Home > Research > Publications & Outputs > Temporal aggregation of random walk processes a...

Research

Links

Text available via DOI:

View graph of relations

Temporal aggregation of random walk processes and implications for economic analysis

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Temporal aggregation of random walk processes and implications for economic analysis. / Ahmad, Yamin S. ; Paya, Ivan.
In: Studies in Nonlinear Dynamics and Econometrics, Vol. 24, No. 2, 2017-0102, 01.04.2020.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Ahmad, YS & Paya, I 2020, 'Temporal aggregation of random walk processes and implications for economic analysis', Studies in Nonlinear Dynamics and Econometrics, vol. 24, no. 2, 2017-0102. https://doi.org/10.1515/snde-2017-0102

APA

Ahmad, Y. S., & Paya, I. (2020). Temporal aggregation of random walk processes and implications for economic analysis. Studies in Nonlinear Dynamics and Econometrics, 24(2), Article 2017-0102. https://doi.org/10.1515/snde-2017-0102

Vancouver

Ahmad YS, Paya I. Temporal aggregation of random walk processes and implications for economic analysis. Studies in Nonlinear Dynamics and Econometrics. 2020 Apr 1;24(2):2017-0102. Epub 2019 Sept 18. doi: 10.1515/snde-2017-0102

Author

Ahmad, Yamin S. ; Paya, Ivan. / Temporal aggregation of random walk processes and implications for economic analysis. In: Studies in Nonlinear Dynamics and Econometrics. 2020 ; Vol. 24, No. 2.

Bibtex

@article{f7ff27ff9dae4f639d4f445a05f499a6,
title = "Temporal aggregation of random walk processes and implications for economic analysis",
abstract = "This paper examines the impact of time averaging and interval sampling data assuming that the data generating process for a given series follows a random walk with iid errors. We provide exact expressions for the corresponding variances, and covariances, for both levels and higher order differences of the aggregated series, as well as that for the variance ratio, demonstrating exactly how the degree of temporal aggregation impacts these properties. We empirically investigate this issue on exchange rates and find that the values of the variance ratios and autocorrelation coefficients at different frequencies are consistent with our theoretical results. We also conduct a simulation exercise that illustrates the potential effect that conditional heteroskedasticity and fat tails may have on the temporal aggregation of a random walk and of a highly persistent autoregressive process.",
author = "Ahmad, {Yamin S.} and Ivan Paya",
year = "2020",
month = apr,
day = "1",
doi = "10.1515/snde-2017-0102",
language = "English",
volume = "24",
journal = "Studies in Nonlinear Dynamics and Econometrics",
issn = "1558-3708",
publisher = "Berkeley Electronic Press",
number = "2",

}

RIS

TY - JOUR

T1 - Temporal aggregation of random walk processes and implications for economic analysis

AU - Ahmad, Yamin S.

AU - Paya, Ivan

PY - 2020/4/1

Y1 - 2020/4/1

N2 - This paper examines the impact of time averaging and interval sampling data assuming that the data generating process for a given series follows a random walk with iid errors. We provide exact expressions for the corresponding variances, and covariances, for both levels and higher order differences of the aggregated series, as well as that for the variance ratio, demonstrating exactly how the degree of temporal aggregation impacts these properties. We empirically investigate this issue on exchange rates and find that the values of the variance ratios and autocorrelation coefficients at different frequencies are consistent with our theoretical results. We also conduct a simulation exercise that illustrates the potential effect that conditional heteroskedasticity and fat tails may have on the temporal aggregation of a random walk and of a highly persistent autoregressive process.

AB - This paper examines the impact of time averaging and interval sampling data assuming that the data generating process for a given series follows a random walk with iid errors. We provide exact expressions for the corresponding variances, and covariances, for both levels and higher order differences of the aggregated series, as well as that for the variance ratio, demonstrating exactly how the degree of temporal aggregation impacts these properties. We empirically investigate this issue on exchange rates and find that the values of the variance ratios and autocorrelation coefficients at different frequencies are consistent with our theoretical results. We also conduct a simulation exercise that illustrates the potential effect that conditional heteroskedasticity and fat tails may have on the temporal aggregation of a random walk and of a highly persistent autoregressive process.

U2 - 10.1515/snde-2017-0102

DO - 10.1515/snde-2017-0102

M3 - Journal article

VL - 24

JO - Studies in Nonlinear Dynamics and Econometrics

JF - Studies in Nonlinear Dynamics and Econometrics

SN - 1558-3708

IS - 2

M1 - 2017-0102

ER -