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Found 8 papers, 1 papers with code
Optimal Dynamic Fees for Blockchain Resources
We develop a general and practical framework to address the problem of the optimal design of dynamic fee mechanisms for multiple blockchain resources.
Automated Market Making and Arbitrage Profits in the Presence of Fees
We consider the impact of trading fees on the profits of arbitrageurs trading against an automated marker marker (AMM) or, equivalently, on the adverse selection incurred by liquidity providers due to arbitrage.
Automated Market Making and Loss-Versus-Rebalancing
Quantitatively, we illustrate how our model's expressions for LP returns match actual LP returns for the Uniswap v2 WETH-USDC trading pair.
Risk-Sensitive Optimal Execution via a Conditional Value-at-Risk Objective
As our problem is a special case of a linear-quadratic-Gaussian control problem with a CVaR objective, these results may be interesting in broader settings.
Policy Gradient Optimization of Thompson Sampling Policies
We study the use of policy gradient algorithms to optimize over a class of generalized Thompson sampling policies.
Thompson Sampling with Information Relaxation Penalties
With this framework, we define an intuitive family of control policies that include Thompson sampling (TS) and the Bayesian optimal policy as endpoints.
Non-parametric Approximate Dynamic Programming via the Kernel Method
This paper presents a novel non-parametric approximate dynamic programming (ADP) algorithm that enjoys graceful, dimension-independent approximation and sample complexity guarantees.
A Smoothed Approximate Linear Program
We present a novel linear program for the approximation of the dynamic programming cost-to-go function in high-dimensional stochastic control problems.
