Cookies?
Library Header Image
LSE Research Online LSE Library Services

Business cycle asymmetries in stock returns: evidence from higher order moments and conditional densities

Perez-Quiros, Gabriel and Timmermann, Allan (2000) Business cycle asymmetries in stock returns: evidence from higher order moments and conditional densities. Financial Markets Group Discussion Papers (360). Financial Markets Group, The London School of Economics and Political Science, London, UK.

[img] Text (dp360) - Published Version
Download (785kB)

Abstract

Markov switching models with time-varying means, variances and mixing weights are applied to characterize business cycle variation in the probability distribution and higher order moments of stock returns. This allows us to provide a comprehensive characterization of risk that goes well beyond the mean and variance of returns. Several mixture models with different specifications of the state transition are compared and we propose a new mixture of Gaussian and student-t distributions that captures outliers in returns. The models produce very similar expected returns and volatilities but imply very different time series for conditional skewness, kurtosis and predictive density. Consistent with economic theory, the gains in predictive accuracy from considering two-state mixture models rather than a single-state specification are higher for small firms than for large firms.

Item Type: Monograph (Discussion Paper)
Official URL: https://www.fmg.ac.uk/
Additional Information: © 2000 The Authors
Divisions: Financial Markets Group
Subjects: H Social Sciences > HC Economic History and Conditions
H Social Sciences > HG Finance
JEL classification: C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C22 - Time-Series Models
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation and Selection
Date Deposited: 04 Jul 2023 10:27
Last Modified: 16 Sep 2023 00:02
URI: http://eprints.lse.ac.uk/id/eprint/119098

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics