Empirical risk minimization is a standard principle for choosing algorithms in learning theory. In this paper we study … covers different types of forecasting applications encountered in the literature. We are concerned with 1-step …

Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification …. There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making …

learning problem in finance: time series forecasting. The underlying idea is to use the attention mechanism and the seq2seq … series forecasting in finance. The first part of this article systematically reviews the Transformer model while highlighting … forecasting. In addition, our paper discusses the issues and considerations when using machine learning models in finance …

We examine the impact of temporal and portfolio aggregation on the quality of Value-at-Risk (VaR) forecasts over a … based on asset class, or into a single portfolio. We compare the impact of aggregation to that of choosing a model for the … that the degree of temporal aggregation is most important. Daily returns form the best basis for VaR forecasts. Modelling …

We examine the impact of temporal and portfolio aggregation on the quality of Value-at-Risk (VaR) forecasts over a … based on asset class, or into a single portfolio. We compare the impact of aggregation to that of choosing a model for the … that the degree of temporal aggregation is most important. Daily returns form the best basis for VaR forecasts. Modelling …

. Two approaches based on the extreme value theory were compared: Block Maxima and the Peaks Over Threshold. Forecasts were …