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An approximate long-memory range-based approach for value at risk estimation

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journal contribution
posted on 2023-06-09, 17:04 authored by Xiaochun MengXiaochun Meng, James W Taylor
This 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 Forecasting

ISSN

0169-2070

Publisher

Elsevier

Issue

3

Volume

34

Page range

377-388

Department affiliated with

  • Accounting and Finance Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2019-03-01

First Open Access (FOA) Date

2020-03-30

First Compliant Deposit (FCD) Date

2019-03-01

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