On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model
Result description
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.
Keywords
Multivariate volatilityrealized covarianceportfolio optimisation
The result's identifiers
Result code in IS VaVaI
Alternative codes found
RIV/00216208:11230/17:10326547
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model
Original language description
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.
Czech name
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Czech description
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Classification
Type
Jimp - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50201 - Economic Theory
Result continuities
Project
GA13-32263S: Multivariate spectral analysis of financial markets
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Forecasting
ISSN
0277-6693
e-ISSN
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Volume of the periodical
36
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
26
Pages from-to
181-206
UT code for WoS article
000394909900006
EID of the result in the Scopus database
2-s2.0-84966539553
Basic information
Result type
Jimp - Article in a specialist periodical, which is included in the Web of Science database
OECD FORD
Economic Theory
Year of implementation
2017