An accurate data-driven and model-agnostic method to compute conditional expectations is presented
Archegos, GameStop, the last days of Libor – markets just about coped in a bleak and disorderly year
Customer churn prediction for commercial banks using customer-value-weighted machine learning models
In this paper the authors propose a framework to address the issue of customer churn prediction, and they quantify customer values with the use of an improved customer value model.
In this paper, the authors propose to approach the calibration problem of local volatility with Bayesian statistics to infer a conditional distribution over functions given observed data.
New technologies such as artificial intelligence (AI) and machine learning promise much in the battle against financial crime, but where are these solutions best deployed? A panel of anti-money laundering and analytics professionals convened for a Risk…
Research into valuation adjustments is back on quants’ to-do list
A multi-step path is forecast using deep learning and parallel computing
Risk USA: machine learning model generates more realistic estimates of trading losses
Risk USA: probability theory may hold key to creating ‘self-aware’ AI
Risk USA: banks “on the precipice” of adopting more complex models, says Goldman exec
In this paper, different machine learning approaches are applied to forecasting future yearly price trends in the natural gas Title Transfer Facility market in the Netherlands.
This webinar features leading compliance and risk management professionals and focuses on how firms can handle regulatory change management, fraud prevention, AML and other compliance needs through the use of an optimal data and AI foundation built for…
Machine learning method edges regression techniques in linking nonlinearities among delinquent borrowers
Tenfold increase in web-enabled devices via 5G and IoT means explosion in cyber threats, says official
Quants use neural networks to upgrade classic options pricing model
Closely watched arbitrage spread poor predictor of a merger deal’s success, quant firm finds
Superior computational grunt of neural networks is attractive to lenders. Lack of explainability is the downside
This study proposes an advanced credit evaluation method for nonperforming consumer loans, which may serve as a new investment opportunity in the post-pandemic era.
Quants achieve more speed by reducing number of dimensions in price calculations
This paper surveys the impressively broad range of machine learning methods and application areas for credit risk.
Comprehensive Capital Analysis and Review consistent yield curve stress testing: from Nelson–Siegel to machine learning
This paper develops different techniques for interpreting yield curve scenarios generated from the FRB’s annual CCAR review.
Differential PCA is introduced to reduce the dimensionality in derivative pricing problems
Firms adapt backtests and tread lightly to address “huge” overfitting risk, magnified by scarce data