Machine learning
Rough volatility’s steampunk vision of future finance
Some of the trickiest puzzles in finance could be solved by blending old and new technologies
Using payments data to nowcast macroeconomic variables during the onset of Covid-19
Economic prediction during a crisis is challenging because of the unprecedented economic impact of such an event, which increases the unreliability of traditionally used linear models that employ lagged data. The authors help to address this challenge by…
The volatility paradigm that’s stirring up options pricing
‘Rough volatility’ models promise better pricing and hedging of options. But will they catch on?
Gradient boosting for quantitative finance
In this paper, the authors discuss how tree-based machine learning techniques can be used in the context of derivatives pricing.
Factor woes prove need for better timing – QuantZ’s Sharma
Investors should switch between factors as alphas change, says quant
Deep hedging strays when volatility gets rough – study
In the most realistic simulations, data-driven approach fared 30% worse than conventional hedging
From use cases to a big data benchmarking framework in clearing houses and exchanges
In this paper, we propose a conceptual framework that links the technical and business benchmarks in the domain of clearing houses and securities exchanges.
As machines disrupt investing, people still have a role to play
Despite AI’s growth, investing still needs human adaptability and judgement, writes Schroders’ Lim
Top 10 op risks 2021: conduct risk
Remote working vastly complicates the job of conduct risk supervisors
Neural network middle-term probabilistic forecasting of daily power consumption
The authors propose a new modeling approach that incorporates trend, seasonality and weather conditions as explicative variables in a shallow neural network with an autoregressive feature.
Forecasting Bitcoin returns: is there a role for the US–China trade war?
In this paper, the authors extend the related literature by examining whether the information on the US–China trade war can be used to forecast the future path of Bitcoin returns, controlling for various explanatory variables.
Technology innovation of the year: Scotiabank
Risk Awards 2021: new risk engine can run nearly a billion XVA calculations per second
Solving final value problems with deep learning
Pricing vanilla and exotic options with a deep learning approach for PDEs
Setting boundaries for neural networks
Quants unveil new technique for controlling extrapolation by neural networks
Deep asymptotics
Introducing a new technique to control the behaviour of neural networks
Degree of influence: volatility shakes markets and quant finance
Volatility and machine learning were among the top research areas for quants this year
Machine learning will create new sales-bots – UBS’s Nuti
Technologists working to automate indications of interest from trading desks
HSBC exec: measure culture through smarter surveillance
Machine learning could help gauge positive sentiment from surveillance logs, says Elhedery
Model misfires raise questions over training data
Quants wrestle with how far into the past their machine learning models should peer
Regions deploys early-warning tool for credit risk
Risk USA: system alerted US superregional to impending defaults during Covid crisis
How to successfully mitigate fraud – AI in Action
Fraud is evolving, with influences spanning technical sophistication through to turmoil and crisis. Most recently, the Covid-19 pandemic has thrown an additional spanner in the works. As the drivers behind these activities are becoming more varied, the…
Risk Technology Awards 2020: Managed support services provider of the year – Exactpro Systems
Co-founder and chief executive of Exactpro Systems, Alexey Zverev, discusses the challenges of maintaining client systems in the current environment, the launch of its new open source microservices-based test automation platform, th2, and how machine…
Machine learning hedge strategy with deep Gaussian process regression
An optimal hedging strategy for options in discrete time using a reinforcement learning technique
AML bill will swamp financial crime teams, banks warn
Proposed US legislation could force firms to run new and old systems in parallel, stretching resources