Tail Behavior of ACD Models and Consequences for Likelihood-Based Estimation

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Standard

Tail Behavior of ACD Models and Consequences for Likelihood-Based Estimation. / Cavaliere, Giuseppe; Mikosch, Thomas Valentin; Rahbek, Anders; Rasmussen, Frederik Vilandt.

I: Journal of Econometrics, Bind 238, Nr. 2, 105613, 2024.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Cavaliere, G, Mikosch, TV, Rahbek, A & Rasmussen, FV 2024, 'Tail Behavior of ACD Models and Consequences for Likelihood-Based Estimation', Journal of Econometrics, bind 238, nr. 2, 105613. https://doi.org/10.1016/j.jeconom.2023.105613

APA

Cavaliere, G., Mikosch, T. V., Rahbek, A., & Rasmussen, F. V. (2024). Tail Behavior of ACD Models and Consequences for Likelihood-Based Estimation. Journal of Econometrics, 238(2), [105613]. https://doi.org/10.1016/j.jeconom.2023.105613

Vancouver

Cavaliere G, Mikosch TV, Rahbek A, Rasmussen FV. Tail Behavior of ACD Models and Consequences for Likelihood-Based Estimation. Journal of Econometrics. 2024;238(2). 105613. https://doi.org/10.1016/j.jeconom.2023.105613

Author

Cavaliere, Giuseppe ; Mikosch, Thomas Valentin ; Rahbek, Anders ; Rasmussen, Frederik Vilandt. / Tail Behavior of ACD Models and Consequences for Likelihood-Based Estimation. I: Journal of Econometrics. 2024 ; Bind 238, Nr. 2.

Bibtex

@article{98ffdcca27a64045807f86f8ba3baef3,
title = "Tail Behavior of ACD Models and Consequences for Likelihood-Based Estimation",
abstract = "We establish new results for estimation and inference in financial durations models, where events are observed over a given time span, such as a trading day, or a week. For the classical autoregressive conditional duration (ACD) models by Engle and Russell (1998), we show that the large sample behavior of likelihood estimators is highly sensitive to the tail behavior of the financial durations. In particular, even under stationarity, asymptotic normality breaks down for tail indices smaller than one or, equivalently, when the clustering behavior of the observed events is such that the unconditional distribution of the durations has no finite mean. Instead, we find that estimators are mixed Gaussian and have non-standard rates of convergence. The results are based on exploiting the crucial fact that for duration data the number of observations within any given time span is random. Our results apply to general econometric models where the number of observed events is random.",
author = "Giuseppe Cavaliere and Mikosch, {Thomas Valentin} and Anders Rahbek and Rasmussen, {Frederik Vilandt}",
year = "2024",
doi = "10.1016/j.jeconom.2023.105613",
language = "English",
volume = "238",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier",
number = "2",

}

RIS

TY - JOUR

T1 - Tail Behavior of ACD Models and Consequences for Likelihood-Based Estimation

AU - Cavaliere, Giuseppe

AU - Mikosch, Thomas Valentin

AU - Rahbek, Anders

AU - Rasmussen, Frederik Vilandt

PY - 2024

Y1 - 2024

N2 - We establish new results for estimation and inference in financial durations models, where events are observed over a given time span, such as a trading day, or a week. For the classical autoregressive conditional duration (ACD) models by Engle and Russell (1998), we show that the large sample behavior of likelihood estimators is highly sensitive to the tail behavior of the financial durations. In particular, even under stationarity, asymptotic normality breaks down for tail indices smaller than one or, equivalently, when the clustering behavior of the observed events is such that the unconditional distribution of the durations has no finite mean. Instead, we find that estimators are mixed Gaussian and have non-standard rates of convergence. The results are based on exploiting the crucial fact that for duration data the number of observations within any given time span is random. Our results apply to general econometric models where the number of observed events is random.

AB - We establish new results for estimation and inference in financial durations models, where events are observed over a given time span, such as a trading day, or a week. For the classical autoregressive conditional duration (ACD) models by Engle and Russell (1998), we show that the large sample behavior of likelihood estimators is highly sensitive to the tail behavior of the financial durations. In particular, even under stationarity, asymptotic normality breaks down for tail indices smaller than one or, equivalently, when the clustering behavior of the observed events is such that the unconditional distribution of the durations has no finite mean. Instead, we find that estimators are mixed Gaussian and have non-standard rates of convergence. The results are based on exploiting the crucial fact that for duration data the number of observations within any given time span is random. Our results apply to general econometric models where the number of observed events is random.

U2 - 10.1016/j.jeconom.2023.105613

DO - 10.1016/j.jeconom.2023.105613

M3 - Journal article

VL - 238

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 2

M1 - 105613

ER -

ID: 369492054