An Optimized Stochastic Techniques related to Option Pricing

• Autorzy: Todorov Venelin , Dimov Ivan , Fidanova Stefka , Apostolov Stoyan

Identyfikatory

DOI

10.15439/2021F52

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

Recently stochastic methods have become very important tool for high performance computing of very high dimensional problems in computational finance. The advantages and disadvantages of the different highly efficient stochastic methods for multidimensional integrals related to evaluation of European style options will be analyzed. Multidimensional integrals up to 100 dimensions related to European options will be computed with highly efficient optimized lattice rules.

Słowa kluczowe

EN

finance; integration; optimisation; pricing; stochastic processes

PL

finanse; integracja; optymalizacja; cennik; procesy stochastyczne

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 247--250
• Opis fizyczny: Bibliogr. 13 poz., wz., tab.

Twórcy

autor

Todorov Venelin

• Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, 8 Acad. G. Bonchev Str., 1113 Sofia, Bulgaria
• Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 25A Acad. G. Bonchev Str., 1113 Sofia, Bulgaria

autor

Dimov Ivan

• Faculty of Mathematics and Informatics, Sofia University, Sofia 1126, Bulgaria

autor

Fidanova Stefka

• Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 25A Acad. G. Bonchev Str., 1113 Sofia, Bulgaria

autor

Apostolov Stoyan

• Faculty of Mathematics and Informatics, Sofia University, Sofia 1126, Bulgaria

Bibliografia

• 1. N. Bakhvalov (2015) On the approximate calculation of multiple integrals, Journal of Complexity 31(4), 502–516.
• 2. P. P. Boyle, Y. Lai and K. Tan, Using lattice rules to value low-dimensional derivative contracts (2001).
• 3. Centeno, V., Georgiev, I. R., Mihova, V., & Pavlov, V. (2019, October). Price forecasting and risk portfolio optimization. In AIP Conference Proceedings (Vol. 2164, No. 1, p. 060006). AIP Publishing LLC.
• 4. Dimov I., Monte Carlo Methods for Applied Scientists, New Jersey, London, Singapore, World Scientific, 2008, 291p.
• 5. Hua, L.K. and Wang, Y., Applications of Number Theory to Numerical analysis, 1981.
• 6. L. K. Hua and Y. Wang, Applications of number theory to numerical analysis, (Springer 1981).
• 7. F. Y. Kuo and D. Nuyens (2016) Application of quasi-Monte Carlo methods to elliptic PDEs with random diffusion coefficients - a survey of analysis and implementation, Foundations of Computational Mathematics 16(6), 1631–1696.
• 8. Y. Lai and J. Spanier, Applications of Monte Carlo/Quasi-Monte Carlo methods in finance: option pricing, Proceedings of the Claremont Graduate University conference (1998).
• 9. S.H. Paskov, Computing high dimensional integrals with applications to finance, Technical report CUCS-023-94, Columbia University (1994).
• 10. I.H. Sloan and P.J. Kachoyan (1987) Lattice methods for multiple integration: Theory, error analysis and examples, SIAM J. Numer. Anal. 24, 116–128.
• 11. I.H. Sloan and S. Joe, Lattice Methods for Multiple Integration, Lattice methods for multiple Integration, (Oxford University Press 1994).
• 12. S.L. Zaharieva, I. Radoslavov Georgiev, V.A. Mutkov and Y. Branimirov Neikov, "Arima Approach For Forecasting Temperature In A Residential Premises Part 2," 2021 20th International Symposium infoteh-jahorina (Infoteh), 2021, pp. 1-5.
• 13. Y. Wang and F. J. Hickernell (2000) An historical overview of lattice point sets, in Monte Carlo and Quasi-Monte Carlo Methods 2000, Proceedings of a Conference held at Hong Kong Baptist University, China.

Uwagi

1. Track 1: Artificial Intelligence in Applications

2. Session: 14th International Workshop on Computational Optimization

3. Short Paper