The distribution of first-passage times and durations in FOREX and future markets

TL;DR

This paper investigates the distribution of first-passage times in financial markets, proposing a Weibull distribution with a power-law tail to better model market durations and improve the understanding of waiting times.

Contribution

It introduces a Weibull distribution with a power-law tail for modeling first-passage times, addressing limitations of previous models and providing a practical formula for optimal crossover points.

Findings

Weibull distribution is more convenient than Mittag-Leffler for long durations.

The Gini index remains stable across different cut-off parameters.

The proposed distribution effectively models durations with Weibull law and moderate tails.

Abstract

Possible distributions are discussed for intertrade durations and first-passage processes in financial markets. The view-point of renewal theory is assumed. In order to represent market data with relatively long durations, two types of distributions are used, namely, a distribution derived from the so-called Mittag-Leffler survival function and the Weibull distribution. For Mittag-Leffler type distribution, the average waiting time (residual life time) is strongly dependent on the choice of a cut-off parameter t_ max, whereas the results based on the Weibull distribution do not depend on such a cut-off. Therefore, a Weibull distribution is more convenient than a Mittag-Leffler type one if one wishes to evaluate relevant statistics such as average waiting time in financial markets with long durations. On the other side, we find that the Gini index is rather independent of the cut-off parameter. Based on the above considerations, we propose a good candidate for describing the distribution of first-passage time in a market: The Weibull distribution with a power-law tail. This distribution compensates the gap between theoretical and empirical results much more efficiently than a simple Weibull distribution. We also give a useful formula to determine an optimal crossover point minimizing the difference between the empirical average waiting time and the one predicted from renewal theory. Moreover, we discuss the limitation of our distributions by applying our distribution to the analysis of the BTP future and calculating the average waiting time. We find that our distribution is applicable as long as durations follow a Weibull-law for short times and do not have too heavy a tail.