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This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers. [ABSTRACT FROM AUTHOR]

Cryptocurrencies have been historically characterised by large price swings and inherent volatility at a much higher scale than traditional financial assets. Understanding the underlying mechanisms and whether, or how, these are priced in through possible risk premia is crucial to bringing cryptocurrencies closer to mainstream financial markets. Using data on 1982 cryptocurrencies form January 1, 2015 till September 30, 2020 and a combination of models involving portfolio-level and Fama–MacBeth analyses, while accounting for cryptocurrency sample selection, we show that the additional risk measured by idiosyncratic volatility is well priced in cryptocurrencies and investors are being paid a risk premium for their holdings. However, a deeper inspection of the dynamics reveals that such a trade-off is mostly valid for the most illiquid cryptocurrencies, which are susceptible to microstructure noise. [ABSTRACT FROM AUTHOR]

Most previous studies on the market efficiency of cryptocurrencies consider time evolution but do not provide insights into the potential driving factors. This study addresses this limitation by examining the time-varying efficiency of the two largest cryptocurrencies, Bitcoin and Ethereum, and the factors that drive efficiency. It uses daily data from August 7, 2016, to February 15, 2023, the adjusted market inefficiency magnitude (AMIMs) measure, and quantile regression. The results show evidence of time variation in the levels of market (in)efficiency for Bitcoin and Ethereum. Interestingly, the quantile regressions indicate that global financial stress negatively affects the AMIMs measures across all quantiles. Notably, cryptocurrency liquidity positively and significantly affects AMIMs irrespective of the level of (in) efficiency, whereas the positive effect of money flow is significant when the markets of both cryptocurrencies are efficient. Finally, the COVID-19 pandemic positively and significantly affected cryptocurrency market inefficiencies across most quantiles. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we examine extreme spillovers among the realized volatility of various energy, metals, and agricultural commodities over the period from September 23, 2008, to June 1, 2020. Using high-frequency (5-min) price data on commodity futures, we compute daily realized volatility and then apply quantile-based connectedness measures. The results show that the connectedness measures estimated at the lower and upper quantiles are much higher than those estimated at the median, implying that realized volatility shocks circulate more intensely during extreme events relative to normal periods, which endangers the stability of the system of volatility connectedness under extreme events such as the COVID19 outbreak. There is evidence of a strong asymmetry between the behaviour of volatility spillovers in lower and upper quantiles, given that the connectedness measures estimated at the upper quantile are the highest. The main results are robust to rolling window size and other alternative choices. Our analyses matter to investors and policy makers who are concerned with the stability of commodity markets. [ABSTRACT FROM AUTHOR]

The cryptomarket has evolved into a complex system of different types of cryptoassets, each playing an important role within the system. These can be clustered into four main groups: market spillovers, portfolio management, herding and market crashes, and regulation. The above-discussed high connectedness of the cryptomarkets raises alarms with possible market implosions and cascades of losses across the markets, often materialising in a herding behaviour of market participants. [Extracted from the article]

Bitcoin's growing use as a financial asset and transaction instrument has economic and monetary effects. In this paper, we examine the short- and long-term interactions between Bitcoin prices and the money supply, consumer price index (CPI), and economic policy uncertainty (EPU) in the US. Using monthly data covering the period July 31, 2010 to August 31 2020, we employ continuous wavelet transforms, wavelet coherence, wavelet-based vector autoregressive Granger causality test, and nonlinear causality test. The results indicate that Bitcoin prices affect money supply and share dynamic inter-shock with CPI, EPU, and money supply. Specifically, the money supply and EPU negatively affect Bitcoin prices. CPI positively affects Bitcoin prices in the short-term, which supports the role of Bitcoin as a hedging asset. A bidirectional volatility transmission exists between Bitcoin prices and each of money supply, CPI, and EPU. Moreover, nonlinear causality test results show a bidirectional causality from Bitcoin price to money supply across all dimensions and a significant causality with CPI and EPU. The findings matter to investors seeking to refine their investment decisions while considering the effect of economic factors and to policymakers and central banks seeking to formulate policy tools using Bitcoin. [ABSTRACT FROM AUTHOR]

This paper is motivated by Bitcoin's rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets. It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns, particularly at the sectoral level of data. We specifically assess Bitcoin prices' ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons, based on daily data from November 22, 2017, to December, 30, 2021. The findings show that Bitcoin prices have significant predictive power for US stock volatility, with an inverse relationship between Bitcoin prices and stock sector volatility. Regardless of the stock sectors or number of forecast horizons, the model that includes Bitcoin prices consistently outperforms the benchmark historical average model. These findings are independent of the volatility measure used. Using Bitcoin prices as a predictor yields higher economic gains. These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors, which is important for practitioners and policymakers. [ABSTRACT FROM AUTHOR]

We investigate the asymmetric effects of climate policy uncertainty (CPU), geopolitical risk (GPR), and crude oil prices (WTI) on the realized volatility of the returns of clean energy prices (CEP) in the USA. Using the non-linear autoregressive distributed lags (NARDL) model on data from January 2001 to December 2021, we provide evidence that the effects of CPU, GPR, and WTI on CEP's returns and realized volatility differ in the short and long run and are asymmetric. An increase and decrease in CPU affect CEP's realized volatility more than returns in the long run. Notably, an increase in CPU positively affects the CEP's returns, and a decrease negatively affects CEP's returns in the short run. Moreover, an increase in GPR exerts higher effects on returns in the short run, while both an increase and a decrease in GPR have significant long-run effects. An increase or decrease in WTI shows higher effects on CEP's returns and realized volatility in the long run, while an increase in WTI shows short-run effects. Our findings provide valuable information for making investment decisions while considering the asymmetric effects of climate policy uncertainty, geopolitical risks, and crude oil prices. [ABSTRACT FROM AUTHOR]

In this paper, we contribute to the old debate on the dynamic correlation between gold and stock markets by considering a spectral approach within the framework of portfolio hedging. Specifically, we consider eight MENA stock markets (Tunisia, Egypt, Morocco, Jordan, UAE, Saudi Arabia, Qatar, and Oman) and examine the optimal composition between gold and the stock market index, with a minimum portfolio risk and a high expected return. Based on the spectral approach, we propose seven portfolio structures and evaluate them through a comparison with the conventional DCC-GARCH method and the most best 10 portfolios constructed by using wavelet approach. The main results show that the spectral-based approach outperforms the DCC-GARCH and the wavelet methods. In fact, the optimal gold-stock composition depends on the spectral density of each stock market index, where a stock market index with a stable spectral density requires more investments in gold than a stock market index with an unstable spectral density. [ABSTRACT FROM AUTHOR]

We study the tail dependence between crude oil and BRIC stock markets using a time-varying optimal copula (TVOC) approach. We show evidence of multiple tail dependence regimes, suggesting that simple static or dynamic copula specifications do not fully characterize the extreme dependence between oil and BRIC stock markets. The identified combinations of asymmetric and extreme positive lower tail dependence justify the application of the TVOC. Interestingly, the positive lower tail dependence between oil and stock markets and risk spillover from oil is higher for Brazil and Russia (oil exporters) than India and China (oil importers). Finally, we assess the effectiveness of hedging and measure the conditional diversification benefits of investing in oil for BRIC stock indices. Notably, the Chinese and Indian equity markets offer higher conditional diversification benefits when combined with oil in an equally weighted portfolio. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to determine the impact of rare disaster risks, captured by the El Niño-Southern Oscillation (ENSO) cycle, on the volatility of Treasury securities of the United States (US) involving 1- to 360-month maturities. We use a random coefficient panel-data-based heterogeneous autoregressive-realized variance (HAR-RV) model over the monthly period of 1961:06 to 2019:12, with the monthly RV derived from the sum of squared daily changes in yield within a month. Our results show a positive and statistically significant (at the 1% level) impact of the ENSO cycle on RV, with the results being robust to alternative metrics of the ENSO, consideration of lagged values, and decomposition of the ENSO cycle into El Niño and La Niña phases, with the former having a relatively stronger effect. Based on the panel estimation method using heterogeneous slope coefficients, we find that the impact on the entire term-structure is positive yet stronger at the two-ends and the middle-part of the term-structure. Our findings have important implications for investors in US Treasury securities. [ABSTRACT FROM AUTHOR]

Economic policy uncertainty generally tends to induce a pessimistic view of future market behaviour. Furthermore, instabilities in global oil prices have serious implications for the economies of oil exporters and importers, due to their over-dependence on crude oil for revenue and production activities, respectively, and thereby on stock market indices. Against limited empirical evidence, this study examines the spillover effects from global economic policy uncertainty (GEPU) and oil price volatility to the volatility of the stock market indices of oil exporters and importers in both developed and emerging economies. The results show that the spillover effect from GEPU to oil exporters is relatively smaller than to oil importers, for both developed and emerging countries. Conversely, the volatility spillovers from oil prices to oil exporters are relatively larger than to oil importers, for both developed and emerging countries. Specifically, the volatility spillovers from oil prices to oil exporters (importers) in emerging countries are relatively stronger compared to oil exporters (importers) in developed countries. The findings indicate that the volatility of the stock markets of emerging countries is more sensitive to global factors such as GEPU and oil price volatility, and that oil exporters and importers in emerging economies are more sensitive to oil price volatility than oil exporters and importers in developed economies, which is in line with previous studies. [ABSTRACT FROM AUTHOR]

This study examines the impact of trust and a national culture of secretiveness on the number of bank relationships per firm. We hypothesize that the degree of openness of a firm and trust between economic agents may influence the willingness of the firm to release sensitive information to its lenders, as well as the decision between maintaining single or multiple bank relationships. Using a sample of over 8000 non-financial firms operating in 12 countries from the eurozone we provide evidence that a national culture of secrecy enhances the number of bank relationships, while trust has the opposite effect. Results are robust to the use of various estimation techniques, alternative definitions of secrecy and trust, controls for firm-level and country-level characteristics, and instrumental variables. The main implication of this finding is that the financial decisions of firms cannot be effectively examined without considering deep-rooted national cultural elements. We also find that the results are mainly driven by small and medium enterprises, implying that the higher informational opaqueness of these firms enhances the role of secrecy and trust. [ABSTRACT FROM AUTHOR]

Natural disasters have caused over a million of deaths and $3 trillion in economic losses during the last 20 years. However, theoretical and empirical studies have not reached a conclusion as for their effect on economic growth, and the results can best be described as mixed. The present study proposes the use of a quantile on quantile (QQ) approach to shed more light on this complex relationship. This approach combines the standard quantile regression analysis with nonparametric estimations and allows us to examine how different quantiles of natural disasters affect different quantiles of GDP growth. Using data from over 100 countries over a 30-years period, we confirm that the results of the QQ approach differ from the ones obtained by standard approaches like fixed effects regressions. We document that the relationship between the intensity of natural disasters and economic growth is mostly negative. Nonetheless, there are some exceptions to this. Our findings reveal that the effect of natural disasters can be occasionally positive, depending on the quantiles that we examine. The magnitude of the effect also differs across different combinations of the quantile of economic growth and the quantile of natural disasters. Finally, we obtain somewhat different results when we estimate separate QQ regressions for groups of countries that differ in terms of climate, economic and democratic development, and across different year lags of the natural disaster index. [ABSTRACT FROM AUTHOR]

We forecast monthly realized volatility (RV) of the oil price based on an extended heterogenous autoregressive (HAR)-RV model that incorporates the role of the El Niño Southern Oscillation (ENSO), as captured by the Equatorial Southern Oscillation Index (EQSOI). Based on the period covering 1986 January to 2020 December and studying various rolling-estimation windows and forecast horizons, we find that the EQSOI has predictive value for oil-price RV particularly at forecast horizons from 2 to 4 years, and for rolling-estimation windows of length 4 to 6 years. We show that this result holds not only based on standard tests of out-of-sample predictability, but also under an asymmetric loss function. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak. To this end, we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns. Notably, we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network. The results show that the network structure and spillovers differ considerably with respect to the market state. During stable times, the network shows a nice sectoral clustering structure which, however, changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure. The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated. The sectoral topology thus has not collapsed into a unified market during the pandemic. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous variables (VARX) model to a daily dataset from 25-July-2016 to 1-April-2020. The results indicate various patterns of spillover in high and low volatility regimes, especially during the COVID-19 outbreak. The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19, especially in the high volatility regime. Notably, the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak, which is consistent with the notion of contagion during stress periods. [ABSTRACT FROM AUTHOR]

We analyze the role of the US–China trade war in forecasting out-of-sample daily realized volatility of Bitcoin returns. We study intraday data spanning from 1st July 2017 to 30th June 2019. We use the heterogeneous autoregressive realized volatility model (HAR-RV) as the benchmark model to capture stylized facts such as heterogeneity and long-memory. We then extend the HAR-RV model to include a metric of US–China trade tensions. This is our primary forecasting variable of interest, and it is based on Google Trends. We also control for jumps, realized skewness, and realized kurtosis. For our empirical analysis, we use a machine-learning technique that is known as random forests. Our findings reveal that US–China trade uncertainty does improve forecast accuracy for various configurations of random forests and forecast horizons. [ABSTRACT FROM AUTHOR]

This study explores the dynamic effects of different oil shocks on real exchange rates in net oil importers and exporters. Specifically, the connectedness measures are combined with the structural vector autoregressive model. The findings show that oil supply shocks have a larger depreciating influence on exchange rates in oil exporters than in importers. All countries are generally more sensitive to oil-specific demand shocks, and this sensitivity can lead to a significant appreciation in real exchange rates, except in Japan and the United Kingdom. Further, the spillover effect between oil shocks and exchange rates has strengthened after the global financial crisis of 2007–08. Our findings provide useful implications for the policy-makers and market risk managers to effectively avoid exchange rate risk induced by oil shocks. [ABSTRACT FROM AUTHOR]

This paper aims to examine short- and long-run asymmetries in the impacts of disaggregated oil price shocks on economic policy uncertainty, stock market uncertainty, treasury rates, and investor (bullish and bearish) sentiment in the US. To this end, we use a nonlinear auto-regressive distributed lag cointegration approach, which allows us to capture both positive and negative disaggregated oil shocks. We find that oil demand shocks are the main drivers of both measures of uncertainty, while oil supply shocks affect treasury rates. However, both oil demand shocks and oil supply shocks affect investor sentiment, with certain differences in the effects of positive and negative shocks. The overall effects of both oil demand and supply shocks—whether positive or negative—are stronger in the long-run than in the short-run. Additionally, we apply rolling causality and reveal evidence of a rather homogenous causal flow from disaggregated oil shocks to the variables studied, particularly around global stress periods. Our findings have implications for asset pricing and portfolio risk management and suggest policy formulations that differentiate between disaggregated positive and negative oil price shocks. [ABSTRACT FROM AUTHOR]

We examine the impact of climate risks on the nexus of clean energy and technology stocks using a time-varying correlation model. We find that physical and transition climate risks are positively associated with the long-term correlation between clean energy and technology stock indices, whereas the effect of transition risk is more robust to different sample periods and alternative stock indices. On the contrary, the short-term correlation tends to decrease after shocks to physical risk, since clean energy stocks react more strongly to physical risk shocks than technology stocks. [ABSTRACT FROM AUTHOR]

Unlike previous research which had focused solely on principal-agent conflicts in the Middle East, this paper enriches corporate governance literature of privately-held family firms in an emerging economy with inadequate legislative and enforcement framework, by examining conflicts between controlling and minority shareholders. Based on a sample of 288 privately-held family firms from Lebanon, we find that the inequality between cash flow and voting rights of the controlling shareholders increases self-dealing behaviour. Similarly, family CEO and political involvement of owner intensify the expropriation of minority shareholders. Conversely, the equity ownership by private equity firms and banks acts as monitor and thus protects minority shareholders from being abused. The relevance of our results is not limited to Lebanon but can be transferable to other Middle Eastern countries, where public and private sectors are joining efforts to customize codes of corporate governance in unlisted family firms in order to promote foreign investment. [ABSTRACT FROM AUTHOR]