaches (regression) to forecast the energy equity prices by utilizing the daily data from 1/6/2011 to 18/1/2022 for four US uncertainty and eight energy equity indices. Results show that the Nonlinear Autoregressive with External (Exogenous) parameters (NARX) of Neural Networks (NN) scored significantly better accuracy than all other (25) ML models and conventional approaches. The study outcomes are beneficial for policymakers, governments, market regulators, investors, hedge and mutual funds, and corporations. They improve stakeholders' resilience to exogenous shocks, blaze the recovery path, and provide evidence-based for assets allocation strategies.
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This study reviews Islamic FinTech research development from 2017 to 2022. The study adopts a hybrid approach combining bibliometric and content analysis to reveal the current research trend of Islamic FinTech research. Using the Scopus database, we retrieve 85 documents and analyze them using RStudio and VOSviewer. The content analysis categorizes the research output in Islamic FinTech into four distinct streams. The study finds potential for cointegrating FinTech into Islamic finance to benefit the unbanked and small-medium-size businesses, the adoption of FinTech in Islamic finance will also help the government improve financial inclusion, conquer financial crises, such as COVID-19, and achieve SDGs for a sustainable nation. However, the lack of legal regulation and the lower financial literacy becomes the primary obstacle to the development of FinTech in Islamic finance.