Complex Systems in Finance and Econometrics by Robert A. Meyers

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By Robert A. Meyers

Advanced platforms in Finance and Econometrics is an authoritative connection with the elemental instruments and ideas of complexity and structures thought as utilized to an knowing of advanced, financial-based enterprise and social platforms. Fractals, nonlinear time sequence modeling, mobile automata, online game concept, community idea and statistical physics are one of the crucial instruments and methods for predicting, tracking, comparing, handling, and decision-making in a variety of fields from wellbeing and fitness care, poverty relief, and effort and the surroundings, to production and caliber coverage, version development, organizational studying. and macro and microeconomics. Sixty of the world’s top specialists current forty seven articles for an viewers of complicated undergraduate and graduate scholars, professors, and execs in all of those fields.

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Thus, we do not expect prices to be more volatile than the value of the discounted dividend stream. For a formal test of excess volatility we follow the technique in [37]. For each time period we calculate the actual 11 12 Agent Based Computational Economics Agent Based Computational Economics, Figure 1 Price Dynamics in the Benchmark Model Agent Based Computational Economics, Figure 2 Return Autocorrelation in Benchmark Model price Pt , and the fundamental value of discounted divif dend stream, Pt , as in Eq.

The EMBs are investors who believe that the price of the stock re- flects all of the currently available information about the stock. As a consequence, they do not try to time the market or to buy bargain stocks. Rather, their investment decision is reduced to the optimal diversification problem. For this portfolio optimization, the ex-ante return distribution is required. However, since the ex-ante distribution is unknown, the EMB investors use the ex-post distribution in order to estimate the ex-ante distribution.

The total return on the stock in period t, which we denote by R t is given by: ˜t P˜t C D R˜ t D ; Pt 1 (1) where P˜t is the stock price at time t. All investors in the model are characterized by a von Neuman-Morgenstern utility function. We assume that all investors have a power utility function of the form: U(W) D W1 ˛ ; 1 ˛ (2) where ˛ is the risk aversion parameter. This form of utility function implies Constant Relative Risk Aversion (CRRA). We employ the power utility function (Eq. (2)) because the empirical evidence suggests that relative risk aversion is approximately constant (see, for example [8,9,18,20]), and the power utility function is the unique utility function which satisfies the CRRA condition.

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