Aim: The Bachelier lectures are docotoral training also opened to researchers and practitioners who would like to follow a specialised high-level class in the field of mathematical finance.
Periodicity and location: The lectures take place at Institut Henri Poincaré between 9h and 11h, according to the schedule indicated in Schedule, before the Bachelier seminar.
Friday 10/01, 24/01, 07/02 and 14/02
Economics of climate change and Green Finance
Friday 28/02, 06/03, 13/03 and 20/03
Longévité
Part 1: Point processes in random environment and application to the study of longevity risk, Sarah Kaakai (LMM, Université du Mans).
Point processes have received much attention in the recent years. Indeed, their flexibility allows for the modeling of a wide range of phenomena, in various fields including for instance biology, finance, insurance, population dynamics or neurosciences.
In this course, I will first present a general overview on representations of point processes with stochastic intensity, with a particular focus on pathwise representations as solution of stochastic differential equations with random coefficients, driven by Poisson random measures. This viewpoint is particularly well-suited to the study of interacting events occurring in a random environment, and of non-Markovian systems. I will show how strong comparison results can be derived from pathwise representations, as well as straightforward tightness results.
In a second part, I will apply these results in order to introduce a new class of heterogeneous population dynamics in random environment. Individuals (or companies) can enter or exit the population, as well as change characteristics, at stochastic rates depending on the whole population and on the random environment. I will show on this model how averaging results can be obtained for point processes in random environment, here when changes of characteristics occur at a faster timescale than entries and exits from/to the population. In particular, I will give a brief overview on the stable convergence, which is a powerful tool naturally extending the convergence in distribution in the presence of a random environment.
Finally, I will illustrate how such averaging results allow us to generate more realistic mortality models than standard demographic tools based on linear models, reflecting the heterogeneity of the underlying population and taking into account the macro environment.
Part 2: Longevity risk and quickest detection problem: from theory to practice, Nicole El Karoui (LPSM, Sorbonne Université) and Stéphane Loisel (ISFA, Université Lyon 1).
After recalling briefly key features of longevity risk,, we explain how to detect as quickly as possible the date where that the actuarial assumptions related to longevity risk are no longer valid.
The problem is put as a quickest detection problem. We introduce the so-called cusum process and show its optimality for a generalized Lorden criterion. We then explain how to design Key Risk Indicators thanks to the cusum process. We analyze its advantages and drawbacks for longevity risk monitoring, as well as for some other insurance risks. The method is illustrated on simulated and real-world case studies.
Lecture notes: Cours 28/02 ⬇, Cours 06/03 ⬇, Cours 13/03 ⬇
Annulé
Friday 08/02 and 15/02
Rough volatility
Friday 29/03, 05/04, 12/04 and 19/04
Auctions in the Energy Sector: An Introduction and Survey
OUTLINE:
Friday 07/06 (G. Peyré) and 21/06 (M. Cuturi)
Optimal Transport & Machine Learning
Friday 26/01, 02/02, 09/02 and 16/02
Analyse XVA
Lecture notes: Part 1, Part 2.
ABSTRACT: Since the crisis, derivative dealers charge to their clients various add-ons, dubbed X-valuation adjustments (XVAs), meant to account for counterparty risk and its capital and funding implications.
XVAs deeply affect the derivative pricing task by making it global, nonlinear, and entity dependent. However, before the technical implications, the fundamental points are to understand what deserves to be priced and what does not, and to establish, not only the pricing, but also the corresponding collateralization, dividend, and accounting policy of a bank.
If banks cannot replicate jump-to-default related cash ows, deals trigger wealth transfers from bank shareholders to creditors and shareholders need to set capital at risk. On this basis, we devise a theory of XVAs, whereby so-called contraliabilities and cost of capital are sourced from bank clients at trade inceptions, on top of the fair valuation of counterparty risk, in order to compensate shareholders for wealth transfer and risk on their capital.
The resulting all-inclusive XVA add-on, to be sourced from clients incrementally at every new deal, reads (CVA + FVA + MVA + KVA), where C sits for credit, F for funding, M for (initial) margin, and where the KVA is a cost of capital risk premium. This formula corresponds to the cost of the possibility for the bank to go into run-off, while staying in line with shareholder interest, from any point in time onward if wished.
Moreover, economic capital (EC) can be used as a funding source by banks, at a risk-free cost instead of the additional credit spread of the bank in the case of unsecured borrowing. This intertwining of EC and FVA leads to an anticipated BSDE (backward stochastic differential equation of the McKean type) for the FVA, with coefficient entailing a conditional risk measure of the one-year-ahead increment of the martingale part of the FVA itself.
Our XVA equations are solved by projection on a reduced filtration myopic to the default of the bank, the latter being assumed to be an invariance time as per Crépey and Song (2017). This assumption, which covers mainstream immersion setups (but not only), expresses the consistency of valuation across different trading desks with different focuses within the bank: the XVA desks versus the different business desks.
Finally, we present a nested Monte Carlo approach implemented on graphics processing units (GPU) to XVA computations. The overall XVA suite involves five compound layers of nested dependence. Higher layers are launched first and trigger nested simulations on-the-fly whenever required in order to compute a metric from a lower layer. With GPUs, error controlled nested Monte Carlo XVA computations are within reach. This is illustrated on XVA computations involving equities, interest rate, and credit derivatives, for both bilateral and central clearing XVA metrics.
Course material: Related papers on https://math.maths.univ-evry.fr/crepey/.
Friday 23/03 and 30/03
Diffusions en interaction de champ moyen suivant le rang
ABSTRACT: Les particules diffusives interagissant suivant le rang permettent de modéliser les capitalisations boursières dans la théorie du portefeuille stochastique de Fernholtz. Nous nous intéresserons au cas particulier où l'interaction est en outre de type champ moyen : les coefficients de diffusion et de dérive de chaque coordonnée (ou particule) dépendent de la fonction de répartition empirique de l'ensemble des particules calculée en cette coordonnée. Nous étudierons tout d'abord la limite de champ moyen où le nombre de coordonnées tend vers l'infini. La fonction de répartition empirique tend alors vers la fonction de répartition de la loi marginale de l'équation différentielle stochastique limite qui est non linéaire au sens de McKean. Nous nous intéresserons ensuite au comportement en temps long de cette diffusion non linéaire en exploitant que la statistique d'ordre des particules est une diffusion à coefficients constants normalement réfléchie à la frontière du simplexe.
Nous interprèterons cette limite en temps long en théorie du portefeuille. Enfin, nous montrerons que la limite petit bruit du système de particules est donnée par la dynamique des particules collantes et nous étudierons la limite de cette dynamique lorsque le nombre de particules tend vers l'infini.
Friday 06/04 and 13/04
Control of McKean-Vlasov equations
ABSTRACT: This lecture is concerned with the optimal control of McKean-Vlasov equations, which has been knowing a surge of interest since the emergence of the mean-field game theory. Such problem is originally motivated from large population stochastic control in mean-field interaction, and finds various applications in economy, finance, or social sciences for modelling motion of socially interacting individuals and herd behavior. It is also relevant for dealing with intermittence questions arising typically in risk management.
In the first part, I focus on the important class of linear-quadratic McKean-Vlasov (LQMcKV) control problem, which provides a major source for examples and applications. We show a direct and elementary method for solving explicitly LQMcKV based on a mean version of the well-known martingale optimality principle in optimal control, and the completion of squares technique. Variations and extensions to the case of infinite horizon, random coefficients and common noise are also addressed. Finally, we illustrate our results with an application to a model of interaction between centralised and distributed generation.
The second part is devoted to the presentation of the dynamic programming approach (in other words, the time consistency approach) for the control of general McKean-Vlasov dynamics. In particular, we introduce the recent mathematical tools that have been developed in this context: differentiability in the Wasserstein space of probability measures, Itô formula along a flow of probability measures and Master Bellman equation. Extensions to stochastic differential games of McKean-Vlasov type are also discussed.
Friday 18/05, 25/05, 01/06 and 15/06 (10h-11h)
Théorie des jeux : Outils de base et application aux réseaux sans fil et réseau d’électricité
Friday 18/11, 25/11 and 2/12
Financial Intermediation at Any Scale for Quantitative Modelling
ABSTRACT: During this series of lectures, we will go from the role of the financial system described as a large network of intermediaries to a fine description of high frequency market makers. The role of regulation in the recent transformations of participants’ practices will be exposed too. The viewpoint taken is the one of a practitioner or a researcher who has to put in place models. Existing models will be reviewed, and new challenges and the stakes of possible improvements will be discussed. Important stylized facts and important mechanisms that models should reproduce will be exposed.
Outline:
MAIN REFERENCES:
ABOUT THE AUTHOR:
Charles-Albert Lehalle is Senior Research Advisor at Capital Fund Management (CFM, Paris) and a member of the CFM-Imperial Institute of Quantitative Finance. He was formerly Global Head of Quantitative Research at Crédit Agricole Cheuvreux, and Global Head of Quantitative Research on Market Microstructure in the Equity Brokerage and Derivative Department of Crédit Agricole Corporate Investment Bank.
With a Ph.D. And an HDR in applied mathematics Charles-Albert Lehalle lectures at the Pierre et Marie Curie "Probability and Finance" and the MASEF/ENSAE Masters in Paris.
Since the financial crisis, Charles-Albert has studied market microstructure evolution and regulatory changes in Europe and the US, and has provided research and expertise on these topics to investors, intermediaries and policy-makers such as the European Commission, the French Senate and the UK Foresight Committee. He has been a member of the Consultative Workgroup on Financial Innovation of the European Market Authority (ESMA) and is part of the Scientific Committee of the French regulator (AMF). Besides, he chairs Euronext’s Index Advisory Group, working on topics like Smart Beta and Factor Investing.
He has published many academic papers about the use of stochastic control and stochastic algorithms to optimize trading flows with respect to flexible constraints. He has also authored papers on post-trade analysis, market impact estimation and modelling the dynamics of limit order books. He co-authored the book "Market Microstructure in Practice".
and co-edited the book "Market Microstructure: Confronting Many Viewpoints", being co-organizer of the eponymous conference taking place every even year in December in Paris. Charles-Albert is one of the managing editors of the “Market Microstructure and Liquidity” academic journal.
Friday 7/4, 21/4, 28/4 and 5/5
Continuous time contract theory models
ABSTRACT: We consider a number of models involving two parties, a principal and an agent. In practice, the principal can be the owner of a firm and the agent can be a manager that is hired to run the firm's operations. The two parties may share the same information or not. The first of these two cases gives rise to a risk sharing problem in which the principal optimally determines the precise actions that the agent has to follow. The second one gives rise to a problem that may involve moral hazard in the sense that the agent can take actions that are not in the best interest of the principal. We develop a complete analysis of the models we consider, with emphasis on the important ideas underlying their analysis. The four lectures are structured to be relatively independent.
Friday 12/5 and 19/5
Equilibrium models with frictions
ABSTRACT:
Part I: Equilibrium Liquidity Premia (Joint work with Bruno Bouchard, Masaaki Fukasawa, and Martin Herdegen) We study equilibrium returns in a continuous-time model, where heterogenous mean-variance investors trade subject to quadratic transaction costs. We show that the unique equilibrium is characterised by a system of coupled but linear forward backward stochastic differential equations. Explicit solutions obtain in a number of concrete settings. The corresponding liquidity premia compared to the frictionless case are mean reverting; they are positive if the more risk-averse agents are net sellers.
Part II: A Risk-Neutral Equilibrium Leading to Uncertain-Volatility Pricing (joint work with Marcel Nutz)
We study the formation of derivative prices in equilibrium between risk-neutral agents with heterogeneous beliefs about the dynamics of the underlying. Under the condition that the derivative cannot be shorted, we prove the existence of a unique equilibrium price and show that it incorporates the speculative value of possibly reselling the derivative. This value typically leads to a bubble; that is, the price exceeds the autonomous valuation of any given agent. Mathematically, the equilibrium price operator is of the same nonlinear form that is obtained in single-agent settings with strong aversion against model uncertainty. Thus, our equilibrium leads to a novel interpretation of this price.
Propagation of uncertainty
Friday 15/01, 22/01, 29/01 and 12/02
An introduction to Feynman-Kac integration and genealogical tree based particle models
Slides: Lecture 1, Lecture 2, Lecture 3, Lecture 4.
Labs (.sce): Lab 1, Lab 2.