Articles & Video
What does 2020 have in store for quant finance?
Understanding new trends, challenges, and solutions
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
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.
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.
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.
The right way to be wrong: learning interest rate interpolation
In which Marcos Costa Santos Carreira shows us the similarities between option pricing and interest rate interpolation.
The convexity profile of systematic strategies and diversification benefits of trend-following strategies
Artur Sepp, Head of Research at Quantica Capital AG, introduces a regime-conditional regression model to measure the risk profile of systematic investment strategies.
Modelling for cryptocurrency trading
Without much historical data, how do you model cryptocurrency trading?
Correlations in modelling energy derivatives
Correlations are very important for energy derivatives, as Roza Galeeva, Professor at NYU, say, however there are almost no liquid markets in correlation. How could we model their behavior accurately and be able to calibrate parameters to the market instruments?
Looking over the horizon – with the help of bubble models
We’re in the business of predicting the unpredictable, and Prof Dr Jerome L Kreuser shares how he predicted the crash of the Bitcoin in Dec 2017.
The value of bond convexity
Commerzbank AG Managing Director and Senior Quantitative Researcher Prof Jessica James’s 101 to convexity
Data’s big year: The last frontiers of quantitative edge
Stocktwits’ Director of Business Development and Revenue Strategy Pierce Crosby on the 2 data trends that quants should look out for in 2019.
Lifetime cost of capital for derivatives (KVA) under the final Basel III framework
Rodney Hoskinson looks at the KVA for the SA CVA and presents his approach to the problem.
What is idiosyncratic Alpha?
Do you know what an idiosyncratic alpha strategy is and what the common pitfalls are?
Volatility seasonality of Bitcoin prices
Anyone looking to invest in cryptocurrencies would have seen that their prices are highly volatile. Anton Golub and Vladimir Petrov explore how.
Innovating in decentralised financial markets
Blockchain and bitcoin have hit the mainstream, but how do you continue to innovate within these new markets?