Tssp-2022-23

General course info

• Boring official web page

Grading

Fall grade = 0.3 HAs + 0.7 October Exam

Final grade = 0.2 Fall grade + 0.25 HAs + 0.15 December Midterm + 0.25 Spring Midterm + 0.15 Final Exam

Actual grades: xlsx, html

Teachers and assistants

Lecturer: Peter Lukianchenko

Class teacher: Boris Demeshev, Sveta Popova, Maria Kirillova, Yan Maximov

Semester I

Week 1. 2022-09-03

Lecture. Markov chains, transition matrix, pdf

Class. Transition matrix, first step analysis.

More:

Cambridge course on Markov chains

Week 2. 2022-09-10

Lecture. Markov chains, stationary distribution, modes of convergence, pdf

Class. Stationary distribution, modes of convergence

Week 3. 2022-09-17

Lecture. Markov process, math modelling, pdf

Class. plim, almost sure lim

Week 4. 2022-09-24

Lecture. Conditional expectation, pdf

Class. Conditional expectation, sigma algebra, 4a, 4b

Week 5. 2022-10-01

Lecture. First-step analysis, sigma algebra pdf

Class. Conditional expectation and variance, sigma algebra, 5a, 5b

Week 6. 2022-10-08

Lecture. Basics of stochastic processes pdf

Class: Martingales, filtration, 6a, 6b

Week 7. 2022-10-15

Lecture. Brownian motion (Wiener process), filtration in continuous time pdf

Class: Poisson process, 7a, 7b

Week 8. 2022-10-22

Lecture. Wiener process (additional exercises) video, pdf

Class: Solve midterm tasks

Week 9. 2022-11-05

Lecture. Stochastic integral, Ito formula pdf

Class: Stochastic integral, L2 convergence

Week 10. 2022-11-12

Lecture. Ito's lemma, BS model pdf

Class: Stochastic integral (Wu dWu), L2 convergence

Week 11. 2022-11-19

Lecture. BS solution pdf

Class: Ito's lemma

Week 12. 2022-11-26

Lecture. Binomial tree, risk-neutral probability pdf

Class: BS model, SDE

Semester II

Week 1. 2023-01-14

Lecture. Intro to Time Series, stationarity, ACF, PACF pdf

Class: White noise, stationarity

Week 1. 2023-01-21

Lecture.

Class: ACF, PACF

Sources

• all past exams
• Wiki 2020-21, Wiki 2021-22
• Git repo 2020-21, Git repo 2021-22
• TG chat 2022-23
• видео семинаров 2022-23 на русском

• Statistics cookbook

MC + MCMC

• James Norris, Markov chains (1998, no kernels)

• Cambridge course on Markov chains

• Chib and Greenberg, Understanding MH algorithm

• Casella, Explaining Gibbs Sampler

• Roberts and Rosenthal, General State Space Markov Chains

• Visualization of MCMC methods

• Charles Geyer, MCMC lecture notes (with a little bit of kernels!)

Stochastic Calculus

• Zastawniak, Basic Stochastic Processes

• Exams of ICEF master course

• Заметки магистерского курса МИЭФ (рус)

• Черновик учебника (рус)

• Черновик задачника (рус)

Time Series

• Forecasting principles and practice (R)

• Shumway, Stoffer Time Series Analysis

• Ruey Tsay web page

• Van der Vaart, Time Series

• Черновик задачника (рус)

UCM

• Harvey Jaeger, Detrending, Stylized Facts and the Business Cycle

• João Tovar Jalles, Structural Time Series Models and the Kalman Filter

• Harvey, Forecasting with UCM

• Chad Fulton

• Rob Hyndman, State Space Models