Neural stochastic differential equations for conditional time series generation using the Signature-Wasserstein-1 metric
Year of publication: |
2023
|
---|---|
Authors: | Díaz Lozano, Pere ; Lozano Bagén, Toni ; Vives, Josep |
Published in: |
The journal of computational finance : JFC. - London : Infopro Digital Risk, ISSN 1755-2850, ZDB-ID 2091445-3. - Vol. 27.2023, 1, p. 1-23
|
Subject: | conditional generative modeling | neural networks | expected signature | rough path theory | Wasserstein generative adversarial networks | neural stochastic differential equations | Neuronale Netze | Neural networks | Theorie | Theory | Stochastischer Prozess | Stochastic process | Analysis | Mathematical analysis | Zeitreihenanalyse | Time series analysis | Prognoseverfahren | Forecasting model |
-
Robust pricing and hedging via neural stochastic differential equations
Gierjatowicz, Patrick, (2022)
-
Barrientos, Jorge Hugo, (2018)
-
Effective crude oil prediction using CHS-EMD decomposition and PS-RNN model
Usha Ruby, A., (2024)
- More ...
-
Approximate option pricing under a two-factor Heston-Kou stochastic volatility model
El-Khatib, Youssef, (2024)
-
Calibration of stochastic volatility models via second order approximation : the Heston model case
Alòs, Elisa, (2012)
-
Calibration of stochastic volatility models via second-order approximation : the Heston case
Alòs, Elisa, (2015)
- More ...