Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/108956
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Title: On a buffered threshold autoregressive stochastic volatility model
Authors: Wang, Q 
Yiu, KFC 
Wong, H 
Issue Date: Nov-2022
Source: Applied stochastic models in business and industry, Nov.-Dec. 2022, v. 38, no. 6, p. 974-996
Abstract: This article introduces a new autoregressive stochastic volatility (SV) model with a new piecewise linear structure such that the regime-switching mechanism has a buffer zone where regime-switching is delayed. The proposed model allows us to model the hysteretic phenomenon of the regime-switching existing on both the mean equation and the volatility equation. A full description of the proposed Markov chain Monte Carlo method is given. In the empirical study, we consider the daily closing prices of NIKKEI stock average, the exchange rate for US Dollar to Japanese Yen and Hang Seng Index. Deviance information criterion measure shows that our proposed model outperforms the classical threshold SV models.
Keywords: Bayesian inference
Buffer zone
Kalman filter
Stochastic volatility
Threshold estimation
Publisher: John Wiley & Sons Ltd.
Journal: Applied stochastic models in business and industry 
ISSN: 1524-1904
EISSN: 1526-4025
DOI: 10.1002/asmb.2689
Rights: © 2022 John Wiley & Sons, Ltd.
This is the peer reviewed version of the following article: Wang Q, Yiu K-FC, Wong H. On a buffered threshold autoregressive stochastic volatility model. Appl Stochastic Models Bus Ind. 2022; 38(6): 974–996, which has been published in final form at https://doi.org/10.1002/asmb.2689. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
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