Paper: arXiv 2307.00410

Authors: Sabiou Inoua, Vernon Smith

Abstract

In retrospect, the experimental findings on competitive market behavior called for a revival of the old, classical, view of competition as a collective higgling and bargaining process (as opposed to price-taking behaviors) founded on reservation prices (in place of the utility function). In this paper, we specialize the classical methodology to deal with speculation, an important impediment to price stability. The model involves typical features of a field or lab asset market setup and lends itself to an experimental test of its specific predictions; here we use the model to explain three general stylized facts, well established both empirically and experimentally: the excess, fat-tailed, and clustered volatility of speculative asset prices. The fat tails emerge in the model from the amplifying nature of speculation, leading to a random-coefficient autoregressive return process (and power-law tails); the volatility clustering is due to the traders’ long memory of news; bubbles are a persistent phenomenon in the model, and, assuming the standard lab present value pattern, the bubble size increases with the proportion of speculators and decreases with the trading horizon.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 4.5/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper provides a strong theoretical model based on classical economics to explain stylized facts like fat tails and volatility clustering via a random-coefficient autoregressive process. However, it relies primarily on conceptual calibration to lab setups rather than rigorous empirical backtesting on live market datasets.

Research Flowchart

  flowchart TD
    A[Goal: Model Speculative Asset Dynamics & Explain Stylized Facts] --> B[Methodology: Classical Market Framework based on Reservation Prices & Bargaining]
    B --> C[Inputs: Microstructure Setup, Speculator Ratio & Trading Horizon]
    C --> D[Computational Engine: Random-Coefficient Autoregressive Return Model with Long Memory]
    D --> E1[Outcome 1: Fat-Tailed Volatility via Amplifying Power-Law Tails]
    D --> E2[Outcome 2: Volatility Clustering driven by Long Memory of News]
    D --> E3[Outcome 3: Persistent Bubbles influenced by Speculator Ratio & Horizon]