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

Meng, Xiaochun and Taylor, James W (2018) An approximate long-memory range-based approach for value at risk estimation. International Journal of Forecasting, 34 (3). pp. 377-388. ISSN 0169-2070

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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.

Item Type: Article
Keywords: Value at Risk; CAViaR; Realized Volatility; Intraday Range; Quantile Regression.
Schools and Departments: University of Sussex Business School > Accounting and Finance
Depositing User: Xiaochun Meng
Date Deposited: 01 Mar 2019 08:52
Last Modified: 04 Nov 2019 10:14

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