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Purpose This study aims to examine the multiscale predictability power of COVID-19 deaths and confirmed cases on the S&P 500 index (USA), CAC30 index (France), BSE index (India), two strategic commodity futures (West Texas intermediate [WTI] crude oil and Gold) and five main uncertainty indices Equity Market Volatility Ticker (EMV), CBOE Volatility Index (VIX), US Economic Policy Uncertainty (EPU), CBOE Crude Oil Volatility Index (OVX) and CBOE ETF Gold Volatility Index (GVZ). Furthermore, the authors analyze the impact of uncertainty indices and COVID-19 deaths and confirmed cases on the price returns of stocks (S&P500, CAC300 and BSE), crude oil and gold. Design/methodology/approach The authors used the wavelet coherency method and quantile regression approach to achieve the objectives. Findings The results show strong multiscale comovements between the variables under investigation. Lead-lag relationships vary across frequencies. Finally, COVID-19 news is a powerful predictor of the uncertainty indices at intermediate (4–16 days) and low (32–64 days) frequencies for EPU and at low frequency for EMV, VIX, OVX and GVZ indices from January to April 2020. The S&P500, CAC30 and BSE indexes and gold prices comove with COVID-19 news at low frequencies during the sample period. By contrast, COVID-19 news and WTI oil moderately correlated at low frequencies. Finally, the returns on equity and commodity assets are influenced by uncertainty indices and are sensitive to market conditions. Originality/value This study contributes to the literature by exploring the time and frequency dependence between COVID-19 news (confirmed and death cases) on the returns of financial and commodity markets and uncertainty indexes. The findings can assist market participants and policymakers in considering the predictability of future prices and uncertainty over time and across frequencies when setting up regulations that aim to enhance market efficiency.

This paper examines frequency dynamic spillovers in return and volatility and the hedging ability of Green Bonds, gold, silver, oil, the US dollar index, and volatility index against downside US stock prices before and during the COVID-19 pandemic outbreak and for the short and long run. To do so, we use the Diebold and Yilmaz (2014), the TVP-VAR model, and the frequency spillover index by Baruník and Křehlík (2018). We show that the short-term volatility spillovers dominate their long-term counterparts. Green Bond is net transmitters of spillovers in the system at the short term and net receivers at the long term. SP500 and silver (USDX and oil) are net transmitters (receivers) of short- and long-term spillovers. Gold and VIX are net receivers of short-term spillovers and net transmitters of long-term spillovers. COVID-19 crisis has more effects on the short-term spillover, which reaches its highest level early 2020. COVID-19 and time horizons lead the direction and the magnitude of spillovers. The Quantile-on-Quantile regression analysis shows significant nonlinear relationships between markets under study. More interestingly, we show that green bonds and gold are safe haven assets for US equity investors during COVID-19. On the other hand, a mixed portfolio offers higher diversification benefits. Finally, hedging effectiveness is dependent on COVID-19 and time horizon.

Refereed/Peer-reviewed This paper examines the time-frequency spillovers and connectedness between the major precious metals futures markets (gold, palladium, platinum, and silver), the West Texas Intermediate (WTI) oil futures, the US stock market, the US 10-year Treasury Bond (T-Bond) market, and the US dollar index. Applying Barunik and Krehlik's (2018) novel framework, the results show that spillovers across markets strengthens in the short-term, suggesting that diversification opportunities are lower in the short-term than the long-term. Furthermore, we find that the net transmitter/receiver of spillover is dependent on the frequency being analyzed, thereby indicating asymmetric spillovers among markets. Moreover, gold and silver (platinum, T-Bonds, and US dollar index) are net transmitters (receivers) of spillover regardless of the time horizon. Palladium is the net transmitter of spillover to the other markets in the short-term period (1–8 days); in contrast, in the long-term period (8–256 days), the S&P 500 index is the net transmitter of spillover. Precious metals futures, crude oil, and T-Bond assets are diversifiers for the US stock market for short-term and the long-term investments. The US dollar index is a strong hedge regardless of the time horizon and a strong (weak) safe haven asset in the short (long) term. Precious metals are a safe haven in the short-term and long-term periods. Crude oil and T-Bonds are not a safe haven for US equity investors. Finally, our result support evidence of diversification benefits and better hedging effectiveness by adding precious metals, WTI, T-Bond, and USDX to S&P500 index, which is higher in the long-term than in the short-term. WTI offers the highest hedging effectiveness, regardless the time investment horizons.

The study examines the implications of structural oil price shocks on one of the most widely observed and pervasive momentum anomalies. We use a structural vector autoregression (SVAR) model to find the time-varying responses of momentum payoffs of India, Japan, the UK, and the US to different structural oil shocks-oil supply global aggregate demand shock, speculative shock, and other oil demand shocks. Structural oil shocks, particularly oil demand and speculative shocks, have important implications as conditioning information in predicting stock return momentum behavior according to the conditional asset pricing model. Overall, the results indicate that structural oil shocks have a significant impact on small cap and loser stock portfolios. Our findings have crucial implications to develop effective momentum investment strategies that consider structural oil shocks as conditioning information. (c) 2022 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved. Refereed/Peer-reviewed

This study examines the connectedness in high-order moments between cryptocurrency, major stock (U.S., U.K., Eurozone, and Japan), and commodity (gold and oil) markets. Using intraday data from 2020 to 2022 and the time and frequency connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) and Baruník and Křehlík (J Financ Econom 16(2):271–296, 2018), we investigate spillovers among the markets in realized volatility, the jump component of realized volatility, realized skewness, and realized kurtosis. These higher-order moments allow us to identify the unique characteristics of financial returns, such as asymmetry and fat tails, thereby capturing various market risks such as downside risk and tail risk. Our results show that the cryptocurrency, stock, and commodity markets are highly connected in terms of volatility and in the jump component of volatility, while their connectedness in skewness and kurtosis is smaller. Moreover, jump and volatility connectedness are more persistent than that of skewness and kurtosis connectedness. Our rolling-window analysis of the connectedness models shows that connectedness varies over time across all moments, and tends to increase during periods of high uncertainty. Finally, we show the potential of gold and oil as hedging and safe-haven investments for other markets given that they are the least connected to other markets across all moments and investment horizons. Our findings provide useful information for designing effective portfolio management and cryptocurrency regulations.

This study investigates tail dependence among five major cryptocurrencies, namely Bitcoin, Ethereum, Litecoin, Ripple, and Bitcoin Cash, and uncertainties in the gold, oil, and equity markets. Using the cross-quantilogram method and quantile connectedness approach, we identify cross-quantile interdependence between the analyzed variables. Our results show that the spillover between cryptocurrencies and volatility indices for the major traditional markets varies substantially across quantiles, implying that diversification benefits for these assets may differ widely across normal and extreme market conditions. Under normal market conditions, the total connectedness index is moderate and falls below the elevated values observed under bearish and bullish market conditions. Moreover, we show that under all market conditions, cryptocurrencies have a leadership influence over the volatility indices. Our results have important policy implications for enhancing financial stability and deliver valuable insights for deploying volatility-based financial instruments that can potentially provide cryptocurrency investors with suitable hedges, as we show that cryptocurrency and volatility markets are insignificantly (weakly) connected under normal (extreme) market conditions.

This study examines the volatility spillovers between the US stock market (SP500 index) and both oil and gold before and during the global health crisis (GHC). We apply the FIAPARCH-DCC model to the 15-minute intraday data. The results showed negative (positive) conditional correlations between the SP500 and gold (oil). The time-varying conditional correlations between markets were higher during COVID-19 spread. Moreover, gold offers more diversification gains than oil does during the pandemic. Hedging is more expensive during a pandemic than before. Oil provides higher hedging effectiveness (HE) than gold for all sub-periods. HE was lower during the COVID-19 outbreak for both oil and gold. These findings have important implications for both equity investors and policymakers.

Refereed/Peer-reviewed Recent literature supports the view that returns of most of the international equity markets are significantly integrated. However, diversification based across different regions remains a focus of attention for the investment community. We examine the presence of returns integration among BRICS, Latin American, and emerging and frontier Asian equity markets by utilizing panel co-integration and panel regression, i.e., fully modified ordinary least square (FMOLS), to examine portfolio diversification opportunities. Our sampling of data comprises of daily stock return from 1st September 1997 to 30th November 2018. Our findings highlight that within the region, portfolio diversification does not provide optimal returns. Furthermore, empirical results from the vector error correction model (VECM) suggest the benefits of portfolio diversification in the short-run. In particular, not all equity markets are significantly linked with gold, oil, and forex markets. Our empirical analysis reveals that within the region, equity-commodity portfolios may lead to greater diversification gains.

The point cloud data provides useful geometric information to 3-D intelligent systems such as autonomous driving, 3-D reconstruction, and hand pose estimation (HPE). Many mobile devices have implemented the 3-D intelligent system with their limited hardware resources. However, previous processors were not designed for accelerating the point cloud based neural network (PNN) which consists of sampling-grouping layers (SGLs) and convolution layers (CLs). In this article, a pipelined PNN processor is proposed for low latency PNN-based 3-D intelligent systems in mobile devices. The processor adopts the pipelined heterogeneous architecture to accelerate both SGLs and CLs in PNNs. The window-based sampling-grouping (WSG) algorithm boosts up the throughput x2.34 higher in SGLs by directly sampling and grouping the 3-D point cloud data from the depth image. Furthermore, the max pooling (MP) prediction core (MPPC) predicts the large-scale (64- and 128-to-1) MP layers, which increases the throughput by x1.31 higher. Besides, the MP prediction on the tiled data can hide the latency of the MPPC and solve the bank conflict problem on the in-out memories in the convolution core (CC). As a result, the processor successfully demonstrates the PNN-based HPE system resulting in 4.45 ms of the processing time with 8.24 mm of HPE error and 266 mW of power consumption.

We examine the spillovers and resource allocation characteristics of a portfolio of precious metal commodities and global/regional equity markets using a directional spillover index and portfolio o...

This study investigates dynamic frequency connectedness for volatility differences among eight popular cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dash, Monero, Ripple, Nem and Stellar). It employs the methodologies of Diebold and Yilmaz (2014; 2016) and Barunik and Křehlik (2018). Furthermore, an analysis of diversification benefits and downside risk reductions is carried out. The results demonstrated dynamic spillovers, which intensified after 2017. Furthermore, Bitcoin, Ethereum, and Litecoin are net transmitters of risk, which can be a contagion source; Dash, Ripple, Monero, Stellar, and Nem are net receivers of risk. Moreover, the short-term risk spillover is more pronounced than the medium- and long-term risk spillovers, which also increased after 2017. The directional spillovers among cryptocurrencies is sensitive to frequencies. Finally, adding a cryptocurrency to a benchmark Bitcoin portfolio provides diversification benefits and downside risk reductions. In contrast, adding Bitcoin to a cryptocurrency portfolio do not offers diversification opportunities.

Herding behaviour is an interesting phenomenon that has a serious impact on the market, leading to inefficient asset prices and high volatility in periods of market turmoil. We analysed the existen...

We examine the impact of COVID-19 pandemic crisis on the pricing efficiency and asymmetric multifractality of major asset classes (S&P500, US Treasury bond, US dollar index, Bitcoin, Brent oil, and gold) within a dynamic framework. Applying permutation entropy on intraday data that covers between April 30, 2019 and May 13, 2020, we show that efficiency of all sample asset classes is deteriorated with the outbreak, and in most cases this deterioration is significant. Results are found to be robust under different analysis schemes. Brent oil is the highest efficient market before and during crisis. The degree of efficiency is heterogeneous among all markets. The analysis by an asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) approach shows evidence of asymmetric multifractality in all markets which rise with the scales. The inefficiency is higher during downward trends before the pandemic crisis as well as during COVID-19 except for gold and Bitcoin. Moreover, the pandemic intensifies the inefficiency of all markets except Bitcoin. Findings reveal increased opportunities for price predictions and abnormal returns gains during the COVID-19 outbreak.

This paper examines the frequency of spillovers between crude oil futures and the Middle East and North Africa (MENA) stock markets. We use the methodologies proposed by Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) and the wavelet coherency approach. The results show time-varying volatility spillovers in the considered markets. The short-term spillovers are higher than their intermediate-term counterparts. The highest jump in spillovers occurs during the COVID-19 outbreak, followed by the global financial crisis and the recent oil price crash. The spillovers are higher for oil-exporting countries than oil-importing countries. Saudi Arabia, Qatar, and the United Arab Emirates (UAE) are the main contributors to spillovers in the short and intermediate terms. Brent oil, Egypt, Morocco, and Turkey are the net receivers of spillovers in the short term, and they switch to become net contributors to spillovers in the intermediate term. Turkey and oil-exporting stock markets receive more spillovers than oil-importing stock markets irrespective of the time frequency. Wavelet analysis shows evidence of co-movements between oil futures and stock markets at intermediate and low frequencies. The lead–lag relationships between crude oil and stock markets are mixed and time-varying. Moreover, a mixed portfolio offers diversification benefits. Hedging is more expensive during the pandemic period and particularly in the intermediate term compared to the short term. Hedging effectiveness is highest during the COVID-19 pandemic in the short and intermediate terms for almost all markets. Refereed/Peer-reviewed