UNMASKING MARKET MYSTERIES: BRIDGING FINANCIAL AND NEURAL FORECASTING IN RISK ASSESSMENT ACROSS GLOBAL MARKETS

RAIMA AMJAD, DR. ZEESHAN AHMED

The study is the discussion of the neural network model integration within the conventional financial forecasting models in order to improve the degree of risk exposure in the global market. The study compares market volatility in major economies using a combination of the statistical forecasting and neural forecasting models such as LSTM using Value at Risk (VaR), Conditional Value at risk-CVaR and Mean Squared error (MSE) as two key indicators. A comparison of these methods shows that neural networks are more powerful to capture non-linear relationships and latent patterns as compared to traditional econometric models. The results show that there is a better predictive accuracy and less estimation errors, especially when the market is volatile. This research paper draws attention to the real-world applications of neural forecasting to strategic decision-making, assessment of sovereign risk, and management of tail-risk. The uniqueness is that it empirically fills the quantitative financial analysis with artificial intelligence, providing a holistic framework that increases the accuracy, flexibility, and interpretability of market risk forecasts to a variety of financial contexts.