Fitting the empirical distribution of intertrade durations

Politi, Mauro; Scalas, Enrico

doi:10.1016/j.physa.2007.11.018

Cited by 68 publications

(66 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the econometric literature, the statistics of inter-trade intervals has been studied empirically and parametric models have been proposed (see, for instance, [15,17,19]). Here, we discuss a few analytical models for the probability density functions f (τ ), which provide reasonable fits to the empirical distributions of inter-trade intervals while at same time possess a convenient analytical expression for their Laplace imagesf (s).…”

Section: Statistics Of Inter-trade Time Intervalsmentioning

confidence: 99%

“…Indeed, using β ≃ 0.75 ÷ 0.85, the Weibull distribution provides a rather accurate representation of the empirical distribution at small and intermediate values of τ . But it goes to zero too fast for large τ values, compared with the slow decay of the empirical probability density function [17]. This justifies using some more flexible distributions, which are related to the Weibull family.…”

Section: Statistics Of Inter-trade Time Intervalsmentioning

confidence: 99%

See 1 more Smart Citation

A Simple Microstructure Return Model Explaining Microstructure Noise and Epps Effects

Sornette

Саичев

2012

SSRN Journal

View full text Add to dashboard Cite

We present a simple microstructure model of financial returns that combines (i) the well-known ARFIMA process applied to tick-by-tick returns, (ii) the bid-ask bounce effect, (iii) the fat tail structure of the distribution of returns and (iv) the non-Poissonian statistics of intertrade intervals. This model allows us to explain both qualitatively and quantitatively important stylized facts observed in the statistics of microstructure returns, including the short-ranged correlation of returns, the long-ranged correlations of absolute returns, the microstructure noise and Epps effects. According to the microstructure noise effect, volatility is a decreasing function of the time scale used to estimate it. Paradoxically, the Epps effect states that cross correlations between asset returns are increasing functions of the time scale at which the returns are estimated. The microstructure noise is explained as the result of the negative return correlations inherent in the definition of the bid-ask bounce component (ii). In the presence of a genuine correlation between the returns of two assets, the Epps effect is due to an average statistical overlap of the momentum of the returns of the two assets defined over a finite time scale in the presence of the long memory process (i).

show abstract

Section: Statistics Of Inter-trade Time Intervalsmentioning

confidence: 99%

Section: Statistics Of Inter-trade Time Intervalsmentioning

confidence: 99%

A Simple Microstructure Return Model Explaining Microstructure Noise and Epps Effects

Sornette

Саичев

2012

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…This is not surprising and can be explained as follows. According to Table 1 We apply the Weibull and the q-exponential distributions to model the weight distributions [34,35]. The Weibull probability density p w (w) can be written as…”

Section: Directed Networkmentioning

confidence: 99%

Statistical properties of world investment networks

Song

Jiang

Zhou

2009

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

Abstract. We have performed a detailed investigation on the world investment networks constructed from the Coordinated Portfolio Investment Survey (CPIS) data of the International Monetary Fund, ranging from 2001 to 2006. The distributions of degrees and node strengthes are scale-free. The weight distributions can be well modeled by the Weibull distribution. The maximum flow spanning trees of the world investment networks possess two universal allometric scaling relations, independent of time and the investment type. The topological scaling exponent is 1.17 ± 0.02 and the flow scaling exponent is 1.03 ± 0.01.

show abstract

“…Recently various authors have introduced several q-type distributions such as q-exponential, q-Weibull, q-gamma etc. Such types of distributions have lots of applications in various contexts such as empirical study of stock markets (Politi and Scalas (2008)), train delays (Briggs and Beck (2007)), DNA sequences (Keylock (2005)) etc. A brief review of q-distributions in complex systems is give in Picoli et al (2009).…”

Section: Introductionmentioning

confidence: 99%

Pathway Extension of Weibull-Gamma Model

Princy¹

2016

JSTA

View full text Add to dashboard Cite

In this paper we develop a new family of Weibull-gamma model which is obtained by compounding a q-Weibull density with a two parameter gamma density. This class can be considered as a natural extension of the Weibullgamma model which accommodates various other useful statistical models. Properties of the proposed model are discussed, including the derivation of its density function. The method of moments is used for estimating the model parameters. The practical importance of the new model is illustrated using cancer survival data.

show abstract

Fitting the empirical distribution of intertrade durations

Cited by 68 publications

References 17 publications

A Simple Microstructure Return Model Explaining Microstructure Noise and Epps Effects

A Simple Microstructure Return Model Explaining Microstructure Noise and Epps Effects

Statistical properties of world investment networks

Pathway Extension of Weibull-Gamma Model

Contact Info

Product

Resources

About