Abstract
The study analyses the volatility behaviour of the CAC 40 index using daily stock market data from January 25th, 2007, to 24th April 24th, 2026. The main purpose of the research is to examine market volatility and identify the most suitable GARCH family models for volatility estimation and forecasting. Daily closing prices were converted to log returns, and various statistical and econometric techniques, including graphical analysis, the Augmented Dickey–Fuller (ADF) test, and the Ljung–Box test, were applied to examine the series' behaviour. The results indicate volatility clustering, volatility persistence, and asymmetric effects in the CAC 40 return series. The ADF test confirmed that the log-return series is stationary, while the Ljung–Box test indicated significant autocorrelation in the data, supporting the use of GARCH-family models. Different models, including GARCH, EGARCH, GJR-GARCH, T-GARCH, and APARCH models with various distributions, were estimated and compared using Log-Likelihood, AIC, and BIC values. Among all the models, the T-GARCH model with skewed Student’s t-distribution was found to be the most suitable for the study. The findings further reveal that negative market news has a stronger effect on volatility than positive news and that market shocks remain for a longer period. The conditional volatility analysis also highlighted major spikes during the 2008 financial crisis and the COVID-19 pandemic. Overall, the study concludes that the selected T-GARCH model effectively captures the volatility behaviour of the CAC 40 Index and can be useful for investors, researchers, and policymakers in understanding market risk and making informed financial decisions.
Cuvinte cheie
CAC 40 index
volatility forecasting
T-GARCH model
Skewed Student’s t-distribution
leverage effects
Istoric articol
Publicat
31.08.2026
Informații autori
Citare recomandată
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