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Title: Nonparametric value-at-risk via sieve estimation Authors:  Philipp Ratz - Universite du Quebec a Montreal (UQAM) (Canada) [presenting]
Abstract: Artificial Neural Networks (ANN) have been employed for a range of modelling and prediction tasks using financial data. However, evidence of their predictive performance, especially for time-series data, has been mixed. Whereas some applications find that ANNs provide better forecasts than more traditional estimation techniques, others find that they barely outperform basic benchmarks. The aim is to guide as to when the use of ANNs might result in better results in a general setting. We propose a flexible nonparametric model and extend existing theoretical results for the rate of convergence to include the popular Rectified Linear Unit (ReLU) activation function and compare the rate to other nonparametric estimators. Finite sample properties are then studied with the help of Monte-Carlo simulations to provide further guidance. An application to estimate the Value-at-Risk of portfolios of varying sizes is also considered to show the practical implications.