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Updated: Oct 2, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Backtesting VaR under the COVID-19 sudden changes in volatility
Brenda Castillo1, Ángel León1, Trino-Manuel Ñíguez2
1Dept. Fundamentos del Análisis Económico (FAE), Universidad de Alicante, Alicante 03690, Spain.
The COVID-19 pandemic significantly increased stock return variance globally. This shift necessitates adjustments in financial risk management models for accurate Value-at-Risk calculations.
Area of Science:
- Financial Economics
- Econometrics
- Pandemic Studies
Background:
- The COVID-19 pandemic introduced unprecedented volatility into global financial markets.
- Understanding shifts in stock return variance is crucial for financial risk management.
Purpose of the Study:
- To analyze the impact of the COVID-19 pandemic on the conditional variance of stock returns globally.
- To assess the effect on downside risk measures like Value-at-Risk.
Main Methods:
- Utilized Hansen's Skewed-t distribution with an extended EGARCH model.
- Employed time series data from major global stock market and sector indices.
- Controlled for sudden changes in volatility and analyzed Value-at-Risk.
Main Results:
- A significant upward shift in return distribution variance was observed post-pandemic announcement.
- The pandemic demonstrably altered stock market volatility dynamics.
- Downside risk measures were notably affected by the increased variance.
Conclusions:
- The pandemic caused a structural break in stock return variance.
- Accurate financial risk management requires accounting for this pandemic-induced volatility shift.
- Reliable Value-at-Risk estimations depend on models that capture these changes.
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