|
|||||
|
|
Quantile autoregression neural network model with applications to evaluating value at risk |
|
| Affiliation: | 1. School of Management, Hefei University of Technology, Hefei 230009, Anhui, PR China;2. Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, Anhui, PR China;3. School of Statistics, Shandong Institute of Business and Technology, Yantai 264005, Shandong, PR China;1. Research Laboratory for Economy, Management and Quantitative Finance (LaREMFiQ), IHEC, University of Sousse, Tunisia;2. LaREMFiQ, IHEC, University of Sousse, Tunisia;1. Financial Engineering Research Unit, Department of Management Science and Technology, Athens University of Economics and Business, 47A Evelpidon Str., 11362 Athens, Greece;2. Bank of Greece, Financial Stability Department, 3 Amerikis Str., 105 64 Athens, Greece |
| Abstract: | 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 (S&P500) and the Financial Times Stock Exchange 100 Index (FTSE100). |
| Keywords: | Artificial neural network Quantile autoregression neural network (QARNN) Quantile autoregression Quantile regression Value-at-risk |
| 本文献已被 ScienceDirect 等数据库收录！ | |