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Segmenting Observed Time Series Using Comovement and Complexity Measures
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Segmentering av Observerade Tidsserier med hjälp av Comovement- och Komplexitetsmått (Swedish)
Abstract [en]

Society depends on unbiased, efficient and replicable measurement tools to tell us more truthfully what is happening when our senses would otherwise fool us. A new approach is made to consistently detect the start and end of historic recessions as defined by the US Federal Reserve. To do this, three measures, correlation (Spearman and Pearson), Baur comovement and Kolmogorov complexity, are used to quantify market behaviour to detect recessions. To compare the effectiveness of each measure the normalized correct Area Under Curve (AUC) fraction is introduced. It is found that for all three measures, the performance is mostly dependent on the type of data and that financial market data does not perform as good as fundamental economical data to detect recessions. Furthermore, comovement is found to be the most efficient individual measure and also most efficient of all measures when compared against several measures merged together.

Abstract [sv]

Samhället är beronde förväntningsriktiga, effektiva och replikerbara mätverktyg för att mer sanningsenligt informera vad som händer när våra sinnen lurar oss. Ett nytt tillvägagångssätt utvecklas för att konsekvent uppmäta början och slut av historiska lågkonjunkturer så som definierats av US Federal Reserve. För att göra detta används tre mätmetoder, korrelation (Spearman och Pearson), Baur comovement och Kolmogorovkomplexitet, för att kvantifiera marknadsbeteendet i avsikt att upptäcka lågkonjunkturer. För att jämföra effektiviteten hos varje metod introduceras normalized correct Area Under Curve (AUC) fraktionen. Det konstateras att effektiviteten hos alla tre metoder är främst beroende av vilken typ av data som används och att finansiell data inte fungerar lika bra som real ekonomiska data för att upptäcka lågkonjunkturer. Vidare visas att comovement är den mest effektiva individualla mätmetoden och även den mest effektiva metoden jämfört med sammanslagna metoder

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:102
Keywords [en]
Time Series, Correlation, Baur Comovement, Kolmogorov Complexity
Keywords [sv]
Tidsserie, Korrelation, Baur Comovement, Kolmogorovkomplexitet
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-252571OAI: oai:DiVA.org:kth-252571DiVA, id: diva2:1319720
Subject / course
Financial Mathematics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2019-06-04 Created: 2019-06-03 Last updated: 2019-06-04Bibliographically approved

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