Multi-step non- and semi-parametric predictive regressions for short and long horizon stock return prediction

Arbeitspapier

In this paper, we propose three new predictive models: the multi-step nonparametric predictive regression model and the multi-step additive predictive regression model, in which the predictive variables are locally stationary time series; and the multi-step time-varying coefficient predictive regression model, in which the predictive variables are stochastically nonstationary. We also establish the estimation theory and asymptotic properties for these models in the short horizon and long horizon case. To evaluate the effectiveness of these models, we investigate their capability of stock return prediction. The empirical results show that all of these models can substantially outperform the traditional linear predictive regression model in terms of both in-sample and out-of-sample performance. In addition, we find that these models can always beat the historical mean model in terms of in-sample fitting, and also for some cases in terms of the out-of-sample forecasting.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP03/18

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Financial Forecasting and Simulation
• Subject: Kernel estimator ; locally stationary process ; series estimator ; stock return prediction

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Cheng, Tingting ; Gao, Jiti ; Linton, Oliver
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2018

Further information

• DOI: doi:10.1920/wp.cem.2018.0318
• Handle: http://hdl.handle.net/10419/189687
• Last update: 10.03.2025, 11:43 AM CET