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Forecasting Value at Risk via Intra-day Return Curves

Rice, Gregory and Wirjanto, Tony and Zhao, Yuqian (2020) 'Forecasting Value at Risk via Intra-day Return Curves.' International Journal of Forecasting, 36 (3). pp. 1023-1038. ISSN 0169-2070

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Abstract

Methods for incorporating high resolution intra-day asset price data into risk forecasts are being developed at an increasing pace. Existing methods, such as those based on realized volatility, rely primarily on reducing the observed intra-day price fluctuations to simple scalar summaries. In this paper, we propose several methods that incorporate full intra-day price information as functional data objects in order to forecast Value at Risk (VaR). The methods we consider are based on the recently proposed functional GARCH models and a new functional linear quantile regression model. In addition to providing daily VaR forecasts, these methods can also be used to forecast intra-day VaR curves, which we develop and study along with companion backtests to evaluate the quality of such intra-day risk measures. Using high-frequency trading data from equity and foreign exchange markets, we first forecast 1-day-ahead daily and intra-day VaR by applying the proposed methods and a host of benchmark models. Empirical evidence suggests that the functional GARCH models estimated from over-night cumulative intra-day return curves exhibit competitive performance relative to benchmark models in daily risk management, and produce valid intra-day VaR curves.

Item Type: Article
Uncontrolled Keywords: Value at Risk, Overnight Cumulative Intra-day Return, Functional GARCH, Intraday VaR Backtesting, Forecasting Comparison; Forecasting comparison; Functional GARCH; Intra-day VaR backtesting; Overnight cumulative intra-day return; Value at risk
Divisions: Faculty of Social Sciences
Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 15 Nov 2019 15:59
Last Modified: 26 Feb 2022 02:00
URI: http://repository.essex.ac.uk/id/eprint/25906

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