Political Uncertainty, COVID-19 pandemic and Stock Market Volatility Transmission

News about referendums and the ongoing evolution of a global contagious increase uncertainty about the development of economic fundamentals reflected by increased volatility in the financial markets. In this paper, employing volatility impulse response functions and assessing the volatility spillovers we examine intra-market volatility transmission in the Athens stock market. We employ a large sample period of daily data that spans from December 1999 to December 2020 and captures major events of the last 20 years especially related to the announcement of the two referendums during the Greek government-debt crisis in 2010 and the economic and political turmoil that increased country instability, the following years, the BREXIT referendum and the COVID-19 pandemic of 2020. Our results demonstrate that negative shocks during the announcement of the referendum produce larger impulse responses than during the announcement of the country lockdowns. Furthermore, we shed light on the existence of the dynamic relationship of volatility spillovers. Volatility spillovers peaked during the COVID-19 pandemic. Dynamic spillover plots demonstrate that during the COVID-19 pandemic, more volatility is transmitted by mid cap firms to large cap firms. Our findings have implications to market participants, policy makers and market regulators.

Exploring the role of non-pharmaceutical interventions (NPIs) in flattening the Greek COVID-19 epidemic curve.

Due to the COVID-19 pandemic originating in China in December 2019, apart from the grave concerns on the exponentially increasing casualties, the affected countries are called to deal with severe repercussions in all aspects of everyday life, from economic recession to national and international movement restrictions. Several regions managed to handle the pandemic more successfully than others in terms of life loss, while ongoing heated debates as to the right course of action for battling COVID-19 have divided the academic community as well as public opinion. To this direction, in this paper, an autoregressive COVID-19 prediction model with heterogeneous explanatory variables for Greece is proposed, taking past COVID-19 data, non-pharmaceutical interventions (NPIs), and Google query data as independent variables, from the day of the first confirmed case-February 26th-to the day before the announcement for the quarantine measures' softening-April 24th. The analysis indicates that the early measures taken by the Greek officials positively affected the flattening of the epidemic curve, with Greece having recorded significantly decreased COVID-19 casualties per million population and managing to stay on the low side of the deaths over cases spectrum. In specific, the prediction model identifies the 7-day lag that is needed in order for the measures' results to actually show, i.e., the optimal time-intervention framework for managing the disease's spread, while our analysis also indicates an appropriate point during the disease spread where restrictive measures should be applied. Present results have significant implications for effective policy making and in the designing of the NPIs, as the second wave of COVID-19 is expected in fall 2020, and such multidisciplinary analyses are crucial in order to understand the evolution of the Daily Deaths to Daily Cases ratio along with its determinants as soon as possible, for the assessment of the respective domestic health authorities' policy interventions as well as for the timely health resources allocation.

Forecasting Power of Infectious Diseases-Related Uncertainty for Gold Realized Variance

We examine the forecasting power of a daily newspaper-based index of uncertainty associated with infectious diseases (EMVID) for gold market returns volatility via the heterogeneous autoregressive realized variance (HAR-RV) model. Our results show that the EMVID index increases realized variance (RV) at the highest level of statistical significance within-sample, while it improves the forecast accuracy of gold realized variance at short-, medium-, and long-run horizons in a statistically significant manner. Importantly, by assessing the role of this index during the recent pandemic, we find strong evidence for its critical role in forecasting gold RV. Such evidence has important portfolio implications for investors during the current period of unprecedented levels of uncertainty resulting from the outbreak of COVID-19.

COVID-19 predictability in the United States using Google Trends time series.

During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this paper, the role of Google query data in the predictability of COVID-19 in the United States at both national and state level is presented. As a preliminary investigation, Pearson and Kendall rank correlations are examined to explore the relationship between Google Trends data and COVID-19 data on cases and deaths. Next, a COVID-19 predictability analysis is performed, with the employed model being a quantile regression that is bias corrected via bootstrap simulation, i.e., a robust regression analysis that is the appropriate statistical approach to taking against the presence of outliers in the sample while also mitigating small sample estimation bias. The results indicate that there are statistically significant correlations between Google Trends and COVID-19 data, while the estimated models exhibit strong COVID-19 predictability. In line with previous work that has suggested that online real-time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers in addressing the most crucial issues: flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of their respective health care systems.