Fast Times, Slow Times: Timescale Separation in Financial Timeseries Data

TL;DR

This paper presents a novel method for decomposing financial time series into slow and fast components based on timescale separation, aiding in risk management and understanding market dynamics.

Contribution

It introduces a generalized eigenvalue approach utilizing variance and tail stationarity criteria to identify multiscale processes in financial data.

Findings

Effective separation of timescales in asset returns and prices

Application to currencies, ETFs, and treasury yields demonstrates practical utility

Facilitates analysis of parameter drift, mean reversion, and tail risk

Abstract

Financial time series exhibit multiscale behavior, with interaction between multiple processes operating on different timescales. This paper introduces a method for separating these processes using variance and tail stationarity criteria, framed as generalized eigenvalue problems. The approach allows for the identification of slow and fast components in asset returns and prices, with applications to parameter drift, mean reversion, and tail risk management. Empirical examples using currencies, equity ETFs and treasury yields illustrate the practical utility of the method.