Simulated maximum likelihood estimation of agent-based models in economics and finance

Kód výsledku v IS VaVaI

RIV/67985556:_____/19:00510031 - isvavai.cz

Nalezeny alternativní kódy

RIV/00216208:11230/19:10407391

Výsledek na webu

http://dx.doi.org/10.1007/978-981-13-8319-9_10

DOI - Digital Object Identifier

10.1007/978-981-13-8319-9_10

Jazyk výsledku

angličtina

Název v původním jazyce

Simulated maximum likelihood estimation of agent-based models in economics and finance

Popis výsledku v původním jazyce

This chapter presents a general simulation-based framework for estimation of agent-based models in economics and finance based on kernel methods. After discussing the distinguishing features between empirical estimation and calibration of economic models, the simulated maximum likelihood estimator is validated for utilization in agent-based econometrics. As the main advantage, the method allows for estimation of nonlinear models for which the analytical representation of the objective function does not exist. We test the properties and performance of the estimator in combination with the seminal Brock and Hommes (J Econ Dyn Control 22:1235–1274, 1998) asset pricing model, where the dynamics are governed by switching of agents between trading strategies based on the discrete choice approach. We also provide links to how the estimation method can be extended to multivariate macroeconomic optimization problems. Using simulation analysis, we show that the estimator consistently recovers the pseudo-true parameters with high estimation precision. We further study the impact of agents' memory on the estimation performance and show that while memory generally deteriorates the precision, the main properties of the estimator remain unaffected.

Název v anglickém jazyce

Simulated maximum likelihood estimation of agent-based models in economics and finance

Popis výsledku anglicky

This chapter presents a general simulation-based framework for estimation of agent-based models in economics and finance based on kernel methods. After discussing the distinguishing features between empirical estimation and calibration of economic models, the simulated maximum likelihood estimator is validated for utilization in agent-based econometrics. As the main advantage, the method allows for estimation of nonlinear models for which the analytical representation of the objective function does not exist. We test the properties and performance of the estimator in combination with the seminal Brock and Hommes (J Econ Dyn Control 22:1235–1274, 1998) asset pricing model, where the dynamics are governed by switching of agents between trading strategies based on the discrete choice approach. We also provide links to how the estimation method can be extended to multivariate macroeconomic optimization problems. Using simulation analysis, we show that the estimator consistently recovers the pseudo-true parameters with high estimation precision. We further study the impact of agents' memory on the estimation performance and show that while memory generally deteriorates the precision, the main properties of the estimator remain unaffected.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

GJ17-12386Y: Multifraktální analýza ve financích: Extrémní události, řízení rizika a portfolia, a komplexita trhů

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Network Theory and Agent-Based Modeling in Economics and Finance

ISBN

978-981-13-8318-2

Počet stran výsledku

24

Strana od-do

203-226

Počet stran knihy

458

Název nakladatele

Springer

Místo vydání

Singapore

Kód UT WoS kapitoly

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