On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00478479" target="_blank" >RIV/67985556:_____/17:00478479 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11230/17:10326547

Výsledek na webu

<a href="http://dx.doi.org/10.1002/for.2423" target="_blank" >http://dx.doi.org/10.1002/for.2423</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/for.2423" target="_blank" >10.1002/for.2423</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model

Popis výsledku v původním jazyce

Recent multivariate extensions of the popular heterogeneous autoregressive model (HAR) for realized volatility leave substantial information unmodelled in residuals. We propose to employ a system of seemingly unrelated regressions to model and forecast a realized covariance matrix to capture this information. We find that the newly proposed gener- alized heterogeneous autoregressive (GHAR) model outperforms competing approaches in terms of economic gains, providing better mean–variance trade-off, while, in terms of statistical precision, GHAR is not substantially dominated by any other model. Our results provide a comprehensive comparison of the performance when realized covariance, subsampled realized covariance and multivariate realized kernel estimators are used. We study the contribution of the estimators across different sampling frequencies, and show that the multivariate realized kernel and subsampled real- ized covariance estimators deliver further gains compared to realized covariance estimated on a 5-minute frequency. In order to show economic and statistical gains, a portfolio of various sizes is used.

Název v anglickém jazyce

On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model

Popis výsledku anglicky

Recent multivariate extensions of the popular heterogeneous autoregressive model (HAR) for realized volatility leave substantial information unmodelled in residuals. We propose to employ a system of seemingly unrelated regressions to model and forecast a realized covariance matrix to capture this information. We find that the newly proposed gener- alized heterogeneous autoregressive (GHAR) model outperforms competing approaches in terms of economic gains, providing better mean–variance trade-off, while, in terms of statistical precision, GHAR is not substantially dominated by any other model. Our results provide a comprehensive comparison of the performance when realized covariance, subsampled realized covariance and multivariate realized kernel estimators are used. We study the contribution of the estimators across different sampling frequencies, and show that the multivariate realized kernel and subsampled real- ized covariance estimators deliver further gains compared to realized covariance estimated on a 5-minute frequency. In order to show economic and statistical gains, a portfolio of various sizes is used.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50201 - Economic Theory

Projekt

<a href="/cs/project/GA13-32263S" target="_blank" >GA13-32263S: Vícerozměrná spektrální analýza finančních trhů</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

Journal of Forecasting

ISSN

0277-6693

e-ISSN

—

Svazek periodika

36

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

26

Strana od-do

181-206

Kód UT WoS článku

000394909900006

EID výsledku v databázi Scopus

2-s2.0-84966539553