Articles & Video
Staying afloat: How is quant finance changing under Covid-19?
Is the worst behind us? Or is it still to come?
Model robustification and the future of quant finance: an interview with J.D. Opdyke, Allstate
Were your models caught by surprise when the pandemic hit? J.D. Opdyke, Allstate, explains how he built a robust framework and shares key learnings from this year.
Smart contracts and regulation: the centres of personalised law meet risk management challenges
Smart contracts promise a tailored experience, but what does that mean from a regulatory standpoint?
How is reinforcement learning different from un/supervised learning?
Marshall Chang, Founder and CIO, A.I. Capital Management, shares how to develop and deploy RL trading algorithms.
Stop doing that! How unintentional bets are hurting your performance
How do you mitigate unintentional exposures that bring down your performance?
Looking forward to backward-looking rates: completing the generalised forward market model
Webinar presented by Fabio Mercurio, Global Head of Quant Analytics, Bloomberg L.P.
Can FX hedges of bonds deliver a free lunch?
Risk-free returns don’t exist – and if they did, even briefly, they would be traded on until they disappeared. Except sometimes, somehow, under just the right circumstances, the elusive free lunch may temporarily appear on the table…
How to forecast Covid-19 default and recovery rates
Find out how the forecasts of credit conditions changed.
Beyond Weisfeiler-Lehman: using substructures for provably expressive graph neural networks
How powerful are graph neural networks?
Five issues quants need to address in 2020
QuantMinds editor-in-chief Vincent Beard now summarises the five key conundrums on quants’ minds this year.
Fourier-based methods for the management of complex insurance products
Modelling for variable annuities products – how could the financial and insurance risks be integrated better?
New research, new breakthroughs, and new opportunities
What are the latest innovations by leading quants?
Differential machine learning
Unreasonably effective pricing and risk approximation by automatic differentiation (AAD) combined with machine learning (ML)
Can models predict black swan events?
Maurizio Garro, Senior Lead IBOR Transition Programme, Lloyds Bank, shares his experience building robust models to predict the combined impact of risk factors.
The right kind of volatility
Is there a right kind of volatility for market makers? And how do market makers make money?
QuantMinds Digital Week
A series of webinars including panels, talks, and presentations spread across 3 days.
The ups and downs of trading crypto and what’s next for blockchain?
On the nature of crypto-trading and blockchain
One year after a CCP member default: lessons learned and the path forward
From the perspective of a clearing member: Marnie Rosenberg, Global Head of Clearinghouse Risk and Strategy, JP Morgan
What are algorithmic regulation and RegTech?
Which way will the regulators go: RegTech or algorithmic regulation?
Should we be worried that the Fed has gone all-in?
Did the Federal Reserve just announce unlimited quantitative easing?