Non-linear predictability in stock and bond returns: when and where is it exploitable?

Non-linear predictability in stock and bond returns: when and where is it exploitable?
Document type
Working Paper
Author(s)
Guidolin, Massimo; Hyde, Stuart; McMillan, David
Publisher
Manchester Business School
Date of publication
1 April 2008
Series
University of Manchester Business School Working Papers. No. 546
Subject(s)
Trends: economic, social and technology trends affecting business
Collection
Business and management
Material type
Reports

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The authors of this paper systematically examine the comparative predictive performance of a number of alternative linear and non-linear models for stock and bond returns in the G7 countries. Besides Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STAR) regime switching (predictive) regression models, we also estimate univariate models in which conditional heteroskedasticity is captured through GARCH, TARCH and EGARCH models and ARCH-in mean effects appear in the conditional mean. Although the authors fail to find a consistent winner/out-performer across all countries and asset markets, it turns out that capturing non-linear effects is of extreme importance to improve forecasting performance. US and UK asset return data are 'special' in the sense that good predictive performance seems to loudly ask for models that capture non-linear dynamics, especially of the Markov switching type.

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