6 papers
The geometry of the adapted Bures--Wasserstein space
Beatrice Acciaio, Daniel Bartl, Anne Grass +2
The adapted Bures--Wasserstein space consists of Gaussian processes endowed with the adapted Wasserstein distance. It can be viewed as the analogue of the classical Bures--Wasserst…
Estimating causal distances with non-causal ones
Beatrice Acciaio, Songyan Hou, Gudmund Pammer
The adapted Wasserstein () distance refines the classical Wasserstein () distance by incorporating the temporal structure of stochastic processes. This makes the -distan…
Entropic adapted Wasserstein distance on Gaussians
Beatrice Acciaio, Songyan Hou, Gudmund Pammer
The adapted Wasserstein distance is a metric for quantifying distributional uncertainty and assessing the sensitivity of stochastic optimization problems on time series data. A com…
Convergence of the Adapted Smoothed Empirical Measures
Songyan Hou
The adapted Wasserstein distance controls the calibration errors of optimal values in various stochastic optimization problems, pricing and hedging problems, optimal stopping probl…
Nested Optimal Transport Distances
Ruben Bontorno, Songyan Hou
Simulating realistic financial time series is essential for stress testing, scenario generation, and decision-making under uncertainty. Despite advances in deep generative models,…
Time-Causal VAE: Robust Financial Time Series Generator
Beatrice Acciaio, Stephan Eckstein, Songyan Hou
We build a time-causal variational autoencoder (TC-VAE) for robust generation of financial time series data. Our approach imposes a causality constraint on the encoder and decoder…