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

Xiaochun Meng () and James W. Taylor

International Journal of Forecasting, 2018, vol. 34, issue 3, 377-388

Abstract: This paper proposes new approximate long-memory VaR models that incorporate intra-day price ranges. These models use lagged intra-day 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 intra-day range and the overnight returns, are able to outperform GARCH-based methods and CAViaR models.

Keywords: Value at risk; CAViaR; Realized volatility; Intra-day range; Quantile regression (search for similar items in EconPapers)
Date: 2018
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Handle: RePEc:eee:intfor:v:34:y:2018:i:3:p:377-388