38 research outputs found
Statistical Learning and Inverse Problems: An Stochastic Gradient Approach
Inverse problems are paramount in Science and Engineering. In this paper, we
consider the setup of Statistical Inverse Problem (SIP) and demonstrate how
Stochastic Gradient Descent (SGD) algorithms can be used in the linear SIP
setting. We provide consistency and finite sample bounds for the excess risk.
We also propose a modification for the SGD algorithm where we leverage machine
learning methods to smooth the stochastic gradients and improve empirical
performance. We exemplify the algorithm in a setting of great interest
nowadays: the Functional Linear Regression model. In this case we consider a
synthetic data example and examples with a real data classification problem
Optimal Trading in Automatic Market Makers with Deep Learning
This article explores the optimisation of trading strategies in Constant
Function Market Makers (CFMMs) and centralised exchanges. We develop a model
that accounts for the interaction between these two markets, estimating the
conditional dependence between variables using the concept of conditional
elicitability. Furthermore, we pose an optimal execution problem where the
agent hides their orders by controlling the rate at which they trade. We do so
without approximating the market dynamics. The resulting dynamic programming
equation is not analytically tractable, therefore, we employ the deep Galerkin
method to solve it. Finally, we conduct numerical experiments and illustrate
that the optimal strategy is not prone to price slippage and outperforms
na\"ive strategies
Avoiding zero probability events when computing Value at Risk contributions
This paper is concerned with the process of risk allocation for a generic
multivariate model when the risk measure is chosen as the Value-at-Risk (VaR).
We recast the traditional Euler contributions from an expectation conditional
on an event of zero probability to a ratio involving conditional expectations
whose conditioning events have stricktly positive probability. We derive an
analytical form of the proposed representation of VaR contributions for various
parametric models. Our numerical experiments show that the estimator using this
novel representation outperforms the standard Monte Carlo estimator in terms of
bias and variance. Moreover, unlike the existing estimators, the proposed
estimator is free from hyperparameters