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The paper investigates long memory, structural breaks, and spurious long memory in the daily trading volume of the largest and most active cryptocurrencies and stablecoins, namely, Bitcoin, Ethereum, Tether, USD coin, Binance coin, Binance USD, Ripple, Cardano, Solana, Dogecoin and Bitcoin cash. The overall results show that both long memory and structural breaks are present in the cryptocurrencies trading volume, and the detected long memory property is not driven by structural breaks but rather true and thus not spurious. Given this, we conduct out-of-sample forecasting and indicate that the ARFIMA model, which accounts for long-range dependence, has a superior forecasting performance over the standard ARIMA model for four cryptocurrencies, namely, Binance coin, Ripple, Cardano, and Dogecoin at most forecasting horizons ahead and the shorter forecasting horizon (1-day ahead) for most cryptocurrencies under investigation.

We examine the dynamics of tail dependence across returns of 105 global energy firms from 26 countries covering the regions of America, Asia Pacific and Europe. A partial correlation-based approach is used to quantify the dependence structure and level of systemic risk under relatively stable and extremely bearish and bullish market conditions. The dependence network of energy stock returns is constructed based on the novel triangulated maximally filtered graph (TMFG). The results reveal a high degree of tail dependence and role played by geographical proximity. The strongest links are found under extreme bearish market conditions. American and European energy firms are more interconnected and contribute more to systemic risk than Asian-Pacific companies. The dependence intensifies during periods of market turmoil, especially during the COVID-19 pandemic. A higher instability in the dependence structure is observed during extremely bearish market circumstances. A simple portfolio trading strategy based on the dependence ranking of energy firms outperforms a naïve equally-weighted buy-and-hold portfolio strategy.

Understanding sectoral dynamic dependence across equity indexes is crucial for investment decisions and designing economic policy. This study examines the sectoral dependence among 82 Pakistani companies using a vine copula approach and daily data from July 1, 2014, to December 17, 2019. Vine copulas represent a flexible dependence approach capable of capturing the dynamic and complex relationships among equity sectors stemming from the nonlinear patterns of asymmetric behavior of returns that cannot be captured by linear dependence models. The results show that the Pakistani stock market is driven by the oil and gas marketing sector and oil and gas exploration sector, with high dependence on commercial banks, fertilizers, power generation and distribution, and automobile parts and accessories. The findings help explain the tail-dependence structure among equity stocks and sectors in the context of understudied emerging markets such as Pakistan, which is vital for portfolio management and designing sector-level policies.

The war in Ukraine and new sanctions imposed on Russia have affected commodity prices and induced historic moves in exchange rate markets. In this paper, we examine the impact of commodity price shocks related to the war in Ukraine on three currencies (Canadian dollar, euro, and Japanese yen). Using four-hour price data for three commodities (wheat, crude oil, and natural gas) and two exchange rates (EUR/CAD and CAD/JPY) from February 1 to April 30, 2022, the analysis based on the quantile autoregressive distributed lag (ARDL) model suggests a long-run association between higher commodity prices and appreciation of the Canadian dollar against the euro and the yen. Furthermore, the dynamic simulated ARDL model shows a positive impact of commodity price shocks on the value of the Canadian dollar against the euro and the yen, which demonstrates the robustness of our findings. Oil price shocks have almost the same impact on the depreciation of the euro and the yen. The driving forces in the depreciation of the euro and the yen, in addition to higher oil prices, are higher gas prices and higher wheat prices, respectively.

AbstractGovernments around the world have responded to the COVID-19 outbreak with a mix of policies. The strictest responses of the New Zealand government are notable, given their abilities to contain and limit the spread of the virus. However, their impacts on stock returns remain unclear. This paper investigates the impact of three policies, namely lockdown, the stimulus package, and the travel ban, on the returns of 14 New Zealand industry stock indices. Using daily data from 1 January 2019 to 25 August 2020, evidence points to a heightened level of integration among the various industry stock indices during the early stages of the pandemic. Only lockdown has had a positive impact on aggregate stock returns, suggesting its ability to raise investors’ confidence in the overall stock market. At the industry level, the impact of the three response policies is generally positive but heterogeneous across industry stock indices. Notably, none of the three adopted policies significantly impact technology, healthcare, and real estate returns.

AbstractThis study uses hourly data to analyse the return and volatility transmission of oil-gold and oil-Bitcoin pairs during the pre-COVID-19 and COVID-19 periods. The results show that the return transmissions vary across the two periods for both pairs. There is a unidirectional volatility spill-over from gold to oil in the pre-COVID-19 period, and from oil to gold during the COVID-19 period. There is a significant volatility spill-over from Bitcoin to oil during the pre-COVID-19 period, whereas no evidence of volatility spill-over between oil and Bitcoin is shown during the COVID-19 period. Based on optimal weights, investors should increase their investments in, (a) gold for a portfolio of oil-gold, and (b) Bitcoin for a portfolio of oil-Bitcoin during the COVID-19 period. All hedge ratios are higher during the COVID-19 period, implying a higher hedging cost compared to the pre-COVID-19 period. The results of hedging effectiveness reveal that the risk-adjusted returns can be improved by constructing a portfolio of oil-gold and oil-Bitcoin during both sample periods. Further results reveal that gold is a strong safe haven and a hedge for the oil market, while Bitcoin serves as a diversifier for the oil market during the COVID-19 period.

Using network analysis on the connectedness of default factors in a credit default swap (CDS) dataset of U.S. and European energy firms, we provide the first evidence of differences in the shape and dynamics of the interconnectedness of the level, slope, and curvature, representing long-, short- and middle-term default factors, respectively. The interconnectedness of the three default factors increases during the European sovereign debt crisis (ESDC), whereas only the interconnectedness of the level factor increases during the oil price crash, and the interconnectedness of both level and slope factors spikes during COVID19. European firms contribute more to the transmission of long-term and short-term default risk from early 2011 till the beginning of the 2014–2105 oil price crash; afterwards, U.S. firms are major default transmitters despite some periods of parity with European firms. The impacts of oil demand and supply shocks on the various interconnectedness are quantile-dependent and more pronounced in the long term for the credit risk of the energy firms.

Uncovering the tail risk spillover among global financial markets helps provide a more comprehensive understanding of the information transmission in extreme market conditions such as the COVID-19 outbreak. In this paper, we examine systemic distress risk spillover between the global stock market and individual stock markets in the countries most affected by the COVID-19 pandemic. Using two important measures of tail dependence risk: conditional value at risk (CoVaR) and delta conditional VaR (ΔCoVaR), we apply the bivariate dynamic conditional correlation (DCC) conditional autoregressive heteroscedastic (GARCH) model. The empirical results reveal that bivariate systemic risk contagion between the global stock market and each individual stock market evolved during the sample period and intensified as COVID-19 spread worldwide. During the COVID-19 period, the developed markets in Europe and North America transmitted and received more marginal extreme risk to and from the entire global market than Asian stock markets. Further analysis involving the connectedness among the value at risk (VaR) series of the sampled stock market indices and the global stock index, shows a high degree of integration in the extreme downside risk of the stock market system, especially during the COVID-19 period. These findings offer practical implications for regulators, policymakers, and portfolio risk managers during the unprecedented uncertainty period provoked by the COVID-19 pandemic.

Using 10 significant cryptocurrencies, we construct emotional spillovers and provide inferences about the transmission of fear and greed shocks. The results show intense emotional bonding signifying a high level of cryptocurrency interdependence through the sentiment channel of crypto traders and investors. The amplitude of idiosyncratic (own) connectedness increases significantly for most cryptocurrencies in their greed phase, implying that positive investor sentiment is more substantial than negative sentiment in driving the trading of individual cryptocurrencies. Though market conditions and the pandemic alter the role of most cryptocurrencies, some cryptocurrencies display persistence. Ethereum cash and, to some extent, Ripple remain considerably isolated, whereas Ethereum and Dash remain highly interlocked with the rest of the cryptocurrencies. Dash is the leading net transmitter, whereas Ripple is the leading net receiver of fear and greed shocks.

Unlike previous studies which tend to consider only return linkages between European emission allowances and electricity markets, this paper examines return and volatility linkages between the two markets and importantly conducts a hedging analysis. Using a dynamic conditional correlation model and daily price data covering European emission allowances and Nord Pool electricity markets from 28th January 2009 to 22th February 2022, which comprise the second and third phases of the European emission allowances trading system, the main results are summarized as follows: Firstly, volatility and returns flow from the Nordic electricity market to the European emission market, with no evidence of a feedback effect, suggesting that rapid changes in demand for electricity may have forced producers to ramp up carbon-intensive facilities. Secondly, the conditional dynamic correlation between the two markets is weak but varies with time, and its sensitivity to crisis periods is noted. Thirdly, the hedging analysis indicates that Nordic electricity market participants can lower their downside risk by including carbon emission allowances in their portfolios, as evidenced by the effectiveness of hedging returns of the Nordic electricity markets with European allowances, irrespective of the COVID-19 sub-periods.

This study examines the perspectives of corporate managers regarding corporate social responsibility (CSR), stakeholder interest management, firm performance, and their preferences in capital budgeting. It uses structural equation modelling (SEM) to test hypotheses. The analysis shows that capital budgeting serves as a mediator, diminishing the direct relationship between CSR and firm performance, and between the management of stakeholder interests and firm performance. The findings indicate that instrumental motives guide corporate managers in their capital budgeting decisions. The paper challenges perspectives that disproportionately emphasize the benevolent and moral dimensions of CSR, highlighting the significance of stakeholder interest management as a credible and essential business practice.

The transition from the reference rate based on interbank offered rates, such as the Turkish lira interbank offer rate (TRLIBOR), to the risk-free rate (RfR), the Turkish lira overnight reference rate (TLREF), in Türkiye is a critical juncture, but it is not clear how it affects the market's ability to incorporate information precisely and promptly. Drawing on the adaptive market hypothesis (AMH), we examine the impact of transitioning from TRLIBOR to TLREF on the efficiency of Turkish financial markets. Our results reveal pronounced and time-varying persistence patterns in both reference rates and highlight heterogeneity in their efficiency, which seems influenced by fluctuations in national political conditions and monetary policies. TLREF consistently demonstrates higher market efficiency than TRLIBOR. The findings offer insights into the dynamics of market efficiency in Türkiye and highlight the broader implications of switching to RfR-based reference rate regimes.

AbstractRecent evidence, based on a linear framework, tends to suggest that while mortgage default risks can predict weekly and monthly housing returns of the United States, the same does not hold at the daily frequency. We, however, indicate that the relationship between daily housing returns with mortgage default risks is in fact nonlinear, and hence a linear predictive model is misspecified. Given this, we use a k-th order nonparametric causality-in-quantiles test, which in turn allows us to test for predictability over the entire conditional distribution of not only housing returns, but also volatility, by controlling for misspecification due to nonlinearity. Based on this model, we show that mortgage default risks do indeed predict housing returns and volatility, barring at the extreme upper end of the respective conditional distributions.

Stock return predictability has always been one of the central themes of finance literature, given its crucial implications for investment decisions, risk management, and financial and monetary policymaking. This paper evaluates the in-sample and out-of-sample stock return predictive power of the global and Saudi geopolitical risk indices and crude oil returns in the context of six Gulf Cooperation Council (GCC) countries. Monthly data from February 2007 to December 2019 and the feasible generalized least square (FGLS) estimator for predictive modelling by Westerlund and Narayan (2012, 2015) are used. Global and Saudi GPR indices show weak evidence of in-sample predictability of excess stock returns. However, the out-of-sample forecasts show that only the global geopolitical risk index provides superior prediction in the context of Kuwaiti and Omani stock markets, compared to the historical average benchmark model. Crude oil prices are shown to be a better predictor in most cases, in both in-sample and out-of-sample forecast models The results imply that crude oil returns can be used for active prediction of GCC stock market returns, once econometric issues are accounted for. The findings remain mostly unaffected when excess risk adjusted returns are used.

We study the dependency structure between option-implied volatilities of gold and silver markets via the application of a copula-based quantile regression. First, we conduct a static analysis and show that the asymptotic lower tail dependence is only pronounced in the low volatility regime of both gold and silver markets. Second, given the existence of a bi-directional causality between the two option-implied volatilities, we consider the lead-lag relationship via non-parametric tail dependence estimators. Results indicate an extreme tail dependency in lower and upper quantiles, with evidence of an asymmetric behavior between/for low and high volatility regimes. Our findings have implications to investors and risk managers. Practically, findings imply evidence of predictability of the probability of gold implied volatility based on the lagged silver implied volatility across different quantiles. Another implication concerns a volatility-based trading strategy, especially during in tandem occurrence of high volatility regimes, which involves the simultaneous selling of an out-of-the money call and put with different strike prices on gold implied volatility.

We construct daily realized volatility, skewness, and kurtosis using 5-min data of eight Chinese commodity futures and the Chinese stock market index from March 26, 2018 to October 22, 2020, then analyse the dynamic spillovers of realized moments among these markets. The results show that the spillover effects between commodity and stock markets intensify during shock periods such as ‘trade disputes between China and the United States’ and ‘COVID-19’. Volatility spillovers are relatively stronger than spillovers in skewness or spillovers in kurtosis; however, spillovers in higher-order moments seem to contain additional information. Shocks from the silver market influence realized moments of other markets. Soybean, corn, aluminium, and oil markets are affected by other markets. The contribution of wheat as a net transmitter to the system of spillovers between stock and commodity markets is only observed at higher-order realized moments. The results from OLS and quantile regressions show that the total spillovers are generally affected by the US stock market, economic uncertainties, and the COVID-19 outbreak.

Firstly, we use the log-periodic power law singularity multi-scale confidence indicator (LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium- and long-term stock market indices of the G7 countries. Secondly, we apply heterogeneous coefficients panel data-based regressions to analyse the impact of investor sentiment, proxied by business and consumer confidence indicators, on the indicators of bubbles of the G7. Controlling for the impacts of output growth, inflation, monetary policy, stock market volatility, and growth in trading volumes, we find that investor sentiment increases the positive and reduces the negative LPPLS-CIs, primarily at the medium- and long-term scales for the G7, considered together, with the result being driven by at least five of the seven countries. Our results have important implications for both investors and policymakers, as the collapse (improvement) of investor sentiment can lead to a crash (recovery) in a bull (bear) market.

This paper examines the volatility transmissions between the oil and stock markets by performing causality-in-variance tests across a number of emerging (oil) importing and exporting nations. The volatility spillover causality tests generally rule out country-to-oil effects, suggesting unidirectional volatility transmissions from oil prices to emerging stock markets, particularly in the case of the net exporting nations of Kuwait, Saudi Arabia and UAE. We also observe significant volatility spillovers from oil prices to emerging importers during the post-global financial crisis period. The findings suggest that the information on oil market volatility combined with the magnitude of volatility spillovers can be utilized towards modeling and monitoring volatility shocks in emerging stock markets.

This paper examines minority expropriation issues in the dominant presence of family ownership using longitudinal (hand-collected) data on 270 Lebanese non-financial firms between 2009 and 2012. The analysis reveals that family-related CEOs and the disparity between cash-flow rights and voting rights facilitate expropriation. However, the presence of private equity and banking firms reduces private benefit extraction. By explicitly focusing on the ownership-expropriation nexus and by promoting the development and implementation of a tailored governance code that considers Lebanese specificities, this paper makes a contribution to the growing literature on the role of corporate governance in emerging markets.

This paper provides a novel perspective in determining the Granger causality of sentiment across the US, Latin America, Eurozone, Japan and Asia (excluding Japan), based on monthly data covering the period of January 2003–November 2017. Using a survey-based sentiment index of “sentix”, our results suggest strong evidence of nonlinearity and structural breaks making the use of linear causality models unreliable. Using a kernel-based multivariate nonlinear causality test, we find that causality runs from Eurozone to the US, Asia and Japan, with Japan also causing the Eurozone sentiment, and Latin America causing the Japanese sentiment. Interestingly, when we apply rolling estimations to detect time-varying causality for the cases of Eurozone and the US, Eurozone and Asia, Eurozone and Japan and Latin America and Japan, the results suggest evidence of bidirectional spillovers during certain months of the recent global financial crisis, and thereafter. Overall, our findings indicate that the sentiments of Japan, Asia and the US are related quite strongly with that of the Eurozone, as well as the sentiments of Japan and Latin America.

In this paper, we first obtain a time-varying measure of volatility connectedness involving fifteen major cryptocurrencies based on a dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model, and then analyze the role of investor sentiment in explaining the movement of the connectedness metric within a quantile-on-quantile framework. Our findings show that lower quantiles of investor happiness, built on Twitter feed data as a proxy for investor sentiment, is positively associated with the entire conditional distribution of connectedness, but the opposite is observed at higher values of investor happiness. In addition, when we look at the effect of sentiment on the common market volatility, we are able to deduce that as investors become exceedingly unhappy, overall market volatility increases and this is associated with high market connectedness. The heightened volatility possibly due to higher trading, seems to suggest that cryptocurrencies are used for hedging when investor sentiment is weak, with evidence in favor of this behavior being relatively stronger than the possible speculative motive associated with happy investors, as low total connectedness is coupled with high common volatility. Our results tend to suggest that, relatively more diversification opportunities are available when investors are happy rather than when sentiment is weak.