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Found 9 papers, 0 papers with code
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.
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.
Estimating the Value-at-Risk by Temporal VAE
Estimation of the value-at-risk (VaR) of a large portfolio of assets is an important task for financial institutions.
Unifying the theory of storage and the risk premium by an unobservable intrinsic electricity price
In this paper we introduce a new concept for modelling electricity prices through the introduction of an unobservable intrinsic electricity price $p(\tau)$.
A Generalised Linear Model Framework for $β$-Variational Autoencoders based on Exponential Dispersion Families
Although variational autoencoders (VAE) are successfully used to obtain meaningful low-dimensional representations for high-dimensional data, the characterization of critical points of the loss function for general observation models is not fully understood.
A lower bound for the ELBO of the Bernoulli Variational Autoencoder
We consider a variational autoencoder (VAE) for binary data.
Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies
Under the Solvency II regime, life insurance companies are asked to derive their solvency capital requirements from the full loss distributions over the coming year.
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.
