Cryptocurrency volatility markets

By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market’s expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture ‘normal’ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.

U.S. Economic Uncertainty Shocks and China’s Economic Activities: A Time-Varying Perspective

This paper presents new empirical evidence concerning the time-varying responses of China’s macroeconomy to U.S. economic uncertainty shocks through a novel TVP-VAR model. The results robustly reveal that a rise in U.S. economic uncertainty would exert sizable, persistent, and significant detrimental effects on China’s gross domestic product (GDP), price level, and short-term interest rate during the period when common shocks take place, such as the global financial crisis around 2008, whereas small and transient effects in the tranquil times. Therefore, China should diversify its international linkages and gradually reduce the dependence on the United States into a certain range to shield the domestic economy, as well as improve the independence of monetary policy. Furthermore, to withstand unfavorable external shocks, China should be prudent on greater opening-up and carry out more intensive intervention when common shocks hit the world economy. Finally, investors should be alert to the potential detrimental impact of U.S. economic uncertainty on Chinese assets’ fundamentals.

Financial Constraints, Stock Returns and R&D in Indian Stock Market

When the accessibility of external finance prohibits a firm from taking the optimum decision related to investment, that firm is called financially constrained. By applying the methodology of Kaplan and Zingales (1997) and Lamont et al. (2001), the current study has created a construct to gauge the level of financial constraints (FC) of the companies which emanate from quantitative information. The study explores whether FC factor is present in the Indian stock market and explores whether the security returns of those firms that are financially constrained move in tandem. The study also attempts to establish the association between security returns and R&D of financially constrained firms. On a sample of 63 R&D reporting companies of S&P BSE 500, traded over the period March 2008 to February 2019, the study used the Fama–French methodology, fixed effect model and the ordered logistic regression. The study finds that firms that are highly constrained earn more returns than low constrained firms. Second, the security returns of firms that are financially constrained move in tandem because these firms are affected by common shocks. This suggests that the FC factor exists in the Indian stock market. Finally, when R&D interacts with the level of FC, then this interaction effect has a negative effect on returns.

Searching for the Peak Google Trends and the COVID-19 Outbreak in Italy

One of the difficulties faced by policy makers during the COVID-19 outbreak in Italy was the monitoring of the virus diffusion. Due to changes in the criteria and insufficient resources to test all suspected cases, the number of ‘confirmed infected’ rapidly proved to be unreliably reported by official statistics. We explore the possibility of using information obtained from Google Trends to predict the evolution of the epidemic. Following the most recent developments on the statistical analysis of longitudinal data, we estimate a dynamic heterogeneous panel. This approach allows to takes into account the presence of common shocks and unobserved components in the error term both likely to occur in this context. We find that Google queries contain useful information to predict number patients admitted to the intensive care units, number of deaths and excess mortality in Italian regions.