Price Linkages Between the US,Japan and UK Stock Markets

This study presents an empirical analysis of the short- and long-term relationships among stock prices in the US, Japan and the UK. We re-examine the evidence of market linkages and cointegration between S&P 500, Nikkei 225 and FTSE-100 stock indices. The results suggest that mature markets are cointegrated, indicating a stationary long-run relationship. Furthermore, Granger causality tests show a bi-directional causality between Nikkei 225–FTSE-100, and unidirectional causalities between S&P 500–FTSE-100 and S&P 500–Nikkei 225. These findings suggest that the potential for diversifying risk by investing in mature markets is limited.     

Nonlinear tail dependence in cryptocurrency-stock market returns: The role of Bitcoin futures

We investigate the median and tail dependence between cryptocurrency and stock market returns of BRICS and Developed countries using a newly developed nonparametric cumulative measure of dependence over the period January 4, 2016 – December 31, 2019 as well as before and after the introduction of Bitcoin futures on December 17, 2017. The new measure is model-free and permits measuring tail risk. The results highlight the leading role of S&P500, Nasdaq and DAX 30 in predicting BRICS and developed countries’ stock market returns. Among BRICS countries, BVSP shows a starring role in predicting stock market returns. BSE 30 is the most predictor of cryptocurrencies, which have a little predictability on stock market returns. Ethereum has the leading role in predicting cryptocurrencies and stock market returns followed by Bitcoin. Tail dependence shows substantial role of S&P500, Nasdaq and BVSP in predicting stock market returns. Subsample analysis show the role of Bitcoin futures in reshaping the mean and tail dependence between cryptocurrency and stock market returns. Our results have important policy implications for portfolio managers, hedge funds and investors.     

Backtesting value-at-risk based on tail losses

Extreme losses caused by leverage and financial derivatives highlight the need to backtest Value-at-Risk (VaR) based on the sizes of tail losses, because the risk measure currently used disregards losses beyond the VaR boundary. While Basel II backtests VaR by counting the number of exceptions, this paper proposes to use the saddlepoint technique by summing the sizes of tail losses. Monte Carlo simulations show that the technique is extremely accurate and powerful, even for small samples. Empirical applications for the proposed backtest find substantial downside tail risks in S&P 500, and demonstrate that risk models which account for jumps, skewed and fat-tailed distributions failed to capture the tail risk during the 1987 stock market crash. Finally, the saddlepoint technique is used to derive a multiplication factor for any risk capital requirement that is responsive to the sizes of tail losses.     

Analysing financial contagion and asymmetric market dependence with volatility indices via copulas

This paper explores the cross-market dependence between five popular equity indices (S&P 500, NASDAQ 100, DAX 30, FTSE 100, and Nikkei 225), and their corresponding volatility indices (VIX, VXN, VDAX, VFTSE, and VXJ). In particular, we propose a dynamic mixed copula approach which is able to capture the time-varying tail dependence coefficient (TDC). The findings indicate the existence of financial contagion and significant asymmetric TDCs for major international equity markets. In some situations, although contagion cannot be clearly detected by stock index movements, it can be captured by dependence between volatility indices. The results imply that contagion is not only reflected in the first moment of index returns, but also the second moment, i.e. the volatility. Results also show that dependence between volatility indices is more easily influenced by financial shocks and reflects the instantaneous information faster than the stock market indices.     

Modelling dynamic dependence and risk spillover between all oil price shocks and stock market returns in the BRICS

This paper investigates the dynamic dependence and risk spillover between BRICS stock returns and different types of oil shocks, combining the Structural VAR model and time-varying copula-GARCH-based CoVaR approach. Our findings indicate that the dependence between BRICS stock returns and oil shocks is time-varying and exhibits different behaviours depending on the shock types in the oil market. In general, the shape of the CoVaRs in each country is comparatively different, depending on its special market situation and domestic policies. There is significant risk spillover from oil-specific demand shock to stock returns in all the BRICS countries. Finally, in Brazil, Russia and India, there is a significant asymmetric effect between upside and downside risk spillover based on oil aggregate demand shock and oil-specific demand shock.     

Lack of consumer confidence and stock returns

This paper investigates the link between the lack of consumer confidence and stock returns during market fluctuations. Using a Markov-switching framework, we first focus on whether the shock to consumer confidence has asymmetric effects on stock returns. We also examine whether the decreased confidence pushes the stock market into bear territory. Empirical evidence using monthly returns on Standard & Poor's S&P 500 price index suggests that market pessimism has larger impacts on stock returns during bear markets. Moreover, the lack of consumer confidence leads to a higher probability of switching to a bear market regime.     

Impacts of COVID-19 outbreak,macroeconomic and financial stress factors on price spillovers among green bond

We examine the impacts of the COVID-19 pandemic and global risk factors on the upside and downside price spillovers of MSCI global, building, financial, industrial, and utility green bonds (GBs). Using copulas, CoVaR, and quantile regression approaches, we show symmetric tail dependence between MSCI global GB and both building and utility GBs. Moreover, the upper tail dependence between MSCI global GB and financial GB intensified during COVID-19. We find asymmetric risk spillovers from MSCI global GB to the remaining GBs. Finally, the COVID-19 spread, the Citi macro risk index, and the financial condition index contribute positively to the quantiles' risk spillovers. The spillover index method shows significant dynamic volatility spillovers from global GB to GB sectors that intensify during the pandemic outbreak, except for financial GB. The causality-in-mean and in-variance from COVID-19, Citi macro risk index, and US financial condition index to the downside and upside spillover effects are sensitive to quantiles     

Static and dynamic connectedness between NFTs,Defi and other assets: Portfolio implication

The paper examines the return and volatility transmission between NFTs, Defi assets, and other assets (oil, gold, Bitcoin, and S&P 500) using the TVP-VAR framework. The results report weak static return and volatility spillovers between NFTs and Defi assets and selected markets, showing that these new digital assets are still relatively decoupled from traditional asset classes. Bitcoin, oil, and half of the NFTs and Defi assets are net transmitters of return and volatility spillovers, whereas rest of the markets are net recipients of spillovers. Our findings show that the dynamic return and volatility connectedness become higher during the initial phase of the COVID-19 pandemic and the cryptocurrency bubble of 2021. We also compute the static and dynamic optimal weights, hedge ratios, and hedging effectiveness for the portfolios of NFTs/other asset and Defi asset/other asset and show that investors and portfolio managers should consider adding NFTs and Defi assets in their portfolios of gold, oil, and stock markets to achieve diversification benefits.     

What do we know about the price spillover between green bonds and Islamic stocks and stock market indices?

This paper investigates the risk transmission, linkages, and directional predictability between green bonds, Islamic stocks, and other asset classes. Using daily data from November 2008 to August 2020, we use the Standard & Poor's (S&P) Green Bond Index to represent the green bond market and the Dow Jones Islamic World Index and the S&P Global Shariah Indices to represent Islamic stocks. The other asset classes considered include the S&P 500 Stock Composite, S&P 500 Bond, and S&P 500 Energy indices. This paper uses the novel quantile cross-spectral (coherency), the windowed scalogram difference (WSD), and the cross-quantilogram (CQ) correlation approaches. The results from the quantile coherency analysis reveal a negative spillover effect from green bond price returns to Islamic stocks in the long run, which indicates that the green bond market poses a long-run systemic risk to Islamic stocks. From the WSD analysis, the results show that the integration between green bonds and Islamic stocks, the S&P 500 Stock Composite, and the S&P 500 Bond index is weaker during volatile market conditions. The CQ correlation suggests that the dependency between green bonds and other asset returns is concentrated in the lower quantiles and that this dependency is weaker at longer lags. Our results underscore the significance of green bonds in investor portfolios as a new investment asset class.     

The impacts of climate policy uncertainty on stock markets: Comparison between China and the US

The interplay between climate policy uncertainty and stock market performance has emerged as a pressing research question in light of the challenges posed by climate change to financial markets. This paper measures China's daily and monthly climate policy uncertainty (CPU) from Jan 2000 to Mar 2022 based on Chinese news data for the first time. Then, the nonlinear and lag impacts of the US CPU and China's CPU on the return, volatility, correlation and tail dependence of China's and US stock markets are investigated and compared by adopting copula function and the distribution lag nonlinear model (DLNM). The data of stock markets includes the Shanghai Composite Index (SSCI) and NASDAQ from Jan 2000 to Mar 2022 from the Choice database, and the Shenzhen Composite Index (SCI) and S&P 500 are used for the robustness test. The empirical results indicate that (1) the growth trend of China’s CPU index is similar to that of the US. However, there are significant differences between the impacts of these two CPUs on stock markets. (2) For China, high CPU decreases current stock market return and increases volatility but decreases it in the future. It could also increase the upper tail dependence between China’s and the US stock markets’ volatilities in current period. (3) For the US, CPU decreases stock market return in the short term but increases it in the long term. High CPU increases volatility in short term, decreases volatility in 5 months and increases it again after 6 months. Both low and high CPU could increase the correlation between China's and US stock markets' volatilities.     

Quantifying the extreme spillovers on worldwide ESG leaders' equity

With the increasing consensus about the severity of climate change, environmental, social, and governance (ESG) philosophy has been rising and remarkably integrated into green investors' portfolio decisions. This paper applies the novel quantile-based VAR spillover index methods and examines the spillovers among worldwide ESG leaders' equity markets under different market states. Overall, the significant spillovers among global ESG leaders' equity can be captured. The Northern American and E.U. markets are the main risk transmitters to the global ESG investment market. Based on the quantile connectedness analysis, the spillovers among worldwide ESG leaders' equity are more significant for extreme market states than the normal states. Meanwhile, the downward tail risk spillover is larger than the upper. Thus, this paper obtains an asymmetric “U” shape curve to describe the total spillovers across different quantiles. The dynamic estimation also supports the asymmetric characteristics of the cross-market spillover effects and suggests external events could shape the pattern of dynamic connectedness. The potential determinants of total and net extreme spillovers are also investigated to help increase investors' awareness of ESG risk management decisions. Our findings eventually provide a crucial understanding of global ESG investment and indicate some targeted investment implications.     

An empirical investigation of multiperiod tail risk forecasting models

In the context of multiperiod tail risk (i.e., VaR and ES) forecasting, we provide a new semiparametric risk model constructed based on the forward-looking return moments estimated by the stochastic volatility model with price jumps and the Cornish–Fisher expansion method, denoted by SVJCF. We apply the proposed SVJCF model to make multiperiod ahead tail risk forecasts over multiple forecast horizons for S&P 500 index, individual stocks and other representative financial instruments. The model performance of SVJCF is compared with other classical multiperiod risk forecasting models via various backtesting methods. The empirical results suggest that SVJCF is a valid alternative multiperiod tail risk measurement; in addition, the tail risk generated by the SVJCF model is more stable and thus should be favored by risk managers and regulatory authorities.     

Dynamic spillovers across oil,gold and stock markets in the presence of major public health emergencies

Extreme events have a systemic impact on global financial markets, leading to significant cross-market spillovers in the oil, gold, and stock markets and raising widespread concerns about market linkages and risk contagion. In this paper, with a focus on both return and volatility, a frontier spillover network analysis is used to examine the strength and scale characteristics of spillovers in the oil, gold and stock markets under major public health emergency shocks. In addition, the paper adopts a marginal spillover and network analysis to evaluate linkage relationships, risk sources and transmission paths in the oil, gold, and stock markets during such events. The results show that the return and volatility spillover effects generated across the oil, gold, and stock markets are significant, with return spillovers being more stable and volatility spillovers being highly sensitive to emergencies. Meanwhile, the COVID-19 pandemic has displayed the strongest return and volatility spillovers. The high intensity of the shocks during the COVID-19 period has changed the usual characteristics of the market, with the gold market becoming the risk receiver and the oil market becoming risk sources.     

Tail dependence between Bitcoin and financial assets: Evidence from a quantile cross-spectral approach

Using daily price data for Bitcoin and 10 representative financial assets from the stock, commodity, gold, foreign exchange and bond markets from 2011 to 2019, we study the tail dependence between returns for Bitcoin and these other financial assets using the novel “quantile cross-spectral dependence” approach of Baruník and Kley (2019). We find evidence of right-tail dependence between Bitcoin returns and the S&P 500 in the long term and weaker normal return dependence between Bitcoin and the US Dollar (USD)–Euro (EUR) foreign exchange rate in the monthly term. In addition, we note that the dependence between Bitcoin and commodity as well as oil, and silver decrease the most within their respective medium return quantiles over the short term. Furthermore, we document a one-way causality running from each of the financial assets considered to Bitcoin in different quantiles of the return distribution. In sum, our findings support the notion that Bitcoin can provide financial diversification in certain return quantiles (i.e., bear, normal, or bull asset conditions) and time frequencies (i.e., short, medium, or long term investment horizon).     

Measuring systemic risk contribution of global stock markets: A dynamic tail risk network approach

Measuring the systemic risk contribution (SRC) of country-level stock markets helps understand the rise of extreme risks in the worldwide stock system to prevent potential financial crises. This paper proposes a novel SRC measurement based on quantifying tail risk propagation's domino effect using ΔCoVaR and the cascading failure network model. While ΔCoVaR captures the tail dependency structure among stock markets, the cascading failure network model captures the nonlinear dynamic characteristics of tail risk contagion to mimic tail risk propagation. As an illustration, we analyze 73 markets' SRCs using a daily closing price dataset from 1990.12.19 to 2020.9.8. The validity test demonstrates that our method outperforms seven classic methods as it helps early warning global financial crises and correlates to many systemic risk determinants, e.g., the market liquidity, leverage, inflation, and fluctuation. The empirical results identify that Southeast European markets have higher SRCs with time-varying and momentum features corresponding to significant financial crisis events. Besides, it needs attention that South American and African markets have displayed increasing risk contributions since 2018. Overall, our results highlight that considering tail risk contagion's dynamic characteristics helps avoid underestimating SRC and supplement a “too cascading impactive to fail” perspective to improve financial crisis prevention.     

Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models

This paper examines the dynamic relationship between the oil market and stock markets from two perspectives: dependence between the crude oil market (WTI) and stock markets of the US and China, and volatility spillovers between them during 1991–2016. We further analyze structural breaks of market dependences and consider the extent of their influence on such relationships. Our vine-copula results show that the dependences between the three paired markets, WTI-US, WTI-China and US-China, vary dynamically across the six identified structural break periods. In particular, the dependence between WTI-US is stronger and more volatile than that between WTI-China during most of the periods. The dependence between US-China remains at a lower level in the earlier periods, but increases in the final period. Our VAR-BEKK-GARCH results demonstrate distinctive volatility spillovers across these periods, with varying directionality, in response to the structural changes. Overall, our results indicate that the oil market stimulates rapid and continual fluctuations in market dependences, which become manifest most acutely in the aftermath of the Financial Crisis of 2007–08, demonstrating the increasing interdependence between the oil and stock markets. Further, the growing influence of China on the dynamics of these relationships, in the period following the Great Recession, presents evidence that it begins to assume an increasingly important role in global economic recovery.     

Stock–bond dependence and flight to/from quality

This paper investigates the stock–bond dependence structure using a dependence-switching copula model. The model allows stock–bond dependence to switch between positive dependence regimes (contagions or crashes of the two markets during downturns or booms in both markets during upturns) and negative dependence regimes (flight-to-quality from stock markets to bond markets or flight-from-quality from bond markets to stock markets). Using data from four developed markets including the US, Canada, Germany, and France for the period between January 1985 and August 2022, we find that the within-country stock–bond (extreme) dependence could be both positive and negative. In the positive dependence regimes, the stock–bond dependence is asymmetric with stronger left tail dependence than the right tail dependence, giving evidence of a higher likelihood of joint stock–bond market crashes or contagions during market downturns than the collective stock–bond market booms. Under the negative dependence regimes, we find both flight-from-quality and flight-to-quality, with flight-to-quality being more dominant in the North American markets while flight-from-quality is more prominent in the European markets. Further, the dependence switches between positive and negative regimes over time. Moreover, the dependence is mainly in the positive regimes before 2000 while mostly in the negative regimes after that, indicating contagions mostly before 2000 and flights afterwards. Further, the dependence switches between positive and negative regimes around financial crises and the COVID-19 pandemic. These results greatly enrich the findings in the existing literature on the co-movements of stock–bond markets and are important for risk management and asset pricing.     

Assessing dependence between financial market indexes using conditional time-varying copulas: applications to Value at Risk (VaR)

We analyse the dynamic dependence structure between broad stock market indexes from the United States (S&P500), Britain (FTSE100), Brazil (BOVESPA) and Mexico (PCMX). We employ Patton’s [Int. Econ. Rev., 2006, 2, 527–556] conditional copula setting and additionally observe the impact of different copula functions on Value at Risk (VaR) estimation. We conclude that the dependence between BOVESPA and the other indexes has intensified since the beginning of 2007. In our case the particular copula form is not crucial for VaR estimation. A goodness-of-fit test based on the parametric bootstrap is also applied. The best fits are obtained via time constant Student-t and time-varying Normal copulas.     

Commodity market financialization,herding and signals: An asymmetric GARCH R-vine copula approach

Institutional investors have significantly increased their exposure to commodity futures after 2004 in the process of commodity market financialization, raising questions about the risk-sharing and price-discovery functions of the market. We identify some symptoms of financialization through examining S&P500, JPM bond index, and 18 S&P GSCI excess return indices, employing ARMA-GARCH R-vine copula approach that can flexibly model high-dimensional multivariate asymmetric tail dependence. We discover three trends: an increased resemblance between the news impact curve of stocks and those of commodities; an increased bi-variate stock-commodity tail dependence; and an increased multivariate tail-dependence across all commodities. We also explore the market structural change underlying these symptoms using an augmented news impact curve. We suggest and provide evidence that herding, in addiction to leverage effect, explains the observed symptoms. The findings have profound implications for commercial hedgers and financial traders, and for regulators who are concerned about the functionalities of commodity futures market.     

Time-frequency spillovers among carbon,fossil energy and clean energy markets: The effects of attention to climate change

We explore the time-frequency spillovers among carbon, fossil energy and clean energy markets, and consider the casual effects of climate change attention. The spillover effects among carbon, fossil energy and clean energy markets are time-varying. Carbon market is a net receiver of spillovers from the oil market and clean energy markets in the short term, but it becomes a net transmitter of spillovers to the coal and gas markets in the long term. Our marginal spillover effects analysis suggests that the COVID-19 pandemic has increased cross-market risk contagion in the long term and that carbon market bears larger input risks. Investors' attention to climate change has significant causal effects on the spillovers, and the causal impact of climate change attention on total spillover has significantly increased during the COVID-19 pandemic. Our findings provide important guidelines for investment in environmental protection and demonstrate the importance of formulating differentiated policies for environmental protection in different time horizons.