A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data.

In this paper, we explore mutual information based stock networks to build regular vine copula structure on high frequency log returns of stocks and use it for the estimation of Value at Risk (VaR) of a portfolio of stocks. Our model is a data driven model that learns from a high frequency time seri...

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Autores principales:	Charu Sharma, Niteesh Sahni
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6cf26294a5e149308a1dfffef7fa3c8c
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spelling	oai:doaj.org-article:6cf26294a5e149308a1dfffef7fa3c8c2021-12-02T20:10:25ZA mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data.1932-620310.1371/journal.pone.0253307https://doaj.org/article/6cf26294a5e149308a1dfffef7fa3c8c2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253307https://doaj.org/toc/1932-6203In this paper, we explore mutual information based stock networks to build regular vine copula structure on high frequency log returns of stocks and use it for the estimation of Value at Risk (VaR) of a portfolio of stocks. Our model is a data driven model that learns from a high frequency time series data of log returns of top 50 stocks listed on the National Stock Exchange (NSE) in India for the year 2014. The Ljung-Box test revealed the presence of Autocorrelation as well as Heteroscedasticity in the underlying time series data. Analysing the goodness of fit of a number of variants of the GARCH model on each working day of the year 2014, that is, 229 days in all, it was observed that ARMA(1,1)-EGARCH(1,1) demonstrated the best fit. The joint probability distribution of the portfolio is computed by constructed an R-Vine copula structure on the data with the mutual information guided minimum spanning tree as the key building block. The joint PDF is then fed into the Monte-Carlo simulation procedure to compute the VaR. If we replace the mutual information by the Kendall's Tau in the construction of the R-Vine copula structure, the resulting VaR estimations were found to be inferior suggesting the presence of non-linear relationships among stock returns.Charu SharmaNiteesh SahniPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253307 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Charu Sharma Niteesh Sahni A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data.
description	In this paper, we explore mutual information based stock networks to build regular vine copula structure on high frequency log returns of stocks and use it for the estimation of Value at Risk (VaR) of a portfolio of stocks. Our model is a data driven model that learns from a high frequency time series data of log returns of top 50 stocks listed on the National Stock Exchange (NSE) in India for the year 2014. The Ljung-Box test revealed the presence of Autocorrelation as well as Heteroscedasticity in the underlying time series data. Analysing the goodness of fit of a number of variants of the GARCH model on each working day of the year 2014, that is, 229 days in all, it was observed that ARMA(1,1)-EGARCH(1,1) demonstrated the best fit. The joint probability distribution of the portfolio is computed by constructed an R-Vine copula structure on the data with the mutual information guided minimum spanning tree as the key building block. The joint PDF is then fed into the Monte-Carlo simulation procedure to compute the VaR. If we replace the mutual information by the Kendall's Tau in the construction of the R-Vine copula structure, the resulting VaR estimations were found to be inferior suggesting the presence of non-linear relationships among stock returns.
format	article
author	Charu Sharma Niteesh Sahni
author_facet	Charu Sharma Niteesh Sahni
author_sort	Charu Sharma
title	A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data.
title_short	A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data.
title_full	A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data.
title_fullStr	A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data.
title_full_unstemmed	A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data.
title_sort	mutual information based r-vine copula strategy to estimate var in high frequency stock market data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/6cf26294a5e149308a1dfffef7fa3c8c
work_keys_str_mv	AT charusharma amutualinformationbasedrvinecopulastrategytoestimatevarinhighfrequencystockmarketdata AT niteeshsahni amutualinformationbasedrvinecopulastrategytoestimatevarinhighfrequencystockmarketdata AT charusharma mutualinformationbasedrvinecopulastrategytoestimatevarinhighfrequencystockmarketdata AT niteeshsahni mutualinformationbasedrvinecopulastrategytoestimatevarinhighfrequencystockmarketdata
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A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data.

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