Ben Wood

• ED at J.P.Morgan

We present a method for finding optimal hedging policies for arbitrary initial portfolios and market states. We develop a novel actor-critic algorithm for solving general risk-averse stochastic control problems and use it to learn hedging strategies across multiple risk aversion levels simultaneously. We demonstrate the effectiveness of the approac...

We present an actor-critic-type reinforcement learning algorithm for solving the problem of hedging a portfolio of financial instruments such as securities and over-the-counter derivatives using purely historic data. The key characteristics of our approach are: the ability to hedge with derivatives such as forwards, swaps, futures, options; incorpo...

We construct realistic spot and equity option market simulators for a single underlying on the basis of normalizing flows. We address the high-dimensionality of market observed call prices through an arbitrage-free autoencoder that approximates efficient low-dimensional representations of the prices while maintaining no static arbitrage in the reco...

We present a numerically efficient approach for learning minimal equivalent martingale measures for market simulators of tradable instruments, e.g. for a spot price and options written on the same underlying. In the presence of transaction cost and trading restrictions, we relax the results to learning minimal equivalent "near-martingale measures"...

We present a numerically efficient approach for learning a risk-neutral measure for paths of simulated spot and option prices up to a finite horizon under convex transaction costs and convex trading constraints. This approach can then be used to implement a stochastic implied volatility model in the following two steps: 1. Train a market simulator...

Neural network based data-driven market simulation unveils a new and flexible way of modelling financial time series without imposing assumptions on the underlying stochastic dynamics. Though in this sense generative market simulation is model-free, the concrete modelling choices are nevertheless decisive for the features of the simulated paths. We...

We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the limited real-world data sets available for the training a...

We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, liquidity constraints or risk limits using modern deep reinforcement machine learning methods. We discuss how standard reinforcement learning methods can be applied to non-linear reward structures, i.e. in our case convex ris...

We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, market impact, liquidity constraints or risk limits using modern deep reinforcement machine learning methods. We discuss how standard reinforcement learning methods can be applied to non-linear reward structures, i.e. in our...

We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, market impact, liquidity constraints or risk limits using modern deep reinforcement machine learning methods. We discuss how standard reinforcement learning methods can be applied to non-linear reward structures, i.e. in our...

Metamaterials have significantly extended the range of electromagnetic properties available to device designers. An interesting application of these new materials is to the problem of cloaking, where the goal is to render an object invisible to electromagnetic radiation within a certain frequency range. Here, I review the concepts behind recently-p...

We report resonant photon tunneling (RPT) through one-dimensional metamaterials consisting of alternating layers of metal and dielectric. RPT via a surface plasmon polariton state permits evanescent light waves with large wavenumbers to be conveyed through the metamaterial. This is the mechanism for sub-wavelength imaging recently demonstrated with...

Electromagnetic metamaterials are a class of materials that have been artificially structured on a subwavelength scale. They are currently the focus of a great deal of interest because they allow access to previously unrealizable properties such as a negative refractive index. Most metamaterial designs have so far been based on resonant elements, s...

Metamaterials are artificial materials structured on a subwavelength scale to provide electromagnetic properties beyond those available in nature. Progress in this young field has been startlingly swift. The last year alone has seen the realization of the first optical negative-index metamaterials and the constructon of two very striking metamateri...

Electromagnetic metamaterials are a class of materials which have been artificially structured on a subwavelength scale. They are currently the focus of a great deal of interest because they allow access to previously unrealisable properties like a negative refractive index. Most metamaterial designs have so far been based on resonant elements, lik...

We present quantum Monte Carlo calculations of the surface energy of the electron gas jellium. Our results agree with the best estimates obtained by other methods, thus appearing to resolve the controversy which currently exists and paving the way for future simulations of real surface systems.

We investigate the problem of designing metamaterial structures which operate at very low frequencies. As an example, we consider the case of a DC magnetic cloak, which requires a variable, anisotropic magnetic permeability with both paramagnetic and diamagnetic components. We show that a structure based on superconducting components is the key to...

We examine some of the optical properties of a metamaterial consisting of thin layers of alternating metal and dielectric. We can model this material as a homogeneous effective medium with anisotropic dielectric permittivity. When the components of this permittivity have different signs, the behavior of the system becomes very interesting: the norm...

We investigate an alternative approach to the connection between plasmons and the ground-state wavefunction of many-electron systems, starting with a classical plasmon Hamiltonian. We obtain a prescription for the part of the wavefunction which determines the long-ranged electron–electron correlations; we then show how this prescription can be used...

The model periodic Coulomb interaction (Williamson et al 1997 Phys.?Rev.?B 55 R4851) is a replacement for the Ewald sum, developed to reduce finite-size errors in the simulation of extended 3D systems. We investigate the generalization of this technique to quasi-2D systems; we show through testing in quantum Monte Carlo simulations that while the n...