Static and dynamic factors in an information-based multi-asset artificial stock market

Authored by Linda Ponta, Silvano Cincotti, Stefano Pastore

Date Published: 2018

DOI: 10.1016/j.physa.2017.11.012

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

An information-based multi-asset artificial stock market characterized by different types of stocks and populated by heterogeneous agents is presented. In the market, agents trade risky assets in exchange for cash. Beside the amount of cash and of stocks owned, each agent is characterized by sentiments and agents share their sentiments by means of interactions that are determined by sparsely connected networks. A central market maker (clearing house mechanism) determines the price processes for each stock at the intersection of the demand and the supply curves. Single stock price processes exhibit volatility clustering and fat-tailed distribution of returns whereas multivariate price process exhibits both static and dynamic stylized facts, i.e., the presence of static factors and common trends. Static factors are studied making reference to the cross-correlation of returns of different stocks. The common trends are investigated considering the variance-covariance matrix of prices. Results point out that the probability distribution of eigenvalues of the cross-correlation matrix of returns shows the presence of sectors, similar to those observed on real empirical data. As regarding the dynamic factors, the variance-covariance matrix of prices point out a limited number of assets prices series that are independent integrated processes, in close agreement with the empirical evidence of asset price time series of real stock markets. These results remarks the crucial dependence of statistical properties of multi-assets stock market on the agents' interaction structure. (C) 2017 Elsevier B.V. All rights reserved.

Tags

Agent-based modeling noise Model Artificial financial market Rates Prices Corruption Financial time-series Heartbeat dynamics Scaling behavior Multifractality