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Title: Estimation and calibration of agent-based models of financial markets using empirical likelihood
Author: Nguyen, Minh Khoa
ISNI:       0000 0004 5348 458X
Awarding Body: University of Essex
Current Institution: University of Essex
Date of Award: 2014
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This thesis introduces the eBAEL (extended Empirical Balanced Augmented Likelihood) method for general analytical moment conditions. It proves that eBAEL has a X2 - limit distribution and provides a consistent estimator. Numerical results demonstrate that the eBAEL ratio statistics exhibits less bias than AEL ratio statistics and has smaller type I errors. This thesis also presents a general framework for calibrating financial agent-based models using eBAEL, where the aim is to find the model parameter for which the true model moments match the given empirical values. It is demonstrated that our proposed approach is able to retrieve that parameter with probability approaching one as the number of simulations increase. Furthermore this thesis demonstrates that the EL approach may also be used for estimating financial agent-based models. In contrast to calibration, estimation via moment matching in particular emphasizes that empirical moments are estimates themselves and the aim is to find a parameter configuration for which the true model moments and true empirical moments coincide. As a numerical benchmark case, the parameters of a Geometric Brownian Motion are calibrated and estimated from its simulated sample paths in comparison to the SMM. In this case the EL approach is able to provide the best mean squared errors for both calibration and estimation and in particular is the most robust calibration method. In terms of calibration efficiency this robustness holds figuratively, as the SMM is only more efficient in cases where it provides worse mean squared errors. Additionally, this thesis also estimates an actual agent-based model of a financial market against empirical moments that are generated at some known model parameter setting. Similarly, the resulting EL mean squared errors are mostly better than those of the SMM.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available