Search

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jedc.2006.11.008 Byline: Xue-Zhong He (a), Youwei Li (b) Abstract: Long-range dependence in volatility is one of the most prominent examples in financial market research involving universal power laws. Its characterization has recently spurred attempts to provide some explanations of the underlying mechanism. This paper contributes to this recent line of research by analyzing a simple market fraction asset pricing model with two types of traders - fundamentalists who trade on the price deviation from estimated fundamental value and trend followers whose conditional mean and variance of the trend are updated through a geometric learning process. Our analysis shows that agent heterogeneity, risk-adjusted trend chasing through the geometric learning process, and the interplay of noisy fundamental and demand processes and the underlying deterministic dynamics can be the source of power-law distributed fluctuations. In particular, the noisy demand plays an important role in the generation of insignificant autocorrelations (ACs) on returns, while the significant decaying AC patterns of the absolute returns and squared returns are more influenced by the noisy fundamental process. A statistical analysis based on Monte Carlo simulations is conducted to characterize the decay rate. Realistic estimates of the power-law decay indices and the (FI)GARCH parameters are presented. Author Affiliation: (a) School of Finance and Economics, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007, Australia (b) School of Management and Economics, Queen's University Belfast, 25 University Square, Belfast, BT7 1NN, UK Article History: Received 10 February 2005; Accepted 20 November 2006

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jebo.2005.08.005 Byline: Carl Chiarella (a), Roberto Dieci (b), Xue-Zhong He (a) Keywords: Heterogeneous beliefs; Asset pricing; Portfolio choice; Bifurcation analysis; Co-movements in stock prices Abstract: This paper develops a dynamic model of a financial market where heterogeneous agents invest among multiple risky assets and a risk-free asset, under a market maker scenario. Particular attention is paid to the case of two risky assets and two agent types, fundamentalists and trend chasers, whose beliefs on both first and second moments of the conditional distribution of returns are based on past observations. Conditions for the stability of the 'fundamental' equilibrium are established and the effect of the correlation between the risky assets is examined. It turns out that investors' anticipated correlation and dynamic portfolio diversification do not always have a stabilizing role, but rather may act as a source of complexity in the financial market. Author Affiliation: (a) School of Finance and Economics, University of Technology, Sydney, Australia (b) Dipartimento di Matematica per le Scienze Economiche e Sociali, Universita degli Studi di Bologna, Italy Article History: Received 30 October 2004; Revised 11 July 2005; Accepted 31 August 2005

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jebo.2004.07.019 Byline: Carl Chiarella (a), Xue-Zhong He (a), Hing Hung (a), Peiyuan Zhu (b) Keywords: Cobweb model; Heterogeneity; Bounded rationality; Geometric decay learning dynamics; Bifurcations Abstract: This paper considers the traditional cobweb model with heterogenous risk averse producers whose supply functions involve their estimates of the conditional mean and variance of the future price. The producers seek to learn these quantities by applying geometric decay processes (GDP) to past prices. The heterogeneity manifests itself in the lag lengths and memory parameters applied to past prices as well as in risk aversion coefficients. We find that each dimension of heterogeneity changes/enriches the cobweb dynamics with respect to the case of homogeneous producers. Author Affiliation: (a) School of Finance and Economics, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007, Australia (b) Department of Electrical and Information Engineering, The University of Sydney, NSW 2006, Australia Article History: Received 20 October 2003; Accepted 21 July 2004

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jedc.2005.08.014 Byline: Carl Chiarella (a), Xue-Zhong He (a), Cars Hommes (b) Abstract: The use of various moving average (MA) rules remains popular with financial market practitioners. These rules have recently become the focus of a number empirical studies, but there have been very few studies of financial market models where some agents employ technical trading rules of the type used in practice. In this paper, we propose a dynamic financial market model in which demand for traded assets has both a fundamentalist and a chartist component. The chartist demand is governed by the difference between current price and a (long-run) MA. Both types of traders are boundedly rational in the sense that, based on a fitness measure such as realized capital gains, traders switch from a strategy with low fitness to the one with high fitness. We characterize the stability and bifurcation properties of the underlying deterministic model via the reaction coefficient of the fundamentalists, the extrapolation rate of the chartists and the lag length used for the MA. By increasing the intensity of choice to switching strategies, we then examine various rational routes to randomness for different MA rules. The price dynamics of the MA rule are also examined and one of our main findings is that an increase of the window length of the MA rule can destabilize an otherwise stable system, leading to more complicated, even chaotic behaviour. The analysis of the corresponding stochastic model is able to explain various market price phenomena, including temporary bubbles, sudden market crashes, price resistance and price switching between different levels. Author Affiliation: (a) School of Finance and Economics, University of Technology, P.O. Box 123 Broadway, Sydney, NSW 2007, Australia (b) CeNDEF, Faculty of Economics and Econometrics, University of Amsterdam, Roetersstraat 11, NL-1018 WB Amsterdam, The Netherlands Article History: Received 28 September 2004; Accepted 12 August 2005 Article Note: (footnote) [star] An earlier version of this paper was prepared while Tony He was visiting CeNDEF, whose hospitality he gratefully acknowledges. This work has been presented at the CEF-conference, July 8-10, 2004.

A model for commodity markets is presented wherein consumers producers and speculators with different levels of risk aversion assess the commodity price fluctuations and develop price stabilization method. The bullish and bearish trends in the commodity markets are also analyzed with the help of the model.

A class of models in which the agent's expectations influence the actual dynamics while the expectations themselves are the outcome of some recursive processes is presented. The dynamics of the resulting expectations and recursive schemes are examined assuming a heterogeneous expectations and that the agents update their expectations by recursive L- and general aL-processes.

By introducing a genetic algorithm with a classifier system as a learning mechanism for uninformed traders into a dynamic limit order market with asymmetric information, this paper examines the effect of the learning on traders’ trading behavior, market liquidity and efficiency. We show that the learning is effective and valuable with respect to information acquisition, forecasting, buy–sell order choice accuracies, and profit opportunity for uninformed traders. It improves information dissemination efficiency and reduces the information advantage of informed traders and hence the value of the private information. In particular, the learning and information become more valuable with higher volatility, less informed traders, and longer information lag. Furthermore, the learning makes not only uninformed but also informed traders submit more limit orders and hence increases market liquidity supply. [ABSTRACT FROM AUTHOR]