Estimating the Conditional Density of Returns Based on Neural Network
نویسندگان
چکیده
This paper adopts a new approach to estimating the conditional probability distribution of asset returns. It is evident that the exact conditional mean or variance is inherently unobservable for time series. In practice, the popular way is to derive from different models such as GARCH by assuming distributions such as normal, student t, or skewed t. Thus the accuracy of forecast strongly depends on the assumption of distribution. The new method avoids the need to assume any distribution by using a neural network (NN) to estimate the potentially nonlinear relationship between VaR (Value at Risk) and returns. Our results show that the forecast from neural network outperforms traditional GARCH models.
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