How volatilities nonlocal in time affect the price dynamics in complex financial systems

TL;DR

This paper uncovers that past volatilities, nonlocal in time, significantly influence future stock returns, challenging traditional local correlation assumptions and supported by empirical data and an agent-based model.

Contribution

It introduces nonlocal-in-time volatility-return correlation measures and demonstrates their significance using empirical data and a novel agent-based model.

Findings

Non-zero volatility-return correlation over two weeks.

Empirical evidence from hundreds of stocks and indices.

Agent-based model explains microscopic origin.

Abstract

What is the dominating mechanism of the price dynamics in financial systems is of great interest to scientists. The problem whether and how volatilities affect the price movement draws much attention. Although many efforts have been made, it remains challenging. Physicists usually apply the concepts and methods in statistical physics, such as temporal correlation functions, to study financial dynamics. However, the usual volatility-return correlation function, which is local in time, typically fluctuates around zero. Here we construct dynamic observables nonlocal in time to explore the volatility-return correlation, based on the empirical data of hundreds of individual stocks and 25 stock market indices in different countries. Strikingly, the correlation is discovered to be non-zero, with an amplitude of a few percent and a duration of over two weeks. This result provides compelling evidence that past volatilities nonlocal in time affect future returns. Further, we introduce an agent-based model with a novel mechanism, that is, the asymmetric trading preference in volatile and stable markets, to understand the microscopic origin of the volatility-return correlation nonlocal in time.