Stock market volatility: An approach based on Tsallis entropy

TL;DR

This paper explores using Tsallis entropy to measure stock market volatility, comparing it with traditional methods like standard deviation, and highlights its ability to capture nonlinear dynamics in various global indexes.

Contribution

It introduces Tsallis entropy as a novel approach for volatility detection and compares its effectiveness with standard deviation across multiple stock indexes.

Findings

Tsallis entropy effectively detects volatility in stock indexes.

It captures nonlinear dynamics better than traditional methods.

Provides a comparative analysis across major global indexes.

Abstract

One of the major issues studied in finance that has always intrigued, both scholars and practitioners, and to which no unified theory has yet been discovered, is the reason why prices move over time. Since there are several well-known traditional techniques in the literature to measure stock market volatility, a central point in this debate that constitutes the actual scope of this paper is to compare this common approach in which we discuss such popular techniques as the standard deviation and an innovative methodology based on Econophysics. In our study, we use the concept of Tsallis entropy to capture the nature of volatility. More precisely, what we want to find out is if Tsallis entropy is able to detect volatility in stock market indexes and to compare its values with the ones obtained from the standard deviation. Also, we shall mention that one of the advantages of this new methodology is its ability to capture nonlinear dynamics. For our purpose, we shall basically focus on the behaviour of stock market indexes and consider the CAC 40, MIB 30, NIKKEI 225, PSI 20, IBEX 35, FTSE 100 and SP 500 for a comparative analysis between the approaches mentioned above.