Groupe de travail

Financial and Actuarial Mathematics, Numerical Probability


Day, hour and place

Thurssay at 16:00, Jussieu, Salle Paul Lévy, 16-26 209 / Sophie Germain salle 1016


Contact(s)

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Previous talks


Year 2023

Financial and Actuarial Mathematics, Numerical Probability
Thursday November 16, 2023, 11AM, Sophie Germain salle 1013
Samuel Daudin (Univ. Nice) On the optimal rate for the convergence problem in mean-field control

The goal of this work is to obtain optimal rates for the convergence problem in mean field control. Our analysis covers cases where the solutions to the limiting problem may not be unique nor stable. Equivalently the value function of the limiting problem might not be differentiable on the entire space. The key argument in our approach consists in mollifying the value function of the limiting problem in order to produce functions that are almost classical sub-solutions to the limiting Hamilton-Jacobi equation (which is a PDE set on the space of probability measures). These sub-solutions can be projected onto finite dimensional spaces and then compared with the value functions associated with the particle systems. In the end, this comparison is used to prove the most demanding bound in the estimates. The key challenge therein is thus to exhibit an appropriate form of mollification. We do so by employing sup-convolution within a convenient functional Hilbert space. This is a joint work with François Delarue and Joe Jackson.

Financial and Actuarial Mathematics, Numerical Probability
Thursday October 19, 2023, 11AM, INRIA 2 Rue Simone Iff, 75012 Paris, France
Robert Denkert (HU Berlin) xtended Mean Field Control Problems with Singular Controls

We consider a novel class of extended mean field control (MFC) problems with singular controls, where the costs depend on the current state, control, and the joint law of the state-control-process. We prove an approximation of general singular controls with purely regular controls. Subsequently, we derive a dynamic programming principle and use it to establish a quasi-variational inequality (QVI) for the value function in the Wasserstein space. Finally, using the master equations of the approximating regular MFC problems, we establish a uniqueness result for our QVI characterisation of the value function of the MFC problem with singular controls. This presentation is based on joint work with Ulrich Horst.

Financial and Actuarial Mathematics, Numerical Probability
Thursday October 19, 2023, 9AM, Inria 2 Rue Simone Iff, 75012 Paris, France
Gudmund Pammer (ETH, Zurich) Stretched Brownian Motion: Analysis of a Fixed-Point Scheme

The fitting problem is a classical challenge in mathematical finance about finding martingales that satisfy specific marginal constraints. Building on the Bass solution to the Skorokhod embedding problem and optimal transport, Backhoff, Beiglböck, Huesmann, and Källblad propose a solution for the two-marginal problem: the stretched Brownian motion. Notably rich in structure, this process is an Ito diffusion and a continuous, strong Markov martingale. Following a similar approach, Conze and Henry-Larbordère recently introduced a novel local volatility model. This model, rooted in an extension of the Bass construction, is efficiently computable through a fixed-point scheme. In our presentation, we reveal the fixed-point scheme's intricate connection to the stretched Brownian motion and analyse its convergence. This presentation is based on joint work with Beatrice Acciaio and Antonio Marini.

Financial and Actuarial Mathematics, Numerical Probability
Thursday October 19, 2023, 11:45AM, Inria 2 Rue Simone Iff, 75012 Paris, France
Aurélien Alfonsi (ENPC) Nonnegativity preserving convolution kernels. Application to Stochastic Volterra Equations in closed convex domains and their approximation.

We define and study convolution kernels that preserve nonnegativity. When the past dynamics of a process is integrated with a convolution kernel like in Stochastic Volterra Equations or in the jump intensity of Hawkes processes, this property allows to get the nonnegativity of the integral. We give characterizations of these kernels and show in particular that completely monotone kernels preserve nonnegativity. We then apply these results to analyze the stochastic invariance of a closed convex set by Stochastic Volterra Equations. We also get a comparison result in dimension one. Last, when the kernel is a positive linear combination of decaying exponential functions, we present a second order approximation scheme for the weak error that stays in the closed convex domain under suitable assumptions. We apply these results to the rough Heston model and give numerical illustrations.

Financial and Actuarial Mathematics, Numerical Probability
Thursday October 19, 2023, 9:45AM, Inria 2 Rue Simone Iff, 75012 Paris, France
Mehdi Talbi (LPSM) Sannikov’s contracting problem with many Agents

This work aims to study an extension of the celebrated Sannikov’s Principal-Agent problem to the multi-Agents case. In this framework, the contracts proposed by the Principal consist in a running payment, a retirement time and a final payment at retirement. After discussing how the Principal may derive optimal contracts in the N-Agents case, we explore the corresponding mean field model, with a continuous infinity of Agents. We then prove that the Principal’s problem can be reduced to a mixed control-and-stopping mean field problem, and we derive a semi-explicit solution of the first best contracting problem. This is a joint work with Thibaut Mastrolia and Nizar Touzi.

Financial and Actuarial Mathematics, Numerical Probability
Thursday October 12, 2023, 11AM, Sophie Germain salle 1016
Céline Labart (Université de Savoie) To be announced.

Financial and Actuarial Mathematics, Numerical Probability
Wednesday July 12, 2023, 11AM, Jussieu, Salle Paul Lévy, 16-26 209
Mortiz Voss (UCLA) On Adaptive Robust Optimal Execution and Machine Learning Surrogates

We study a stochastic optimal order execution problem in discrete time in the presence of price impact. First, we propose a unified price impact model which nests various existing models in the literature. Second, we propose a numerical algorithm based on dynamic programming and neural network surrogates to solve the resulting optimal stochastic control problem. Third, we extend our modeling framework to account for model uncertainty in the sense of unknown model parameters and propose an adaptive robust optimization approach that combines dynamic learning with a robust (worst-case) optimization.

Financial and Actuarial Mathematics, Numerical Probability
Thursday June 15, 2023, 11AM, Jussieu, Salle Paul Lévy, 16-26 209
Thomas Kruse (Bergischen Universität Wuppertal) Multilevel Picard approximations for high-dimensional semilinear parabolic PDEs and further applications

We present the multilevel Picard approximation method for high-dimensional semilinear parabolic PDEs. A key idea of our method is to combine multilevel approximations with Picard fixed-point approximations. We prove in the case of semilinear heat equations with Lipschitz continuous nonlinearities that the computational effort of the proposed method grows polynomially both in the dimension and in the reciprocal of the required accuracy. Moreover, we present further applications of the multilevel Picard approximation method and illustrate its efficiency by means of numerical simulations. The talk is based on joint works with Weinan E, Martin Hutzenthaler, Arnulf Jentzen, Tuan Nguyen and Philippe von Wurstemberger.

Financial and Actuarial Mathematics, Numerical Probability
Thursday May 11, 2023, 11AM, Jussieu, Salle Paul Lévy, 16-26 209
Botao Li (LPSM) Simplified Models and Optimization Algorithms in Deep Learning

In general, deep learning models are considered black boxes and are not able to be solved analytically. However, some simplified models are easy enough to analyze while sharing characteristics with realistic deep-learning models. In this talk, we present results obtained in the simplified models that aim at providing insight into deep learning in general. We demonstrate that, in some toy models, the stochastic gradient descent algorithm (SGD) with a constant learning rate can converge to local maxima; adaptive methods can have the same problem; SGD can escape a saddle point arbitrarily slow; it may prefer a sharp minimum rather than a flat one. We also present the expression for the global minima of a deep linear network with weight decay and dropout.

Financial and Actuarial Mathematics, Numerical Probability
Thursday April 13, 2023, 11AM, Jussieu, Salle Paul Lévy, 16-26 209
Xavier Erny (CMAP) Propagation du chaos conditionnelle de modèles en champ moyen

Nous étudions un système de particules en interaction (de type champ moyen) dans une normalisation diffusive. Chaque particule interagit avec les autres de manière ponctuelle à un taux aléatoire. A chaque interaction, la dynamique des particules est perturbée par une quantité aléatoire centrée de l'ordre de N^{−1/2}. Nous prouvons que ce système converge, quand N tend vers l'infini, vers une équation différentielle stochastique dirigée par un mouvement brownien W, qui est créé par le théorème central limite. Ce mouvement brownien régit les mouvements de toutes les particules, et créé un bruit commun dans le système limite. Conditionnellement à W, les particules sont indépendantes dans le système limite. C'est la propriété de propagation du chaos conditionnelle. Pour prouver la convergence en loi du système fini vers le système limite, nous introduisons un nouveau type de problème de martingale adapté à notre cadre de travail. Les techniques utilisées dans les preuves reposent sur le fait que l'on étudie des systèmes échangeables.

Financial and Actuarial Mathematics, Numerical Probability
Thursday April 13, 2023, 10AM, Jussieu, Salle Paul Lévy, 16-26 209
Pierre Lavigne (Institut Louis Bachelier) Decarbonization of financial markets: a mean field game approach

We present a model of a financial market where a large number of firms determine their dynamic emission strategies under climate transition risk in the presence of both green-minded and neutral investors. The firms aim to achieve a trade-off between financial and environmental performance, while interacting through the stochastic discount factor, determined in equilibrium by the investors’ allocations.

We formalize the problem in the setting of mean-field games and prove the existence and uniqueness of a Nash equilibrium for firms. We then present a convergent numerical algorithm for computing this equilibrium and illustrate the impact of climate transition risk and the presence of green-minded investors on the market decarbonization dynamics and share prices.

We show that uncertainty about future climate risks and policies leads to higher overall emissions and higher spreads between share prices of green and brown companies. This effect is partially reversed in the presence of environmentally concerned investors, whose impact on the cost of capital spurs companies to reduce emissions.

Joint work with Peter Tankov.

Financial and Actuarial Mathematics, Numerical Probability
Thursday April 6, 2023, 11AM, Jussieu, Salle Paul Lévy, 16-26 209
Mahmoud Khabou (INSA Toulouse) The normal approximation of compound Hawkes functionals

We derive quantitative bounds in the Wasserstein distance for the approximation of stochastic integrals of deterministic and non-negative integrands with respect to Hawkes processes by a normally distributed random variable. Our results are specifically applied to compound Hawkes processes, and improve on the current literature where estimates may not converge to zero in large time, or have been obtained only for specific kernels such as the exponential or Erlang functions.

Financial and Actuarial Mathematics, Numerical Probability
Thursday April 6, 2023, 10AM, Jussieu, Salle Paul Lévy, 16-26 209
Ziad Kobeissi (INRIA ILB) Temporal Difference Learning with Continuous Time and State in the Stochastic Setting

Classical methods in reinforcement learning allow to efficiently approximate discrete-time value functions using observations. In this talk, we investigate the extensions of such methods to the continuous-time case in a stochastic regime. Mathematically, this boils down to numerically solving BSDEs through learning from observations. Here, we focus our attention on the problem of policy evaluation and more precisely on the basic method TD(0) (for temporal difference). This corresponds to learning the value function of an uncontrolled SDE associated with a reward function. Using vanishing time steps, we propose two adaptations of the TD(0) algorithm: the first is similar to the one in discrete time and is model-free; the second is model-based and is obtained by adding a zero-expectation term, resulting in a reduction of its variance. In the linear setting, we prove multiple convergence results for the two algorithms, the model-based one is more flexible and enjoys better convergence rates. In particular, using the Polyak-Juditsky averaging method and a constant learning step, we obtain a convergence rate similar to the state of the art on the simpler problem of linear regression using SGD. Finally, we present simulations showing numerical evidence of our theoretical analysis.

Financial and Actuarial Mathematics, Numerical Probability
Thursday March 30, 2023, 11AM, Jussieu, Salle Paul Lévy, 16-26 209
Mehdi Talbi (ETH Zurich) Mean field games of stopping times

We are interested in the study of stochastic games for which each player faces an optimal stopping problem. In our setting, the players may interact through the criterion to optimize as well as through their dynamics. After briefly discussing the N-players game, we formulate the corresponding mean field problem. In particular, we introduce a weak formulation of the game for which we are able to prove existence of Nash equilibria for a large class of criteria. We also prove that equilibria for the mean field problem provide approximated Nash equilibria for the N-players game, and we formally derive the master equation associated with our mean field game. Joint work with Dylan Possamaï.

Financial and Actuarial Mathematics, Numerical Probability
Thursday March 16, 2023, 11AM, Jussieu, Salle Paul Lévy, 16-26 209
Rudy Morel (Ecole Normale Supérieure) A statistical model of financial time-series through Scattering Spectra

We introduce the wavelet scattering spectra which provide non-Gaussian models of time-series having stationary increments.

These spectra are an extension of the standard wavelet spectrum and are defined as the diagonal of a certain non-linear correlation matrix on wavelet coefficients.

They characterize a wide range of non-Gaussian properties of multi-scale processes. This is analyzed for a variety of processes in the Finance literature.

We prove that self-similar processes have scattering spectra which are scale invariant. This property can be tested statistically on a single realization and defines a class of wide-sense self-similar processes.

We build maximum entropy models conditioned by scattering spectra coefficients, and generate new time-series with a microcanonical sampling algorithm.

Besides capturing statistical properties of observed time-series, these models can be used to predict future volatility and are shown to capture non-trivial statistical properties of the option smile.

Financial and Actuarial Mathematics, Numerical Probability
Thursday March 9, 2023, 11AM, Jussieu, Salle Paul Lévy, 16-26 209
Ofelia Bonesini (Imperial College) Correlated equilibria for mean field games with progressive strategies

In a discrete space and time framework, we study the mean field game limit for a class of symmetric N-player games based on the notion of correlated equilibrium. We give a definition of correlated solution that allows to construct approximate N-player correlated equilibria that are robust with respect to progressive deviations. We illustrate our definition by way of an example with explicit solutions.

Financial and Actuarial Mathematics, Numerical Probability
Thursday February 16, 2023, 11AM, Jussieu, Salle Paul Lévy, 16-26 209
William Hammersley (Univ. Nice) A prospective regularising common noise for mean field systems

Financial and Actuarial Mathematics, Numerical Probability
Thursday February 2, 2023, 4PM, Jussieu, Salle Paul Lévy, 16-26 209
Nabil Kazi-Tani (Université de Lorraine) The role of correlation in diffusion control ranking games

We study Nash equilibriums in 2-players continuous time stochastic differential games, where the players are allowed to control the diffusion coefficient of their state process. We consider zero-sum ranking games, in the sense that the criteria to optimize only depends on the difference of the two players state processes. We explicitly compute the players optimal strategies, depending on the correlation of the Brownian motions driving the two state equations: in particular, if the correlation coefficient is smaller than some explicit threshold, then the optimal strategies consist of strong controls, whereas if the correlation exceeds the threshold, then the optimal controls are mixed strategies. To characterize these equilibria, we rely on a relaxed formulation of the game, allowing the players to randomize their actions. This is a joint work with Stefan Ankirchner and Julian Wendt (University of Jena).

Financial and Actuarial Mathematics, Numerical Probability
Thursday January 26, 2023, 4:30PM, Jussieu, Salle Paul Lévy, 16-26 209
Manal Jakani (Le Mans Université) Approximation of reflected SDEs in time-dependent domains and applications to Generalized BSDEs and PDE in time-dependent domain

We consider a class of reflected SDEs in non-smooth convex time-dependent domains. We provide a strong approximation for this type of equations using a sequence of standard SDEs. As a consequence, we obtain an approximation scheme for generalized BSDEs using standard BSDEs. As a by-product, we get an approximation for the solution of a system of PDEs with nonlinear boundary conditions in time-dependent domains.

Financial and Actuarial Mathematics, Numerical Probability
Thursday January 19, 2023, 4:30PM, Jussieu, Salle Paul Lévy, 16-26 209
Andrea Mazzon (LMU München) Detecting asset price bubbles using deep learning

In this paper we employ deep learning techniques to detect financial asset bubbles by using observed call option prices. The proposed algorithm is widely applicable and model-independent. We test the accuracy of our methodology in numerical experiments within a wide range of models and apply it to market data of tech stocks in order to assess if asset price bubbles are present. Under a given condition on the pricing of call options under asset price bubbles, we provide a theoretical foundation of our approach for positive and continuous stochastic processes. When such a condition is not satisfied, we focus on local volatility models. To this purpose, we give a new necessary and sufficient condition for a process with time-dependent local volatility function to be a strict local martingale. This is a joint work with Francesca Biagini, Lukas Gonon and Thilo Meyer-Brandis.

Financial and Actuarial Mathematics, Numerical Probability
Thursday January 5, 2023, 4PM, Jussieu, Salle Paul Lévy, 16-26 209
Ahmed Kebaier (Université d'Evry) The interpolated drift implicit Euler scheme Multilevel Monte Carlo method for pricing Barrier options and applications to the CIR and CEV models

Recently, Giles et al. [2019] proved that the efficiency of the Multilevel Monte Carlo (MLMC) method for evaluating Down-and-Out barrier options for a diffusion process (Xt)t∈[0,T] with globally Lipschitz coefficients, can be improved by combining a Brownian bridge technique and a conditional Monte Carlo method provided that the running minimum inft∈[0,T]Xt has a bounded density in the vicinity of the barrier. In the present work, thanks to the Lamperti transformation technique and using a Brownian interpolation of the drift implicit Euler scheme of Alfonsi [2013], we show that the efficiency of the MLMC can be also improved for the evaluation of barrier options for models with non-Lipschitz diffusion coefficients under certain moment constraints. We study two example models: the Cox-Ingersoll-Ross (CIR) and the Constant of Elasticity of Variance (CEV) processes for which we show that the conditions of our theoretical framework are satisfied under certain restrictions on the models parameters. In particular, we develop semi-explicit formulas for the densities of the running minimum and running maximum of both CIR and CEV processes which are of independent interest. Finally, numerical tests are processed to illustrate our results.

Financial and Actuarial Mathematics, Numerical Probability
Thursday January 5, 2023, 5PM, Jussieu, Salle Paul Lévy, 16-26 209
Thomas Wagenhofer (TU Berlin) Weak error estimates for rough volatility models

We consider a rough volatility model where the volatility is a (smooth) function of a Riemann-Liouville Brownian motion with Hurst parameter H in (0,1/2). When simulating these models, one often uses a discretization of stochastic integrals as an approximation. These integrals can be interpreted as log-stock-prices and in financial applications such as in pricing, the most relevent quantities are expectations of (payoff) functions.

Our main result is that moments of these integrals have a weak error rate of order 3H+1/2 if H<1/6 and order 1 otherwise. For this we first derive a moment formula for both the discretization and the true stochastic integral. We then use this formula and properties of Gaussian random variables to prove our main theorems. Furthermore, we show that this convergence rate also holds for slightly more general payoffs and also provide a lower bound. Note that our rate of 3H+1/2 is in stark contrast to the strong error rate which is of order H.


Year 2022

Financial and Actuarial Mathematics, Numerical Probability
Thursday December 15, 2022, 4PM, Jussieu, Salle Paul Lévy, 16-26 209
Nizar Touzi (CMAP, Ecole Polytechnique) Arrêt optimal en champ moyen

Financial and Actuarial Mathematics, Numerical Probability
Thursday December 15, 2022, 5PM, Jussieu, Salle Paul Lévy, 16-26 209
Olivier Bokanowski (Univ. Paris Cité, LJLL) Neural Networks for First Order HJB Equations

We consider a deterministic optimal control problem, in a finite horizon context, and propose deep neural network approximations for Bellman's dynamic programming principle, corresponding also to some first-order Hamilton-Jacobi-Bellman (HJB) equations. This work follows the work of Huré, Pham, Bachouch, and Langrené (SIAM J. Numer. Anal., vol. 59 (1), 2021, pp. 525-557) where algorithms are proposed in a stochastic context. However, we need to develop an entirely new approach in order to deal with the propagation of errors in the deterministic setting, where no diffusion is present in the dynamics. Our analysis gives precise error estimates in an average norm. The algorithms are then illustrated on several academic numerical examples related to front propagations models in the presence of obstacle constraints (modelized by an optimal maximum running cost problem) showing the relevance of the approach for average dimensions (e.g. from $2$ to $8$), in particular in situations where the value functions is non-smooth. This is a joint work with Xavier Warin and Averil Prost.

Financial and Actuarial Mathematics, Numerical Probability
Thursday November 24, 2022, 4:30PM, Jussieu, Salle Paul Lévy, 16-26 209
Pierre Bras (LPSM, Sorbonne Université) Total variation convergence of the Euler-Maruyama scheme in small time with unbounded drift

We give bounds for the total variation distance between the solution of a stochastic differential equation in $R^d$ and its one step Euler-Maruyama scheme in small time. We show that for small t, the total variation is of order $t^{1/3}$ , and more generally of order $t^{r/(2r+1)}$ if the noise coefficient $\sigma$ of the SDE is elliptic and $C^2r_b$, $r \in N$, using multi-step Richardson-Romberg extrapolation. The Richardson-Romberg extrapolation is a method used in numerical analysis to improve the convergence rates of numerical schemes and relies on a linear combination of Taylor expansions with null coefficients up to some order; we adapt this method to our case for theoretical purposes.

Financial and Actuarial Mathematics, Numerical Probability
Thursday October 20, 2022, 4PM, Jussieu, Salle Paul Lévy, 16-26 209
Damien Lamberton (Université Gustave Eiffel) Régularité de la frontière libre d'un problème d'arrêt optimal : une approche probabiliste

Dans ce travail en collaboration avec Tiziano De Angelis (de l'Université de Turin), nous proposons une approche probabiliste de la dérivabilité de la frontière libre d'un problème d'arrêt optimal d'une diffusion uni-dimensionnelle.

Financial and Actuarial Mathematics, Numerical Probability
Thursday October 20, 2022, 5PM, Jussieu, Salle Paul Lévy, 16-26 209
Aurélien Alfonsi (Ecole des Ponts) Approximation of Stochastic Volterra Equations with kernels of completely monotone type (Joint work with Ahmed Kebaier)

In this work, we develop a multifactor approximation for d-dimensional Stochastic Volterra Equations (SVE) with Lipschitz coefficients and kernels of completely monotone type that may be singular. First, we prove an L2-estimation between two SVEs with different kernels, which provides a quantification of the error between the SVE and any multifactor Stochastic Differential Equation (SDE) approximation. For the particular rough kernel case with Hurst parameter lying in (0, 1/2), we propose various approximating multifactor kernels, state their rates of convergence and illustrate their efficiency for the rough Bergomi model. Second, we study a Euler discretization of the multifactor SDE and establish a convergence result towards the SVE that is uniform with respect to the approximating multifactor kernels. These obtained results lead us to build a new multifactor Euler scheme that reduces significantly the computational cost in an asymptotic way compared to the Euler scheme for SVEs. Finally, we show that our multifactor Euler scheme outperforms the Euler scheme for SVEs for option pricing in the rough Heston model.

Financial and Actuarial Mathematics, Numerical Probability
Thursday October 6, 2022, 4:30PM, Jussieu, Salle Paul Lévy, 16-26 209
Michaël Allouche (Ecole Polytechnique) Estimation of extreme quantiles from heavy-tailed distributions with neural networks

We propose new parametrizations for neural networks in order to estimate extreme quantiles in both non-conditional and conditional heavy-tailed settings. All proposed neural network estimators feature a bias correction based on an extension of the usual second-order condition to an arbitrary order. The convergence rate of the uniform error between extreme log-quantiles and their neural network approximation is established. The finite sample performances of the non-conditional neural network estimator are compared to other bias-reduced extreme-value competitors on simulated data. The source code is available at https://github.com/michael-allouche/nn-quantile-extrapolation.git. Finally, the conditional neural network estimators are implemented to investigate the behavior of extreme rainfalls as functions of their geographical location in the southern part of France.

Financial and Actuarial Mathematics, Numerical Probability
Thursday July 7, 2022, 4PM, Sophie Germain salle 1016
Anthony Reveillac (INSA Toulouse) Malliavin calculus for Hawkes functionals and application to Insurance

In this talk we will present the basics of the Malliavin calculus we developed for the Hawkes process. We will recall this mathematical object together with one key representation which is at the core of our analysis. As an application of our theoretical findings, we will present some quantifications of the risk for (re-)Insurance contracts. This talk is based on several joint works with Caroline Hillairet; Lorick Huang; Mahmoud Khabou and Mathieu Rosenbaum.

Financial and Actuarial Mathematics, Numerical Probability
Thursday June 23, 2022, 5PM, Sophie Germain salle 1016
Boualem Djehiche (KTH Stockholm) On zero-sum Dynkin games of mean field type.

I will review recent results on a class of zero-sum game problems of mean-field type which extend the classical zero-sum Dynkin game problems. The results I will highlight include sufficient conditions under which such a game admits a value and a saddle point and a characterization of the value of the game in terms of a specific class of doubly reflected backward stochastic differential equations (BSDEs) of mean-field type. Moreover, a corresponding system of weakly interacting values of zero-sum Dynkin games is introduced and is shown to converge to the value of the zero-sum mean-field Dynkin game. In particular, a propagation of chaos result is derived.

This is a joint work with Roxana Dumistrescu.

Financial and Actuarial Mathematics, Numerical Probability
Thursday June 2, 2022, 5PM, Jussieu, Salle Paul Lévy, 16-26 209 / Sophie Germain salle 1016
Marcos Lopes De Prado (ADIA) Open problems in Finance

Financial and Actuarial Mathematics, Numerical Probability
Thursday May 12, 2022, 5PM, Sophie Germain salle 1016
Maximilien Germain (Université Paris Cité, LPSM) A level-set approach to the control of state-constrained McKean-Vlasov equations: application to portfolio selection

We consider the control of McKean-Vlasov dynamics (or mean-field control) with probabilistic state constraints. We rely on a level-set approach which provides a representation of the constrained problem in terms of an unconstrained one with exact penalization and running maximum or integral cost. Our work extends (Bokanowski, Picarelli, and Zidani, SIAM J. Control Optim. 54.5 (2016), pp. 2568–2593) and (Bokanowski, Picarelli, and Zidani, Appl. Math. Optim. 71 (2015), pp. 125–163) to a mean-field setting. The reformulation as an unconstrained problem is particularly suitable for the numerical resolution of the problem, that is achieved from an extension of a machine learning algorithm from (Carmona, Laurière, arXiv:1908.01613 to appear in Ann. Appl. Prob., 2022). An application focuses on a mean-variance portfolio selection problem with probabilistic constraints on the wealth. We also illustrate our approach for a direct numerical resolution of the primal Markowitz continuous-time problem without relying on duality.

Financial and Actuarial Mathematics, Numerical Probability
Thursday April 28, 2022, 4PM, Sophie Germain salle 1016
Nabil Khazi-Tani (IECL, université de Lorraine) To be announced.

Financial and Actuarial Mathematics, Numerical Probability
Thursday March 17, 2022, 4PM, Sophie Germain salle 1016
Pierre Cardaliaguet (Ceremade, Université Paris-Dauphine) On the convergence rate for the optimal control of McKean-Vlasov dynamics

In this talk I will report on a joint work with S. Daudin (Paris Dauphine), J. Jackson (U. Texas) and P. Souganidis (U. Chicago). We are interested in the convergence problem for the optimal control of McKean-Vlasov dynamics, also known as mean field control. We establish an algebraic rate of convergence of the value functions of N-particle stochastic control problems towards the value function of the corresponding McKean-Vlasov problem. This convergence rate is established in the presence of both idiosyncratic and common noise, and in a setting where the value function for the McKean-Vlasov problem need not be smooth. Then I will discuss a quantitative propagation of chaos property for the optimal trajectories of the optimal control of the particle system. It relies on the regularity of the value function of the limit problem on an open and dense subset of the state space.

Financial and Actuarial Mathematics, Numerical Probability
Thursday March 17, 2022, 5PM, Sophie Germain salle 1016
Haoyang Cao (École Polytechnique) Identifiability in Inverse Reinforcement Learning

Inverse reinforcement learning attempts to reconstruct the reward function in a Markov decision problem, using observations of agent actions. As already observed in Russell [1998] the problem is ill-posed, and the reward function is not identifiable, even under the presence of perfect information about optimal behavior. We provide a resolution to this non-identifiability for problems with entropy regularization. For a given environment, we fully characterize the reward functions leading to a given policy and demonstrate that, given demonstrations of actions for the same reward under two distinct discount factors, or under sufficiently different environments, the unobserved reward can be recovered up to a constant. We also give general necessary and sufficient conditions for reconstruction of time-homogeneous rewards on finite horizons, and for action-independent rewards, generalizing recent results of Kim [2021] and Fu [2018].

Financial and Actuarial Mathematics, Numerical Probability
Thursday March 3, 2022, 5PM, Sophie Germain salle 1016
Jodi Dianetti (Bielefeld University) Submodular mean field games: Existence and approximation of solutions

We study mean field games with scalar Itô-type dynamics and costs that are submodular with respect to a suitable order relation on the state and measure space. The submodularity assumption has a number of interesting consequences. Firstly, it allows us to prove existence of solutions via an application of Tarski's fixed point theorem, covering cases with discontinuous dependence on the measure variable. Secondly, it ensures that the set of solutions enjoys a lattice structure: in particular, there exist a minimal and a maximal solution. Thirdly, it guarantees that those two solutions can be obtained through a simple learning procedure based on the iterations of the best-response-map. Our approach also allows to treat submodular mean field games with common noise, as well as mean field games with singular controls, optimal stopping and reflecting boundary conditions

Financial and Actuarial Mathematics, Numerical Probability
Thursday February 17, 2022, 4PM, Sophie Germain salle 1016
David Métivier (CMAP, Ecole Polytechnique) Interpretable hidden Markov model for stochastic weather generation and climate change analysis

The challenges raised by climate change force industrials to carefully analyze the resilience of their assets to anticipate future weather conditions. In particular, the estimation of future extreme hydrometeorological events, like the frequency of long-lasting dry spells, is critical for hydropower or nuclear generation. Stochastic Weather Generators (SWG) are essential tools to determine these future risks, as they can quickly sample climate statistics from models. They can be either trained on historical data or from simulated data like expert climate change scenarios. In our work, the SWG described and validated with France historical data is based on a spatial Hidden Markov Model (HMM). It generates correlated multisite rain occurrences and amounts, with special attention to the correct reproduction of the distribution of dry and wet spells. The hidden states are viewed as global climate states, e.g., dry all over France, rainy in the north, etc. The resulting model is fully interpretable. We describe how the model recovers large-scale structures that are compared with North Atlantic Oscillations. The model achieves good performances, specifically in terms of extremes, where for example, statistics of drought at the scale of France are well replicated. The model architecture allows easy integration of other weather variables like temperature. In a last part, we show how the model parameters change when trained on RCP climate scenarios.

Financial and Actuarial Mathematics, Numerical Probability
Thursday February 17, 2022, 5PM, Sophie Germain salle 1016
Sergio Pulido (LaMME, ENSIEE) The rough Heston model with self-exciting jumps

We introduce a novel affine Volterra stochastic volatility model by adding jumps with a self-exciting structure to the dynamics of the variance process and the log returns in the well-known rough Heston model. In particular, the variance process is given in terms of the solution of a stochastic affine Volterra equation of convolution type with jumps. Thanks to the affine structure of the model we can provide explicit formulas for the Laplace transforms of quantities like log-prices and their quadratic variation, variance swap rates, and integrated variance. These formulas can be exploited to price options using Fourier inversion techniques. The procedure to get the explicit expressions for these transforms essentially relies on the martingale property of complex-valued exponential processes and comparison results for (deterministic) Riccati-Volterra equations. In addition, when the Brownian component in the spot variance dynamics is equal to zero, we devise an exact and efficient conditional simulation scheme. We illustrate with numerical examples the behavior of the implied volatility smiles for options written on the underlying asset and on the VIX. Joint work with Alessandro Bondi, Giulia Livieri and Simone Scotti.

Financial and Actuarial Mathematics, Numerical Probability
Thursday February 3, 2022, 4PM, Jussieu, Salle Paul Lévy, 16-26 209
Peter Tankov (CREST, ENSAE) Optimal Exploration of an Exhaustible Resource with Stochastic Discoveries

The standard Hotelling model assumes that the stock of an exhaustible resource is known. &nbsp;We expand on the model by Arrow and Chang that introduced stochastic discoveries and for the first time completely solve such a model using impulse control. The model has two state variables: the ``proven reserves as well as a finite unexplored area available for exploration with constant marginal cost, resulting in a Poisson process of new discoveries. &nbsp;We prove &nbsp;that &nbsp;a frontier of critical levels of ``proven reserves exists, above which exploration is stopped, and below which it happens at infinite speed. This frontier is increasing in the explored area, and higher ``proven'' reserve levels along this critical threshold are indicative of more scarcity, not less. In our stochastic generalization of Hotelling's rule, price expectations conditional on the current state rise at the rate of interest across exploratory episodes. However, the state-dependent conditional &nbsp;expected &nbsp;path of prices realized prior to exhaustion of the exploratory area rises at a rate lower than the rate of interest, consistent with &nbsp;most &nbsp;empirical tests based on observed price histories.

Financial and Actuarial Mathematics, Numerical Probability
Thursday February 3, 2022, 5PM, Jussieu, Salle Paul Lévy, 16-26 209
Alexandre Pannier (Imperial College, Londres) Rough multi-factor volatility models for SPX and VIX

After a short review of VIX and rough volatility models, we provide explicit small-time formulae for the at-the-money implied volatility, skew and curvature in a large class of models. Our general setup encompasses both European options on a stock and VIX options, thereby providing new insights on their joint calibration. This framework also allows to consider rough volatility models and their multi-factor versions; in particular we develop a detailed analysis of the two-factor rough Bergomi model. The tools used are essentially based on Malliavin calculus for Gaussian processes. This is a joint work with A. Jacquier and A. Muguruza.

Financial and Actuarial Mathematics, Numerical Probability
Thursday January 20, 2022, 5PM, Jussieu, Salle Paul Lévy, 16-26 209
Philippe Bergault (Ecole Polytechnique) A mean field game of market making against strategic traders

We design a market making model à la Avellaneda–Stoikov in which the market takers act strategically, in the sense that they design their trading strategy based on a specific signal. The market maker chooses her quotes based on the average market takers' behaviour, modelled through a mean field interaction. We derive, up to the resolution of a coupled HJB–Fokker–Planck system, the optimal controls of the market maker and the representative market taker. This approach is flexible enough to incorporate different behaviours for the market takers and takes into account the impact of their strategies on the price process. Joint work with Bastien Baldacci and Dylan Possamai.