Universal randomised signatures for generative time series modelling
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Erdinc Akyildirim & Matteo Gambara & Josef Teichmann & Syang Zhou, 2022. "Applications of Signature Methods to Market Anomaly Detection," Papers 2201.02441, arXiv.org, revised Feb 2022.
- Adriano Koshiyama & Nick Firoozye & Philip Treleaven, 2021.
"Generative adversarial networks for financial trading strategies fine-tuning and combination,"
Quantitative Finance, Taylor & Francis Journals, vol. 21(5), pages 797-813, May.
- Adriano Koshiyama & Nick Firoozye & Philip Treleaven, 2019. "Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination," Papers 1901.01751, arXiv.org, revised Mar 2019.
- Christa Cuchiero & Janka Moller, 2023. "Signature Methods in Stochastic Portfolio Theory," Papers 2310.02322, arXiv.org, revised Oct 2024.
- Magnus Wiese & Robert Knobloch & Ralf Korn & Peter Kretschmer, 2020. "Quant GANs: deep generation of financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1419-1440, September.
- Takahashi, Shuntaro & Chen, Yu & Tanaka-Ishii, Kumiko, 2019. "Modeling financial time-series with generative adversarial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
- Magnus Wiese & Ben Wood & Alexandre Pachoud & Ralf Korn & Hans Buehler & Phillip Murray & Lianjun Bai, 2021. "Multi-Asset Spot and Option Market Simulation," Papers 2112.06823, arXiv.org.
- Magnus Wiese & Lianjun Bai & Ben Wood & Hans Buehler, 2019. "Deep Hedging: Learning to Simulate Equity Option Markets," Papers 1911.01700, arXiv.org.
- Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2023. "Arbitrage-Free Neural-SDE Market Models," Applied Mathematical Finance, Taylor & Francis Journals, vol. 30(1), pages 1-46, January.
- Solveig Flaig & Gero Junike, 2022. "Scenario Generation for Market Risk Models Using Generative Neural Networks," Risks, MDPI, vol. 10(11), pages 1-28, October.
- Mohamed Hamdouche & Pierre Henry-Labordere & Huyên Pham, 2023. "Generative modeling for time series via Schrödinger bridge," Working Papers hal-04063041, HAL.
- Mohamed Hamdouche & Pierre Henry-Labordere & Huy^en Pham, 2023. "Generative modeling for time series via Schr{\"o}dinger bridge," Papers 2304.05093, arXiv.org.
- Hans Buhler & Blanka Horvath & Terry Lyons & Imanol Perez Arribas & Ben Wood, 2020. "A Data-driven Market Simulator for Small Data Environments," Papers 2006.14498, arXiv.org.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2022. "Estimating risks of option books using neural-SDE market models," Papers 2202.07148, arXiv.org.
- Weilong Fu & Ali Hirsa & Jorg Osterrieder, 2022. "Simulating financial time series using attention," Papers 2207.00493, arXiv.org.
- Alexandre Miot, 2020. "Adversarial trading," Papers 2101.03128, arXiv.org.
- Hans Buhler & Blanka Horvath & Terry Lyons & Imanol Perez Arribas & Ben Wood, 2020. "A Data-driven Market Simulator for Small Data Environments," Papers 2006.14498, arXiv.org.
- Chung I Lu & Julian Sester, 2024. "Generative model for financial time series trained with MMD using a signature kernel," Papers 2407.19848, arXiv.org, revised Dec 2024.
- Beatrice Acciaio & Anastasis Kratsios & Gudmund Pammer, 2022. "Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer," Papers 2201.13094, arXiv.org, revised Mar 2023.
- Sohyeon Kwon & Yongjae Lee, 2024. "Can GANs Learn the Stylized Facts of Financial Time Series?," Papers 2410.09850, arXiv.org.
- Haoyang Cao & Xin Guo, 2021. "Generative Adversarial Network: Some Analytical Perspectives," Papers 2104.12210, arXiv.org, revised Sep 2021.
- Emiel Lemahieu & Kris Boudt & Maarten Wyns, 2023. "Generating drawdown-realistic financial price paths using path signatures," Papers 2309.04507, arXiv.org.
- Rama Cont & Mihai Cucuringu & Renyuan Xu & Chao Zhang, 2022. "Tail-GAN: Learning to Simulate Tail Risk Scenarios," Papers 2203.01664, arXiv.org, revised Mar 2023.
- Magnus Wiese & Phillip Murray & Ralf Korn, 2023. "Sig-Splines: universal approximation and convex calibration of time series generative models," Papers 2307.09767, arXiv.org.
- Chung I Lu, 2023. "Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisation," Papers 2307.07694, arXiv.org, revised Jul 2023.
- Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021. "Black-box model risk in finance," Papers 2102.04757, arXiv.org.
- Xiaoyu Tan & Zili Zhang & Xuejun Zhao & Shuyi Wang, 2022. "DeepPricing: pricing convertible bonds based on financial time-series generative adversarial networks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-38, December.
- Magnus Wiese & Phillip Murray, 2022. "Risk-Neutral Market Simulation," Papers 2202.13996, arXiv.org.
- Gautier Marti & Victor Goubet & Frank Nielsen, 2021. "cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope," Papers 2107.10606, arXiv.org.
- Gero Junike & Solveig Flaig & Ralf Werner, 2023. "Validation of machine learning based scenario generators," Papers 2301.12719, arXiv.org, revised Dec 2024.
- Szymon Kubiak & Tillman Weyde & Oleksandr Galkin & Dan Philps & Ram Gopal, 2023. "Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe," Papers 2311.16004, arXiv.org.
- Michael Karpe, 2020. "An overall view of key problems in algorithmic trading and recent progress," Papers 2006.05515, arXiv.org.
- Solveig Flaig & Gero Junike, 2022. "Scenario Generation for Market Risk Models Using Generative Neural Networks," Risks, MDPI, vol. 10(11), pages 1-28, October.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2406.10214. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.