Backtesting Extreme Value Theory models of expected shortfall



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Novales Cinca, Alfonso and Garcia-Jorcano, Laura (2019) Backtesting Extreme Value Theory models of expected shortfall. [ Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE); nº 24, 2019, ISSN: 2341-2356 ]

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We use stock market data to analyze the quality of alternative models and procedures for fore- casting expected shortfall (ES) at different significance levels. We compute ES forecasts from conditional models applied to the full distribution of returns as well as from models that focus on tail events using extreme value theory (EVT). We also apply the semiparametric filtered historical simulation (FHS) approach to ES forecasting to obtain 10-day ES forecasts. At the 10-day hori- zon we also combine FHS with EVT. The performance of the different models is assessed using six different ES backtests recently proposed in the literature. Our results suggest that conditional EVT-based models produce more accurate 1-day and 10-day ES forecasts than do non-EVT based models. Under either approach, asymmetric probability distributions for return innovations tend to produce better forecasts. Incorporating EVT in parametric or semiparametric approaches also improves ES forecasting performance. These qualitative results are also valid for the recent crisis period, even though all models then underestimate the level of risk. FHS narrows the range of numerical forecasts obtained from alternative models, thereby reducing model risk. Combining EVT and FHS seems to be best approach for obtaining accurate ES forecasts.

Item Type:Working Paper or Technical Report
Uncontrolled Keywords:Extreme value theory; Skewed distributions; Expected shortfall; Backtesting; Filtered historical simulation.
Subjects:Social sciences > Economics > Banks and credit unions
Social sciences > Economics > Econometrics
Series Name:Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE)
ID Code:57130
Deposited On:27 Sep 2019 08:56
Last Modified:30 Sep 2019 12:04

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