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Purpose – This study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers. Design/methodology/approach – The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation. Findings – Generally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors. Originality/value – The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.

This paper examines the diversification benefits of commodity indices during the COVID-19 pandemic by analyzing both static and dynamic risk spillovers for the period from January 2, 1998 to September 16, 2020. Using variance decomposition forecasting, we employed static and dynamic analyses based on the estimation of 50-day moving window spillover indices. Globally, the results show significant spillovers between markets during the COVID-19 pandemic crisis. The results show that stock markets are highly interdependent with other financial markets (in both directions), and that commodity markets (except energy) and the bond market are recipients of shocks emanating from stock markets. The main contribution of this paper is to study the return and volatility spillovers between stock and commodity indices before and during the pandemic. This study of shock transmission mechanisms will enable investors to develop optimal diversification and hedging strategies during the crisis. In this context, we found that commodities and US government bonds could offer diversification benefits to investors. In addition, some of these assets may serve as hedging instruments or safe havens during the COVID-19 crisis.

Abstract In this paper, we design and apply the Long Short-Term Memory (LSTM) neural network approach to predict several financial classes’ time series under COVID-19 pandemic crisis period. We use the S&P GSCI commodity indices and their sub-indices and consider the stock market indices for different regions. Based on the daily prices, the results show that the proposed LSTM network can form a robust prediction model to determine the optimal diversification strategies. Our prediction model achieved RMSEs and MAEs too small for the different selected financial assets, showing the predictive power of our LSTM network especially during the COVID-19 health crisis. In addition, our LSTM network outperforms ARIMA-type models for all selected assets.