Title:
Pricing Path-Dependent Derivative Securities Using Monte Carlo Simulation and Intra-Market Statistical Trading Model

Simple item page
dc.contributor.advisor Goldsman, David
dc.contributor.advisor Deng, Shijie
dc.contributor.author Lee, Sungjoo en_US
dc.contributor.committeeMember Spruill, M. C.
dc.contributor.committeeMember Alexopoulos, Christos
dc.contributor.committeeMember Kang, Keebom
dc.contributor.department Industrial and Systems Engineering en_US
dc.date.accessioned 2005-03-01T19:42:53Z
dc.date.available 2005-03-01T19:42:53Z
dc.date.issued 2004-12-09 en_US
dc.description.abstract This thesis is composed of two parts. The first parts deals with a technique for pricing American-style contingent options. The second part details a statistical arbitrage model using statistical process control approaches. We propose a novel simulation approach for pricing American-style contingent claims. We develop an adaptive policy search algorithm for obtaining the optimal policy in exercising an American-style option. The option price is first obtained by estimating the optimal option exercising policy and then evaluating the option with the estimated policy through simulation. Both high-biased and low-biased estimators of the option price are obtained. We show that the proposed algorithm leads to convergence to the true optimal policy with probability one. This policy search algorithm requires little knowledge about the structure of the optimal policy and can be naturally implemented using parallel computing methods. As illustrative examples, computational results on pricing regular American options and American-Asian options are reported and they indicate that our algorithm is faster than certain alternative American option pricing algorithms reported in the literature. Secondly, we investigate arbitrage opportunities arising from continuous monitoring of the price difference of highly correlated assets. By differentiating between two assets, we can separate common macroeconomic factors that influence the asset price movements from an idiosyncratic condition that can be monitored very closely by itself. Since price movements are in line with macroeconomic conditions such as interest rates and economic cycles, we can easily see out of the normal behaviors on the price changes. We apply a statistical process control approach for monitoring time series with the serially correlated data. We use various variance estimators to set up and establish trading strategy thresholds. en_US
dc.description.degree Ph.D. en_US
dc.format.extent 699711 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/4914
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Pricing options en_US
dc.subject Monte Carlo simulations
dc.subject Trading model
dc.title Pricing Path-Dependent Derivative Securities Using Monte Carlo Simulation and Intra-Market Statistical Trading Model en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Deng, Shijie
local.contributor.advisor Goldsman, David
local.contributor.corporatename H. Milton Stewart School of Industrial and Systems Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication b68b0406-b232-41f1-bcd1-82b80871b650
relation.isAdvisorOfPublication e34280e7-a117-44ac-ac35-2141fe0b4dd1
relation.isOrgUnitOfPublication 29ad75f0-242d-49a7-9b3d-0ac88893323c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
lee_sungjoo_200412_phd.pdf
Size:
683.31 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Theses and Dissertations