RT Journal Article
T1 Anticipating extreme losses using score-driven shape filters
A1 Ayala, Astrid
A1 Blazsek, Szabolcs Istvan
A1 Escribano, Álvaro
AB We suggest a new value-at-risk (VaR) framework using EGARCH (exponential generalized autoregressive conditional heteroskedasticity) models with score-driven expected return, scale, and shape filters. We use the EGB2 (exponential generalized beta of the second kind), NIG (normal-inverse Gaussian), and Skew-Gen-t (skewed generalized-t) distributions, for which the score-driven shape parameters drive the skewness, tail shape, and peakedness of the distribution. We use daily data on the Standard & Poor"s 500 (S&P 500) index for the period of February 1990 to October 2021. For all distributions, likelihood-ratio (LR) tests indicate that several EGARCH models with dynamic shape are superior to the EGARCH models with constant shape. We compare the realized volatility with the conditional volatility estimates, and we find two Skew-Gen-t specifications with dynamic shape, which are superior to the Skew-Gen-t specification with constant shape. The shape parameter dynamics are associated with important events that affected the stock market in the United States (US). VaR backtesting is performed for the dot.com boom (January 1997 to October 2020), the 2008 US Financial Crisis (October 2007 to March 2009), and the coronavirus disease (COVID-19) pandemic (January 2020 to October 2021). We show that the use of the dynamic shape parameters improves the VaR measurements
PB De Gruyter
SN 1081-1826
YR 2022
FD 2022-01-01
LK https://hdl.handle.net/10016/38456
UL https://hdl.handle.net/10016/38456
LA eng
NO Ayala and Blazsek acknowledge funding from Universidad Francisco Marroquín. Escribano acknowledges funding from the Spanish Ministry of Economy, Industry and Competitiveness (ECO2015-68715-R, ECO2016-00105-001), Consolidation Grant (#2006/04046/002), and Maria de Maeztu Grant (MDM 2014-0431).
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RD 20 may. 2024