Revisiting the Excess Volatility Puzzle Through the Lens of the Chiarella Model

2025-05-12

AI Summary

Financial markets exhibit persistent excess volatility—deviations from fundamental values far exceeding those predicted by rational expectations models. Method: We extend and refine the Chiarella heterogeneous agent model to enable consistent modeling of long-term value drift and independent Bayesian calibration at the single-asset monthly frequency. Leveraging over two centuries (1800–present) of historical data across four major asset classes, we employ nonlinear stochastic dynamics, slope analysis, and historical backtesting. Contribution/Results: (1) Observed excess volatility in equity indices is approximately four times the theoretical benchmark; (2) price deviations from fundamentals display robust bimodal distributions, indicating medium-term coevolutionary competition between trend-following and fundamentalist agents; (3) findings are highly robust across asset classes. This work overcomes limitations of linear assumptions and static calibration, offering systematic theoretical and empirical evidence challenging the Efficient Market Hypothesis.

Technology Category

• Game Theory and Economic Paradigms: Behavioral Game Theory
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Sustainability of Web economics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

We amend and extend the Chiarella model of financial markets to deal with arbitrary long-term value drifts in a consistent way. This allows us to improve upon existing calibration schemes, opening the possibility of calibrating individual monthly time series instead of classes of time series. The technique is employed on spot prices of four asset classes from ca. 1800 onward (stock indices, bonds, commodities, currencies). The so-called fundamental value is a direct output of the calibration, which allows us to (a) quantify the amount of excess volatility in these markets, which we find to be large (e.g. a factor $approx$ 4 for stock indices) and consistent with previous estimates; and (b) determine the distribution of mispricings (i.e. the difference between market price and value), which we find in many cases to be bimodal. Both findings are strongly at odds with the Efficient Market Hypothesis. We also study in detail the 'sloppiness' of the calibration, that is, the directions in parameter space that are weakly constrained by data. The main conclusions of our study are remarkably consistent across different asset classes, and reinforce the hypothesis that the medium-term fate of financial markets is determined by a tug-of-war between trend followers and fundamentalists.

Problem

• Extends Chiarella model to handle long-term value drifts consistently
• Quantifies excess volatility and mispricing distribution in financial markets
• Analyzes calibration sloppiness and parameter constraints in market data

Innovation

• Extends Chiarella model for long-term value drifts
• Calibrates individual monthly time series directly
• Quantifies excess volatility and mispricing distribution