Mathematical Market Microstructure: w/o Rationality Assumptions
Just like the view on micro world made us rethink our theories about the laws of physics previously based on macro world experience, algorithmic trading at extremely low latency exposes us to new phenomena and demands new mathematical models for their analysis.
Objectives of this course are: introducing students to some models that have become important for analysis of market microstructure in recent years and show how they can be applied to low latency trading and risk management.
We start with a review of the main features of the market behavior at ultra-low latency, explain why we prefer to look at the market events with “frog’s eye” and concentrate on mathematical models consistent with Principle of Ma.
During the course we study stochastic processes that describe market behavior at the microstructure level. Among them are Ordered Probit and Decomposition time series models, Poisson, Cox, Ammeter, Hawkes and other processes. Students will design real time quantitative trading strategies and apply them in simulated trading environment developed for the course.
Demonstrations and applications will be implemented in R. Students will work with some real market data examples. Classes consist of lecture part and in-class workshop, followed by real time trading projects. Students are required to come with their laptop computers with installed R. Some background in probability theory, statistical methods and statistical data analysis with R is recommended.
Grades for this course are calculated based on real time simulated trading P&L of 4 trading projects.
This is a five-week course taught in the first half of the quarter.