Stochastic processes and applications

2026/2027

Учебный год

ENG

Обучение ведется на английском языке

4

Кредиты

Кто читает:

Департамент больших данных и информационного поиска

Статус:

Курс обязательный

Когда читается:

3-й курс, 1, 2 модуль

Преподаватели

Кириллова Мария Андреевна

Лукьянченко Петр Павлович

Числова Алёна Игоревна

Course Syllabus

Abstract

This course is conducted at Data Science and Business Analytics program and is provided to 3rd-year undergraduates who have studied a course covering basic probability and statistical inference. A half of this course introduces concepts of Markov chains, random walks, martingales. The course requires basic knowledge in probability theory and linear algebra. It introduces students to the modeling, quantification and analysis of uncertainty. The main objective of this course is to developthe skills needed to do empirical research in fields operating with a concept of Stochastic processes and its applications. The course aims to provide students with techniques and receipts for estimation and assessment of quality of economic models with time series data.

Learning Objectives

• The main objective of this course is to develop the skills needed to do empirical research in fields of stochastic processes and application in finance

Expected Learning Outcomes

• Students get an understanding of techniques and receipts for estimation and assessment of the quality of economic models with time-series data.

Course Contents

• Discrete-time martingale theory.
• Continuous-time stochastic processes.
• Stochastic calculus and differential equations.
• Continuous-time financial models.

Assessment Elements

• Final exam
• Fall midterm
• Homework

Interim Assessment

• 2026/2027 2nd module
  0.35 * Fall midterm + 0.25 * Homework + 0.4 * Final exam

Bibliography

Recommended Core Bibliography

• Applied econometric time series, Enders, W., 2015
• Stochastic processes : theory for applications, Gallager, R. G., 2015
• The econometric modelling of financial time series, Mills, T. C., 2004
• Time series models, Harvey, A. C., 1993

Recommended Additional Bibliography

• An introduction to mathematical statistics and its applications, Larsen, R. J., 2012
• Mathematical statistics with applications, Wackerly, D. D., 2008
• Probability and statistical inference, Hogg, R. V., 2015

Authors

• Lukianchenko Petr Pavlovich