Domenico Mignacca, Attilio Meucci (auth.), Erricos John's Computational Methods in Decision-Making, Economics and PDF

By Domenico Mignacca, Attilio Meucci (auth.), Erricos John Kontoghiorghes, Berc Rustem, Stavros Siokos (eds.)

ISBN-10: 1441952306

ISBN-13: 9781441952301

ISBN-10: 1475736134

ISBN-13: 9781475736137

Computing has turn into crucial for the modeling, research, and optimization of platforms. This publication is dedicated to algorithms, computational research, and determination versions. The chapters are geared up in components: optimization types of choices and versions of pricing and equilibria.

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Additional resources for Computational Methods in Decision-Making, Economics and Finance

Example text

1) its. 0) Initialise the following parameters: • Past horizon p • Future horizon • Branching b • Risk level r • number of Simulations S Set current time to t f = p, and initial portfolio value to V = 1. Analyze the past p time periods (from t - p + 1 to t). Use logarithmic regression to estimate the growth rate of each stock. Calculate the covariance matrix of the residuals from this regression. Forecast a scenario tree f time periods into the future, branching b at each time period. The root of the scenario tree incorporates the present data.

Otherwise, we lower the estimate of z and re-solve the new (B-1) model. A bisection, or related search, method can be employed to find the optimal solution to the desired accuracy. In the next section, we implement a procedure similar to the one developed herein within a policy-optimization model for a large US pension plan. z 4. Pension Plan Example In this section, we describe results with optimizing a real-world portfolio for a large US pension plan. As discussed in the previous section, the dynamically balanced approach is taken in concert with selected no-trade-zones in order to reduce transaction costs while capitalizing on volatility pumping.

Maximise the resulting linear program to obtain W max' Multistage stochastic programming in computational finance 43 • Foranumberofequally-spacedpoints'W E ['Wmin' 'Wmax:] , solve QP('W), and output the resulting points of the efficient frontier. Foliage has the ability to handle simple box constraints on the decision variables, as well as percentage constraints, as discussed above. 10), which would ignore variance in intermediate time periods and optimise only the variance of the final wealth.

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Computational Methods in Decision-Making, Economics and Finance by Domenico Mignacca, Attilio Meucci (auth.), Erricos John Kontoghiorghes, Berc Rustem, Stavros Siokos (eds.)


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