Modeling bimodal stock price dynamics by a parsimonious diffusion process

Abstract

We extend the double-well potential to a three-parameter model in order to capture the momentum and reversal effects in stock price dynamics. The proposed model is characterized by three parameters that control momentum, reversal, and volatility. By varying these parameters, the model can represent two distinct price patterns: (i) a mean-reverting pattern with a unimodal distribution, and (ii) a momentum pattern with a bimodal distribution. We develop an estimation method and establish its asymptotic properties, along with a simulation study to evaluate its finite sample performance. An empirical application using high-frequency data is provided to demonstrate the effectiveness of our proposed model in analyzing price dynamics.