Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

The interest in forecasting the Value at Risk (VaR) has been growing over the last two
decades, due to the practical relevance of this risk measure for financial and insurance …

Value at Risk (VaR) forecasts can be produced from conditional autoregressive VaR
models, estimated using quantile regression. Quantile modeling avoids a distributional …

Combining provides a pragmatic way of synthesising the information provided by individual
forecasting methods. In the context of forecasting the mean, numerous studies have shown …

Abstract Value-at-Risk (VaR) is a popular measure of market risk. To convey information
regarding potential exceedances beyond the VaR, Expected Shortfall (ES) has become the …

The Gaussianity assumption has been consistently criticized as a main limitation of the
Variational Autoencoder (VAE) despite its efficiency in computational modeling. In this …

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 …

This paper considers nonlinear expectile regression models to estimate conditional
expected shortfall (ES) and Value-at-Risk (VaR). In the literature, the asymmetric least …

Support vector regression is a promising method for time-series prediction, as it has good
generalisability and an overall stable behaviour. Recent studies have shown that it can …

A new framework for the joint estimation and forecasting of dynamic value at risk (VaR) and
expected shortfall (ES) is proposed by our incorporating intraday information into a …