Using monthly returns to model conditional heteroscedasticity

Joseph, Nathan L. (2003). Using monthly returns to model conditional heteroscedasticity. Applied Economics, 35 (7), pp. 791-801.

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.

Publication DOI: https://doi.org/10.1080/0003684021000088536
Divisions: Aston Business School > Accounting
Aston Business School > Accounting Research Group
Uncontrolled Keywords: non-linearity,multivariate model,monthly financial series,Economics and Econometrics
Full Text Link: http://www.info ... sue=7&spage=791
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2003-05-10
Authors: Joseph, Nathan L. ( 0000-0002-2182-0847)

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