Directed Continuous-Time Random Walk with memory

TL;DR

This paper introduces a directed continuous-time random walk model with memory, where each step depends on the previous, to better understand autocorrelation phenomena in high-frequency financial data.

Contribution

The paper develops a novel directed CTRW model with memory, focusing on positive jumps and their dependence, to analyze autocorrelation in financial returns.

Findings

Bid-ask bounce explains only a small part of observed long-term autocorrelation.

Model captures short-term negative autocorrelation in high-frequency data.

Long-term autocorrelation requires additional explanations beyond bid-ask bounce.

Abstract

We propose a new Directed Continuous-Time Random Walk (CTRW) model with memory. As CTRW trajectory consists of spatial jumps preceded by waiting times, in Directed CTRW, we consider the case with only positive spatial jumps. Moreover, we consider the memory in the model as each spatial jump depends on the previous one. Our model is motivated by the financial application of the CTRW presented in [Phys. Rev. E 82:046119][Eur. Phys. J. B 90:50]. As CTRW can successfully describe the short term negative autocorrelation of returns in high-frequency financial data (caused by the bid-ask bounce phenomena), we asked ourselves to what extent the observed long-term autocorrelation of absolute values of returns can be explained by the same phenomena. It turned out that the bid-ask bounce can be responsible only for the small fraction of the memory observed in the high-frequency financial data.