Articles & Video
New research, new breakthroughs, and new opportunities
What are the latest innovations by leading quants?
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?
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.
QuantMinds Digital Week
A series of webinars including panels, talks, and presentations spread across 3 days.
Innovations in quant trading strategies and modelling
People and markets have changed hugely, and quants need to adapt to this new equilibrium. Therefore, innovation, both technological and strategic, are in the spotlight in this QuantMinds eMagazine.
Machine learning with quantum annealing
2018 Quant of the Year Alexei Kondratyev shares his passion for machine learning and quantum computing.
Learn this about options: Pricing is hedging
PhD Candidate at École Polytechnique Marcos Costa Santos Carriera takes a look at applying Q-Learning to option pricing, and its impact on hedging strategies.
Artificial intelligence, machine learning, and data in quant finance
In this compilation of articles, FutureQuantMinds explore AI, ML, and the impact of data on quant finance
Another STAC-A2 record for Intel – what are these guys doing?
James Reinders, James Reinders Consulting, reviews some of the research done by the Securities Technology Analysis Center (STAC) to find the best solutions that allow quant finance to enter its next evolutionary phase.
Listening to the financial heartbeat with agent-based models
How the study of agent-based models in directional-change intrinsic time can help you avoid another Flash Crash
Presentation: Weather derivatives – supporting hedging against climate change
by Laura Ballotta, Reader in Financial Mathematics at Cass Business School, from QuantMinds International 2019
Optimal portfolio strategy to control maximum cryptocurrency investment drawdowns
How successful is your crypto investment strategy? Patrick Tan, CEO of Novum Technologies, looks at the drawdown risks to inform his decisions.
Rebalancing portfolios with crash/rally indicators
In this case study, Prof. Dr. Jerome Kreuser shows how to optimise a portfolio using crash/rally indicators.
Demystifying AAD – what is the role of adjoint algorithmic differentiation in quant finance?
Leading quant finance experts show us what adjoint algorithmic differentiation means for this sector
On vectorisation of automatic adjoint C++ Code
To vectorise or not? If you ask Johannes Lotz, Klaus Leppkes, Uwe Naumann from RWTH Aachen University, Germany, the answer is an obvious yes.
Modelling volatility, convexity, and option pricing – new approaches and challenges
How are quant finance pioneers achieving more accurate results?
Correlations in modelling energy derivatives, Part II
NYU Professor Roza Galeeva shares her approach to modelling correlations for energy derivatives.
Holistic view of XVAs – Chebyshev interpolation for speedup and unification
Kathrin Glau, Lecturer in Financial Mathematics at Queen Mary University of London and EPFL FELLOW, tackles the issue of a computational bottleneck in XVA calculations!
Building a deep learning neural network
B. Horvath, A. Muguruza and M. Tomas discuss the challenges of building a neural network that can tackle all arising challenges.