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Estimating Expected Shortfall Using Parametric and Non-Parametric Approaches

Hedman, Fredrik LU and Håkansson, Emil LU (2020) NEKN02 20201
Department of Economics
Abstract
With the implementation of the Fundamental Review of the Trading Book in January of 2022, financial institutions will be obligated to implement Expected Shortfall as a means of determining market risk capital. With the transition from Value at Risk to Expected Shortfall, the question of how to accurately forecast Expected Shortfall arises. This paper investigates the forecasting ability of non-parametric and parametric approaches used for estimating Expected Shortfall. More specifically, the paper considers, Basic Historical Simulation, Age-Weighted Historical Simulation, Volatility- Weighted Historical Simulation as well as parametric models based on a Normal distribution, t-distribution and on Extreme Value Theory. As a number of... (More)
With the implementation of the Fundamental Review of the Trading Book in January of 2022, financial institutions will be obligated to implement Expected Shortfall as a means of determining market risk capital. With the transition from Value at Risk to Expected Shortfall, the question of how to accurately forecast Expected Shortfall arises. This paper investigates the forecasting ability of non-parametric and parametric approaches used for estimating Expected Shortfall. More specifically, the paper considers, Basic Historical Simulation, Age-Weighted Historical Simulation, Volatility- Weighted Historical Simulation as well as parametric models based on a Normal distribution, t-distribution and on Extreme Value Theory. As a number of previous studies have investigated the ability of various estimation approaches’ ability not to underestimate market risk, the concept of overestimation of risk is introduced. The empirical results indicate that while the conditional Peaks Over Threshold approach yields the most satisfactory results when only underestimation is of a concern, the Volatility-Weighted Historical Simulation most accurately forecasts Expected shortfall when the concept of overestimation is introduced. (Less)
Please use this url to cite or link to this publication:
author
Hedman, Fredrik LU and Håkansson, Emil LU
supervisor
organization
course
NEKN02 20201
year
type
H1 - Master's Degree (One Year)
subject
keywords
Value at Risk, Expected Shortfall, Normal distribution, t-distribution, Historical Simulation, Extreme Value Theory, Peaks Over Threshold
language
English
id
9018400
date added to LUP
2020-08-29 11:17:57
date last changed
2020-08-29 11:17:57
@misc{9018400,
  abstract     = {{With the implementation of the Fundamental Review of the Trading Book in January of 2022, financial institutions will be obligated to implement Expected Shortfall as a means of determining market risk capital. With the transition from Value at Risk to Expected Shortfall, the question of how to accurately forecast Expected Shortfall arises. This paper investigates the forecasting ability of non-parametric and parametric approaches used for estimating Expected Shortfall. More specifically, the paper considers, Basic Historical Simulation, Age-Weighted Historical Simulation, Volatility- Weighted Historical Simulation as well as parametric models based on a Normal distribution, t-distribution and on Extreme Value Theory. As a number of previous studies have investigated the ability of various estimation approaches’ ability not to underestimate market risk, the concept of overestimation of risk is introduced. The empirical results indicate that while the conditional Peaks Over Threshold approach yields the most satisfactory results when only underestimation is of a concern, the Volatility-Weighted Historical Simulation most accurately forecasts Expected shortfall when the concept of overestimation is introduced.}},
  author       = {{Hedman, Fredrik and Håkansson, Emil}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Estimating Expected Shortfall Using Parametric and Non-Parametric Approaches}},
  year         = {{2020}},
}