Details
| Original language | English |
|---|---|
| Pages (from-to) | 114-129 |
| Number of pages | 16 |
| Journal | Econometrics and Statistics |
| Volume | 19 |
| Early online date | 24 Jun 2020 |
| Publication status | Published - Jul 2021 |
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
ASJC Scopus subject areas
- Mathematics(all)
- Statistics and Probability
- Economics, Econometrics and Finance(all)
- Economics and Econometrics
- Decision Sciences(all)
- Statistics, Probability and Uncertainty
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In: Econometrics and Statistics, Vol. 19, 07.2021, p. 114-129.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Cyclical fractional cointegration
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 -