Estimation of financial agent-based models with simulated maximum likelihood

Kukačka, Jiří; Baruník, Jozef

doi:10.1016/j.jedc.2017.09.006

Cited by 59 publications

(14 citation statements)

References 84 publications

(61 reference statements)

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“…Despite the model is relatively simple, different contributions have tried to match the statistical properties of its output with those observed in real financial markets (Boswijk et al, 2007;Recchioni et al, 2015;Lamperti, 2016;Kukacka and Barunik, 2016). This makes the model an ideal test case for our surrogate: it is relatively cheap in terms of computational needs, it offers a reasonably large parameter space and it has been extensively studied in the literature.…”

Section: Experimental Design and Empirical Settingmentioning

confidence: 99%

Agent-based model calibration using machine learning surrogates

Lamperti

Roventini

Sani

2018

Journal of Economic Dynamics and Control

182

117

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Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and accurate approximation of model behaviour, dramatically reducing computation time. In that, our machine-learning surrogate facilitates large scale explorations of the parameter-space, while providing a powerful filter to gain insights into the complex functioning of agent-based models. The algorithm introduced in this paper merges model simulation and output analysis into a surrogate meta-model, which substantially ease ABM calibration. We successfully apply our approach to the Brock and Hommes (1998) asset pricing model and to the "Island" endogenous growth model (Fagiolo and Dosi, 2003). Performance is evaluated against a relatively large outof-sample set of parameter combinations, while employing different user-defined statistical tests for output analysis. The results demonstrate the capacity of machine learning surrogates to facilitate fast and precise exploration of agent-based models' behaviour over their often rugged parameter spaces. KEY WORDSAgent based model; calibration; machine learning; surrogate; meta-model. JELC15, C52, C63.

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Section: Experimental Design and Empirical Settingmentioning

confidence: 99%

Agent-based model calibration using machine learning surrogates

Lamperti

Roventini

Sani

2018

Journal of Economic Dynamics and Control

182

117

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“…Alfarano and Milakovi c (2009) studied the herding behaviour of traders via a probabilistic model based on finance agent simulation. Franke and Westerhoff (2012), Chen and Lux (2018), and Barde (2016) employed estimated moments in their simulation approach, whereas Kukacka and Barunik (2017) used the maximum likelihood in their simulation method. Diverse estimation methods have been applied by means of the particle filtering approach for state-space models with latent variables.…”

Section: Agent-based Simulation Literature Reviewmentioning

confidence: 99%

Big data tools for Islamic financial analysis

Mnif

Jarboui

Hassan

et al. 2020

Intell Sys Acc Fin Mgmt

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Summary Behavioural science states that emotions, principles and the manner of thinking can affect the behaviour of individuals and even investors in their decision making on financial markets. In this paper, we have tried to measure the investor sentiment by three means of big data. The first is based on a search query of a list of words related to Islamic context. The second is inferred from the engagement degree on social media. The last measure of sentiment is built, based on the Twitter API classified into positive and negative directions by a machine learning algorithm based on the naive Bayes method. Then, we investigate whether these sensations and emotions have an impact on the market sentiment and the price fluctuations by means of a vector autoregression model and Granger causality analysis. In the final step, we apply the agent‐based simulation by means of the sequential Monte Carlo method with the control of our Twitter measure on Islamic index returns. We show, then, that the three social media sentiment measures present a remarkable impact on the contemporaneous and lagged returns of the different Islamic assets studied. We also give an estimation of the parameters of the latent variables relative to the agent model studied.

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“…To this end, transition probabilities of return time series, observed and simulated, are discretized and compared in terms of contexttree weighted Markov approximations (Barde 2016(Barde , 2017 or JS divergence (Lamperti 2018). Alternatively, predictive likelihoods are estimated on the continuous time series of returns via (Guerini and Moneta 2017) fitting a VAR model or (Kukacka and Barunik 2017) kernel density estimation and again compared and matched with model predictions. These methods go beyond moment matching and allow to generate and evaluate model predictions.…”

Section: Introductionmentioning

confidence: 99%

Bayesian estimation and likelihood-based comparison of agent-based volatility models

Bertschinger

Mozzhorin

2020

J Econ Interact Coord

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The statistical description and modeling of volatility plays a prominent role in econometrics, risk management and finance. GARCH and stochastic volatility models have been extensively studied and are routinely fitted to market data, albeit providing a phenomenological description only. In contrast, agent-based modeling starts from the premise that modern economies consist of a vast number of individual actors with heterogeneous expectations and incentives. Observed market statistics then emerge from the collective dynamics of many actors following heterogeneous, yet simple rules. On the one hand, such models generate volatility dynamics, qualitatively matching several stylized facts. On the other hand, they illustrate the possible role of different mechanisms, such as chartist trading and herding behavior. Yet, rigorous and quantitative statistical fits are still mostly lacking. Here, we propose Hamiltonian Monte Carlo, an efficient and scalable Markov chain Monte Carlo algorithm, as a general method for Bayesian inference of agent-based models. In particular, we implement several models by Vikram and Sinha, Franke and Westerhoff and Alfarano, Lux and Wagner in Stan, an accessible probabilistic programming language for Bayesian modeling. We also compare the performance of these models with standard econometric models of the GARCH and stochastic volatility families. We find that the best agent-based models are on par with stochastic volatility models in terms of predictive likelihood, yet exhibit challenging posterior geometries requiring care in model comparison and sophisticated sampling algorithms.

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Estimation of financial agent-based models with simulated maximum likelihood

Cited by 59 publications

References 84 publications

Agent-based model calibration using machine learning surrogates

Agent-based model calibration using machine learning surrogates

Big data tools for Islamic financial analysis

Bayesian estimation and likelihood-based comparison of agent-based volatility models

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