Temporal structure and gain/loss asymmetry for real and artificial stock indices

TL;DR

This paper investigates the gain/loss asymmetry in stock indices, showing it depends on temporal structure and is linked to increased dependence among stocks during downturns, with implications for understanding market dynamics.

Contribution

It reveals that gain/loss asymmetry depends on temporal dependence and demonstrates its relation to increased stock dependence during market downturns, using artificial indices and dependence measures.

Findings

Gain/loss asymmetry vanishes when temporal structure is destroyed.

Artificial indices exhibit gain/loss asymmetry, enabling analysis of constituent dependence.

Stock returns show higher dependence during downturns than upturns.

Abstract

We demonstrate that the gain/loss asymmetry observed for stock indices vanishes if the temporal dependence structure is destroyed by scrambling the time series. We also show that an artificial index constructed by a simple average of a number of individual stocks display gain/loss asymmetry - this allows us to explicitly analyze the dependence between the index constituents. We consider mutual information and correlation based measures and show that the stock returns indeed have a higher degree of dependence in times of market downturns than upturns.