Please use this identifier to cite or link to this item: http://arl.liuc.it/dspace/handle/2468/6716
Title: Studying the influence of economic sectors on stocks through a partial dependence analysis
Authors: De Luca, Giovanni
Nai Ruscone, Marta
Rivieccio, Giorgia
Issue Date: 2019
Publisher: ECOSTA econometrics and statistics
Bibliographic citation: De Luca Giovanni, Nai Ruscone Marta, Rivieccio Giorgia (2019), Studying the influence of economic sectors on stocks through a partial dependence analysis. In: CFE-CMStatistics 2019: 13th International conference on computational and financial econometrics (CFE 2019) and 12th International conference of the ERCIM (European Research Consortium for Informatics and Mathematics) working group on computational and methodological statistics (CMStatistics 2019), Senate House & Birkbeck University of London, UK, 14-16 December 2019: programme and abstracts. S.l.: ECOSTA econometrics and statistics, p. 37, article number E1186. ISBN 978-9963-2227-8-0.
Abstract: Understanding the complex nature of financial markets is still a great challenge. In particular, a challenge is to understand the underlying mechanisms of influence that operate in financial markets. A method is discussed to estimate how a company is influenced by an economic sectors after identifying the partial dependence structure of each asset with the assets of the sector excluding the influence of the market. An effective way used to capture the dependence structure of a multivariate time-series is the copula function. However the variety of copula functions and ease of estimation dramatically reduce when the dimension of the multivariate time-series increases. On the other hand, bivariate copula functions are popular and effective in capturing the dependence structure of a 2-dimensional continuous random vector. A simple strategy to continue to use bivariate copula functions for modelling multivariate time-series is the recourse to the vine copulas. The procedure provides a picture of the relative influence of the economic sectors on each stock in terms of Kendall’s t and tail dependence.
URI: http://arl.liuc.it/dspace/handle/2468/6716
Journal/Book: CFE-CMStatistics 2019: 13th International conference on computational and financial econometrics (CFE 2019) and 12th International conference of the ERCIM (European Research Consortium for Informatics and Mathematics) working group on computational and methodological statistics (CMStatistics 2019), Senate House & Birkbeck University of London, UK, 14-16 December 2019: programme and abstracts
ISBN: 978-9963-2227-8-0
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