Semiparametric nonlinear quantile regression model for financial returns

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00472346" target="_blank" >RIV/67985556:_____/17:00472346 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1515/snde-2016-0044" target="_blank" >http://dx.doi.org/10.1515/snde-2016-0044</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1515/snde-2016-0044" target="_blank" >10.1515/snde-2016-0044</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Semiparametric nonlinear quantile regression model for financial returns

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Semiparametric nonlinear quantile regression model for financial returns

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

<a href="/cs/project/GBP402%2F12%2FG097" target="_blank" >GBP402/12/G097: DYME-Dynamické modely v ekonomii</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Studies in Nonlinear Dynamics and Econometrics

ISSN

1081-1826

e-ISSN

—

Svazek periodika

21

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

81-97

Kód UT WoS článku

000394467800006

EID výsledku v databázi Scopus

2-s2.0-85013269709