The impact of the coronavirus crisis on the market price of risk

Constructing a positive sentiment index for COVID-19: Evidence from G20 stock markets

The present study investigates the degree of market responses through the scope of investors' sentiment during the COVID-19 pandemic across G20 markets by constructing a novel positive search volume index for COVID-19 (COVID19+). Our key findings, obtained using a Panel-GARCH model, indicate that an increased COVID19+ index suggests that investors decrease their COVID-19 related crisis sentiment by escalating their Google searches for positively associated COVID-19 related keywords. Specifically, we explore the predictive power of the newly constructed index on stock returns and volatility. According to our findings, investor sentiment positively (negatively) predicts the stock return (volatility) during the COVID-19. This is the first study assessing global sentiment by proposing a novel proxy and its impacts on the G20 equity market.

The COVID-19 pandemic, consumption and sovereign credit risk: Cross-country evidence

Many recent studies investigate the economic effect of the COVID-19 pandemic in multiple aspects, while whether and how the sovereign credit risk reacts to the shock is still underexplored. Using a sample of forty developed and developing countries and employing staggered difference-in-differences models, we find that the sovereign credit risk measured by sovereign credit default swap spreads significantly increases after the COVID-19 pandemic outbreak, and the adverse effect is more pronounced for short-term credit risk. The reason is that the pandemic causes severe concerns about aggregate consumption contraction in addition to the fiscal capacity and the volatility of exports. We also find that fiscal stimuli stabilizing consumer spending alleviate the adverse effect of the COVID-19 pandemic, while debt relief does not matter. Overall, practitioners and policy makers should attach more importance to consumption and its recovery during the pandemic when making decisions.

Efficient Asset Allocation: Application of Game Theory-Based Model for Superior Performance

In this paper, we compared the models for selecting the optimal portfolio based on different risk measures to identify the periods in which some of the risk measures dominated over others. For decades, the best known return-risk model has been Markowitz’s mean-variance model. Based on the criticism of the classical Markowitz model, a whole series of risk measures and models for selecting the optimal portfolio have been developed, which are divided into two groups: symmetrical and downside risk measures. Based on the tools provided by game theory, we presented a minimax model for selecting the optimal portfolio based on the maximum loss as a measure of risk. Recent research has shown the adequacy of the application of this risk measure and its dominance concerning variance in certain circumstances. Theoretically, the model based on maximum loss as a measure of risk relies on a much smaller number of assumptions that must be satisfied. In the empirical part of the paper, we analyzed the real return performance, structure, correlation, stability, and predictive efficiency of the model based on maximum loss return as a measure of risk and compared it with the other famous models to determine whether the maximum loss-based risk measure model is more suitable for use in certain circumstances than conventional return-risk models. We compared portfolios created based on different models over the period of 2000–2020 from a selected sample of stocks that are components of the STOXX Europe 600 index, which covers 90% of the free market capitalization in the European capital market. The observed period included 3 bear market periods, including the period of market decline during the COVID-19 crisis. Our analysis showed that there was no significant difference in portfolio returns depending on the selected model using the “buy-and-hold” strategy, but there were crisis periods. The results showed a significantly higher stability of portfolios selected on the criterion of minimizing the maximum loss than others. In periods of market decline, this portfolio achieved the best performance and had a shorter recovery period than others. This allowed superior use of the minimax model at least for investors with a pronounced risk aversion.

Banking research in the time of COVID-19☆

Despite the devastating worldwide human and economic tolls of the COVID-19 crisis, it has created some positive economic and financial surprises and opportunities for research. This paper highlights two such favorable surprises – the shortest U.S. recession on record and the avoidance of any banking crisis – and a number of research opportunities. The paper ties the “economic surprise” of the short recession to the speed and size of U.S. stimulus programs during COVID-19 – faster and larger than for the Global Financial Crisis (GFC). We connect the “financial surprise” of the resilient banking sector to prudential policies put in place during and after the GFC that fortified U.S. banks prior to COVID-19. These twin “surprises” are also mutually reinforcing – if either the economy or banking system had failed, so would the other. The paper also reviews extant COVID-19 banking research and suggest paths for future research. It recommends that particular attention be paid to research outside of the U.S. – where fewer favorable “surprises” may be present – as the best way to advance knowledge in this area.

Impact of COVID-19 on Performance Evaluation Large Market Capitalization Stocks and Open Innovation

This research is an event study that evaluates the performance of large market capitalization shares using a performance model that is adjusted to risks due to the COVID-19 outbreak. The study measured the performance of large market capitalization stocks which represented each tick size on the Indonesian Stock Exchange during the COVID-19 pandemic using the Sharpe Index, the Treynor Ratio, and Jensen’s Alpha. The sample selection used a purposive sampling technique and 24 stocks were selected as samples in the study. We used the daily closing price of stocks, the Indonesia composite index, and average risk-free rate return (BI rate). By using Jensen’s Alpha, this study found that FREN was the highest beta with a value of 1.8189, indicating that the index was an effective and well-diversified stock. FREN is low priced and the highest market capitalization stock in its tick size (third tier stocks). Jensen’s Alpha is good for measuring the performance of large capitalization and low-priced stocks. There are eight stocks that always have negative values in each method of measuring stock performance, which indicates that these stocks underperformed during COVID-19.