Modeling interaction of trading volume in financial dynamics

TL;DR

This paper introduces a herding model for financial trading that captures the power-law distributions and long-range correlations observed in real market data by modeling trading volume interactions.

Contribution

A novel dynamic herding model incorporating trading volume interactions that reproduces key statistical properties of financial markets.

Findings

Reproduces power-law distributions of trading volume, trades, and returns.

Generates long-range correlated time series.

Results are robust across different trading probability forms.

Abstract

A dynamic herding model with interactions of trading volumes is introduced. At time $t$, an agent trades with a probability, which depends on the ratio of the total trading volume at time $t-1$ to its own trading volume at its last trade. The price return is determined by the volume imbalance and number of trades. The model successfully reproduces the power-law distributions of the trading volume, number of trades and price return, and their relations. Moreover, the generated time series are long-range correlated. We demonstrate that the results are rather robust, and do not depend on the particular form of the trading probability.