Cyclical fractional cointegration

Michelle Voges; Philipp Sibbertsen

doi:10.1016/j.ecosta.2020.05.004

Cyclical fractional cointegration

Michelle Voges^*, Philipp Sibbertsen

^*Korrespondierende*r Autor*in für diese Arbeit

Institut für Statistik

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Abstract

The concept of cyclical long memory is extended to a multivariate setting and definitions of cyclical fractional cointegration are provided. Furthermore, cyclical long-memory models that exhibit these characteristics are proposed and a cyclical multiple local Whittle estimator for the cyclical memory parameters and the cyclical cointegrating vector is derived. A series of Monte Carlo studies shows that the proposed method works well in finite samples. Finally, an application to financial high-frequency data underlines the usefulness of the method in practical applications where cyclical fractional cointegration between realized volatility and trading volume is found for a daily cycle.

Originalsprache	Englisch
Seiten (von - bis)	114-129
Seitenumfang	16
Fachzeitschrift	Econometrics and Statistics
Jahrgang	19
Frühes Online-Datum	24 Juni 2020
DOIs	https://doi.org/10.1016/j.ecosta.2020.05.004
Publikationsstatus	Veröffentlicht - Juli 2021

ASJC Scopus Sachgebiete

Statistik und Wahrscheinlichkeit
Volkswirtschaftslehre und Ökonometrie
Statistik, Wahrscheinlichkeit und Ungewissheit

Zugriff auf Dokument

10.1016/j.ecosta.2020.05.004

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

Auswirkungen von Strukturbrüchen auf Inferenzmethoden bei Zeitreihen mit langem Gedächtnis
Sibbertsen, P. (Projektleiter*in (Principal Investigator))
1 Dez. 2017 → 30 Nov. 2021
Projekt: Forschung

Dieses zitieren

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title = "Cyclical fractional cointegration",

abstract = "The concept of cyclical long memory is extended to a multivariate setting and definitions of cyclical fractional cointegration are provided. Furthermore, cyclical long-memory models that exhibit these characteristics are proposed and a cyclical multiple local Whittle estimator for the cyclical memory parameters and the cyclical cointegrating vector is derived. A series of Monte Carlo studies shows that the proposed method works well in finite samples. Finally, an application to financial high-frequency data underlines the usefulness of the method in practical applications where cyclical fractional cointegration between realized volatility and trading volume is found for a daily cycle.",

keywords = "C52, C58), Fractional cointegration (C32, Multivariate time series, Seasonal/Cyclical long memory",

author = "Michelle Voges and Philipp Sibbertsen",

note = "Funding Information: We are very grateful to Christian Leschinski for helpful comments. Special thanks are also due to two anonymous referees whose suggestions improved the paper a lot. Financial support of DFG under grant SI 745/9-2 is gratefully acknowledged. ",

year = "2021",

month = jul,

doi = "10.1016/j.ecosta.2020.05.004",

language = "English",

volume = "19",

pages = "114--129",

journal = "Econometrics and Statistics",

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AU - Voges, Michelle

AU - Sibbertsen, Philipp

N1 - Funding Information: We are very grateful to Christian Leschinski for helpful comments. Special thanks are also due to two anonymous referees whose suggestions improved the paper a lot. Financial support of DFG under grant SI 745/9-2 is gratefully acknowledged.

PY - 2021/7

Y1 - 2021/7

N2 - The concept of cyclical long memory is extended to a multivariate setting and definitions of cyclical fractional cointegration are provided. Furthermore, cyclical long-memory models that exhibit these characteristics are proposed and a cyclical multiple local Whittle estimator for the cyclical memory parameters and the cyclical cointegrating vector is derived. A series of Monte Carlo studies shows that the proposed method works well in finite samples. Finally, an application to financial high-frequency data underlines the usefulness of the method in practical applications where cyclical fractional cointegration between realized volatility and trading volume is found for a daily cycle.

AB - The concept of cyclical long memory is extended to a multivariate setting and definitions of cyclical fractional cointegration are provided. Furthermore, cyclical long-memory models that exhibit these characteristics are proposed and a cyclical multiple local Whittle estimator for the cyclical memory parameters and the cyclical cointegrating vector is derived. A series of Monte Carlo studies shows that the proposed method works well in finite samples. Finally, an application to financial high-frequency data underlines the usefulness of the method in practical applications where cyclical fractional cointegration between realized volatility and trading volume is found for a daily cycle.

KW - C52, C58)

KW - Fractional cointegration (C32

KW - Multivariate time series

KW - Seasonal/Cyclical long memory

UR - http://www.scopus.com/inward/record.url?scp=85088223034&partnerID=8YFLogxK

U2 - 10.1016/j.ecosta.2020.05.004

DO - 10.1016/j.ecosta.2020.05.004

M3 - Article

AN - SCOPUS:85088223034

VL - 19

SP - 114

EP - 129

JO - Econometrics and Statistics

JF - Econometrics and Statistics

ER -

Cyclical fractional cointegration

Abstract

ASJC Scopus Sachgebiete

Zugriff auf Dokument

Andere Dateien und Links

Projekte

Auswirkungen von Strukturbrüchen auf Inferenzmethoden bei Zeitreihen mit langem Gedächtnis

Dieses zitieren