Quantile autoregression neural network model with applications to evaluating value at risk

We develop a new quantile autoregression neural network (QARNN) model based on an artificial neural network architecture. The proposed QARNN model is flexible and can be used to explore potential nonlinear relationships among quantiles in time series data. By optimizing an approximate error function and standard gradient based optimization algorithms, QARNN outputs conditional quantile functions recursively. The utility of our new model is illustrated by Monte Carlo simulation studies and empirical analyses of three real stock indices from the Hong Kong Hang Seng Index (HSI), the US S&P500 Index eywords: rtificial neural network uantile autoregression neural network QARNN) uantile autoregression uantile regression (S&P500) and the Financial Times Stock Exchange 100 Index (FTSE100). © 2016 Elsevier B.V. All rights reserved.