TAILIEUCHUNG - Time series properties of an arti"cial stock market

The fourth empirical nding is the fact that the di¤erent volatility factors are due to systematic risk, and therefore are priced in the cross section of stock returns. In a recent contribution, Adrian and Rosenberg (2008) show that volatility factor models compare favorably to benchmark models in explaining the cross section of stock returns. While the statistical knowledge of stock return volatility is impressive, several questions remain regarding their economic explanation. For example, why does stock return volatility cluster? why is it that stock return volatility is composed of several factors? what do these factors represent? why do they matter in the cross section of stock returns?. | ELSEVIER Journal of Economic Dynamics Control 23 1999 1487-1516 JOURNAL OF Economic Dynamics Control locate econbase Time series properties of an artificial stock market Blake LeBarona b W. Brian Arthur Richard Palmer Graduate School of International Economics and Finance Brandeis University 415 South Street Mailstop 021 Waltham MA 02453-2728 USA bSanta Fe Institute 1399 Hyde Park Road Santa Fe NM 87501 USA Dept. of Physics Box 90305 Duke University Durham NC 27708 USA Accepted 20 November 1998 Abstract This paper presents results from an experimental computer simulated stock market. In this market artificial intelligence algorithms take on the role of traders. They make predictions about the future and buy and sell stock as indicated by their expectations of future risk and return. Prices are set endogenously to clear the market. Time series from this market are analyzed from the standpoint of well-known empirical features in real markets. The simulated market is able to replicate several of these phenomenon including fundamental and technical predictability volatility persistence and leptokurtosis. Moreover agent behavior is shown to be consistent with these features in that they condition on the variables that are found to be signi cant in the time series tests. Agents are also able to collectively learn a homogeneous rational expectations equilibrium for certain parameters giving both time series and individual forecast values consistent with the equilibrium parameter values. 1999 Elsevier Science . All rights reserved. JEL classification G12 G14 D83 Keywords Learning Asset pricing Evolution Financial time series Corresponding author. 0165-1889 99 -see front matter 1999 Elsevier Science . All rights reserved. PII S0 165 - 1 889 98 0008 1-5 1488 B. LeBaron et al. Journal of Economic Dynamics Control 23 1999 1487-1516 1. Introduction The picture of financial markets as groups of interacting agents continually adapting to new information and

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