Recently, great strides have been made in predicting volatility in the financial market. However, the so widely used GARCH model suffers from several problems, like the normality assumption which can lead to unreliable estimates and forecasts. In order to solve this problem, it is possible to introduce different new assumptions in the classical GARCH framework. In particular, the GED-GARCH model supposes that the innovation distribution follows a Generalized Error Distribution (GED). In this paper, after a brief theoretical discussion of the statistical model, we show empirically that it is possible to obtain better results using the GED-GARCH(1,1) instead of GARCH(1,1) based on the normality or t-student assumptions.

Improving Volatility Forecasts with GED-GARCH Model: Evidence from U.S. Stock Market

Paolo Carmelo Cozzucoli
2019

Abstract

Recently, great strides have been made in predicting volatility in the financial market. However, the so widely used GARCH model suffers from several problems, like the normality assumption which can lead to unreliable estimates and forecasts. In order to solve this problem, it is possible to introduce different new assumptions in the classical GARCH framework. In particular, the GED-GARCH model supposes that the innovation distribution follows a Generalized Error Distribution (GED). In this paper, after a brief theoretical discussion of the statistical model, we show empirically that it is possible to obtain better results using the GED-GARCH(1,1) instead of GARCH(1,1) based on the normality or t-student assumptions.
GARCH
Generalized Error Distribution
Volatility
t-student
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/295108
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