Abstract/Details

Evolutionary algorithms for statistics and finance


2003 2003

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Abstract (summary)

Several models in econometrics and finance have been proven to be computationally intractable due to their complexity. In this dissertation, we propose an evolutionary-genetic-algorithm for solving these types of problems. We extend the models so that less restrictive assumptions are required and we cope with the increased complexity by using a modified version of the evolutionary algorithm proposed for the simpler cases.

More specifically, we study closer the estimation of switching regression models as introduced by Quandt (1958). The applicability of the proposed algorithms is examined through disequilibrium models; models that provide supply and demand functions for markets, when the price is not adjusted so that the quantity supplied equals the quantity demanded. We focus on the computational aspect of the deterministic switching regression models and we suggest a self-evolving genetic algorithm for solving these types of problems. As an illustration, we present results from Monte Carlo simulations and thereafter we apply the algorithm to the disequilibrium model proposed for the gasoline market during the “energy crisis”.

We further extend the “general model” for markets in disequilibrium by incorporating dynamic relationships, and we examine the applicability of the proposed genetic algorithm in this more complex and realistic problem.

Subsequently, the proposed genetic algorithm for the markets in disequilibrium is applied to financial models, where the structure and computational complexity are comparable with those of the switching regression models. As example, we apply the algorithm to minimizing portfolio tracking error with respect to a pre-specified index.

The proposed genetic algorithm possesses unique characteristics that maximize the fitness of the algorithm itself for each individual problem. This is achieved through a Self-Evolving process that teaches the genetic algorithm what internal parameters improve the algorithm's fitness.

Indexing (details)


Subject
Operations research;
Computer science;
Finance;
Statistics;
Algorithms;
Studies
Classification
0796: Operations research
0984: Computer science
0508: Finance
0463: Statistics
Identifier / keyword
Social sciences, Applied sciences, Pure sciences, Disequilibrium, Evolutionary algorithms, Portfolio management, Tracking errors
Title
Evolutionary algorithms for statistics and finance
Author
Karavas, Vassilios N.
Number of pages
83
Publication year
2003
Degree date
2003
School code
0118
Source
DAI-B 64/06, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
Advisor
Nakosteen, Robert A.
University/institution
University of Massachusetts Amherst
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3096291
ProQuest document ID
305322761
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/305322761
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