Just like everyone else, we are incredibly disappointed that we won’t be able to join our quant community in person this coming May. And whilst the postponement was the right decision, we strongly believe that now, more than ever, people need to stay connected as we adjust to a new way of working over the coming months.
To bring the quant community together, we are proud to launch our QuantMinds Digital Week on the 26-28th May.
What is the QuantMinds Digital Week?
A series of webinars including panels, talks, and presentations spread across 3 days.
Join us live to ask your questions from these leading quants:
- Marcos Lopez de Prado, CIO, True Positive Technologies on Tue 26 May, 4pm BST | 11am EDT
- Alexandre Antonov, Chief Analyst, Danske Bank on Wed 27 May, 2pm BST | 9am EDT
- Svetlana Borovkova, Associate Professor Of Quantitative Finance, Vrije Universiteit Amsterdam on Thu 28 May, 2pm BST | 9am EDT
- Fabio Mercurio, Global Head of Quant Analytics, Bloomberg L.P. on Thu 28 May, 4pm BST | 11am EDT
Here’s what’s on the agenda.
Machine learning asset allocation
Presented by Marcos Lopez de Prado, CIO, True Positive Technologies
Tue 26 May, 4pm BST | 11am EDT
Convex optimisation solutions tend to be unstable, to the point of entirely offsetting the benefits of optimisation. For example, in the context of financial applications, it is known that portfolios optimised in sample often underperform the naïve (equal weights) allocation out of sample. This instability can be traced back to two sources:
- noise in the input variables; and
- signal structure that magnifies the estimation errors in the input variables.
There is abundant literature discussing noise induced instability. In contrast, signal induced instability is often ignored or misunderstood. We introduce a new optimisation method that is robust to signal induced instability.
Neural networks with asymptotics control
Presented by Alexandre Antonov, Chief Analyst, Danske Bank
Wed 27 May, 2pm BST | 9am EDT
Artificial neural networks (ANNs) have recently been proposed as accurate and fast approximators in various derivatives pricing applications. ANNs typically excel in fitting functions they approximate at the input parameters they are trained on, and often are quite good in interpolating between them. However, for standard ANNs, their extrapolation behaviour – an important aspect for financial applications – cannot be controlled due to complex functional forms typically involved. We overcome this significant limitation and develop a new type of neural networks that incorporate large-value asymptotics, when known, allowing explicit control over extrapolation.
This new type of asymptotics-controlled ANNs is based on two novel technical constructs, a multi-dimensional spline interpolator with prescribed asymptotic behaviour, and a custom ANN layer that guarantees zero asymptotics in chosen directions. Asymptotics control brings a number of important benefits to ANN applications in finance such as well-controlled behaviour under stress scenarios, graceful handling of regime switching, and improved interpretability.
Corona-immunise your portfolio: from global macro trends to corona-proof quant investing
Presented by Svetlana Borovkova, Associate Professor Of Quantitative Finance, Vrije Universiteit Amsterdam
Thu 28 May, 2pm BST | 9am EDT
One can only speculate how the world will look like after the coronavirus epidemic. But some of the macroeconomic and consumer trends we can observe already now. Using alternative data such as sentiment and search behaviour, I will outline several emerging trends and translate them into scenarios, which can be used to assess stock portfolios in terms of their resistance in the post-corona world. I will address factors such as quality and sustainability, but also other, new post-corona factors will play important role in immunising your stock portfolio against corona effects. Finally, I will touch upon risk and modelling challenges of recently observed negative oil prices.
Looking forward to backward-looking rates: completing the generalised forward market model
Presented by Fabio Mercurio, Global Head of Quant Analytics, Bloomberg L.P.
Thu 28 May, 4pm BST | 11am EDT
In this talk, we show how the generalised forward market model (FMM) introduced by Lyashenko and Mercurio (2019) can be extended to make it a complete term-structure model describing the evolution of all points on a yield curve, as well as that of the bank account. The ability to model the bank account, in addition to the forward curve, is going to be of crucial importance once Libor rates are replaced with setting-in-arrears rates in derivative and cash contracts, where fixings are defined in terms of the realised bank account values.
To achieve our goal, we “embed” the FMM into a Markovian HJM model with separable volatility structure by aligning the HJM and FMM dynamics of the forward term rates modelled by the FMM. This FMM-aligned HJM model is effectively a hybrid between an instantaneous forward-rate model and a LMM, and shares the advantages of both approaches, with the caveat that the number of variables to simulate could be too high.
A more efficient approach is then derived by expressing the zero-coupon bonds and the bank account as functions of the modelled forward term rates and their volatilities. In this FMM-HJM construct, FMM acts as a “coarse” model capturing a “macro” structure of the market such as the covariance structure of the set of modelled rates, while the FMM-aligned HJM serves as a finer modelling environment used to fill the gaps left by the coarser FMM.
The problem of recovering the whole yield curve evolution from the modelled set of Libor rates has been extensively discussed in the LMM literature, and is often referred to as Libor-rate interpolation, or front- and back-stub interpolations. Contrary to existing methods, the approach we propose is not only arbitrage-free by construction, but it also allows for the generation of bank account values.