Measuring DAX Market Risk: A Neural Network Volatility Mixture Approach

In this paper we propose a framework for estimation and quality control of conditional neural network volatility models for market risk management. In a first step, we derive a conditional volatility model based on gaussian mixture densities, that can be used with linear or neural regression models (extendable even to rule systems or decision trees). In a second step, we introduce performance measures, that measure two different properties of the models’ volatility forecasts important to riskmanagement. The proposed framework is being tested on daily DAX (German stock index) data. Results show, that the neural network volatility mixture approach outperforms GARCH models.