Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Expansion Based Methods for Pricing Financial Options |
| Organization Unit | |
| Authors |
|
| Supervisors |
|
| Language |
|
| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 39 |
| Date | 2020 |
| Abstract Text | This paper focuses on the Edgeworth expansions for financial option valuation using Hermite polynomials and logistic polynomials with the calibration of S&P 500 index option data. Our approach expresses the value of an option by replicating an infinite series of polynomials, whose coefficients are composed of variance, skewness, kurtosis, and higher moments of the underlying density distribution. This new formula is a computationally convenient alternative compared with Fourier transform method. Our analysis establishes two different tail conditions in order to work with the convergence of different series. Hermite series diverges for fat-tailed distributions while logistic series converges. In this paper, Heston (1993) model is applied and calibrated on S&P 500 index option data. All the MATLAB codes to achieve the model is contained in the appendix. |
| Export |
BibTeX
|