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Title:A non-parametric approach of heteroskedasticity robust estimation of Vector-Autoregressive (VAR) models PDF Logo
Authors:Grobys, Klaus
Issue Date:2012
Citation:[Journal:] Journal of Finance and Investment Analysis [ISSN:] 2241-0996 [Publisher:] International Scientific Press [Volume:] 1 [Year:] 2012 [Issue:] 1 [Pages:] 55-67
Abstract:This contribution studies the application of heteroskedasticity robust estimation of Vector-Autoregressive (VAR) models. VAR models have become one of the most applied models for the analysis of multivariate time series. Econometric standard software usually provides parameter estimators that are not robust against unknown forms of heteroskedasticity. Different bootstrap methodologies are available which are able to generate heteroskedasticity robust parameter estimates. However, common literature is mostly focused on univariate time series models. This study applies a natural extension of the non-parametric pairs bootstrap methodology to different VAR models, taking into account empirical stock market data of the FTSE 100, DAX 30 and S&P 500. A comparison shows that the t-values of the bootstrap models' parameters are considerably lower than the ordinary ones and that the determinants of the covariance matrices are clearly smaller.
Subjects:VAR models
pairs bootstrapping
heteroskedasticity robust estimation
non-parametric approach
stock market data
Document Type:Article
Appears in Collections:Journal of Finance and Investment Analysis

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