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Journal

2008 | 41 | 5 | 185-193

Article title

Organization in Finance Prepared by Stochastic Differential Equations with Additive and Nonlinear Models and Continuous Optimization

Title variants

Languages of publication

EN

Abstracts

EN
A central element in organization of financal means by a person, a company or societal group consists in the constitution, analysis and optimization of portfolios. This requests the time-depending modeling of processes. Likewise many processes in nature, technology and economy, financial processes suffer from stochastic fluctuations. Therefore, we consider stochastic differential equations (Kloeden, Platen and Schurz, 1994) since in reality, especially, in the financial sector, many processes are affected with noise. As a drawback, these equations are hard to represent by a computer and hard to resolve. In our paper, we express them in simplified manner of approximation by both a discretization and additive models based on splines. Our parameter estimation refers to the linearly involved spline coefficients as prepared in (Taylan and Weber, 2007) and the partially nonlinearly involved probabilistic parameters. We construct a penalized residual sum of square for this model and face occuring nonlinearities by Gauss-Newton's and Levenberg-Marquardt's method on determining the iteration step. We also investigate when the related minimization program can be written as a Tikhonov regularization problem (sometimes called ridge regression), and we treat it using continuous optimization techniques. In particular, we prepare access to the elegant framework of conic quadratic programming. These convex optimation problems are very well-structured, herewith resembling linear programs and, hence, permitting the use of interior point methods (Nesterov and Nemirovskii, 1993).

Publisher

Journal

Year

Volume

41

Issue

5

Pages

185-193

Physical description

Dates

published
2008-09-01
online
2008-12-18

Contributors

References

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  • Hastie, T., Tibshirani, R. & Friedman, J. H. (2001). The Element of Statistical Learning, Springer Verlag, New York.
  • Işcanoğlu Çekiç, A., Weber G.-W. & Taylan, P. (2007). Predicting Default Probabilities with Generalized Additive Models for Emerging Markets, Invited Lecture, Graduate Summer School on New Advances in Statistics, METU, August 11-24, 2007.
  • Kloeden, P. E, Platen, E. & Schurz, H. (1994). Numerical Solution of SDE Through Computer Experiments, Springer Verlag, New York.
  • Nash, G., and Sofer, A. (1996). Linear and nonlinear programming, McGraw-Hill, New York.
  • Nemirovski, A.(2002) Five lectures on modern convex optimization, Available from:
  • Nesterov, Y. E. & Nemirovskii, A. S. (1994) Interior Point Methods in Convex Programming, SIAM Publications, Philadelphia.
  • Øksendal, B. K. (2003). Stochastic Differential Equations: An Introduction with Applications, Springer, Berlin.
  • Seydel, R. U. (2003). Tools for Computational Finance, Springer, Berlin.
  • Taylan, P. & Weber, G.-W. (2007). New approaches to regression in financial mathematics by additive models, J. Comp. Techn.12(2): 3-22.
  • Taylan, P. & Weber, G.-W. (2007) Approximation of stochastic differential equations by additive models using splines and conic programming, to appear in the proceedings of CASYS'07, Eighth International Conference on Computing Anticipatory Systems, Edited by Dubois, D. M. American Institute of Physics.
  • Taylan, P., Weber, G.-W. & Beck, A. (2007) New approaches to regression by generalized additive, models and continuous optimization for modern applications in finance, science and technology, Optimization56(5-6): 675-698.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.doi-10_2478_v10051-008-0020-8
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