Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Algorithmic evaluation of Parameter Estimation for Hidden Markov Models in Finance
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Modeling financial time series is of great importance for being successful within the financial market. Hidden Markov Models is a great way to include the regime shifting nature of financial data. This thesis will focus on getting an in depth knowledge of Hidden Markov Models in general and specifically the parameter estimation of the models. The objective will be to evaluate if and how financial data can be fitted nicely with the model. The subject was requested by Nordea Markets with the purpose of gaining knowledge of HMM’s for an eventual implementation of the theory by their index development group. The research chiefly consists of evaluating the algorithmic behavior of estimating model parameters. HMM’s proved to be a good approach of modeling financial data, since much of the time series had properties that supported a regime shifting approach. The most important factor for an effective algorithm is the number of states, easily explained as the distinguishable clusters of values. The suggested algorithm of continuously modeling financial data is by doing an extensive monthly calculation of starting parameters that are used daily in a less time consuming usage of the EM-algorithm.

Place, publisher, year, edition, pages
2014.
Series
TRITA-MAT-E, 2014:12
Keyword [en]
Hidden Markov Models, Parameter Estimation, Expectation Maximization
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-141187OAI: oai:DiVA.org:kth-141187DiVA: diva2:696772
Subject / course
Mathematical Statistics
Educational program
Master of Science - Mathematics
Supervisors
Examiners
Available from: 2014-02-15 Created: 2014-02-11 Last updated: 2014-02-15Bibliographically approved

Open Access in DiVA

fulltext(2637 kB)98 downloads
File information
File name FULLTEXT01.pdfFile size 2637 kBChecksum SHA-512
d776ecd63bea9baf594a02763664abae1c687bad9754aeb9026986ac8ff9c65a8bde0bcf0f37275c96e4fe392e29dbdaedd6905efc33f5e0fbbff86cda036d9e
Type fulltextMimetype application/pdf

By organisation
Mathematical Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 98 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 425 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf