A non-parametric approach of heteroskedasticity robust estimation of Vector-Autoregressive (VAR) models

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Finance and Investment Analysis ; ISSN: 2241-0996 ; Volume: 1 ; Year: 2012 ; Issue: 1 ; Pages: 55-67 ; International Scientific Press

Classification and Topics

• Classification: Wirtschaft ; Estimation: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Construction and Estimation ; General Financial Markets: General (includes Measurement and Data)
• Subject: VAR models ; pairs bootstrapping ; heteroskedasticity robust estimation ; non-parametric approach ; stock market data

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Grobys, Klaus
• Event: Veröffentlichung
• (when): 2012

Further information

• Handle: http://hdl.handle.net/10419/58007
• Last update: 10.03.2025, 11:44 AM CET