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
Technologists working to automate indications of interest from trading desks
Machine learning could help gauge positive sentiment from surveillance logs, says Elhedery
Quants wrestle with how far into the past their machine learning models should peer
Risk USA: system alerted US superregional to impending defaults during Covid crisis
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…
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…
An optimal hedging strategy for options in discrete time using a reinforcement learning technique
Proposed US legislation could force firms to run new and old systems in parallel, stretching resources