An Agent-Based Model With Realistic Financial Time Series: A Method for Agent-Based Models Validation
Luis Goncalves de Faria

TL;DR
This paper introduces a validation methodology for agent-based financial models by comparing their generated data against universal stylised facts observed in real-world financial time series, ensuring models' empirical relevance.
Contribution
It presents a systematic approach to validate agent-based models using stylised facts, enhancing their empirical credibility in financial market simulations.
Findings
The methodology effectively distinguishes models that replicate key stylised facts.
Validation against empirical data improves model reliability and realism.
The approach is applicable across various financial instruments and time scales.
Abstract
This paper proposes a methodology to empirically validate an agent-based model (ABM) that generates artificial financial time series data comparable with real-world financial data. The approach is based on comparing the results of the ABM against the stylised facts -- the statistical properties of the empirical time-series of financial data. The stylised facts appear to be universal and are observed across different markets, financial instruments and time periods, hence they can serve to validate models of financial markets. If a given model does not consistently replicate these stylised facts, then we can reject it as being empirically inadequate. We discuss each stylised fact, the empirical evidence for it, and introduce appropriate metrics for testing the presence of these in model generated data. Moreover we investigate the ability of our model to correctly reproduce these stylised…
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Stock Market Forecasting Methods
