RangeVaR_s3_XM2_name.pdf (337.94 kB)
An approximate long-memory range-based approach for value at risk estimation
journal contribution
posted on 2023-06-09, 17:04 authored by Xiaochun Meng, James W TaylorThis paper proposes new approximate long-memory VaR models that incorporate intraday price ranges. These models use lagged intraday range with the feature of considering different range components calculated over different time horizons. We also investigate the impact of the market overnight return on the VaR forecasts, which has not yet been considered with the range in VaR estimation. Model estimation is performed using linear quantile regression. An empirical analysis is conducted on 18 market indices. In spite of the simplicity of the proposed methods, the empirical results show that they successfully capture the main features of the financial returns and are competitive with established benchmark methods. The empirical results also show that several of the proposed range-based VaR models, utilizing both the intraday range and the overnight returns, are able to outperform GARCH-based methods and CAViaR models.
History
Publication status
- Published
File Version
- Accepted version
Journal
International Journal of ForecastingISSN
0169-2070Publisher
ElsevierExternal DOI
Issue
3Volume
34Page range
377-388Department affiliated with
- Accounting and Finance Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2019-03-01First Open Access (FOA) Date
2020-03-30First Compliant Deposit (FCD) Date
2019-03-01Usage metrics
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