Asymmetric volatility connectedness between Islamic stock and commodity markets

This paper examines the asymmetric volatility connectedness amongst the Dow Jones Islamic Market Index (DJIM) and the Brent crude oil, gold, and silver markets. We use the Diebold and Yilmaz methodology to examine asymmetric volatility connectedness associated with bad (negative semi-variance) and good (positive semi-variance) volatility in these markets. We identify significant volatility connectedness between the DJIM and commodity markets, with the DJIM and Brent oil markets being the largest net contributors of spillovers. Furthermore, the evidence on semi-volatility connectedness displays asymmetric behavior. Bad volatilities are associated with net transmission of spillovers to other markets, except for silver. Our results have significant implications for investors dealing with the DJIM and commodity markets in terms of asset management and diversification.     

Quantile connectedness between Chinese stock and commodity futures markets

In this study, we examine the static and dynamic connectedness between the conventional Chinese stock market and commodity futures (aluminum, gold, copper, steel rebar, natural rubber, and zinc). Our results show that both steel rebar and gold receive whereas zinc and copper transmit changes across all quantiles. However, spillover behavior of aluminum, natural rubber, and CSI 300 vary across different quantiles. Our results have implications for investors who are considering a mix of Chinese conventional stocks and commodity futures in their portfolios. Our findings also provide insights for investing under different market conditions by providing results for static as well as dynamic connectedness between CSI 300 and the commodities market.     

Interconnected multilayer networks: Quantifying connectedness among global stock and foreign exchange markets

This paper proposes a novel interconnected multilayer network framework based on variance decomposition and block aggregation technique, which can be further served as a tool of linking and measuring cross-market and within-market contagion. We apply it to quantifying connectedness among global stock and foreign exchange (forex) markets, and demonstrate that measuring volatility spillovers of both stock and forex markets simultaneously could support a more comprehensive view for financial risk contagion. We find that (i) stock markets transmit the larger spillovers to forex markets, (ii) the French stock market is the largest risk transmitter in multilayer networks, while some Asian stock markets and most forex markets are net risk receivers, and (iii) interconnected multilayer networks could signal the financial instability during the global financial crisis and the COVID-19 crisis. Our work provides a new perspective and method for studying the cross-market risk contagion.     

Integration of emerging stock markets with global stock markets

Financial integration for emerging economies should be seen as a long-term objective. In this paper, we examine stock market integration among five selected emerging stock markets (Brazil, China, Mexico, Russia and Turkey) and developed markets of the US, UK and Germany. The bounds testing approach to cointegration and error-correction modeling are used on monthly data from January 2001 to December 2014 to determine the short-run and long-run relationship between emerging stock market returns and the returns of the developed stock markets. The results show evidence of the existence of short-run integration among stock markets in emerging countries and the developed markets. However, the long-run coefficients for stock market returns in all emerging countries show a significant relationship only with Germany stock market return. The empirical findings in this study have important implications for academicians, international investors, and policymakers in emerging markets.     

Extreme coexceedances in new EU member states’ stock markets

We analyze the financial integration of the new European Union (EU) member states’ stock markets using the negative (positive) coexceedance variable that counts the number of large negative (large positive) returns on a given day across the countries. A similar analysis is performed for the old EU countries. We use a multinomial logit model to investigate how persistence, asset classes, and volatility are related to the coexceedance variables. We find that the effects differ (a) between negative and positive coexceedance variables (b) between old and new EU member states, and (c) before and after the EU enlargement in 2004, suggesting a closer connection of new EU stock markets to those in Western Europe.     

International stock market leadership and its determinants

We study time-varying price leadership between international stock markets using a Markov switching causality model. We demonstrate variations in the causality pattern over time, with the US being the dominant country in causing other markets. We examine the factors which determine a country’s role in the causal relationship. For country-specific factors, we show that trades openness increases price leadership. We also find that the lead–lag relationship between the stock markets is weaker during crisis periods, confirming the “wake-up call” hypothesis, with markets and investors focusing substantially more on idiosyncratic, country-specific characteristics during the crisis.     

Oil shocks and stock markets: Dynamic connectedness under the prism of recent geopolitical and economic unrest

In this study we examine the dynamic structural relationship between oil price shocks and stock market returns or volatility for a sample of both net oil–exporting and net oil–importing countries between 1995:09 and 2013:07. We accomplish that, by extending the Diebold and Yilmaz (2014) dynamic connectedness measure using structural forecast error variance decomposition. The results for both stock market returns and volatility suggest that connectedness varies across different time periods, and that this time–varying character is aligned with certain developments that take place in the global economy. In particular, aggregate demand shocks appear to act as the main transmitters of shocks to stock markets during periods characterised by economic–driven events, while supply–side and oil–specific demand shocks during periods of geopolitical unrest. Furthermore, differences regarding the directions and the strength of connectedness can be reported both between and within the net oil–importing and net oil–exporting countries. These results are of particular importance to investors and portfolio managers, given the recent financialisation of the oil market.     

Are the China-related stock markets segmented with both world and regional stock markets?

Implementing the Capital Asset Pricing Model framework, this study investigates the integration of three China-related stock markets, namely, the A-, B- and H-share markets, with both the Hong Kong stock market and the world market. An analysis of market segmentation versus integration using the Jorion and Schwartz model suggests that the A-share market was a segmented market during the period 1995–2004. However, evidence of a higher-level integration between the A- and B-share markets, and the A-share and Hong Kong stock markets is found in the sub-period tests. The hypothesis that the B- and H-share markets are becoming increasingly integrated with the world stock market is not supported.     

Time and frequency dynamics of connectedness and hedging performance in global stock markets: Bitcoin versus conventional hedges

Some studies have revealed the hedging ability of Bitcoin against stock markets, but the knowledge of how it compares with other hedges is in its infancy. This paper presents the first study on time-frequency domain connectedness and hedging among five hedges (Bitcoin, crude oil, commodities, gold and the U.S. dollar (USD) index) and four stock indices (developed markets ex U.S., emerging markets ex China, U.S. and China). We find that the connectedness between hedges and stock markets varies by time across time horizons. Specifically, the connectedness between Bitcoin and stock indices is the smallest among all hedges, especially for the short horizon. Gold and USD are isolated from other markets at longer horizons. The hedging ratio, optimal portfolio weights and hedging effectiveness also vary across investment horizons. For short-term investment, gold has better hedging effectiveness, especially for emerging stock markets and the U.S. stock market. For median- and long-term investment, USD has better performance, especially for developed markets ex U.S. and emerging stock markets. Additionally, although Bitcoin has good hedging properties, it has high volatility compared with other hedging assets. In other words, if Bitcoin is included in a portfolio, investors should pay attention to its wide variation. These empirical findings highlight the important role that gold and USD play in hedging against global stock markets.     

Dynamics and determinants of credit risk discovery: Evidence from CDS and stock markets

This paper investigates the dynamics and drivers of credit risk discovery between stock and CDS markets in the US. Our research is distinguished from the existing literature in three aspects: 1) we employ an improved method to measure the information share; 2) we discover new drivers of credit risk discovery; and 3) we assess the impact of central clearing counterparty (CCP) on the CDS market. By using the generalized information share (GIS) by Lien and Shrestha (2014), we address the issue that the CDS and stock prices do not have one-to-one cointegration relation. The empirical results support the use of GIS instead of more conventional measures. We also find that eliminating transitory price components increases the information share of the CDS market in the earlier period of the sample. The economic condition and funding cost turn out to affect the information share of the CDS market negatively. Another interesting finding is that the CDS of investment grade firms possess higher information shares compared to speculative grade firms. Finally, CCP seems to reduce the information share of CDS, which suggests that the CDS market is driven largely by insider trading.     

Extreme downside risk spillover from the United States and Japan to Asia-Pacific stock markets

This paper proposes a binary response model approach to measure and forecast extreme downside risks in Asia-Pacific markets given information on extreme downside risks in the U.S. and Japanese markets. The extreme downside risk of a market is measured as the occurrence of extreme downside movement—market returns falling below left-tail Value at Risk in a Markov switching framework. The empirical findings are consistent with the following notions. First, extreme downside movements of the S&P 500 and Nikkei 225 are significantly predictive for the likelihood of extreme downside movements in all the investigated Asia-Pacific markets. Second, the majority of Asia-Pacific markets become more sensitive to Japan's extreme downside risk when the Japanese market switches into high volatility periods, whereas the U.S. spillover effect is intensified only on Taiwan during high volatility periods in the U.S. Third, mainland China is the least sensitive to extreme downside risk in the U.S. and Japan, Australia is the most sensitive to the U.S., and Singapore is the most sensitive to Japan.     

Extreme return–volume dependence in East-Asian stock markets: A copula approach

A copula approach is used to examine the extreme return–volume relationship in six emerging East-Asian equity markets. The empirical results indicate that there is significant and asymmetric return–volume dependence at extremes for these markets. In particular, extremely high returns (large gains) tend to be associated with extremely large trading volumes, but extremely low returns (big losses) tend not to be related to either large or small volumes.     

Extreme spillover effect of COVID-19 pandemic-related news and cryptocurrencies on green bond markets: A quantile connectedness analysis

We provide the first empirical study on the role of panic and stress related to the COVID-19 pandemic, including six uncertainties and the four most traded cryptocurrencies, on three green bond market volatilities. Based on daily data covering the period from January 1, 2020 to January 31, 2022, we combine Diebold and Yilmaz's (2012, 2014) time domain spillover approach and Ando et al.'s (2022) quantile regression framework to investigate the time-frequency spillover connectedness among markets and measure the direction and intensity of the net transmission effect under extreme negative and positive event conditions, and normal states. We further provide novel insights into the green finance literature by examining sensitivity to quantile analysis of the net transfer mechanism between green bonds, cryptocurrencies, and pandemic uncertainty. Regarding the network connectedness analysis, the results reveal strong net information spillover transmission among markets under the bearish market. In extremely negative event circumstances, the MSCI Euro green bond acts as the leading net shock receiver in the system, whereas COVID-19 fake news appears as the largest net shock contributor, followed by BTC. According to sensitivity to quantile analysis, the net dynamic shock transfer mechanism is time-varying and quantile-dependent. Overall, our work uncovers crucial implications for investors and policymakers.     

Predictability of stock markets with disequilibrium trading

This paper analyses the predictability of a hypothetical market with freely negotiated prices on which exists a censoring of one-period returns which are in excess of an arbitrary level (‘floor’ and ‘ceiling’). It is shown that the expected value of returns (adjusted for drift) conditional on last period information regarding the censoring are equal to zero (and therefore the market is not predictable in mean) if there is no intertemporal spillover on the market. A simple simulation model is proposed and applied for the analysis of the effects of intertemporal and cross-spillovers resulting from quantity constraints. Statistical predictability tests are proposed, based on the corrected Student-t statistic of a regression of returns of some information concerning the previous censoring. An illustrative empirical analysis of six main time series of returns on the Warsaw Stock Exchange confirms their ex-ante, but not ex-post, predictability.     

Volatility dependences of stock markets with structural breaks

We develop a Vector Heterogeneous Autoregression model with Continuous Volatility and Jumps (VHARCJ) where residuals follow a flexible dynamic heterogeneous covariance structure. We employ the Bayesian data augmentation approach to match the realised volatility series based on high-frequency data from six stock markets. The structural breaks in the covariance are captured by an exogenous stochastic component that follows a three-state Markov regime-switching process. We find that the stock markets have higher volatility dependence during turmoil periods and that breakdowns in volatility dependence can be attributed to the increase in market volatilities. We also find positive correlations between the Asian stock markets, the European stock market, and the UK stock market. The US stock market has positive correlations with all other markets for most of the sample periods, indicating the leading position of US stock market in the global stock markets. In addition, the proposed three-state VHARCJ model with Dynamic Conditional Correlation (DCC) and break structure under student-t distribution has a superior density forecast performance as compared to the competing models. The forecast models with structural breaks outperform those without structural breaks based on the log predicted likelihood, the log Bayesian factor, and the root mean square loss function.     

Non-random topology of stock markets

We have analysed the cross correlations of daily fluctuations for [iopmath latex="$N=6358$"] N = 6358 [/iopmath] US stock prices during the year 1999. From those [iopmath latex="$N(N-1)/2$"] N(N-1)/2 [/iopmath] correlation coefficients, the minimum spanning tree (MST) has been built. We have investigated the topology exhibited by the MST. Even though the average coordination number of stocks is [iopmath latex="$langle n rangleapprox 2$"] n2 [/iopmath], the variance [iopmath latex="$sigma$"] [/iopmath] of the topological distribution [iopmath latex="$f(n)$"] f(n) [/iopmath] diverges! More precisely, we have found that [iopmath latex="$f(n) sim n^{-2.2}$"] f(n)~n ^-2.2 [/iopmath] holds over two decades. We have studied the topological correlations for neighbouring nodes: an extremely broad set of local configurations exists, confirming the divergence of [iopmath latex="$sigma$"] [/iopmath].     

Social media bots and stock markets

This study examines the link between information spread by social media bots and stock trading. Based on a large sample of tweets mentioning 55 companies in the FTSE 100 composites, we find significant relations between bot tweets and stock returns, volatility, and trading volume at both daily and intraday levels. These results are also confirmed by an event study of stock response following abnormal increases in the volume of tweets. The findings are robust to various specifications, including controlling for traditional news channel, alternative measures of volatility, information flows in pretrading hours, and different measures of sentiment.     

Quantile connectedness between energy,metal, and carbon markets

This study employs a quantile connectedness approach to examine the dynamic linkages and tail risk connectedness between energy, metal, and carbon markets. Results show that the connectedness between energy, metal, and carbon markets is about 51% at the mean or median and 87% under extreme conditions. This means that the spillover effects of the two tails are much stronger than those under the conditional mean and normal markets, and the spillover effect between markets is heterogeneous under different market conditions. The connectedness between energy, metal, and carbon markets is time-varying, and the volatility is relatively small under extreme positive and negative conditions. Notably, the dynamic connectedness of energy, metal, and carbon markets is different in extreme upward and downward markets, which reflects the asymmetry and tail dependence of spillover effects between markets and indicates that spillover effects are different between the periods of upward and downward markets. In addition, the results of portfolio strategy show that holding short positions in the carbon market is an effective investment choice.     

COVID-19 and time-frequency connectedness between green and conventional financial markets

Against the backdrop of the exponentially growing trend in green finance investments and the calls for green recovery in the post-COVID world, this study presents the time-frequency connectedness between green and conventional financial markets by using the spillover models of Diebold and Yilmaz (2012) and Baruník and Křehlík (2018). Covering a sample period from January 01, 2008, to July 31, 2020, we aim to explore the dynamics of connectedness between conventional and green investments in fixed income, equity, and energy markets. Additionally, we determine the role of market-wide uncertainty in altering the connectedness structure by performing a subsample analysis for the ongoing COVID-19 pandemic crisis period. Our results show that competing energy investments are not connected, and there is only one-way spillovers from the conventional bonds in the fixed-income investments. Additionally, we observe a low (high) intergroup connectedness for conventional (green) investments. Moreover, the frequency-based analysis shows that connectedness between these competing markets is more pronounced during the short-run. The subsample analysis for the pandemic crisis period shows similar results except for the disconnection between bond markets in the short-run frequency. Our time-varying analysis shows peaks and troughs in the connectedness between climate-friendly and conventional investments that suggest different global events such as the Eurozone Debt Crisis and Shale Oil Revolution drives the association between alternate investments. Similarly, we observe an enhanced connectedness during the recent COVID-19 period, suggesting that financial stability would be a significant factor in determining the smooth transition to green investments.     

Extreme downside risk and expected stock returns

We propose a measure for extreme downside risk (EDR) to investigate whether bearing such a risk is rewarded by higher expected stock returns. By constructing an EDR proxy with the left tail index in the classical generalized extreme value distribution, we document a significantly positive EDR premium in cross-section of stock returns even after controlling for market, size, value, momentum, and liquidity effects. The EDR premium is more prominent among glamor stocks and when high market returns are expected. High-EDR stocks are generally characterized by high idiosyncratic risk, large downside beta, lower coskewness and cokurtosis, and high bankruptcy risk. The EDR premium persists after these characteristics are controlled for. Although Value at Risk (VaR) plays a significant role in explaining the EDR premium, it cannot completely subsume the EDR effect.