Dipartimento di Scienze economico-sociali e matematico-statistiche

Descrizione del progetto

Pandemic crises have become a more frequent phenomenon and are predicted to become even more likely in the future. Given these considerations, they represent an important source of risk at both the local and the global levels. The distinctive objective of our research project is the examination of economic dynamics in several markets across different stages of a pandemic crisis. The recent literature on the COVID-19 pandemic situation has highlighted very interesting socio-economic dynamics mostly at the onset of the crisis. How markets adjust in the aftermath of an epidemic shock is still an open question. We intend to focus on a `long-run’ perspective, that is, study the interactions between the real economy and financial markets over an entire pandemic cycle. Given the interconnection between production activities (see, Davis et al. (2021)), corporate financing (that is, capital markets (Ellul et al. 2020)), and the distribution and level of households’ income and wealth (see, Deaton (2021)), we believe that a proper analysis must be broad and granular. That is, we must study several markets and several sectors across several geographical regions. Since pandemic crises represent extreme (tail) events, we intend to produce and share new non-conventional measures of risk. In addition, in order to maximize the social impact of our project, we will focus mainly on European countries. Specifically, we plan to provide new and non-conventional high-frequency indicators that can immediately inform policymakers and practitioners about the status of the economy. Our data will be non-conventional for two reasons. First, some of our indicators are related to information embodied in non-conventional financial assets (such as equity futures, options, cryptocurrencies) very useful to assess tail events. Second, some of our estimators will be obtained from non-conventional sources, i.e., by collecting announcements and news from social media across several locations. We will adopt both new text analysis methods and new econometric techniques for tail events. In addition, we will look at the interplay between asset prices, announcements, and news shocks collected in real-time from social media (e.g., Google, Twitter). For the sake of exposition, in what follows we organize our research agenda into two parts related to the common theme `Measuring Pandemic Risks’: PART 1 - New High-Frequency Indicators & Financial Markets PART 2 – Market-Implied Risk Measures and Macroeconomic Forecasts