3 citations · 7 across the 8 of their papers we have counts for
9 papers · 1 filter
PIVOT: Bridging Black-Scholes Implied-Volatility and Price Objectives via Differentiable Jäckel Operator
Raeid Saqur, Yannick Limmer, Anastasis Kratsios +2
Modern option-learning systems operate in two coordinates: price space, where markets quote and no-arbitrage constraints are most naturally enforced, and implied volatility (IV) sp…
SANOS Smooth strictly Arbitrage-free Non-parametric Option Surfaces
Hans Buehler, Blanka Horvath, Anastasis Kratsios +2
We present a simple, numerically efficient but highly flexible non-parametric method to construct representations of option price surfaces which are both smooth and strictly arbitr…
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…
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…
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…
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…
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…
Deep Hedging: Learning to Simulate Equity Option Markets
Magnus Wiese, Lianjun Bai, Ben Wood +1
We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess t…
Deep Hedging
Hans Bühler, Lukas Gonon, Josef Teichmann +1
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 usi…