Adaptive signal processing of asset price dynamics with predictability analysis

Authors:
Rogemar S. Mamon;Christina Erlwein;R. Bhushan Gopaluni
Affiliations:
Department of Statistical and Actuarial Sciences, University of Western Ontario, London, ON, Canada and CARISMA, School of Information Systems, Computing and Mathematics, Brunel University, West L ...;CARISMA, School of Information Systems, Computing and Mathematics, Brunel University, West London, United Kingdom;Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, BC, Canada
Venue:
Information Sciences: an International Journal
Year:
2008

Citing 6
Cited 3

Exact adaptive filters for Markov chains observed in Gaussian noise

Automatica (Journal of IFAC)
Stock market volatility and regime shifts in returns

Information Sciences: an International Journal
Short-Term Variations and Long-Term Dynamics in Commodity Prices

Management Science
Gold Price, Neural Networks and Genetic Algorithm

Computational Economics
A hybrid genetic-neural architecture for stock indexes forecasting

Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
A descriptive method to evaluate the number of regimes in a switching autoregressive model

Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05

A Fuzzy Asymmetric GARCH model applied to stock markets

Information Sciences: an International Journal
Applying a combined fuzzy systems and GARCH model to adaptively forecast stock market volatility

Applied Soft Computing
Adaptive Fuzzy-GARCH model applied to forecasting the volatility of stock markets using particle swarm optimization

Information Sciences: an International Journal

Quantified Score

Hi-index	0.07

Visualization

Abstract

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 benchmarked with ARCH and GARCH models with respect to price predictability and forecasting errors.