Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Wiese, Magnus"'
In this article we introduce a portfolio optimisation framework, in which the use of rough path signatures (Lyons, 1998) provides a novel method of incorporating path-dependencies in the joint signal-asset dynamics, naturally extending traditional fa
Externí odkaz:
http://arxiv.org/abs/2308.15135
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 linear transformations and the signature transform as a seamless substit
Externí odkaz:
http://arxiv.org/abs/2307.09767
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
Externí odkaz:
http://arxiv.org/abs/2207.07467
Autor:
Wiese, Magnus, Murray, Phillip
Publikováno v:
AAAI 2022 Workshop on AI in Financial Services: Adaptiveness, Resilience & Governance
We develop a risk-neutral spot and equity option market simulator for a single underlying, under which the joint market process is a martingale. We leverage an efficient low-dimensional representation of the market which preserves no static arbitrage
Externí odkaz:
http://arxiv.org/abs/2202.13996
Autor:
Wiese, Magnus, Wood, Ben, Pachoud, Alexandre, Korn, Ralf, Buehler, Hans, Murray, Phillip, Bai, Lianjun
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 effic
Externí odkaz:
http://arxiv.org/abs/2112.06823
Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines. In this work, we develop high-fidelity time-series generators, the SigWGAN, by combining continuous-time stoch
Externí odkaz:
http://arxiv.org/abs/2111.01207
Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high dimensional probability measures. However, these methods struggle to capture the temporal dependence of joint probability distributions i
Externí odkaz:
http://arxiv.org/abs/2006.05421
Publikováno v:
NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy
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 re
Externí odkaz:
http://arxiv.org/abs/1911.01700
Publikováno v:
Quantitative Finance, 2020
Modeling financial time series by stochastic processes is a challenging task and a central area of research in financial mathematics. As an alternative, we introduce Quant GANs, a data-driven model which is inspired by the recent success of generativ
Externí odkaz:
http://arxiv.org/abs/1907.06673
Deep generative networks such as GANs and normalizing flows flourish in the context of high-dimensional tasks such as image generation. However, so far exact modeling or extrapolation of distributional properties such as the tail asymptotics generate
Externí odkaz:
http://arxiv.org/abs/1907.03361