Estimating value at risk: LSTM vs. GARCH. (arXiv:2207.10539v1 [q-fin.RM])

Estimating value-at-risk on time series data with possibly heteroscedastic
dynamics is a highly challenging task. Typically, we face a small data problem
in combination with a high degree of non-linearity, causing difficulties for
both classical and machine-learning estimation algorithms. In this paper, we
propose a novel value-at-risk estimator using a long short-term memory (LSTM)
neural network and compare its performance to benchmark GARCH estimators.

Our results indicate that even for a relatively short time series, the LSTM
could be used …

arxiv garch lstm risk value