2020/07/17 by Matthieu Garcin, Garcin, Matthieu, Jules Klein +3
Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Financial Risk and Volatility Modeling #Market Dynamics and Volatility #Methodology (stat.ME) #Statistical Finance (q-fin.ST)
paper · pdf · doi:10.48550/arxiv.2007.09043
openalex publication_date 2020/07/17 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
The time-varying kernel density estimation relies on two free parameters: the bandwidth and the discount factor. We propose to select these parameters so as to minimize a criterion consistent with the traditional requirements of the validation of a probability density forecast. These requirements are both the uniformity and the independence of the so-called probability integral transforms, which are the forecast time-varying cumulated distributions applied to the observations. We thus build a new numerical criterion incorporating both the uniformity and independence properties by the mean of an adapted Kolmogorov-Smirnov statistic. We apply this method to financial markets during the COVID-19 crisis. We determine the time-varying density of daily price returns of several stock indices and, using various divergence statistics, we are able to describe the chronology of the crisis as well as regional disparities. For instance, we observe a more limited impact of COVID-19 on financial markets in China, a strong impact in the US, and a slow recovery in Europe.