Please use this identifier to cite or link to this item:
|Title:||Quantile autoregression neural network model with applications to evaluating value at risk|
|Keywords:||Artificial neural network;Quantile autoregression neural network;Quantile autoregression;Quantile regression;Value-at-riska|
|Citation:||Applied Soft Computing,49: pp. 1 - 12,(2016)|
|Abstract:||We develop a new quantile autoregression neural network (QARNN) model based on an artificial neuralnetwork architecture. The proposed QARNN model is flexible and can be used to explore potential non-linear relationships among quantiles in time series data. By optimizing an approximate error functionand standard gradient based optimization algorithms, QARNN outputs conditional quantile functionsrecursively. The utility of our new model is illustrated by Monte Carlo simulation studies and empiricalanalyses of three real stock indices from the Hong Kong Hang Seng Index (HSI), the US S&P500 Index(S&P500) and the Financial Times Stock Exchange 100 Index (FTSE100).|
|Appears in Collections:||Dept of Mathematics Research Papers|
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.