The predictive power of Bitcoin prices for the realized volatility of US stock sector returns.

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.

COVID-19 and oil market risks: Evidence from new datasets.

We evaluate the predictive value of the newly constructed six COVID-19 indices for oil market risks from 31st December, 2019 (when COVID-19 started) to 28th December, 2021. We show that, on average, higher values of the COVID-19 indices appear to have heightened oil market risks albeit with the converse for Vaccine index regardless of the choice of oil price proxy. The predictive value of the indices is sustained over multiple out-of-sample forecasts and we attribute the outcome to the increased uncertainties associated with the pandemic. Therefore, measures aimed at mitigating these uncertainties can help moderate the oil market risks.•Testing the predictive value of the newly constructed COVID-19 measures for the out-of-sample forecasting of oil market risks.•Increased uncertainties associated with the pandemic tend to raise the level of oil market risks.•Measures aimed at mitigating these uncertainties can help moderate the oil market risks.

COVID-19 pandemic and financial innovations.

This study is motivated around the COVID-19 pandemic as a source of rising financial market risks. Hence, we investigate whether pandemic-induced risks can be hedged by alternative investment in financial innovations captured in exchange traded funds (ETFs). We explore the hedging effectiveness of sectoral ETFs along with a battery of robustness measures. Following the predictability analyses, we find that financial innovations captured in ETFs can effectively hedge both pandemic-induced and financially engineered market risks especially after controlling for the role of oil price in the predictive model. Our model provides better in-sample and out-of-sample forecasting accuracy and economic gains than the benchmark model and this is more pronounced for the COVID-19 pandemic period.

The U.S. Nonfarm Payroll and the out-of-sample predictability of output growth for over six decades.

We examine the predictive prowess of the U.S. Nonfarm Payroll (USNFP) for output growth in the U.S. covering over six decades from 1947 to 2021. Using two different measures of output growth (with Gross Domestic Product growth being used for the main analysis and growth in Industrial Production Index for robustness check), our predictability results show that the U.S. Nonfarm Payroll offers some predictive information for output growth in the U.S. and the out-of-sample forecast results equally attest to the superiority of the USNFP-based model over the model that ignores it. Our findings have implications for policy directions in the U.S. and various national and regional governments, multilateral agencies and investors whose economic and financial conditions are directly or indirectly linked with the U.S. economy.

Global evidence of the COVID-19 shock on real equity prices and real exchange rates: A counterfactual analysis with a threshold-augmented GVAR model.

In this study, we offer a global perspective on the impacts of the COVID-19 pandemic on financial markets using a multi-country Threshold-Augmented Global Vector Autoregressive Model of Chudik et al. (2020). We document a negative impact of the pandemic on real equity prices across countries (except the United States) and country groupings with the highest negative impact recorded in 2020Q2. The biggest losers are the emerging economies while the biggest gainers are the United States whose real stock prices remain positive and the Euro Area that achieved real exchange rate appreciation when the financial markets were mostly vulnerable. Our results support the effectiveness of the quantitative easing policy regime in the Euro Area during the COVID-19 pandemic and also suggest hedging role for the US stocks among other suggested safe assets.

Assessing the hedging potential of gold and other precious metals against uncertainty due to epidemics and pandemics.

We assess the hedging capabilities of four prominent precious metals namely gold, palladium, platinum and silver against market risks due to epidemics and pandemics. The research objective is informed by the COVID-19 pandemic which amplifies health risks with attendant concerns for financial markets. We utilize the health-related uncertainty index developed by Baker et al. (Equity market volatility: infectious disease tracker [INFECTDISEMVTRACK], 2020) which measures uncertainty in the financial markets due to infectious diseases including the COVID-19 pandemic and construct a predictive model that accommodates the salient features of both the predictand and predictor series. Our results support the safe haven property only for gold before and during the COVID-19 pandemic. We push the analysis further for in-sample and out-of-sample forecast evaluation and find that accounting for uncertainty due to infectious diseases improves the forecast of the four precious metals relative to the benchmark model (historical average). We highlight for investors that the gold market remains the safest market among the precious metals particularly during the COVID-19 pandemic.

The return volatility of cryptocurrencies during the COVID-19 pandemic: Assessing the news effect.

In this paper, we test the role of news in the predictability of return volatility of digital currency market during the COVID-19 pandemic. We use hourly data for cryptocurrencies and daily data for the news indicator, thus, the GARCH MIDAS framework which allows for mixed data frequencies is adopted. We validate the presupposition that fear-induced news triggered by the COVID-19 pandemic increases the return volatilities of the cryptocurrencies compared with the period before the pandemic. We also establish that the predictive model that incorporates the news effects forecasts the return volatility better than the benchmark (historical average)model.

Hedging oil price risk with gold during COVID-19 pandemic.

This paper assesses the role of gold as a safe haven or hedge against crude oil price risks. We employ the asymmetric VARMA-GARCH model, using daily data from January 2016 to August 2020. To account for the impact of COVID-19 pandemic, we partitioned the data into two to reflect the periods before and during the pandemic. Our empirical results find gold as a significant safe haven against oil price risks. The optimal portfolio and hedging analyses conducted also validate the hedging effectiveness of gold against risk associated with oil. The robustness of our results is further confirmed using three other prominent precious metals - silver, platinum, and palladium. In sum, our results are useful for investors and portfolio managers that are desirous of using gold and other precious metals as portfolio rebalancing tools to minimize or circumvent risks associated with volatile oil returns.

COVID-19 pandemic and the crude oil market risk: hedging options with non-energy financial innovations.

This study examines the hedging effectiveness of financial innovations against crude oil investment risks, both before and during the COVID-19 pandemic. We focus on the non-energy exchange traded funds (ETFs) as proxies for financial innovations given the potential positive correlation between energy variants and crude oil proxies. We employ a multivariate volatility modeling framework that accounts for important statistical features of the non-energy ETFs and oil price series in the computation of optimal weights and optimal hedging ratios. Results show evidence of hedging effectiveness for the financial innovations against oil market risks, with higher hedging performance observed during the pandemic. Overall, we show that sectoral financial innovations provide resilient investment options. Therefore, we propose that including the ETFs in an investment portfolio containing oil could improve risk-adjusted returns, especially in similar financial crisis as witnessed during the pandemic. In essence, our results are useful for investors in the global oil market seeking to maximize risk-adjusted returns when making investment decisions. Moreover, by exploring the role of structural breaks in the multivariate volatility framework, our attempts at establishing robustness for the results reveal that ignoring the same may lead to wrong conclusions about the hedging effectiveness.

Assessing the safe haven property of the gold market during COVID-19 pandemic.

This study examines the safe haven prowess of gold against some exogenous shocks due to the COVID-19 pandemic. We further make a comparison of our findings with those obtained for the period before it. Our results confirm the potential of gold market to serve as a safe haven during the pandemic albeit with a higher effectiveness before the pandemic. Further results suggest that gold consistently offers better safe haven properties than the US stocks as well as other precious metals like Silver, Palladium and Platinum regardless of the period. Finally, we find that the predictive model that accounts for uncertainties outperforms the benchmark model that ignores the same both for the in- and out-of-sample forecast analyses.

Gold and US sectoral stocks during COVID-19 pandemic.

In this study, we examine the hedging relationship between gold and US sectoral stocks during the COVID-19 pandemic. We employ a multivariate volatility framework, which accounts for salient features of the series in the computation of optimal weights and optimal hedging ratios. We find evidence of hedging effectiveness between gold and sectoral stocks, albeit with lower performance, during the pandemic. Overall, including gold in a stock portfolio could provide a valuable asset class that can improve the risk-adjusted performance of stocks during the COVID-19 pandemic. In addition, we find that the estimated portfolio weights and hedge ratios are sensitive to structural breaks, and ignoring the breaks can lead to overestimation of the hedging effectiveness of gold for US sectoral stocks. Since the analysis involves sectoral stock data, we believe that any investor in the US stock market that seeks to maximize risk-adjusted returns is likely to find the results useful when making investment decisions during the pandemic.

The COVID-19 global fear index and the predictability of commodity price returns.

In this paper, we subject the global fear index (GFI) for the COVID-19 pandemic to empirical scrutiny by examining its predictive power in the predictability of commodity price returns during the pandemic. One of the attractions to the index lies in its coverage as all the countries and by extension regions and territories in the world are considered in the construction of the index. Our results show evidence of a positive relationship between commodity price returns and the global fear index, confirming that commodity returns increase as COVID-19 related fear rises. By way of extension, we further establish that commodity market offers better safe-haven properties than the stock market given the negative association between GFI and the latter. Finally, the GFI series improves the forecast accuracy of the predictive model for commodity price returns and its forecast outcome outperforms the historical average (constant returns) model both for the in-sample and out-of-sample forecasts. Our results are robust to alternative measures of pandemics.

Predicting stock returns in the presence of COVID-19 pandemic: The role of health news.

This study derives its motivation from the current global pandemic, COVID-19, to evaluate the relevance of health-news trends in the predictability of stock returns. We demonstrate this by using data covering top-20 worst-hit countries, distinctly in terms of reported cases and deaths. The results reveal that the model that incorporates health-news index outperforms the benchmark historical average model, indicating the significance of health news searches as a good predictor of stock returns since the emergence of the pandemic. We also find that accounting for "asymmetry" effect, adjusting for macroeconomic factors and incorporating financial news improve the forecast performance of the health news-based model. These results are consistently robust to data sample (both for the in-sample and out-of-sample forecast periods), outliers and heterogeneity.