activity
20202023
most citedDeep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions

3 citations · 5 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG2023★ 1 cited

Sig-Splines: universal approximation and convex calibration of time series generative models

Magnus Wiese, Phillip Murray, Ralf Korn

We propose a novel generative model for multivariate discrete-time time series data. Drawing inspiration from the construction of neural spline flows, our algorithm incorporates li…

q-fin.CP2022★ 3 cited

Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions

Phillip Murray, Ben Wood, Hans Buehler +2

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-aver…

q-fin.CP2022

Deep Bellman Hedging

Hans Buehler, Phillip Murray, Ben Wood

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 der…

q-fin.CP2021

Multi-Asset Spot and Option Market Simulation

Magnus Wiese, Ben Wood, Alexandre Pachoud +4

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 cal…

q-fin.CP2021★ 1 cited

Deep Hedging: Learning to Remove the Drift under Trading Frictions with Minimal Equivalent Near-Martingale Measures

Hans Buehler, Phillip Murray, Mikko S. Pakkanen +1

We present a machine learning approach for finding minimal equivalent martingale measures for markets simulators of tradable instruments, e.g. for a spot price and options written…

q-fin.CP2021

Deep Hedging: Learning Risk-Neutral Implied Volatility Dynamics

Hans Buehler, Phillip Murray, Mikko S. Pakkanen +1

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…

math.ST2020

Feasible Inference for Stochastic Volatility in Brownian Semistationary Processes

Phillip Murray, Riccardo Passeggeri, Almut E. D. Veraart +1

This article studies the finite sample behaviour of a number of estimators for the integrated power volatility process of a Brownian semistationary process in the non semi-martinga…