Stochastic resonance of periodic volatility in financial markets with stock crashes

Abstract

We investigate the stochastic resonance of periodic volatility in two financial markets with stock crashes for Dow Jones component stocks and Hang Seng index, based on the modified Heston model with an effective potential to describe the stock crashes. We introduce a cosine term to Heston model and develop a modified Heston model with periodic stochastic volatility for capturing the periodicity of the volatility process or volatility clustering which was observed in historical financial data sets. The proposed model was tested against Dow Jones industrial and Hang Seng index data. The experimental results demonstrate that the proposed model fits the historical data well when compared with the original Heston model. The signal power amplification (SPA) is calculated and studied to investigate the stochastic resonance of the proposed dynamic system. Experimental results suggest that: (i) optimal values of volatility parameters can be identified which maximize the effects of systematic and non-systematic randomness to the market periodicity; (ii) different values of correlation strength between noise sources will cause critical phenomenon and induce single or multiple resonances.