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On periodic autoregressive stochastic volatility models: structure and estimation.

Author: Nadia Boussaha; Fayçal Hamdi
Publisher: [Abingdon, Oxfordshire?] : Taylor & Francis, 2018.
Edition/Format:   Downloadable article : Document   Computer File : English
Publication:Journal of statistical computation and simulation
Summary:
ABSTRACT: To capture both the volatility evolution and the periodicity feature in the autocorrelation structure exhibited by many nonlinear time series, a Periodic AutoRegressive Stochastic Volatility (PAR - SV) model is proposed. Some probabilistic properties, namely the strict and second-order periodic stationarity, are provided. Furthermore, conditions for the existence of higher-order moments are established.  Read more...
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Material Type: Document, Internet resource
Document Type: Internet Resource, Article, Computer File
All Authors / Contributors: Nadia Boussaha; Fayçal Hamdi
ISSN:0094-9655
OCLC Number: 1052027932
Notes: In: Journal of statistical computation and simulation, Vol. 88, no. 9 (2018), p.1637-1668.
Description: 1 online resource

Abstract:

ABSTRACT: To capture both the volatility evolution and the periodicity feature in the autocorrelation structure exhibited by many nonlinear time series, a Periodic AutoRegressive Stochastic Volatility (PAR - SV) model is proposed. Some probabilistic properties, namely the strict and second-order periodic stationarity, are provided. Furthermore, conditions for the existence of higher-order moments are established. The autocovariance structure of the squares and higher order powers of the PAR - SV process is studied. Its dynamic properties are shown to be consistent with financial time series empirical findings. Ways in which the model may be estimated are discussed. Finally, a simulation study of the performance of the proposed estimation methods is provided and the PAR - SV is applied to model the spot rates of the euro and US dollar both against the Algerian dinar. The empirical analysis shows that the proposed PAR - SV model can be considered as a viable alternative to the periodic generalized autoregressive conditionally heteroscedastic (PGARCH) model.

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