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Zero-Inflated Hidden Markov Models and Optimal Trading Strategies in High-Frequency Foreign Exchange Trading
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
Noll-utökade Dolda Markovmodeller och Optimala Handelsstrategier för Högfrekvent Valutahandel (Swedish)
Abstract [en]

The properties of high-frequency foreign exchange markets and how well they can be modeled using Hidden Markov Models will be studied in this thesis. Specifically, a Zero-inflated Poisson HMM will be implemented and evaluated for high-frequency price data for the EURSEK exchange rate. Furthermore, a trading strategy aimed at distributing large volumes optimally is developed and evaluated. The results show that the price model performs better than a random walk for some prediction horizons, both when used as a price predictor and as a classifier. The initial tests of the strategy indicate that it has good performance compared to the market benchmark. Both the price model and the strategy needs to undergo more testing before any final conclusions can be made.

Abstract [sv]

Egenskaperna hos högfrekventa valutamarknader och hur dessa kan modelleras med Dolda Markovmodeller behandlas i detta examensarbete. Noll-utökade Poisson distributioner, tillsammans med Dolda Markovmodeller, implementeras och utvärderas för högfrekvent växelkursdata för valutaparet EURSEK. Vidare, utvecklas och utvärderas en handelsstrategi med målet att distribuera stora volymer optimalt. Resultaten visar att prismodellen presterar bättre än en slumpvandring för en del prediktionshorisonter, både när den används för prisprediktion och för klassificering. Initiala tester av strategin indikerar att prestandan är bra jämfört med marknadens prestandamått. Både prismodellen och strategin behöver dock undersökas mer innan några definitiva slutsatser kan dras.

Place, publisher, year, edition, pages
2018.
Series
TRITA-SCI-GRU ; 2018:005
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-221098OAI: oai:DiVA.org:kth-221098DiVA, id: diva2:1180809
External cooperation
SEB
Subject / course
Financial Mathematics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2018-02-06 Created: 2018-02-06 Last updated: 2018-02-06Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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