Forecasting Value at Risk (VaR) and Expected Shortfall (ES) in the Tehran Stock Exchange Index Using Stateful Recurrent Neural Network (Stateful-RNN) Models

The increasing importance of financial risk measurement, driven by regulatory requirements and market uncertainties, necessitates precise forecasting tools for Value at Risk (VaR) and Expected Shortfall (ES). This study introduces three novel models based on Stateful Recurrent Neural Networks (RNNs) and Feedforward Neural Networks (FNNs) for predicting VaR and ES. These models are evaluated against traditional econometric models, including Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, using a dataset of the Tehran Exchange Divedend and Price Index (TEDPIX) spanning more than three decades. This research presents a comprehensive comparison between Stateful and Non-Stateful RNN models, demonstrating that Stateful RNNs significantly enhance forecasting performance. First, the data is standardized before being fed into machine learning models and then used as input for the models. The results indicate strong predictive performance, while a reduction in the FZ۰ loss score highlights the superior accuracy of these models. In the empirical study, the proposed models are applied to the Tehran Exchange Divedend and Price Index (TEDPIX). The models’ performance is assessed using the FZ۰ loss function and backtesting methods, such as DQ regression tests and DES tests. The findings reveal that Stateful RNN-based models outperform traditional econometric models in terms of both accuracy and stability. In particular, the STRNN-II and STRNN-III models consistently rank highest, demonstrating their superior ability to capture the complex dynamics of financial risk. Moreover, these models do not rely on strong distributional assumptions, offering greater flexibility than traditional parametric models like GARCH.

کلیدواژه ها:

Risk models ، Value-at-Risk ، Expected Shortfall ، Machine Learning ، Neural Networks