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- W2537534257 abstract "It is important to have a scalable parallel numerical parameter optimization algorithm for a dynamical system used in financial applications where time limitation is crucial. We use Message Passing Interface parallel programming and present such a new parallel algorithm for parameter estimation. For example, we apply the algorithm to the asset flow differential equations that have been developed and analyzed since 1989 (see [3-6] and references contained therein). We achieved speed-up for some time series to run up to 512 cores (see [10]). Unlike [10], we consider more extensive financial market situations, for example, in presence of low volatility, high volatility and stock market price at a discount/premium to its net asset value with varying magnitude, in this work. Moreover, we evaluated the convergence of the model parameter vector, the nonlinear least squares error and maximum improvement factor to quantify the success of the optimization process depending on the number of initial parameter vectors." @default.
- W2537534257 created "2016-10-28" @default.
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- W2537534257 date "2016-01-01" @default.
- W2537534257 modified "2023-10-18" @default.
- W2537534257 title "Evaluation of a new parallel numerical parameter optimization algorithm for a dynamical system" @default.
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- W2537534257 doi "https://doi.org/10.1063/1.4965416" @default.
- W2537534257 hasPublicationYear "2016" @default.
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