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Estimation of Financial Agent-Based Models
Kukačka, Jiří ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee) ; ZWINKELS, REMCO C. J. (referee) ; GERBA, EDDIE EDIN (referee)
This thesis proposes computational framework for empirical estimation of Finan- cial Agent-Based Models (FABMs) that does not rely upon restrictive theoretical assumptions. First, we develop a two-step estimation methodology for one of the his- torically first FABMs-the stochastic cusp catastrophe model. Our method al- lows us to apply catastrophe theory to stock market returns with time-varying volatility and to model stock market crashes. The methodology is empirically tested on nearly 27 years of U.S. stock market returns. We find that the U.S. stock market's downturns were more likely to be driven by the endogenous market forces during the first half of the studied period, while during the sec- ond half of the period, the exogenous forces seem to be driving the market's instability. The results suggest that the proposed methodology provides an important shift in the application of catastrophe theory to stock markets. Second, we customise a recent methodology of the Non-Parametric Simu- lated Maximum Likelihood Estimator (NPSMLE) based on kernel methods by Kristensen & Shin (2012) and elaborate its capability for FABMs estimation purposes. To start with, we apply the methodology to the most famous and widely analysed model of Brock & Hommes (1998). We extensively test finite sample properties of the...

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