liu.seSearch for publications in DiVA
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
Optimizing the Number of Time-steps Used in Option Pricing
Linköping University, Department of Computer and Information Science.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Optimering av Antal Tidssteg inom Optionsprissättning (Swedish)
Abstract [en]

Calculating the price of an option commonly uses numerical methods and can becomputationally heavy. In general, longer computations result in a more precisresult. As such, improving existing models or creating new models have been thefocus in the research field. More recently the focus has instead shifted towardcreating neural networks that can predict the price of a given option directly.This thesis instead studied how the number of time-steps parameter can beoptimized, with regard to precision of the resulting price, and then predict theoptimal number of time-steps for other options. The number of time-stepsparameter determines the computation time of one of the most common models inoption pricing, the Cox-Ross-Rubinstein model (CRR). Two different methodsfor determining the optimal number of time-steps were created and tested. Bothmodels use neural networks to learn the relationship between the input variablesand the output. The first method tried to predict the optimal number oftime-steps directly. The other method instead tried to predict the parameters ofan envelope around the oscillations of the option pricing method. It wasdiscovered that the second method improved the performance of the neuralnetworks tasked with predicting the optimal number of time-steps. It was furtherdiscovered that even though the best neural network that was found significantlyoutperformed the benchmark method, there was no significant difference incalculation times, most likely because the range of log moneyness and pricesthat were used. It was also noted that the neural network tended tounderestimate the parameter and that might not be a desirable property of asystem in charge of estimating a price in the financial sector.

Place, publisher, year, edition, pages
2019. , p. 58
Keywords [en]
Option pricing, binomial trees, machine learning, deep learning, discretization methods, optimization, recombinant tree, convergence
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:liu:diva-159648ISRN: LIU-IDA/LITH-EX-A--19/056--SEOAI: oai:DiVA.org:liu-159648DiVA, id: diva2:1342920
External cooperation
Nasdaq
Subject / course
Computer Engineering
Supervisors
Examiners
Available from: 2019-08-19 Created: 2019-08-14 Last updated: 2019-08-19Bibliographically approved

Open Access in DiVA

fulltext(1164 kB)10 downloads
File information
File name FULLTEXT01.pdfFile size 1164 kBChecksum SHA-512
fb37a4c48710d17c0427502e48548dcca3d4817dcb8ef87ed8d509e02104eebb8b4661fff40ff56a364196f7301be906542622d46118efa1f132f147f93020f6
Type fulltextMimetype application/pdf

By organisation
Department of Computer and Information Science
Computer Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 10 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: 22 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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