TY - GEN
T1 - Evaluation of a new parallel numerical parameter optimization algorithm for a dynamical system
AU - Duran, Ahmet
AU - Tuncel, Mehmet
PY - 2016/10/20
Y1 - 2016/10/20
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84995421886&partnerID=8YFLogxK
U2 - 10.1063/1.4965416
DO - 10.1063/1.4965416
M3 - Conference contribution
AN - SCOPUS:84995421886
T3 - AIP Conference Proceedings
BT - Numerical Computations
A2 - Sergeyev, Yaroslav D.
A2 - Mukhametzhanov, Marat S.
A2 - Dell'Accio, Francesco
A2 - Mukhametzhanov, Marat S.
A2 - Kvasov, Dmitri E.
A2 - Sergeyev, Yaroslav D.
A2 - Kvasov, Dmitri E.
PB - American Institute of Physics Inc.
T2 - 2nd International Conference on Numerical Computations: Theory and Algorithms, NUMTA 2016
Y2 - 19 June 2016 through 25 June 2016
ER -