Anomalous volatility scaling in high frequency financial data

TL;DR

This paper investigates the anomalous scaling behavior of intra-day stock market volatility using empirical mode decomposition, revealing deviations from classical models and linking them to market development levels.

Contribution

It introduces the use of empirical mode decomposition to analyze volatility scaling, highlighting deviations from fractional Brownian motion in real market data.

Findings

Empirical mode decomposition reveals distinct scaling laws in stock volatility.

Deviations from classical models are larger in less developed markets.

The method provides insights into market maturity through volatility analysis.

Abstract

Volatility of intra-day stock market indices computed at various time horizons exhibits a scaling behaviour that differs from what would be expected from fractional Brownian motion (fBm). We investigate this anomalous scaling by using empirical mode decomposition (EMD), a method which separates time series into a set of cyclical components at different time-scales. By applying the EMD to fBm, we retrieve a scaling law that relates the variance of the components to a power law of the oscillating period. In contrast, when analysing 22 different stock market indices, we observe deviations from the fBm and Brownian motion scaling behaviour. We discuss and quantify these deviations, associating them to the characteristics of financial markets, with larger deviations corresponding to less developed markets.