Articles & Video
To the future of QuantMinds and beyond
Svetlana Borovkova, Aymeric Kalife, Jesus Rodriguez and many more share their research on sustainability and factor investing, customer risk appetite, crypto and more.
Faster, more accurate pricing using polynomial approximations
Introducing a fast and generic expansion of discounted moments of stochastic processes that is applicable to a wide array of pricing problems.
How quants and actuaries can team up for the valuation of complex derivatives
With the introduction of exotic derivatives, we abandon the existence of a replicating portfolio and therefore valuation becomes problematic and ambiguous.
Practical insight into black basket analytics for mid-curves and spread-options and trading crypto asset volatility
Featuring key sessions from the Pricing, Trading, and Volatility day.
Looking forward to backward-looking rates: Modelling and calibrating the volatility decay
From QuantMinds in Focus 2021, Interest Rate & IBOR day.
Through the hedge and backwards - QuantMinds eMagazine Q2 2021
Bringing investment strategies, financial models, and mathematics up to speed
Optimal VaR adaptation in transient environments
Although potential remedies have been available for some time, the concrete implementation of solutions in running VaR systems seems to be more challenging.
Analysing Covid-19 Data with AWS Data Exchange, Amazon Redshift, and Tableau
How to create COVID-19 dashboards using Tableau and different AWS services, such as AWS Data Exchange, AWS COVID-19 Data Lake, Amazon Redshift, and Amazon Athena.
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.
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.
The right kind of volatility
Is there a right kind of volatility for market makers? And how do market makers make money?
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.
Quant finance’s machine learning journey: Are we there yet?
In which practitioners and academics weigh in on machine learning applications and its development in finance.
Beyond the “passive vs active” asset management paradigm
In which Aymeric Kalife (Paris Dauphine University) explores 4 key ways to maximise alpha generation and innovate your strategies to keep customers happy.
What is an alpha of trend-following CTAs?
Artur Sepp, Director of Research at Quantica Capital AG, introduces a new way to measure skill-based alpha.
Computational intelligence: a principled approach for the era of data exploration
De-noising the AI hype is an exercise of intellectual honesty and recognising computational intelligence as a more realistic representation of the current progress achieved in the field of machine intelligence is part of it.
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.
A neural network approach to understanding implied volatility movements
Presentation by John Hull, Maple Financial Professor Of Derivatives & Risk Management, Joseph L. Rotman School of Management at University Of Toronto, from QuantMinds International 2019
How to survive the new challenges in quant finance?
The latest QuantMinds eMagazine delves into the use of machine learning, alternative data, blockchain applications, diversity, and more!
Modelling volatility, convexity, and option pricing – new approaches and challenges
How are quant finance pioneers achieving more accurate results?