Ivonne Schwartz, Mark Kirstein
arXiv 17 Aug 2022 · Econometrics · 1 citations (OpenAlex)
arXiv:2208.08169 · PDF · DOI · OpenAlex · Extracted main text
One challenge in the estimation of financial market agent-based models (FABMs) is to infer reliable insights using numerical simulations validated by only a single observed time series. Ergodicity (besides stationarity) is a strong precondition for any estimation, however it has not been systematically explored and is often simply presumed. For finite-sample lengths and limited computational resources empirical estimation always takes place in pre-asymptopia. Thus broken ergodicity must be considered the rule, but it remains largely unclear how to deal with the remaining uncertainty in non-ergodic observables. Here we show how an understanding of the ergodic properties of moment functions can help to improve the estimation of (F)ABMs. We run Monte Carlo experiments and study the convergence behaviour of moment functions of two prototype models. We find infeasibly-long convergence times for most. Choosing an efficient mix of ensemble size and simulated time length guided our estimation and might help in general.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Grazzini, Jakob, Richiardi, Matteo (2015) Estimation of ergodic agent-based models by simulated minimum distance | 1.000 | 6 | 3 | 100% |
| 2 | Franke, Reiner, Westerhoff, Frank (2012) Structural stochastic volatility in asset pricing dynamics: Estimation and model contest | 0.874 | 7 | 2 | 100% |
| 3 | Alfarano, Simone, Lux, Thomas, Wagner, Friedrich (2008) Time variation of higher moments in a financial market with heterogeneous agents: An analytical approach | 0.874 | 5 | 2 | 100% |
| 4 | (2018) Agent-based Models in Economics | 0.737 | 3 | 2 | 100% |
| 5 | Franke, Reiner (2009) Applying the method of simulated moments to estimate a small agent-based asset pricing model | 0.737 | 3 | 2 | 100% |
| 6 | Chen, Zhenxi, Lux, Thomas (2018) Estimation of Sentiment Effects in Financial Markets: A Simulated Method of Moments Approach | 0.693 | 5 | 1 | 100% |
| 7 | (2006) Handbook of Computational Economics | 0.644 | 4 | 1 | 100% |
| 8 | Bertschinger, Nils, Mozzhorin, Iurii (2021) Bayesian estimation and likelihood-based comparison of agent-based volatility models | 0.644 | 2 | 2 | 100% |
| 9 | Duffie, Darrell, Singleton, Kenneth J (1993) Simulated Moments Estimation of Markov Models of Asset Prices | 0.644 | 2 | 2 | 100% |
| 10 | Fagiolo, Giorgio, Guerini, Mattia, Lamperti, Francesco, Moneta, Ales… (2019) Validation of Agent-Based Models in Economics and Finance | 0.644 | 2 | 2 | 100% |
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