Abstract
We first present a brief review of nonlinear asset-pricing models and contributions in which such models have been used as benchmarks to evaluate the effectiveness of a number of regulatory policy measures. We then illustrate the functioning of one particular asset-pricing model—the seminal framework by Brock and Hommes (J Econ Dyn Control 22:1235–1274, 1998)—and its possible stabilization via a central authority that seeks to counter the destabilizing trading behavior of speculators. Our paper underlines that tools from the field of nonlinear dynamical systems may foster our understanding of the functioning of asset markets, thereby enabling policymakers to design better trading environments in the future.
Highlights
Financial markets regularly display severe bubbles and crashes
Nonlinear asset-pricing models, featuring interactions between heterogeneous interacting speculators that can result in complex price dynamics, provide important insights into the functioning of financial markets and may help policymakers derive strategies that are conductive to improving market stability
Our paper shows that tools from the field of nonlinear dynamical systems may foster our understanding of the functioning of financial markets, thereby enabling policymakers to design better trading environments in the future
Summary
Financial markets regularly display severe bubbles and crashes. The detailed historical accounts compiled by Galbraith [38], Kindleberger and Aliber [52] and Shiller [76] highlight the fact that the instability of financial markets may harm the real economy. Nonlinear asset-pricing models, featuring interactions between heterogeneous interacting speculators that can result in complex (chaotic) price dynamics, provide important insights into the functioning of financial markets and may help policymakers derive strategies that are conductive to improving market stability. Menkhoff and Taylor [59] and Hommes [46] present empirical and experimental evidence showing that speculators believe in two contrasting trading concepts They use technical trading rules [60], assuming the continuation of the current market trend. The interplay between destabilizing technical trading rules and stabilizing fundamental trading rules can produce complex (chaotic) dynamics that closely resembles the dynamics of actual financial markets Since these models possess a high degree of realism— they are based on empirical observations, display a plausible internal functioning, and match important statistical properties of financial markets—policymakers can use them as artificial laboratories to evaluate the effects of regulatory policies. A number of derivations and discussions are presented in Appendix A
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