A Trend-Switching Financial Time Series Model with Level-Duration Dependence. (25th December 2012)

• Record Type: Journal Article
• Authors: Wang, Qingsheng; Ling, Aifan; Huang, Tao; Jiang, Yong; Chen, Min
• Other Names: Hong Wei-Chiang Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2012(2012)
• Publication Date: 2012-12-25
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2012/345093
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : The financial time series model that can capture the nonlinearity and asymmetry of stochastic process has been paid close attention for a long time. However, it is still open to completely overcome the difficult problem that motivates our researches in this paper. An asymmetric and nonlinear model with the change of local trend depending on local high-low turning point process is first proposed in this paper. As the point process can be decomposed into the two different processes, a high-low level process and an up-down duration process, we then establish the so-called trend-switching model which depends on both level and duration (Trend-LD). The proposed model can predict efficiently the direction and magnitude of the local trend of a time series by incorporating the local high-low turning point information. The numerical results on six indices in world stock markets show that the proposed Trend-LD model is suitable for fitting the market data and able to outperform the traditional random walk model.