Randomized Quasi-Monte Carlo Methods for Basket Option Pricing Where Underlying Assets Follow a Time-Changed Meixner Lévy Process
(2016) In Master's Theses in Mathematical Sciences FMS820 20152Mathematical Statistics
- Abstract
- Using derivative securities can help investors increase their expected returns as well as minimize their exposure to risk. For a risk-averse investor, options can offer both insurance and leverage and for a more risk-loving investor they can be used as speculation. Basket option is a kind of option whose payoff de- pends on an arbitrary portfolio of assets. The basket is made out of a weighted sum of assets. Pricing these kinds of options require multivariate asset pricing techniques which still remains a challenge. We aim to price basket options by using different Monte Carlo methods and compare their performance. We will test both quasi-Monte Carlo methods as well as randomized quasi-Monte Carlo methods in order to try to speed up the... (More)
- Using derivative securities can help investors increase their expected returns as well as minimize their exposure to risk. For a risk-averse investor, options can offer both insurance and leverage and for a more risk-loving investor they can be used as speculation. Basket option is a kind of option whose payoff de- pends on an arbitrary portfolio of assets. The basket is made out of a weighted sum of assets. Pricing these kinds of options require multivariate asset pricing techniques which still remains a challenge. We aim to price basket options by using different Monte Carlo methods and compare their performance. We will test both quasi-Monte Carlo methods as well as randomized quasi-Monte Carlo methods in order to try to speed up the convergance rate. We will assume a L ́evy market model with stochastic volatility through an integrated CIR-process as a stochastic time change. More specifically we are going to model the data using the Meixner distribution. In order to calibrate the model parameters we use S&P 500 index vanilla options and the fast Fourier transform (FFT). (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8884919
- author
- Säfwenberg, Gustav LU
- supervisor
- organization
- course
- FMS820 20152
- year
- 2016
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Basket options Randomized quasi-Monte Carlo Time-changed Lévy Process Meixner Distribution Fast Fourier Transform
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMS-3303-2016
- ISSN
- 1404-6342
- other publication id
- 2016:E32
- language
- English
- id
- 8884919
- date added to LUP
- 2016-11-16 10:34:36
- date last changed
- 2016-11-16 10:34:36
@misc{8884919, abstract = {{Using derivative securities can help investors increase their expected returns as well as minimize their exposure to risk. For a risk-averse investor, options can offer both insurance and leverage and for a more risk-loving investor they can be used as speculation. Basket option is a kind of option whose payoff de- pends on an arbitrary portfolio of assets. The basket is made out of a weighted sum of assets. Pricing these kinds of options require multivariate asset pricing techniques which still remains a challenge. We aim to price basket options by using different Monte Carlo methods and compare their performance. We will test both quasi-Monte Carlo methods as well as randomized quasi-Monte Carlo methods in order to try to speed up the convergance rate. We will assume a L ́evy market model with stochastic volatility through an integrated CIR-process as a stochastic time change. More specifically we are going to model the data using the Meixner distribution. In order to calibrate the model parameters we use S&P 500 index vanilla options and the fast Fourier transform (FFT).}}, author = {{Säfwenberg, Gustav}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Randomized Quasi-Monte Carlo Methods for Basket Option Pricing Where Underlying Assets Follow a Time-Changed Meixner Lévy Process}}, year = {{2016}}, }