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

By Qifa Xu, Xi Liu, Cuixia Jiang, Keming Yu. 2016

58 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Finance, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Heng Huang
2. Kari Torkkola
3. Ruofeng Wen

In Finance

1. Michael Bohlke-Schneider
2. David Salinas
3. Valentin Flunkert
4. Jan Gasthaus

In Management Science and Operations Research

1. Bernie Wang
2. Tim Januschowski
3. Jan Gasthaus
4. Syama Sundar Rangapuram
5. Dhruv Madeka
6. Danielle C. Maddix
7. David Salinas
8. Bryan Lim

Citations in the corpus

• A Multi-Horizon Quantile Recurrent Forecaster (2017, background): “Xu et al, 2016 designed a quantile autoregressive neural net for stock price prediction.”
• Probabilistic Forecasting with Spline Quantile Function RNNs (2019, background): “The literature contains further variants (Feng et al., 2010; Xu et al., 2016) all of which su↵er from the quantile crossing problem.”
• GluonTS: Probabilistic Time Series Models in Python (2019, methodology): “These techniques have been combined with deep learning and employed in the context of time series forecasting (Xu et al., 2016; Wen et al., 2017).”
• Multi-Horizon Time Series Forecasting with Temporal Attention Learning (2019, methodology): “Many recent forecasting models [29, 31] are designed to produce quantile estimations which are evaluated with quantile loss functions.”