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Stochastic Finance @ Warwick Seminars

Unless otherwise stated, the seminars will take place on Wednesdays at 11:00 am, in B3.03 (Zeeman).

15th October

Pavel Gapeev (LSE)

Title: Perpetual American Standard and Lookback Options in Models with Progressively Enlarged Filtrations
 
Abstract:
We derive closed-form solutions to optimal stopping problems related to the pricing of perpetual American standard and lookback put and call options in extensions of the Black-Merton-Scholes model under progressively enlarged filtrations. It is assumed that the information available from the market is modelled by Brownian filtrations progressively enlarged with the random times at which the underlying process attains its global maximum or minimum, that is, the last hitting times for the underlying risky asset price of its running maximum or minimum over the infinite time interval, which are supposed to be progressively observed by the holders of the contracts. We show that the optimal exercise times are the first times at which the asset price process reaches certain lower or upper stochastic boundaries depending on the current values of its running maximum or minimum depending on the occurrence of the random times of the global maximum or minimum of the risky asset price process. The proof is based on the reduction of the original necessarily three-dimensional optimal stopping problems to the associated free-boundary problems and their solutions by means of the smooth-fit and either normal-reflection or normal-entrance conditions for the value functions at the optimal exercise boundaries and the edges of the state spaces of the processes, respectively.
This is joint work with Libo Li (Sydney).
   
12th November

Meet the MF Group: short talks by PhD students. All welcome especially CDT students and Masters students.

Jing Liu

Title: Optimal Stopping of a Brownian Excursion and an $\alpha-$dimensional Bessel Bridge

Florian Gutekunst

Title: Optimal Investment and Consumption in a Stochastic Factor Model

Edward Wang

Title: Nonzero-sum Dynkin games under a generalised order condition

   
13th November (11:00 am, B3.03)

Xin Guo (UC Berkeley)

Title: From LLM to RL and diffusion models, via (rough) differential equations

Abstract:

Transfer learning is a machine learning technique that leverages knowledge acquired in one domain to enhance performance on a related task. It plays a central role in the success of large language models (LLMs) such as GPT and BERT, which leverage pretraining to enable broad generalization across downstream applications. In this talk, I will discuss how reinforcement learning (RL), and in particular continuous time RL, can benefit from transfer learning principles. I will present convergence results formulated through stability analysis for stochastic control systems, using rough differential equation techniques. Finally, I will show how this analysis yields a natural corollary establishing robustness guarantees for a class of score-based generative diffusion models. Based on joint work with Zijiu Lyu of UC Berkeley.

   
3rd December

Xinyu Li (Oxford)

Title: An alpha-potential game framework for N-player potential games

Abstract:

We propose and study a general form of dynamic N-player non-cooperative games called alpha-potential games, where the change of a player's objective function upon her unilateral deviation from her strategy is equal to the change of an alpha-potential function up to an error alpha. Analogous to the static potential game (which corresponds to alpha=0), the alpha-potential game framework is shown to reduce the challenging task of finding alpha-Nash equilibria for a dynamic game to minimizing the alpha-potential function. Moreover, an analytical characterization of alpha-potential functions is established, with alpha represented in terms of the magnitude of the asymmetry of objective functions' second-order derivatives. For stochastic differential games in which the state dynamic is a controlled diffusion, alpha is characterized in terms of the number of players, the choice of admissible strategies, and the intensity of interactions and the level of heterogeneity among players. To analyze the alpha-NE, the associated optimization problem is embedded into a conditional McKean–Vlasov control problem. A verification theorem is established to construct alpha-NE based on solutions to an infinite-dimensional Hamilton-Jacobi-Bellman equation, which is reduced to a system of ordinary differential equations for linear-quadratic games.
We also analyze a broad class of distributed games with jump diffusions using the alpha-potential gameframework. We demonstrate that analyzing the alpha-Nash equilibria reduces to solving a finite-dimensional control problem. Beyond the viscosity and verification characterizations for the general games, we explicitly and in detail examine how spatial population distributions and interaction rules influence the structure of alpha-Nash equilibria in these distributed settings, and in particular for crowd motion games. Our theoretical results are supported by numerical implementations using policy gradient-based algorithms, further demonstrating the computational advantages of the alpha-potential game framework in computing Nash equilibria for general dynamic games.
   
10th December

Jonathan Tam (Oxford)

Title: Bayesian dynamic portfolio optimization with informative constraints

Abstract:

There is a recent debate on whether sustainable investing necesarily impact portfolio performance negatively. We model the financial impact of portfolio constraints by attributing the performance of dynamic portfolios to contributions from individual constraints. We consider a mean-variance portfolio problem with unknown asset returns. Investors impose a dynamic constraint based on a firm characteristic that contains information about returns, such as the environmental, social, and governance (ESG) score. We characterize the optimal investment strategy through two stochastic Riccati equations. Using this framework, we demonstrate that, depending on the correlation between returns and firm characteristics, incorporating the constraint can, in certain cases, enhance portfolio performance compared to a passive benchmark that disregards the information embedded in these constraints. Our results shed light on the role of implicit information contained in constraints in determining the performance of a constrained portfolio.
This project is joint work with Ruixun Zhang (Peking University), Yufei Zhang (Imperial College London) and Xunyu Zhou (Columbia University).

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