Forecasting extreme financial risk: A score-driven approach
Fernanda Fuentes,
Rodrigo Herrera and
Adam Clements
International Journal of Forecasting, 2023, vol. 39, issue 2, 720-735
Abstract:
This paper develops a new class of dynamic models for forecasting extreme financial risk. This class of models is driven by the score of the conditional distribution with respect to both the duration between extreme events and the magnitude of these events. It is shown that the models are a feasible method for modeling the time-varying arrival intensity and magnitude of extreme events. It is also demonstrated how exogenous variables such as realized measures of volatility can easily be incorporated. An empirical analysis based on a set of major equity indices shows that both the arrival intensity and the size of extreme events vary greatly during times of market turmoil. The proposed framework performs well relative to competing approaches in forecasting extreme tail risk measures.
Keywords: Forecasting; Score-driven models; Time-varying parameters; Extreme value theory; Value at Risk; Expected Shortfall; Realized volatility (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:2:p:720-735
DOI: 10.1016/j.ijforecast.2022.02.002
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