File(s) under permanent embargo

Structural instability and predictability

journal contribution
posted on 01.11.2019, 00:00 authored by N Devpura, Paresh Narayan, Susan SharmaSusan Sharma
We propose a structural break predictive regression model that accounts for predictor persistency, endogeneity, heteroscedasticity, and a structural break. Monte Carlo (MC) simulations indicate that this test performs satisfactorily compared to competitor estimators. We employ a popular U.S. data set (the period January 1927 to December 2016) that includes stock market returns and multiple predictors. We show, consistent with the MC results, evidence of a structural break. Our analysis reveals that a structural break–based predictive regression model fits the data reasonably well in predicting stock price returns.

History

Journal

Journal of international financial markets, institutions and money

Volume

63

Article number

101145

Pagination

1 - 13

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

1042-4431

Language

eng

Publication classification

C1 Refereed article in a scholarly journal; C Journal article