id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-200147-ans8d3oa	Arimond, Alexander	Neural Networks and Value at Risk	2020-05-04		.txt	text/plain	8597	440	52	Specifically, we estimate VaR thresholds using classic methods (i.e. Mean/Variance, Hidden Markov Model) 1 as well as machine learning methods (i.e. feed forward, convolutional, recurrent), which we advance via initialization of input parameter and regularization of incentive function. Using equity markets and long term bonds as test assets in the global, US, Euro area and UK setting over an up to 1,250 weeks sample horizon ending in August 2018, we investigate neural networks along three design steps relating (i) to the initialization of the neural network's input parameter, (ii) its incentive function according to which it has been trained and which can lead to extreme outputs if it is not regularized as well as (iii) the amount of data we feed. Whereas our paper is focused on advancing machine learning techniques and is therefore following Billio and Pellizon (2000) anchored in a regime based asset allocation setting 1 to account for time varying economic states (CPZ, 2020), we still believe that the nonlinearity and flexible form especially of recurrent neural networks maybe of interesting to the VaR (forecasting) literature (Billio et al.	./cache/cord-200147-ans8d3oa.txt	./txt/cord-200147-ans8d3oa.txt