Using monthly returns to model conditional heteroscedasticity

Nathan L. Joseph*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

This empirical study examines the extent of non-linearity in a multivariate model of monthly financial series. To capture the conditional heteroscedasticity in the series, both the GARCH(1,1) and GARCH(1,1)-in-mean models are employed. The conditional errors are assumed to follow the normal and Student-t distributions. The non-linearity in the residuals of a standard OLS regression are also assessed. It is found that the OLS residuals as well as conditional errors of the GARCH models exhibit strong non-linearity. Under the Student density, the extent of non-linearity in the GARCH conditional errors was generally similar to those of the standard OLS. The GARCH-in-mean regression generated the worse out-of-sample forecasts.

Original languageEnglish
Pages (from-to)791-801
Number of pages11
JournalApplied Economics
Volume35
Issue number7
DOIs
Publication statusPublished - 10 May 2003

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Generalized autoregressive conditional heteroscedasticity
Conditional heteroscedasticity
Nonlinearity
Empirical study
Out-of-sample forecasting
Multivariate models
Student-t distribution
GARCH model

Keywords

  • non-linearity
  • multivariate model
  • monthly financial series

Cite this

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Using monthly returns to model conditional heteroscedasticity. / Joseph, Nathan L.

In: Applied Economics, Vol. 35, No. 7, 10.05.2003, p. 791-801.

Research output: Contribution to journalArticle

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AB - This empirical study examines the extent of non-linearity in a multivariate model of monthly financial series. To capture the conditional heteroscedasticity in the series, both the GARCH(1,1) and GARCH(1,1)-in-mean models are employed. The conditional errors are assumed to follow the normal and Student-t distributions. The non-linearity in the residuals of a standard OLS regression are also assessed. It is found that the OLS residuals as well as conditional errors of the GARCH models exhibit strong non-linearity. Under the Student density, the extent of non-linearity in the GARCH conditional errors was generally similar to those of the standard OLS. The GARCH-in-mean regression generated the worse out-of-sample forecasts.

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