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Found 10 papers, 3 papers with code
Signature Trading: A Path-Dependent Extension of the Mean-Variance Framework with Exogenous Signals
We achieve this by representing a trading strategy as a linear functional applied to the signature of a path (which we refer to as "Signature Trading" or "Sig-Trading").
Sig-Splines: universal approximation and convex calibration of time series generative models
We propose a novel generative model for multivariate discrete-time time series data.
Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions
We present a method for finding optimal hedging policies for arbitrary initial portfolios and market states.
Risk-Neutral Market Simulation
We develop a risk-neutral spot and equity option market simulator for a single underlying, under which the joint market process is a martingale.
Multi-Asset Spot and Option Market Simulation
We construct realistic spot and equity option market simulators for a single underlying on the basis of normalizing flows.
Sig-Wasserstein GANs for Time Series Generation
Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines.
Conditional Sig-Wasserstein GANs for Time Series Generation
The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterises the law of the time-series model.
Deep Hedging: Learning to Simulate Equity Option Markets
We construct realistic equity option market simulators based on generative adversarial networks (GANs).
Quant GANs: Deep Generation of Financial Time Series
Modeling financial time series by stochastic processes is a challenging task and a central area of research in financial mathematics.
Copula & Marginal Flows: Disentangling the Marginal from its Joint
However, so far exact modeling or extrapolation of distributional properties such as the tail asymptotics generated by a generative network is not available.
