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Complex Systems in Finance and Econometrics by Robert A. Meyers

By Robert A. Meyers

Advanced platforms in Finance and Econometrics is an authoritative connection with the elemental instruments and ideas of complexity and platforms conception as utilized to an figuring out of complicated, financial-based enterprise and social structures. Fractals, nonlinear time sequence modeling, mobile automata, online game conception, community conception and statistical physics are one of the crucial instruments and methods for predicting, tracking, comparing, dealing with, and decision-making in quite a lot of fields from overall healthiness care, poverty relief, and effort and the surroundings, to production and caliber insurance, version development, organizational studying. and macro and microeconomics. Sixty of the world’s prime specialists current forty seven articles for an viewers of complex undergraduate and graduate scholars, professors, and execs in all of those fields.

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We next turn to examine the effect of introducing into the market EMB investors, which model empirically and experimentally documented elements of investors’ behavior. The Introduction of a Small Minority of EMB Investors In this section we will show that the introduction of a small minority of heterogeneous EMB investors generates many Agent Based Computational Economics of the empirically observed market “anomalies” which are absent in the benchmark model, and indeed, in most other rational-representative-agent models.

In this simulation 90% of the investors are RII, and the remaining 10% are heterogeneous EMB ¯ D 40, and m D 10. The price pattern investors with m seems very realistic with “smoother” and more irregular cycles. Crashes are dramatic, but infrequent and unpredictable. The heterogeneous EMB population model generates the following empirically observed market phenomena: Return Autocorrelation: Momentum and Mean-Reversion In the heterogeneous EMB population model trends are generated by the same positive feedback mechanism that generated cycles in the homogeneous case: high (low) returns tend to make the EMB investors more (less) aggressive, this generates more high (low) returns, etc.

3) which is about 2–3 periods long, the upper plateau (segment b–c in Fig. 3) which is about 10 periods long, the crash that occurs during one or two peri- ods, and the lower plateau (segment d–e in Fig. 3) which is again about 10 periods long, for a total of about 23–25 periods. Thus, we expect positive autocorrelation for lags of about 23–25 periods, because this is the lag between one point and the corresponding point in the next (or previous) cycle. We also expect negative autocorrelation for lags of about 10–12 periods, because this is the lag between a boom and the following (or previous) crash, and vice versa.

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