Machine learning
Podcast: UBS’s Gordon Lee on conditional expectations and XVAs
Top quant explains why XVA desks need a neighbour and a reverend
Dynamically controlled kernel estimation
An accurate data-driven and model-agnostic method to compute conditional expectations is presented
Review of 2021: Default, revolt, reform
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.
Probabilistic machine learning for local volatility
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.
Language barrier: quants slog to teach investing bots to read
Training models to interpret text can be dull; doing it badly can be costly
Moonshots and machines: can AI solve the problems of fincrime?
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…
Degree of influence 2021: XVA marks the spot
Research into valuation adjustments is back on quants’ to-do list
Multi-horizon forecasting for limit order books
A multi-step path is forecast using deep learning and parallel computing
RBC applies ‘deep hedging’ to stress scenarios
Risk USA: machine learning model generates more realistic estimates of trading losses
Quants see promise in Bayesian machine learning
Risk USA: probability theory may hold key to creating ‘self-aware’ AI
Scalability could trump complexity in machine learning debate
Risk USA: banks “on the precipice” of adopting more complex models, says Goldman exec
Forecasting natural gas price trends using random forest and support vector machine classifiers
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.
Data and AI: addressing increasing regulation for smarter compliance
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…
Neural networks show fewer false positives on bad loans – study
Machine learning method edges regression techniques in linking nonlinearities among delinquent borrowers
Fed: banks may need AI risk systems to cope with smart devices
Tenfold increase in web-enabled devices via 5G and IoT means explosion in cyber threats, says official
An ‘optimal’ way to calculate future P&L distributions?
Quants use neural networks to upgrade classic options pricing model
Machines say: ‘Ignore the spread in merger arb’
Closely watched arbitrage spread poor predictor of a merger deal’s success, quant firm finds
More banks flirt with machine learning for CCAR
Superior computational grunt of neural networks is attractive to lenders. Lack of explainability is the downside
Best AI and machine learning innovation: GBG
Asia Risk Awards 2021
Forecasting consumer credit recovery failure: classification approaches
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.
Podcast: turbo-charging derivatives pricing
Quants achieve more speed by reducing number of dimensions in price calculations
A survey of machine learning in credit risk
This paper surveys the impressively broad range of machine learning methods and application areas for credit risk.