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Modeling the Term Structure of Interest Rates with Restricted Boltzmann Machines
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Modellering av avkastningskurvan med restricted Boltzmann machines (Swedish)
Abstract [en]

This thesis investigates if Gaussian restricted Boltzmann machines can be used to model the Swedish term structure of interest rates. The models are evaluated based on the ability to make one-day-ahead forecasts and the ability to generate long term scenarios. The results are compared to simple benchmark models, such as assuming a random walk. The effects of preprocessing the input data with principal component analysis are also investigated.

The results show that the ability to make one-day-ahead forecasts, measured as a mean squared error, is comparable to a random walk benchmark, both in-sample and out-of-sample. The ability to generate long term scenarios show promising results. The scenarios are evaluated based on visual properties and one-year-ahead forecast errors on semi-out-of-sample data. The results outperform the benchmark models.

The main focus of the thesis is not to optimize performance of the models, but instead to serve as an introduction to modeling the term structure of interest rates with Gaussian restricted Boltzmann machines.

Abstract [sv]

Denna uppsats undersöker huruvida Gaussian restricted Boltzmann machines kan användas för att modellera avkastningskurvan baserad på svensk data. De testade modellerna utvärderas baserat på förmågan att förutsäga morgondagens avkastningskurva och förmågan att generera långsiktiga scenarier för avkastningskurvan. Resultaten jämförs med enkla jämförelsemodeller, så som att anta en slumpvandring. Effekten av att använda principalkomponentanalys för att preparera indatan undersöks också.

Resultaten visar att förmågan att förutsäga morgondagens avkastningskurva, mätt som medelkvadratfel, är jämförbar med att anta en slumpvandring, både in-sample och out-of-sample. Förmågan att generera långsiktiga scenarier visar på lovande resultat baserat på synbara egenskaper och förmågan till att göra ettåriga förutsägelser för semi-out-of-sample data.

Uppsatsens huvudfokus är inte att optimera prestandan för modellerna, utan istället att vara en introduktion till hur avkastningskurvan kan modelleras med Gaussian restricted Boltzmann machines.

Place, publisher, year, edition, pages
2018.
Series
TRITA-SCI-GRU ; 2018:241
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-229486OAI: oai:DiVA.org:kth-229486DiVA, id: diva2:1215619
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2018-06-08 Created: 2018-06-08 Last updated: 2018-08-25Bibliographically approved

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