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Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce

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IMPACT FACTOR 2016: 0.649

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1558-3708
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Volume 21, Issue 1 (Feb 2017)

Issues

A semiparametric nonlinear quantile regression model for financial returns

Krenar Avdulaj
  • Corresponding author
  • Institute of Economic Studies, Charles University in Prague, Opletalova 26, 110 00 Prague, Czech Republic
  • Institute of Information Theory and Automation, The Czech Academy of Sciences, Pod Vodarenskou Vezi 4, 182 00 Prague, Czech Republic
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Jozef Barunik
  • Institute of Economic Studies, Charles University in Prague, Opletalova 26, 110 00 Prague, Czech Republic
  • Institute of Information Theory and Automation, The Czech Academy of Sciences, Pod Vodarenskou Vezi 4, 182 00 Prague, Czech Republic
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-06-03 | DOI: https://doi.org/10.1515/snde-2016-0044

Abstract

Accurately measuring and forecasting value-at-risk (VaR) remains a challenging task at the heart of financial economic theory. Recently, quantile regression models have been used successfully to capture the conditional quantiles of returns and to forecast VaR accurately. In this paper, we further explore nonlinearities in data and propose to couple realized measures with the nonlinear quantile regression framework to explain and forecast the conditional quantiles of financial returns. The nonlinear quantile regression models are implied by the copula specifications and allow us to capture possible nonlinearities, tail dependence, and asymmetries in the conditional quantiles of financial returns. Using high frequency data that covers most liquid US stocks in seven sectors, we provide ample evidence of asymmetric conditional dependence with different levels of dependence, which are characteristic for each industry. The backtesting results of estimated VaR favour our approach.

This article offers supplementary material which is provided at the end of the article.

Keywords: copula quantile regression; realized volatility; value-at-risk

JEL Classification: C14; C32; C58; F37; G32

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About the article

Published Online: 2016-06-03

Published in Print: 2017-02-01


Citation Information: Studies in Nonlinear Dynamics & Econometrics, ISSN (Online) 1558-3708, ISSN (Print) 1081-1826, DOI: https://doi.org/10.1515/snde-2016-0044.

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