Výsledky vyhledávání - "Iuliia Manziuk"

Market Making and Incentives Design in the Presence of a Dark Pool: A Stackelberg Actor–Critic Approach

Autor: Bastien Baldacci, Iuliia Manziuk, Thibaut Mastrolia, Mathieu Rosenbaum

Publikováno v: Operations Research. 71:727-749

A Stackelberg actor–critic approach to optimal market making and incentives design with dark pools. We consider the issue of a market maker acting at the same time in the lit and dark pools of an exchange. The exchange wishes to establish a suitabl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7cff3e3b911790e2ebf9f2ae3a0a94b3
https://doi.org/10.1287/opre.2022.2406

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Optimal control on graphs: existence, uniqueness, and long-term behavior

Autor: Olivier Guéant, Iuliia Manziuk

Publikováno v: ESAIM: Control, Optimisation and Calculus of Variations
ESAIM: Control, Optimisation and Calculus of Variations, EDP Sciences, 2020, 26, pp.a22. ⟨10.1051/cocv/2019071⟩

International audience; The literature on continuous-time stochastic optimal control seldom deals with the case of discrete state spaces. In this paper, we provide a general framework for the optimal control of continuous-time Markov chains on finite

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf4fc231e2eaa95097a9bcc55a2c7bf0
https://hal.archives-ouvertes.fr/hal-02987853/document

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Adaptive trading strategies across liquidity pools

Autor: Bastien Baldacci, Iuliia Manziuk

In this paper, we provide a flexible framework for optimal trading in an asset listed on different venues. We take into account the dependencies between the imbalance and spread of the venues, and allow for partial execution of limit orders at differ

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76b99306bb3893941649cc3ca8eb792d
http://arxiv.org/abs/2008.07807

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Accelerated share repurchase and other buyback programs: what neural networks can bring

Autor: Jiang Pu, Iuliia Manziuk, Olivier Guéant

Publikováno v: Quantitative Finance
Quantitative Finance, Taylor & Francis (Routledge), 2020, 20 (8), pp.1389-1404. ⟨10.1080/14697688.2020.1729397⟩

International audience; When firms want to buy back their own shares, they have a choice between several alternatives. If they often carry out open market repurchase, they also increasingly rely on banks through complex buyback contracts involving op

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9fce95f760ae35e6ae062a0d5d8c95fb
https://hal.archives-ouvertes.fr/hal-03252518

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Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality

Autor: Olivier Guéant, Iuliia Manziuk

Publikováno v: Applied Mathematical Finance
Applied Mathematical Finance, Taylor & Francis (Routledge): SSH Titles, 2019, 26 (5), pp.387-452. ⟨10.1080/1350486X.2020.1714455⟩

International audience; In corporate bond markets, which are mainly OTC markets, market makers play a central role by providing bid and ask prices for bonds to asset managers. Determining the optimal bid and ask quotes that a market maker should set

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e581c768b8dc2f9a350fa1189ae5da4
https://hal.archives-ouvertes.fr/hal-03252505

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