Asset allocation under threshold autoregressive models
Article first published online: 29 APR 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Applied Stochastic Models in Business and Industry
Volume 28, Issue 1, pages 60–72, January/February 2012
How to Cite
Song, N., Siu, T. K., Ching, W.-K., Tong, H. and Yang, H. (2012), Asset allocation under threshold autoregressive models. Appl. Stochastic Models Bus. Ind., 28: 60–72. doi: 10.1002/asmb.897
Chair Professor of Statistics.
Professor of Actuarial Science.
- Issue published online: 10 FEB 2012
- Article first published online: 29 APR 2011
- Manuscript Revised: 27 FEB 2011
- Manuscript Accepted: 27 FEB 2011
- Manuscript Received: 7 MAY 2010
- asset allocation;
- SETAR model;
- STAR model;
- conditional heteroscedasticity;
- dynamical programming;
- stochastic dynamical system
We discuss the asset allocation problem in the important class of parametric non-linear time series models called the threshold autoregressive model in (J. Roy. Statist. Soc. Ser. A 1977; 140:34–35; Patten Recognition and Signal Processing. Sijthoff and Noordhoff: Netherlands, 1978; and J. Roy. Statist. Soc. Ser. B 1980; 42:245–292). We consider two specific forms, one self-exciting (i.e. the SETAR model) and the other smooth (i.e. the STAR) model developed by Chan and Tong (J. Time Ser. Anal. 1986; 7:179–190). The problem of maximizing the expected utility of wealth over a planning horizon is considered using a discrete-time dynamic programming approach. This optimization approach is flexible enough to deal with the optimal asset allocation problem under a general stochastic dynamical system, which includes the SETAR model and the STAR model as particular cases. Numerical studies are conducted to demonstrate the practical implementation of the proposed model. We also investigate the impacts of non-linearity in the SETAR and STAR models on the optimal portfolio strategies. Copyright © 2011 John Wiley & Sons, Ltd.