Machine Learning and Monte Carlo Methods meet Economics and Finance Mini-Conference

Speakers

Giray Okten, Florida State University, Jean Pierre Fouque, and Michael Ludkovski, University of California, Santa Barbara

Event Details

The Aggregate Economics Consortium holds its first Mini-Conference of the year titled Machine Learning and Monte Carlo Methods meets Economics and Finance. We will be joined by Professor Giray Okten from Flordia State University and UCSB's very own Professor Jean Pierre Fouque and Professor Mike Ludkovski. This event will be available to both Economists and Statisticians. The schedule, titles, abstracts, and paper links can be found below.

Schedule:

1:30pm-2:15pm—Jean Pierre Fouque, University of California, Santa Barbara

Title: "Reinforcement Learning Algorithm for Mixed Mean Field Control Games"

Abstract: We present a new combined mean field control game (MFCG) problem which can be interpreted as a competitive game between collaborating groups and its solution as a Nash equilibrium between the groups. Within each group the players coordinate their strategies. An example of such a situation is a modification of the classical trader's problem. Groups of traders maximize their wealth. They are faced with transaction cost for their own trades and a cost for their own terminal position. In addition they face a cost for the average holding within their group. The asset price is impacted by the trades of all agents. We propose a three-timescale reinforcement learning algorithm to approximate the solution of such MFCG problems. We test the algorithm on benchmark linear-quadratic specifications for which we have analytic solution

2:30pm-3:15pm—Michael Ludkovski, University of California, Santa Barbara

Title: "Machine Learning for Optimal Stopping Problems"

*This talk with combine two papers. Abstracts listed below

Paper 1: "Towards a Unified Implementation of Regression Monte Carlo Algorithms"

Abstract: We introduce mlOSP, a computational template for Machine Learning for Optimal Stopping Problems. The template is implemented in the R statistical environment and publicly available via a GitHub repository. mlOSP presents a unified numerical implementation of Regression Monte Carlo (RMC) approaches to optimal stopping, providing a state-of-the-art, open-source, reproducible and transparent platform. Highlighting its modular nature, we present multiple novel variants of RMC algorithms, especially in terms of constructing simulation designs for training the regressors, as well as in terms of machine learning regression modules. Furthermore, mlOSP nests most of the existing RMC schemes, allowing for a consistent and verifiable benchmarking of extant algorithms. The article contains extensive R code snippets and figures, and serves as a vignette to the underlying software package.

Paper 2: "Regression Monte Carlo for Impulse Control"

Abstract: I develop a numerical algorithm for stochastic impulse control in the spirit of Regression Monte Carlo for optimal stopping. The approach consists in generating statistical surrogates (aka functional approximators) for the continuation function. The surrogates are recursively trained by empirical regression over simulated state trajectories. In parallel, the same surrogates are used to learn the intervention function characterizing the optimal impulse amounts. I discuss appropriate surrogate types for this task, as well as the choice of training sets. Case studies from forest rotation and irreversible investment illustrate the numerical scheme and highlight its flexibility and extensibility. Implementation in \texttt{R} is provided as a publicly available package posted on GitHub.

3:30pm-4:30pm—Giray Okten, Florida State University

Title: "Stochastic Deterministic Simulation with Applications in Economics and Finance"

Abstract: Monte Carlo and quasi-Monte Carlo simulation are popular numerical methods used in a wide variety of disciplines including economics and finance.  In this talk, I will give a survey of pseudorandom and low-discrepancy sequences, discuss how they are used in computing, and present some examples from time series forecasting and option pricing.