Search
Skip to Search Results- 1American Option
- 1Central Limit Theorem
- 1Heavy tails
- 1Heston Model
- 1Linear Processes
- 1Long range dependence
Results for "supervisors_tesim:"Kouritzin, Mike (Mathematical and Statistical Sciences)""
-
Spring 2014
We introduce two kinds of particle filters, one is weighted particle filter and the other is resampling particle filter. We prove the Strong Law of Large Numbers and Central Limit Theorem for both particle filters. Then, we show that the resampling particle filter is better than the weighted one.
-
Marcinkiewicz Strong Law of Large Numbers for Products of Long Range Dependent and Heavy Tailed Linear Processes
DownloadFall 2019
Classical methods of inference are often rendered inapplicable while dealing with data exhibiting heavy tails, which gives rise to infinite variance and frequent extremes, and long memory, which induces inertia in the data. In this thesis, we develop the Marcinkiewicz Strong Law of Large Numbers...
-
Fall 2016
In this thesis, we are focusing on developing an efficient simulation algorithm to price the path-dependent options, which remains a challenging problem in derivatives finance. The Heston model, a widely used stochastic volatility model, will first be introduced. Then, we will discuss and...