COVID-19 policy actions and inflation targeting in South Asia.

In this paper, we examine the impact of policy actions undertaken by governments during the COVID-19 pandemic on Consumer Price Index (CPI) in five major South Asian nations, namely, Bangladesh, India, Nepal, Pakistan and Sri Lanka. Using panel fixed effects regression with robust standard errors, we show the relative importance of monetary and financial interventions on reducing CPI while fiscal interventions, direct grants and aid are insignificant. Further, delving into nature of policy interventions, our study finds evidence of negative impact of Credit Support, and Healthcare Support on CPI in South Asian nations. While our investigation is preliminary, it provides insights into additional understanding of effectiveness of policy actions on inflation targeting.

Did green debt instruments aid diversification during the COVID-19 pandemic?

Faced with a persistent pandemic, investors are concerned about portfolio diversification. While the literature on COVID-19 has evolved impressively, limited work remains on diversification opportunities. We contribute to the literature by exploring the volatility and co-movement of different sovereign debt instruments, including green sukuk, sukuk, bond and Islamic and conventional equity indices for Indonesia. Our results consistently point towards increased asset co-movement and weak profitability during the pandemic. Interestingly, sukuk and green sukuk have a 14% correlation with stocks, suggesting potential diversification prospects in times of extreme shocks.

Predicting the European stock market during COVID-19: A machine learning approach.

This research attempts to explore the total of 21 potential internal and external shocks to the European market during the Covid-19 Crisis. Using the time series of 1 Jan 2020 to 26 June 2020, I employ a machine learning technique, i.e. Least Absolute Shrinkage and Selection Operator (LASSO) to examine the research question for its benefits over the traditional regression methods. This further allows me to cater to the issue of limited data during the crisis and at the same time, allows both variable selection and regularization in the analysis. Additionally, LASSO is not susceptible to and sensitive to outliers and multi-collinearity. The European market is mostly affected by indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index. There is a significant difference in the predictors before and after the pandemic announcement by WHO. Before the Pandemic period announcement by WHO, Europe was hit by the gold market, EUR/USD exchange rate, Dow Jones index, Switzerland, Spain, France, Italy, Germany, and Turkey and after the announcement by WHO, only France and Germany were selected by the lasso approach. It is found that Germany and France are the most predictors in the European market.•A LASSO approach is used to predict the European stock market index during COVID-19•European market is mostly affected by the indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index.•There is a significant difference in the predictors before and after the pandemic announcement by WHO.

Deaths, panic, lockdowns and US equity markets: The case of COVID-19 pandemic.

This study investigates the impact of COVID-19 pandemic on the microstructure of US equity markets. In particular, we explain the liquidity and volatility dynamics via indexes that capture multiple dimensions of the pandemic. Our results suggest that increases in confirmed cases and deaths due to coronavirus are associated with a significant increase in market illiquidity and volatility. Similarly, declining sentiment and the implementations of restrictions and lockdowns contribute to the deterioration of liquidity and stability of markets.