The article analyzes financial time series using neural networks. The researchers use feedforward and recurrent neural networks to estimate volatility and market risk. They prove the accuracy of these estimates under different complexities. By introducing new distributions, they improve the estimation of stochastic variance models. Testing on German stocks shows the effectiveness of these methods compared to traditional GARCH models.