Multifractal Analysis and Local Hoelder Exponents Approach to Detecting Stock Markets Crashes

TL;DR

This paper explores the use of multifractal analysis and local Hoelder exponents to identify precursors to stock market crashes by analyzing the local regularity of financial time series.

Contribution

It introduces a novel approach using local Hoelder exponents to detect critical market events based on changes in local regularity patterns.

Findings

Identification of specific local Hoelder exponent behaviors before crashes

Demonstration of the method's potential for early warning signals

Enhanced understanding of financial time series regularity patterns

Abstract

This paper is devoted to problem of detecting critical events at finiacial markets using methods of multifractal analysis. Namely, the local regularity of time-series is studied. As a result, one can find out a special behavior or signal of regularity before crashes. This spesial behaviour of local Hoelder exponents inherent in financial time series can be used in detecting critcal events or crashes at financial markets.