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Modeling exchange rate using symmetric and asymmetric GARCH models
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Modellering av växelkurser meddelst symmetriska och asymmetriska GARCH-modeller (English)
Abstract [en]

This paper attempts to study GARCH-type models, with emphasis on fitting GARCH models to exchange rate return series. The symmetric GARCH(1,1) model is compared with the asymmetric EGARCH(1,1) model. Both models are analysed with different conditional distributions, namely Normal, Student's t and skew Student's t for the return innovation. Parameter estimation is performed using a maximum-likelihood approximation. The model performance is assessed by looking at the lowest AIC and BIC. Four exchange rate returns are studied using daily data over the period from 2002 to 2015. Moreover, essential ideas of return time series and stylised facts will be analysed. Our results indicate that the asymmetric GARCH model improves generally estimation with fat-tailed densities in the conditional variance. Furthermore, persistence has found to be reduced with the use of heavy-tailed distributions. Asymmetry presence has been detected in the EGARCH model. Besides, we found that "good news" tend to increase volatility in comparison with "bad news".

Abstract [sv]

Syftet med uppsatsen är att studera modeller av GARCH-typ, och fokus ligger på att anpassa GARCH-modeller efter växelkurstidsserier. Den symmetriska GARCH(1,1)-modellen jämförs med den asymmetriska EGARCH(1,1)-modellen. Modellerna analyseras för olika fördelningar, såsom normal- och t-fördelning, på avkastningarnas brustermer.  För att estimera parametrarna används maximum likelihood-metoden. Modellens prestanda bedöms sedan utifrån AIC- och BIC-kriterierna. Studien är baserad på daglig data från fyra valutapar under perioden 2002 till 2015. Resultaten indikerar att den asymmetriska GARCH-modellen förbättrar estimeringen generellt sett. Genom att använda tjocksvansade fördelningar finner man att persistensen minskar. EGARCH-modellen fångar dessutom upp asymmetrier i avkastningarna, på så sätt att volatiliteten ökar mer vid "goda nyheter" än vid "dåliga nyheter".

Place, publisher, year, edition, pages
2016.
Series
TRITA-MAT-E, 2016:69
National Category
Mathematical Analysis
Identifiers
URN: urn:nbn:se:kth:diva-195824OAI: oai:DiVA.org:kth-195824DiVA: diva2:1046430
Subject / course
Mathematical Statistics
Educational program
Master of Science - Mathematics
Supervisors
Examiners
Available from: 2016-11-14 Created: 2016-11-10 Last updated: 2016-11-14Bibliographically approved

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