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Large‐scale volatility models: theoretical properties of professionals’ practice
Author(s) -
Zaffaroni Paolo
Publication year - 2008
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2007.00571.x
Subject(s) - econometrics , autoregressive conditional heteroskedasticity , volatility (finance) , multivariate statistics , monte carlo method , mathematics , set (abstract data type) , scale (ratio) , simple (philosophy) , computer science , statistics , physics , quantum mechanics , programming language , philosophy , epistemology
. This article examines the way in which GARCH models are estimated and used for forecasting by practitioners in particular using the highly popular Riskmetrics TM approach. Although it permits sizable computational gains and provide a simple way to impose positive semi‐definitiveness of multivariate version of the model, we show that this approach delivers non‐consistent parameter’ estimates. The novel theoretical result is corroborated by a set of Monte Carlo exercises. A set of empirical applications suggest that this could cause, in general, unreliable forecasts of conditional volatilities and correlations.