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An Analysis of Asynchronous Data
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Risk analysis and financial decision making requires true and appropriate estimates of correlations today and how they are expected to evolve in the future. If a portfolio consists of assets traded in markets with different trading hours, there could potentially occur an underestimation of the right correlation. This is due the asynchronous data - there exist an asynchronicity within the assets time series in the portfolio. The purpose of this paper is twofold. First, we suggest a modified synchronization model of Burns, Engle and Mezrich (1998) which replaces the first-order vector moving average with an first-order vector autoregressive process. Second, we study the time-varying dynamics along with forecasting the conditional variance-covariance and correlation through a DCC model. The performance of the DCC model is compared to the industrial standard RiskMetrics Exponentially Weighted Moving Averages (EWMA) model. The analysis shows that the covariance of the DCC model is slightly lower than of the RiskmMetrics EWMA model. Our conclusion is that the DCC model is simple and powerful and therefore a promising tool. It provides good insight into how correlations are likely to evolve in the short-run time horizon.

Abstract [en]

När man mäter risk och vid finansiellt beslutfattande är det viktigt att det finns skattningar på dagens korrelation samt dess förväntade utveckling. Om en portfölj består av tillgångar som handlas på olika finansiella marknader finns det risk att korrelationen är underskattad. Detta beror på att det existerar en assynkronitet mellan tidsserierna i portföljen, det vill säga att dem handlas under olika tider på dygnet. Syftet med denna uppsats är tvåfaldig. Först anges en modifierad synkroniseringsmodell som antar en AR(1) process istället för en MA(1) som föreslogs av Burns, Engle och Mezrich (1998). Sedan studerar vi den dagliga betingande korrelations- samt varians-kovariansmatrisen med hjälp av en DCC modell. Utförandet av DCC modellen jämförs sedan med RiskMetrics EMWA. Analysen visar i ett konkret exempel att varians-kovariansmatrisen för DCC modellen är längre än för RiskMetrics EMWA modellen. Vår slutsats är att DCC modellen ger sken av att vara ett enkelt och kraftfullt verktyg för att mäta och få en god inblick i hur korrelationer sannolikt kommer att utvecklas på kort sikt. 

Place, publisher, year, edition, pages
2013. , 54 p.
Series
TRITA-MAT-E, 2013:21
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-122307OAI: oai:DiVA.org:kth-122307DiVA: diva2:624027
External cooperation
Handelsbanken
Subject / course
Mathematical Statistics
Educational program
Master of Science - Mathematics
Uppsok
Physics, Chemistry, Mathematics
Supervisors
Examiners
Available from: 2013-05-29 Created: 2013-05-17 Last updated: 2013-05-29Bibliographically approved

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CiteExportLink to record
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