Quants are using the theory of rough paths to distil the essence of financial datasets
Risk Live: big speed-ups for quantum-powered models could prompt bigger questions from regulators
Signatures can provide the synthetic data to train deep hedging strategies
$110 billion quant investor creates automated system to spot greenwashers
Synthetic data made with machine learning will struggle to capture the caprice of financial markets
Traditionally quants have learnt to pick data apart. Soon they might spend more time making it up
A general framework for the identification and categorization of risks: an application to the context of financial markets
This paper is, to the best of the authors' knowledge, the first to develop an algorithm-based and generally applicable framework that generates an extensive and integrated identification and categorization scheme of certain risks by using text mining and…
AI may help fund manager count emissions that companies fail to report
Some of the trickiest puzzles in finance could be solved by blending old and new technologies
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…
‘Rough volatility’ models promise better pricing and hedging of options. But will they catch on?
In this paper, the authors discuss how tree-based machine learning techniques can be used in the context of derivatives pricing.
Investors should switch between factors as alphas change, says quant
In the most realistic simulations, data-driven approach fared 30% worse than conventional hedging
In this paper, we propose a conceptual framework that links the technical and business benchmarks in the domain of clearing houses and securities exchanges.
Despite AI’s growth, investing still needs human adaptability and judgement, writes Schroders’ Lim
Remote working vastly complicates the job of conduct risk supervisors
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.
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.
Risk Awards 2021: new risk engine can run nearly a billion XVA calculations per second
Pricing vanilla and exotic options with a deep learning approach for PDEs
Quants unveil new technique for controlling extrapolation by neural networks
Introducing a new technique to control the behaviour of neural networks
Volatility and machine learning were among the top research areas for quants this year