3 citations · 6 across the 5 of their papers we have counts for
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nlin.SI2024★ 3 cited
Abundant Superintegrable Systems and Hessian Structures
John Armstrong, Andreas Vollmer
We show that a large class of non-degenerate second-order (maximally) superintegrable systems gives rise to Hessian structures, which admit natural (Hessian) coordinates adapted to…
q-fin.CP2024
Deep Gamma Hedging
John Armstrong, George Tatlow
We train neural networks to learn optimal replication strategies for an option when two replicating instruments are available, namely the underlying and a hedging option. If the pr…
math.NA2024★ 2 cited
Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach
Reza Arabpour, John Armstrong, Luca Galimberti +2
Predicting the conditional evolution of Volterra processes with stochastic volatility is a crucial challenge in mathematical finance. While deep neural network models offer promise…