Evolving financial regulations have increased the demand for advanced risk assessment methods. This study presents a GMMN-GARCH model, combining GARCH for volatility modelling in time series with GMMN to capture complex interdependencies among variables. Principal component analysis (PCA) was used to reduce data dimensionality and improve computational efficiency. The model demonstrated robustness and flexibility, accurately reflecting the distribution of log-returns while adapting to current market conditions. Overall, it provides an effective and reliable approach for risk assessment in contemporary financial environments.
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