A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Improved tail risk forecasting model for assets using CAViaR with spillover effects.
Paper proposes a method to estimate multiple dynamic quantiles jointly.
Value-at-Risk (VaR) is an institutional measure of risk favored by financial regulators. VaR may be interpreted as a quantile of future portfolio values conditional on the information available, where the most common quantile used is 95%. Here we demonstrate Conditional Autoregressive Value at Risk, first introduced by…
We address the problem of abnormal event detection from trajectory data. In this paper, a new adversarial approach is proposed for building a deep neural network binary classifier, trained in an unsupervised fashion, that can distinguish normal from abnormal trajectory-based events without the need for setting manual d…
This study improves tail risk forecasting by integrating overnight information into semi-parametric models.
GRF models predict cryptocurrency VaR better than other methods.
CAESar improves risk forecasting by combining VaR and ES estimates.
Paper uses AI to predict tail risks in US financial markets.
The paper develops a method to forecast financial risk multiple steps ahead using quantile time series and historical simulation.