نمذجة تقلبات العوائد اليومية لمؤشر CAC 40 بتطبيق نموذج APGARCH

الملخص EN

Stock market volatility plays a central role in finance.

Decisions such as portfolio selection and diversification, and risk management rely heavily on volatility estimates.

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.

The ARCH model of Engle (1982) was the first model that tried to account for some of these characteristics.

This was succeeded later by the more flexible GARCH model of Bollerslev (1986).

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 Power GARCH (APGARCH) model proposed by Ding et al.

(1993).

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 CAC 40 index for the period between 2014 and 2018 using the APGARCH model on a sample of 1022 observations.

We show that this model is far more superior in capturing the leverage effect than alternative conditional volatility models.