Abstract
The purpose of the paper is to explore the relative biases in the estimation of the Full BEKK model as compared with the Diagonal BEKK model, which is used as a theoretical and empirical benchmark. Chang and McAleer et al., 2017 show that univariate GARCH is not a special case of multivariate GARCH, specifically, the Full BEKK model, and demonstrate that Full BEKK, which, in practice, is estimated almost exclusively, has no underlying stochastic process, regularity conditions, or asymptotic properties. Diagonal BEKK (DBEKK) does not suffer from these limitations, and hence provides a suitable benchmark. We use simulated financial returns series to contrast estimates of the conditional variances and covariances from DBEKK and BEKK. The results of non-parametric tests suggest evidence of considerable bias in the Full BEKK estimates. The results of quantile regression analysis show there is a systematic relationship between the two sets of estimates as we move across the quantiles. Estimates of conditional variances from Full BEKK, relative to those from DBEKK are relatively lower in the left tail and higher in the right tail. The BEKK model is a commonly applied multivariate volatility model frequently used in modelling and forecasting volatilities in financial applications. Our results suggest that it is subject to considerable bias and this should be considered by potential users.
Highlights
Conditional volatility models are the most widely estimated univariate and multivariate models of time-varying volatility applied to financial data, in the high frequency data domains that are measured in days, hours and minutes
In the empirical analysis which follows, we report the results of fitting the multivariate GARCH models, Diagonal BEKK (DBEKK) and FullBEKK, to the simulated financial return series
The estimates from DBEKK are used as the benchmark, given that it has established statistical regularity conditions
Summary
Conditional volatility models are the most widely estimated univariate and multivariate models of time-varying volatility (or dynamic risk) applied to financial data, in the high frequency data domains that are measured in days, hours and minutes. The Full BEKK model, (see Baba and [7], and Engle and Kroner [8]) is subject to uncertainty regarding the existence of its underlying stochastic processes, regularity conditions, and asymptotic properties. These properties have either not yet been established, or are assumed rather than derived, yet these conditions and properties are essential for the existence of the likelihood function, and valid statistical analysis of the empirical estimates. This means that when the model is estimated its coefficients and statistical properties are subject to uncertainty and may not be valid
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