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Parameter Estimation of a non-Equilibrium Asset Pricing Model and Performance Analysis of the Calibration in Terms of Sloppiness
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Prices of assets traded in stock markets often exhibit out of equilibrium behaviours, e.g. bubbles and recessions. Yukalov et al. have developed a model to describe these dynamics, and this Master thesis focuses on the problem of calibrating it using an Evolutionary algorithm and the Simulated Annealing method. In general, the parameter estimation performs far from desired, and a Sloppy model analysis of the deterministic system shows that the performance is linked to the sloppiness structure of the model. Accounting for sloppiness, the calibration results can be seen in a different light and the model could still be useful for predictions. Thus, the prediction performance on both synthetic and real-world data is studied, with good results in artificial markets and poor performance using real prices.

Abstract [sv]

Aktier handlas ofta för priser som skiljer sig från jämviktspriser, exempelvis under finansbubblor eller i recessioner. Yukalov et al. har tagit fram en modell för att beskriva dessa beteenden, och i den här Masteruppsatsen undersöks modellkalibrering genom en Evolutionär algoritm och ’the Simulated Annealing method’. Generellt är modellparametrarna dåligt uppskattade och en Sloppy model-analys av det deterministiska systemet visar att kalibreringsresultatet är beroende av modellens sloppiness struktur. Med detta i åtanke kan kalibreringsresultatet tolkas annorlunda och modellen kan fortfarande vara användbar för prediktion. Således är prediktionsprecisionen studerad för både syntestisk och riktig data, med god precision för simulerade marknader men sämre resultat för verkliga priser.

Place, publisher, year, edition, pages
2014.
Series
TRITA-MAT-E, 2014:31
National Category
Mathematical Analysis
Identifiers
URN: urn:nbn:se:kth:diva-146275OAI: oai:DiVA.org:kth-146275DiVA: diva2:723414
External cooperation
ETH Zürich
Subject / course
Mathematical Statistics
Educational program
Master of Science - Mathematics
Supervisors
Examiners
Available from: 2014-06-10 Created: 2014-06-10 Last updated: 2014-06-10Bibliographically approved

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Citation style
  • apa
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