The present study examines the development offinancial and macroeconomic variables during and in relation to the spread of the novel coronavirus (Covid-19) in European countries and its containment measures from February to October 2020.The research is divided into two analyses, each driven by a different quantitative approach: the first analysis consists of three models built on the relationship between four relevant stock markets’ daily performance (London, Milan, Frankfurt and Paris) and two regressors derived from the stock exchange’s country: the spread of the novel coronavirus in European countries relative to their population, the hardness of government containment measures. The three kinds of models employed describe the relationship are respectively linear(OLS), autoregressive moving-average with exogenous regressors (ARIMAX), and distributed lag. The resulting models for each stock exchange are later tested for forecasting accuracy through RMSE and MAPE error estimators in order to measure their precision in describing the actual underlying relationship. Only one linear model (Milan stock exchange) is reported by the in-sample statistics to be significant, and in the majority of cases the most accurate forecasts are produced by the distributed lag model. The second analysis is an exploratory research of three macroeconomic variables in 30 European countries’ (Industrial Production; Unemployment Rate; Consumer Price Index), which is conducted through partitioning-based k-means clustering, once per month from March to August included. Countries are assigned membership to clusters on the base of the monthly score of their three macroeconomic variables, and the result is framed and plotted for each month so as to observe changes in the clusters’ composition and the relative adjustment of their distance. A continuous comparison with the concerned and most relevant countries’ lockdowns and Covid-19 spread and death toll reveals a stronger recession and an consistently stronger recovery for the hardest-enforcing nations, while countries which adopted different methods and lighter measures respond diversely to the development of the pandemic: some nations may still incur in heavy economic difficulties due to the population’s sensitivity and possibly individual precaution in response to the rising death toll, while others (notably Sweden) appear not to be as heavily affected by the high spread and death rate.
All the calculations in the research have been executed through R-language in R-studio environment. The full code is included in the project’s appendix.
|Educations||MSc in Supply Chain Management , (Graduate Programme) Final Thesis|
|Number of pages||91|
|Supervisors||Lisbeth la Cour|