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Semiparametric nonlinear quantile regression model for financial returns
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SYSNO ASEP 0472346 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Semiparametric nonlinear quantile regression model for financial returns Author(s) Avdulaj, Krenar (UTIA-B)
Baruník, Jozef (UTIA-B) RID, ORCIDNumber of authors 2 Source Title Studies in Nonlinear Dynamics and Econometrics - ISSN 1081-1826
Roč. 21, č. 1 (2017), s. 81-97Number of pages 17 s. Publication form Print - P Language eng - English Country US - United States Keywords copula quantile regression ; realized volatility ; value-at-risk Subject RIV AH - Economics OECD category Applied Economics, Econometrics R&D Projects GBP402/12/G097 GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000394467800006 EID SCOPUS 85013269709 DOI 10.1515/snde-2016-0044 Annotation 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 nonlineari- ties 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 dif- ferent levels of dependence, which are characteristic for each industry. The backtesting results of estimated VaR favour our approach. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2018
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