Pricing exotic options using improved strong convergence
Today, better numerical approximations are required for multi-dimensional SDEs to improve on the poor performance of the standard Monte Carlo integration. With this aim in mind, the material in the thesis is divided into two main categories, stochastic calculus and mathematical finance. In the forme...
| Autor principal: | |
|---|---|
| Altres autors: | |
| Format: | Thesis |
| Idioma: | English |
| Publicat: |
2008
|
| Matèries: |
| _version_ | 1826274088542273536 |
|---|---|
| author | Schmitz Abe, K |
| author2 | Shaw, W |
| author_facet | Shaw, W Schmitz Abe, K |
| author_sort | Schmitz Abe, K |
| collection | OXFORD |
| description | Today, better numerical approximations are required for multi-dimensional SDEs to improve on the poor performance of the standard Monte Carlo integration. With this aim in mind, the material in the thesis is divided into two main categories, stochastic calculus and mathematical finance. In the former, we introduce a new scheme or discrete time approximation based on an idea of Paul Malliavin where, for some conditions, a better strong convergence order is obtained than the standard Milstein scheme without the expensive simulation of the Lévy Area. We demonstrate when the conditions of the 2−Dimensional problem permit this and give an exact solution for the orthogonal transformation (θ Scheme or Orthogonal Milstein Scheme). Our applications are focused on continuous time diffusion models for the volatility and variance with their discrete time approximations (ARV). Two theorems that measure with confidence the order of strong and weak convergence of schemes without an exact solution or expectation of the system are formally proved and tested with numerical examples. In addition, some methods for simulating the double integrals or Lévy Area in the Milstein approximation are introduced. For mathematical finance, we review evidence of non-constant volatility and consider the implications for option pricing using stochastic volatility models. A general stochastic volatility model that represents most of the stochastic volatility models that are outlined in the literature is proposed. This was necessary in order to both study and understand the option price properties. The analytic closed-form solution for a European/Digital option for both the Square Root Model and the 3/2 Model are given. We present the Multilevel Monte Carlo path simulation method which is a powerful tool for pricing exotic options. An improved/updated version of the ML-MC algorithm using multi-schemes and a non-zero starting level is introduced. To link the contents of the thesis, we present a wide variety of pricing exotic option examples where considerable computational savings are demonstrated using the new θ Scheme and the improved Multischeme Multilevel Monte Carlo method (MSL-MC). The computational cost to achieve an accuracy of O(e) is reduced from O(e−3) to O(e−2) for some applications. |
| first_indexed | 2024-03-06T22:38:09Z |
| format | Thesis |
| id | oxford-uuid:5a9fb837-238f-46a7-976a-fe3bae0e7b09 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T22:38:09Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | oxford-uuid:5a9fb837-238f-46a7-976a-fe3bae0e7b092022-03-26T17:16:54ZPricing exotic options using improved strong convergenceThesishttp://purl.org/coar/resource_type/c_db06uuid:5a9fb837-238f-46a7-976a-fe3bae0e7b09Stochastic processesMathematicsProbability theory and stochastic processesFinanceDiscrete mathematics (statistics)Computer science (mathematics)Mathematical financeEnglishOxford University Research Archive - Valet2008Schmitz Abe, KShaw, WGiles, MToday, better numerical approximations are required for multi-dimensional SDEs to improve on the poor performance of the standard Monte Carlo integration. With this aim in mind, the material in the thesis is divided into two main categories, stochastic calculus and mathematical finance. In the former, we introduce a new scheme or discrete time approximation based on an idea of Paul Malliavin where, for some conditions, a better strong convergence order is obtained than the standard Milstein scheme without the expensive simulation of the Lévy Area. We demonstrate when the conditions of the 2−Dimensional problem permit this and give an exact solution for the orthogonal transformation (θ Scheme or Orthogonal Milstein Scheme). Our applications are focused on continuous time diffusion models for the volatility and variance with their discrete time approximations (ARV). Two theorems that measure with confidence the order of strong and weak convergence of schemes without an exact solution or expectation of the system are formally proved and tested with numerical examples. In addition, some methods for simulating the double integrals or Lévy Area in the Milstein approximation are introduced. For mathematical finance, we review evidence of non-constant volatility and consider the implications for option pricing using stochastic volatility models. A general stochastic volatility model that represents most of the stochastic volatility models that are outlined in the literature is proposed. This was necessary in order to both study and understand the option price properties. The analytic closed-form solution for a European/Digital option for both the Square Root Model and the 3/2 Model are given. We present the Multilevel Monte Carlo path simulation method which is a powerful tool for pricing exotic options. An improved/updated version of the ML-MC algorithm using multi-schemes and a non-zero starting level is introduced. To link the contents of the thesis, we present a wide variety of pricing exotic option examples where considerable computational savings are demonstrated using the new θ Scheme and the improved Multischeme Multilevel Monte Carlo method (MSL-MC). The computational cost to achieve an accuracy of O(e) is reduced from O(e−3) to O(e−2) for some applications. |
| spellingShingle | Stochastic processes Mathematics Probability theory and stochastic processes Finance Discrete mathematics (statistics) Computer science (mathematics) Mathematical finance Schmitz Abe, K Pricing exotic options using improved strong convergence |
| title | Pricing exotic options using improved strong convergence |
| title_full | Pricing exotic options using improved strong convergence |
| title_fullStr | Pricing exotic options using improved strong convergence |
| title_full_unstemmed | Pricing exotic options using improved strong convergence |
| title_short | Pricing exotic options using improved strong convergence |
| title_sort | pricing exotic options using improved strong convergence |
| topic | Stochastic processes Mathematics Probability theory and stochastic processes Finance Discrete mathematics (statistics) Computer science (mathematics) Mathematical finance |
| work_keys_str_mv | AT schmitzabek pricingexoticoptionsusingimprovedstrongconvergence |