Adaptive signal processing of asset price dynamics with predictability analysis

作     者：Mamon, Rogemar S. Erlwein, Christina Gopaluni, R. Bhushan 

作者机构：Univ Western Ontario Western Sci Ctr Dept Stat & Actuarial Sci London ON N6A 5B7 Canada Brunel Univ Sch Informat Syst Comp & Math CARISMA Uxbridge Middx England Univ British Columbia Dept Chem & Biol Engn Vancouver BC V5Z 1M9 Canada 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2008年第178卷第1期

页      面：203-219页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：British Academy, (OCG-41559) Marie Curie 

主　　题：Hidden Markov model optimal parameter estimation regime-switching model gold price 

摘      要：In this paper we illustrate the optimal filtering of log returns of commodity prices in which both the mean and volatility are modulated by a hidden Markov chain with finite state space. The optimal estimate of the Markov chain and the parameters of the price model are given in terms of discrete-time recursive filters. We provide an application on a set of high frequency gold price data for the period 1973-2006 and analyse the h-step ahead price predictions against the Diebold-Kilian metric. Within the modelling framework where the mean and volatility are switching regimes, our findings suggest that a two-state hidden Markov model is sufficient to describe the dynamics of the data and the gold price is predictable up to a certain extent in the short term but almost impossible to predict in the long term. The proposed model is also bench-marked with ARCH and GARCH models with respect to price predictability and forecasting errors. (C) 2007 Published by Elsevier Inc.