Modeling the variance of return intervals toward volatility prediction
Modeling the variance of return intervals toward volatility prediction
Interval-valued time series has been attracting increasing interest. There have been fruitful results on mean models, but variance models largely remain unexploited. In this paper, we propose a conditional heteroskedasticity model for the return interval process, which aims at capturing the underlying variance structure. Under the general framework of random sets, the model properties are investigated. Parameters are estimated by the maximum likelihood method, and the asymptotic properties are established. Empirical application to stocks and financial indices data sets suggests that our model overall outperforms the traditional GARCH for both in-sample estimation and out-of-sample prediction of the volatility.
interval-valued time series, stationarity, maximum likelihood estimate, random sets, price range, Volatility
Modeling the Variance of Return Intervals Toward Volatility
Sun, Yan
b85c8694-c05c-47ee-baab-2c070d509de3
Lian, Guanghua
be7c005c-ff0c-4b7e-8e0b-4aa47fc15f75
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
Loveland, Jennifer
bf3fc213-5660-45d2-9c80-898a7547b2e5
Blackhurst, Isaac
8ffc9ec9-0268-4efd-92c9-4ea5cc3a4cb7
Sun, Yan
b85c8694-c05c-47ee-baab-2c070d509de3
Lian, Guanghua
be7c005c-ff0c-4b7e-8e0b-4aa47fc15f75
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
Loveland, Jennifer
bf3fc213-5660-45d2-9c80-898a7547b2e5
Blackhurst, Isaac
8ffc9ec9-0268-4efd-92c9-4ea5cc3a4cb7
Sun, Yan, Lian, Guanghua, Lu, Zudi, Loveland, Jennifer and Blackhurst, Isaac
(2019)
Modeling the variance of return intervals toward volatility prediction.
Journal of Time Series Analysis.
(Modeling the Variance of Return Intervals Toward Volatility).
Abstract
Interval-valued time series has been attracting increasing interest. There have been fruitful results on mean models, but variance models largely remain unexploited. In this paper, we propose a conditional heteroskedasticity model for the return interval process, which aims at capturing the underlying variance structure. Under the general framework of random sets, the model properties are investigated. Parameters are estimated by the maximum likelihood method, and the asymptotic properties are established. Empirical application to stocks and financial indices data sets suggests that our model overall outperforms the traditional GARCH for both in-sample estimation and out-of-sample prediction of the volatility.
Text
Int-GARCH_jtsa_1_final
- Accepted Manuscript
More information
Accepted/In Press date: 15 November 2019
e-pub ahead of print date: 15 December 2019
Keywords:
interval-valued time series, stationarity, maximum likelihood estimate, random sets, price range, Volatility
Identifiers
Local EPrints ID: 436478
URI: http://eprints.soton.ac.uk/id/eprint/436478
DOI: Modeling the Variance of Return Intervals Toward Volatility
ISSN: 0143-9782
PURE UUID: 564bbe79-5801-4cc7-90b6-1dd3447d894c
Catalogue record
Date deposited: 11 Dec 2019 17:30
Last modified: 17 Mar 2024 05:07
Export record
Altmetrics
Contributors
Author:
Yan Sun
Author:
Guanghua Lian
Author:
Jennifer Loveland
Author:
Isaac Blackhurst
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
