Premium
Multivariate high‐frequency‐based volatility (HEAVY) models
Author(s) -
Noureldin Diaa,
Shephard Neil,
Sheppard Kevin
Publication year - 2011
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1260
Subject(s) - multivariate statistics , econometrics , autoregressive conditional heteroskedasticity , autoregressive model , volatility (finance) , inference , heteroscedasticity , covariance , computer science , economics , statistics , mathematics , artificial intelligence
SUMMARY This paper introduces a new class of multivariate volatility models that utilizes high‐frequency data. We discuss the models' dynamics and highlight their differences from multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models. We also discuss their covariance targeting specification and provide closed‐form formulas for multi‐step forecasts. Estimation and inference strategies are outlined. Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model out‐of‐sample, with the gains being particularly significant at short forecast horizons. Forecast gains are obtained for both forecast variances and correlations. Copyright © 2011 John Wiley & Sons, Ltd.