Assessing market uncertainty by means of a time-varying intermittency parameter for asset price fluctuations

TL;DR

This paper introduces a method using maximum likelihood estimation to quantify and analyze the time evolution of volatility clustering in asset prices through a dynamic intermittency parameter, revealing its correlation with market uncertainty.

Contribution

It presents a novel approach to measure and track the intermittency parameter {} in high-frequency financial data, enabling assessment of market uncertainty over time.

Findings

Intermittency parameter {} decreases during high market uncertainty.

Method effectively captures volatility clustering from short-term data.

Results align with market risk indicators like investment grade spreads.

Abstract

Maximum likelihood estimation applied to high-frequency data allows us to quantify intermittency in the fluctu- ations of asset prices. From time records as short as one month these methods permit extraction of a meaningful intermittency parameter {\lambda} characterising the degree of volatility clustering of asset prices. We can therefore study the time evolution of volatility clustering and test the statistical significance of this variability. By analysing data from the Oslo Stock Exchange, and comparing the results with the investment grade spread, we find that the estimates of {\lambda} are lower at times of high market uncertainty.