COVID's worst single day was 4x the 99% VaR. The 2022 rate shock broke the stock-bond negative correlation embedded in the covariance matrix — both TLT and LQD fell alongside equities, invalidating ...

Abstract: Traditional study has some limitation on GARCH models to describe VAR in a market of great volatility, so the purpose of this paper is to look for an effective GARCH model for measuring VAR ...

A powerful approach to probabilistic modelling involves supplementing a set of observed variables with additional latent, or hidden, variables. By defining a joint distribution over visible and latent ...

This is a production-quality Market Risk Value-at-Risk (VaR) system implementing industry-standard methodologies used in major investment banks (HSBC, Barclays, JPMC, Standard Chartered). The system ...

As chaos grips markets in the wake of the US government imposing a vast array of import tariffs, banks’ trading risk models are coming under strain. European bank sources, however, say it is too early ...

Machine learning is an aspect of Artificial Intelligence, which provides computers the ability to teach themselves to automatically improve and learn. It happens to be one of the greatest fields ...

We introduce a new framework for counterparty risk model backtesting based on Bayesian methods. This provides a conceptually sound approach for analyzing model performance that is also straightforward ...

Interpretable latent variable models that probabilistically link behavioral observations to an underlying latent process have increasingly been used to draw inferences on cognition from observed ...