Department of Economics and Business Economics

Integer-valued trawl processes: A class of stationary infinitely divisible processes

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Integer-valued trawl processes : A class of stationary infinitely divisible processes. / Barndorff-Nielsen, Ole E.; Lunde, Asger; Shephard, Neil; Veraart, Almut.

In: Scandinavian Journal of Statistics, Vol. 41, 2014, p. 693-724.

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@article{5fafad896b3044ab93a6f186088cd290,
title = "Integer-valued trawl processes: A class of stationary infinitely divisible processes",
abstract = "This paper introduces a new continuous-time framework for modelling serially correlated count and integer-valued data. The key component in our new model is the class of integer-valued trawl processes, which are serially correlated, stationary, infinitely divisible processes. We analyse the probabilistic properties of such processes in detail and, in addition, study volatility modulation and multivariate extensions within the new modelling framework. Moreover, we describe how the parameters of a trawl process can be estimated and obtain promising estimation results in our simulation study. Finally, we apply our new modelling framework to high-frequency financial data.",
keywords = "L{\'e}vy bases, Stationarity, Stochastic volatility, Time change, Trawl processes",
author = "Barndorff-Nielsen, {Ole E.} and Asger Lunde and Neil Shephard and Almut Veraart",
note = "Campus adgang til artiklen / Campus access to the article",
year = "2014",
doi = "10.1111/sjos.12056",
language = "English",
volume = "41",
pages = "693--724",
journal = "Scandinavian Journal of Statistics",
issn = "0303-6898",
publisher = "Wiley-Blackwell Publishing Ltd.",

}

RIS

TY - JOUR

T1 - Integer-valued trawl processes

T2 - A class of stationary infinitely divisible processes

AU - Barndorff-Nielsen, Ole E.

AU - Lunde, Asger

AU - Shephard, Neil

AU - Veraart, Almut

N1 - Campus adgang til artiklen / Campus access to the article

PY - 2014

Y1 - 2014

N2 - This paper introduces a new continuous-time framework for modelling serially correlated count and integer-valued data. The key component in our new model is the class of integer-valued trawl processes, which are serially correlated, stationary, infinitely divisible processes. We analyse the probabilistic properties of such processes in detail and, in addition, study volatility modulation and multivariate extensions within the new modelling framework. Moreover, we describe how the parameters of a trawl process can be estimated and obtain promising estimation results in our simulation study. Finally, we apply our new modelling framework to high-frequency financial data.

AB - This paper introduces a new continuous-time framework for modelling serially correlated count and integer-valued data. The key component in our new model is the class of integer-valued trawl processes, which are serially correlated, stationary, infinitely divisible processes. We analyse the probabilistic properties of such processes in detail and, in addition, study volatility modulation and multivariate extensions within the new modelling framework. Moreover, we describe how the parameters of a trawl process can be estimated and obtain promising estimation results in our simulation study. Finally, we apply our new modelling framework to high-frequency financial data.

KW - Lévy bases

KW - Stationarity

KW - Stochastic volatility

KW - Time change

KW - Trawl processes

U2 - 10.1111/sjos.12056

DO - 10.1111/sjos.12056

M3 - Journal article

VL - 41

SP - 693

EP - 724

JO - Scandinavian Journal of Statistics

JF - Scandinavian Journal of Statistics

SN - 0303-6898

ER -