Modeling The Volatility of DAX30 Index Using EGARCH Model
Abstract
Financial time series exhibit a number of characteristics, the most important of which are volatility clustering, heavy tail of underlying distribution, and the leverage effect. Nevertheless, both models failed to properly model the leverage effect. Although the literature has seen many proposed models, perhaps the most striking of these models is the Asymmetric EGARCH model. Its ability to account for the asymmetric effect of good and bad news on volatility has made it one of the most adopted model for volatility. In this paper, we will attempt to model the daily returns of the DAX30 index for the period between 2014 and 2019 using the EGARCH model on a sample of 1264 observations. We show that this model is far more superior in capturing the leverage effect than alternative conditional volatility models.
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References
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