Thu, 28 Nov 2024
16:00
L4

Regurgitative Training in Finance: Generative Models for Portfolios

Adil Rengim Cetingoz
(Centre d'Economie de la Sorbonne)
Further Information

Please join us for refreshments outside the lecture room from 15:30.

Abstract
Simulation methods have always been instrumental in finance, but data-driven methods with minimal model specification, commonly referred to as generative models, have attracted increasing attention, especially after the success of deep learning in a broad range of fields. However, the adoption of these models in practice has not kept pace with the growing interest, probably due to the unique complexities and challenges of financial markets. This paper aims to contribute to a deeper understanding of the development, use and evaluation of generative models, particularly in portfolio and risk management. To this end, we begin by presenting theoretical results on the importance of initial sample size, and point out the potential pitfalls of generating far more data than originally available. We then highlight the inseparable nature of model development and the desired use case by touching on a very interesting paradox: that generic generative models inherently care less about what is important for constructing portfolios (at least the interesting ones, i.e. long-short). Based on these findings, we propose a pipeline for the generation of multivariate returns that meets conventional evaluation standards on a large universe of US equities while providing interesting insights into the stylized facts observed in asset returns and how a few statistical factors are responsible for their existence. Recognizing the need for more delicate evaluation methods, we suggest, through an example of mean-reversion strategies, a method designed to identify bad models for a given application based on regurgitative training, retraining the model using the data it has itself generated.
 

 
Thu, 28 Nov 2024
17:00
L4

The Index of Constant Mean Curvature Surfaces in Three-Manifolds

Luca Seemungal
(University of Leeds)
Abstract
Constant mean curvature (CMC) surfaces are special geometric variational objects, closely related to minimal surfaces. The key properties of a CMC surface are its area, mean curvature, genus, and index. The index of a CMC surface measures its stability: the index counts how many ways one can perturb the surface to decrease the area while keeping the enclosed volume constant. In this talk we discuss relationships between these key properties. In particular we present recent joint work with Ben Sharp, where we bound the index of CMC surfaces linearly from above by genus and the correct scale-invariant quantity involving mean curvature and area.

 
Thu, 05 Dec 2024
16:00
L4

Mean Field Games in a Stackelberg problem with an informed major player

Dr Philippe Bergault
(Université Paris Dauphine-PSL)
Further Information

Please join us for refreshments outside the lecture room from 15:30.

Abstract

We investigate a stochastic differential game in which a major player has a private information (the knowledge of a random variable), which she discloses through her control to a population of small players playing in a Nash Mean Field Game equilibrium. The major player’s cost depends on the distribution of the population, while the cost of the population depends on the random variable known by the major player. We show that the game has a relaxed solution and that the optimal control of the major player is approximatively optimal in games with a large but finite number of small players. Joint work with Pierre Cardaliaguet and Catherine Rainer.

Wed, 04 Dec 2024
11:00
L4

Effective Mass of the Polaron and the Landau-Pekar-Spohn Conjecture

Chiranjib Mukherjee
(University of Münster)
Abstract

According to a conjecture by Landau-Pekar (1948) and by Spohn (1986), the effective mass of the Fröhlich Polaron should diverge in the strong coupling limit like a quartic power of the coupling constant. In a recent joint with R. Bazaes, M. Sellke and S.R.S. Varadhan, we prove this conjecture.

Mon, 18 Nov 2024
16:30
L4

Short- and long-time behavior in evolution equations: the role of the hypocoercivity index

Anton Arnold
(Vienna University of Technology)
Abstract

The "index of hypocoercivity" is defined via a coercivity-type estimate for the self-adjoint/skew-adjoint parts of the generator, and it quantifies `how degenerate' a hypocoercive evolution equation is, both for ODEs and for evolutions equations in a Hilbert space. We show that this index characterizes the polynomial decay of the propagator norm for short time and illustrate these concepts for the Lorentz kinetic equation on a torus. Discrete time analogues of the above systems (obtained via the mid-point rule) are contractive, but typically not strictly contractive. For this setting we introduce "hypocontractivity" and an "index of hypocontractivity" and discuss their close connection to the continuous time evolution equations.

This talk is based on joint work with F. Achleitner, E. Carlen, E. Nigsch, and V. Mehrmann.

References:
1) F. Achleitner, A. Arnold, E. Carlen, The Hypocoercivity Index for the short time behavior of linear time-invariant ODE systems, J. of Differential Equations (2023).
2) A. Arnold, B. Signorello, Optimal non-symmetric Fokker-Planck equation for the convergence to a given equilibrium, Kinetic and Related Models (2022).
3) F. Achleitner, A. Arnold, V. Mehrmann, E. Nigsch, Hypocoercivity in Hilbert spaces, J. of Functional Analysis (2025).
 

Tue, 12 Nov 2024

14:00 - 15:00
L4

On forbidden configurations in point-line incidence graphs

Nora Frankl
(Open University)
Abstract

The celebrated Szemeredi-Trotter theorem states that the maximum number of incidences between $n$ points and $n$ lines in the plane is $\mathcal{O}(n^{4/3})$, which is asymptotically tight.

Solymosi conjectured that this bound drops to $o(n^{4/3})$ if we exclude subconfigurations isomorphic to any fixed point-line configuration. We describe a construction disproving this conjecture. On the other hand, we prove new upper bounds on the number of incidences for configurations that avoid certain subconfigurations. Joint work with Martin Balko.

Fri, 13 Dec 2024
12:00
L4

Asymptotic Higher Spin Symmetries in Gravity.

Nicolas Cresto
(Perimeter Institute)
Abstract

 I will first give a short review of the concepts of Asymptotically Flat Spacetimes, IR triangle and Noether's theorems. I will then present what Asymptotic Higher Spin Symmetries are and how they were introduced as a candidate for an approximate symmetry of General Relativity and the S-matrix. Next, I'll move on to the recent developments of establishing these symmetries as Noether symmetries and describing how they are canonically and non-linearly realized on the asymptotic gravitational phase space. I will discuss how the introduction of dual equations of motion encapsulates the non-perturbativity of the analysis. Finally I'll emphasize the relation to twistor, especially with 2407.04028. Based on 2409.12178 and 2410.15219

Wed, 20 Nov 2024
11:00
L4

Quadratic and $p^\mathrm{th}$ variation of stochastic processes through Schauder expansions

Yuchen Fan
(University of Oxford)
Abstract
We present a class of stochastic processes which admit a unique quadratic variation along any sequence of partitions $(\pi^n)_{n\geq 1}$ with $\sum_{n\geq 1}|\pi^n|<\infty$, which generalizes the previous results for finitely refining partitions. This class of processes contains some signed Takagi-Landsberg functions with random coefficients and standard Brownian motions, and these processes admit $\frac{1}{4}$-Hölder continuous version. We study the quadratic and $p^\mathrm{th}$ variation of signed Takagi-Landsberg functions with random coefficients. Finally, we seek some generalizations and applications of our results.


 

Thu, 31 Oct 2024
16:00
L4

Re(Visiting) Large Language Models in Finance

Eghbal Rahimikia
(University of Manchester)
Abstract

This study introduces a novel suite of historical large language models (LLMs) pre-trained specifically for accounting and finance, utilising a diverse set of major textual resources. The models are unique in that they are year-specific, spanning from 2007 to 2023, effectively eliminating look-ahead bias, a limitation present in other LLMs. Empirical analysis reveals that, in trading, these specialised models outperform much larger models, including the state-of-the-art LLaMA 1, 2, and 3, which are approximately 50 times their size. The findings are further validated through a range of robustness checks, confirming the superior performance of these LLMs.

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