About: Estimation of time-varying kernel densities and chronology of the impact of COVID-19 on financial markets

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About: Estimation of time-varying kernel densities and chronology of the impact of COVID-19 on financial markets Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Estimation of time-varying kernel densities and chronology of the impact of COVID-19 on financial markets
Creator	Garcin, Matthieu Klein, Jules Laaribi, Sana
Source	ArXiv
abstract	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.
has issue date	2020-07-17(xsd:dateTime)
has license	arxiv
sha1sum (hex)	f11ea8e07aebec1874562bd2408f738af103d74f
resource representing a document's title	Estimation of time-varying kernel densities and chronology of the impact of COVID-19 on financial markets
resource representing a document's body	covid:f11ea8e07aebec1874562bd2408f738af103d74f#body_text
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