Stochastic Processes with applications to Financial Data Science

(3 Credits)

This course provides mathematical foundations in Markov random processes. and applications of this knowledge to Financial engineering. This course is part of the Finance minor offered by the Department of Finance and Risk Engineering. Following a general introduction to the Markov property and the basic concepts of Markov Chains, a number of quantitative modeling approaches will be introduced: synchronizing automata, random walks, Markov Chain Monte Carlo, Hidden Markov Models, and reinforcement learning. Financial applications to bond credit ratings, default events, asset price models, volatility models, trading strategies, market dynamics, order book, and price formation will be covered. | Perquisites: One of {MA-UY.2114, MATH-UA.123, MATH-UA.133} AND One of {MA-UY.1044, MA-UY.2034, MA-UY.3054, MATH-UA.140, MATH-UA.148} AND One of {MA-UY.2224, MA-UY.3014, MA-UY. 3514, MATH-UA.333, MATH-UA.338} AND One of {CS-UY.1114, CSCI-UA.101}