We present a stochastic simulation model of a prototype financial market. The model covers a number of endogeneous interactions between economic agents: agents may change their mood with respect to the future price development under the influence of other traders’ obeserved behaviour and they may switch between strategies (chartist or fundamentalist) according to observed differences in profits. The particular behavioural variant adopted by an agent also determines her decision to enter on the long or short side of the market. Short-run imbalances between demand and supply lead to price adjustments by a market maker or auctioneer in the usual Walrasian manner. Our interest in this paper is in exploring the behaviour of our model when testing for the presence of chaos or non-linearity in the simulated data. First, attempts to determine the fractal dimension of the underlying process gave unsatisfactory results in that we experienced a lack of convergence of the estimate. Explicit tests for non-linearity and dependence (The BDS and Kaplan tests) also give very unstable results in that we can find both acceptance and strong rejection of IIDness in different realisations of our model. All in all, this behaviour is very similar to experience collected with empirical data and our results may point towards an explanation of why robustness of inference in this area is low. Estimating GARCH models, we also find that most of the nonlinearity is contained in second moments.

Testing for Nonlinearity in an Artificial Financial Market

MARCHESI, MICHELE
1999-01-01

Abstract

We present a stochastic simulation model of a prototype financial market. The model covers a number of endogeneous interactions between economic agents: agents may change their mood with respect to the future price development under the influence of other traders’ obeserved behaviour and they may switch between strategies (chartist or fundamentalist) according to observed differences in profits. The particular behavioural variant adopted by an agent also determines her decision to enter on the long or short side of the market. Short-run imbalances between demand and supply lead to price adjustments by a market maker or auctioneer in the usual Walrasian manner. Our interest in this paper is in exploring the behaviour of our model when testing for the presence of chaos or non-linearity in the simulated data. First, attempts to determine the fractal dimension of the underlying process gave unsatisfactory results in that we experienced a lack of convergence of the estimate. Explicit tests for non-linearity and dependence (The BDS and Kaplan tests) also give very unstable results in that we can find both acceptance and strong rejection of IIDness in different realisations of our model. All in all, this behaviour is very similar to experience collected with empirical data and our results may point towards an explanation of why robustness of inference in this area is low. Estimating GARCH models, we also find that most of the nonlinearity is contained in second moments.
artificial financial market, chaos, non-linearity, ARCH models
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/11301
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