Blockchains are characterized by algorithms for synchronizing data between network nodes (consensus), methods of data storage, and the programs executed on them (smart contracts). The talk will examine each of these aspects using selected tasks as examples: fault-tolerant random number generation, trees for provable responses to user queries, and risk of a platform for cryptocurrency-backed lending.

References:

[1] Krasnoselskii, M., Melnikov, G., & Yanovich, Y. (2020). No-dealer: Byzantine fault-tolerant random number generator. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[2] Krasnoselskii, M., Melnikov, G., & Yanovich, Y. (2021). DisCO: Peer-to-Peer Random Number Generator in Partial Synchronous Systems. In 2021 3rd Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS).
[3] Chaleenutthawut, Y., Davydov, V., Evdokimov, M., Kasemsuk, S., Kruglik, S., Melnikov, G., & Yanovich, Y. (2024). Loan Portfolio Dataset From MakerDAO Blockchain Project. IEEE Access.

Aiming to analyze the impact of environmental transition on the value of assets and on asset stranding, we study optimal stopping and divestment timing decisions for an economic agent whose future revenues depend on the realization of a scenario from a given set of possible futures. Since the future scenario is unknown and the probabilities of individual prospective scenarios are ambiguous, we adopt the smooth model of decision making under ambiguity aversion of Klibanoff et al (2005), framing the optimal divestment decision as an optimal stopping problem with learning under ambiguity aversion. We then prove a minimax result reducing this problem to a series of standard optimal stopping problems with learning. The theory is illustrated with two examples: the problem of optimally selling a stock with ambigous drift, and the problem of optimal divestment from a coal-fired power plant under transition scenario ambiguity.

In this paper we study optimal investment when the investor can peek some time units into the future, but cannot fully take advantage of this knowledge because of quadratic transaction costs. In the Bachelier setting with exponential utility, we give an explicit solution to this control problem with intrinsically infinite-dimensional memory. This is made possible by solving the dual problem where we make use of the theory of Gaussian Volterra integral equations. Joint work with Peter Bank and Miklos Rasonyi.

In this paper, we provide a general approach to reformulating any continuous-time stochastic Stackelberg differential game under closed-loop strategies as a single-level optimisation problem with target constraints. More precisely, we consider a Stackelberg game in which the leader and the follower can both control the drift and the volatility of a stochastic output process, in order to maximise their respective expected utility. The aim is to characterise the Stackelberg equilibrium when the players adopt "closed-loop strategies", i.e. their decisions are based solely on the historical information of the output process, excluding especially any direct dependence on the underlying driving noise, often unobservable in real-world applications. We first show that, by considering the-second-order-backward stochastic differential equation associated with the continuation utility of the follower as a controlled state variable for the leader, the latter’s unconventional optimisation problem can be reformulated as a more standard stochastic control problem with stochastic target constraints. Thereafter, adapting the methodology developed by Soner and Touzi or Bouchard, Élie, and Imbert, the optimal strategies, as well as the corresponding value of the Stackelberg equilibrium, can be characterised through the solution of a well-specified system of Hamilton-Jacobi-Bellman equations. For a more comprehensive insight, we illustrate our approach through a simple example, facilitating both theoretical and numerical detailed comparisons with the solutions under different information structures studied in the literature. This is a joint work with Camilo Hernández, Nicolás Hernández Santibáñez, and Emma Hubert.

Since the seminal work of Ho and Stoll, later refined by Avellaneda and Stoikov, algorithmic market-making models have evolved to incorporate increasingly realistic features such as trade sizes, complex price dynamics, tiering, externalization, and market impact. These models have been applied to a wide range of assets, from illiquid corporate bonds to highly liquid foreign exchange markets to cryptocurrencies (price-aware AMMs). This talk will provide an overview of the key developments of the past decade, highlighting both theoretical advancements and practical applications. It will then focus on recent applications of these models in the gold market by Barzykin, Bergault and I.

A recent approach to statistical inference is based on the concept of an e-variable: a nonnegative sample statistic whose expected value is at most one if a given null hypothesis is true. This approach has been found to produce strong statistical error bounds and high statistical power and is easily extendible to sequential, or online, settings. E-variables admit a natural interpretation as the payoff of a financial bet. In this talk I will discuss how classical ideas from mathematical finance, in particular the numeraire portfolio, enables an optimality theory for e-variables that significantly generalizes earlier results. Our results also lead to a duality theory which yields the so-called reverse information projection in complete generality. Our work showcases the power of financial methods in a setting where information-theoretic tools have traditionally been preferred. (Joint work with Aaditya Ramdas and Johannes Ruf.)

We investigate the convergence properties of sample-average approximations (SAA) for set-valued systemic risk measures. We assume that the systemic risk measure is defined using a general aggregation function with some continuity properties and value-at-risk applied as a monetary risk measure. Our focus is on the theoretical convergence of its SAA under Wijsman and Hausdorff topologies for closed sets. After building the general theory, we provide an in-depth study of an important special case where the aggregation function is defined based on the Eisenberg-Noe network model. In this case, we provide mixed-integer programming formulations for calculating the SAA sets via their weighted-sum and norm-minimizing scalarizations. To demonstrate the applicability of our findings, we conduct a comprehensive sensitivity analysis by generating a financial network based on the preferential attachment model and modeling the economic disruptions via a Pareto distribution.

Several models of partially observed diffusion processes depending on unknown parameters are presented. The proposed algorithms of adaptive Kalman-Bucy filters and parameter estimators have recurrent structure and the questions of their asymptotic optimality are discussed. The properties of the filters and estimators are studied in the asymptotics of small noise. For some nonlinear partially observed systems the construction and properties of the corresponding extended adaptive Kalman filters are discussed too.

For algorithms of machine learning, rate estimates are often provided in the Wasserstein metric or some variant thereof. There has been spectacular recent progress in the techniques for establishing such estimates.
At the same time, powerful methods have been developed in Malliavin calculus that enable to infer total variation convergence from weak convergence.
After presenting an overview of the developments above, we show some new results on total variation convergence that do not rely on Malliavin calculus nevertheless they are applicable to various stochastic systems.

We develop a continuous time framework for sequential goals-based wealth management.
A stochastic factor process drives asset price dynamics as well as the client’s goal amount and income. We prove the weak dynamic programming principle for the value function of our control problem, which we show to be the unique viscosity solution of the corresponding Hamilton-Jacobi-Bellman equation. We develop an equivalent and computationally efficient representation of the Hamiltonian, which yields the optimal portfolio within a factor-dependent opportunity set defined by the maximum and minimum variance hypersurfaces. Our analysis shows that it is optimal to fund an expiring goal up to the level where the marginal benefit of additional fundedness is exceeded by the opportunity cost of diverting wealth from future goals. An all-or-nothing investor is more risk averse towards an approaching goal deadline if she is well funded, but more risk seeking if she is not on track with upcoming goals, compared to an investor with flexible goals.