By Andrea Consiglio
Agent-based computational modeling with its intrinsic multidisciplinary method is gaining expanding popularity within the social sciences, quite in economics, company and finance. The method is now favourite to compute analytical versions numerically and attempt them for departures from theoretical assumptions, and to supply stand-alone simulation types for difficulties which are analytically intractable.This quantity is dedicated to contemporary contributions to the sector from either the social sciences and desktop sciences. It provides purposes of agent-based computational methodologies and instruments within the social sciences, focusing strongly at the makes use of, requisites and constraints of agent-based types hired through social scientists. themes contain agent-based macroeconomics, the emergence of norms and conventions, the dynamics of social and monetary networks, and behavioral versions in monetary markets.
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Extra resources for Artificial Markets Modeling: Methods and Applications
Comparing them to the ones obtained with real market data, we can see that returns properties similar to reality can be obtained with our interaction-based model. These properties are further veriﬁed by the use of the Augmented Dickey-Fuller test which tests for the null hypothesis “The serie has a unit root”. 0 time series: the presence of a unit-root is rejected at a high conﬁdence level as with real data (right column). 0 5 10 15 Lag 20 25 30 35 0 5 10 15 20 25 30 35 Lag Fig. 4. ACF of returns and squared returns obtained in our experiments.
Technical report, The Federal Reserve Bank of St. Louis, 2004. M. Raberto, A. Teglio, and S. Cincotti. A general equilibrium model of a production economy with asset markets. Physica A, 370(1):75–80, 2006. B. Taylor. Discretion versus policy rule in practice. In CarnegieRochester Conference Series on Public Policy, volume 39, pages 195– 214, 1993. L. Tesfatsion and K. Judd. Agent-Based Computational Economics, volume 2 of Handbook of Computational Economics. North Holland, 2006. E. Walsh. Monetary theory and policy.
Computational Economics, 22:255–272, 2003. M. Raberto, S. Cincotti, C. M. Focardi, and M. Marchesi. Price formation in an artiﬁcial market: limit order book versus matching of supply and demand. Nonlinear Dynamics and Heterogenous Interacting Agents, 2005. , the management of ﬁnancial portfolios using chartism or moving average indicators for instance) generally focuses on single “signals” giving the opportunity to buy or sell a ﬁnancial commodity frequently a well diversiﬁed portfolio (see the extensive survey of Park and Irwin, 2004).
Artificial Markets Modeling: Methods and Applications by Andrea Consiglio
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