Time-scale effects on the gain-loss asymmetry in stock indices

TL;DR

This paper investigates the time-scale of auto-correlations causing gain-loss asymmetry in stock indices, revealing a shorter characteristic time-scale than previously thought, which has decreased with the advent of program trading.

Contribution

It introduces a generalized time-window shuffling method to identify the auto-correlation time-scale and links it to market phenomena like program trading and the leverage effect.

Findings

Auto-correlations have a characteristic time-scale less than 25 trading days.

This time-scale has decreased with the rise of program trading.

Connections between auto-correlations and the leverage effect are established.

Abstract

The gain-loss asymmetry, observed in the inverse statistics of stock indices is present for logarithmic return levels that are over $2\%$, and it is the result of the non-Pearson type auto-correlations in the index. These non-Pearson type correlations can be viewed also as functionally dependent daily volatilities, extending for a finite time interval. A generalized time-window shuffling method is used to show the existence of such auto-correlations. Their characteristic time-scale proves to be smaller (less than $25$ trading days) than what was previously believed. It is also found that this characteristic time-scale has decreased with the appearance of program trading in the stock market transactions. Connections with the leverage effect are also established.