Tssp-2022-23 — различия между версиями
Kkorolev (обсуждение | вклад) (→Teachers and assistants) |
Kkorolev (обсуждение | вклад) (→Teachers and assistants) |
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Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] | Lecturer: [https://www.hse.ru/org/persons/14276760 Peter Lukianchenko] | ||
| − | Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova, [https://t.me/LebesgueH | + | Class teacher: [https://www.hse.ru/staff/bbd Boris Demeshev], [https://www.hse.ru/org/persons/14288706 Sveta Popova], Maria Kirillova, [https://t.me/LebesgueH Yan Maximov] |
==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ==== | ==== [https://github.com/bdemeshev/tssp_2022-23/raw/main/ha/tssp_ha.pdf Home assignments] ==== | ||
Версия 00:02, 15 января 2023
Содержание
[убрать]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
Teachers and assistants
Lecturer: Peter Lukianchenko
Class teacher: Boris Demeshev, Sveta Popova, Maria Kirillova, Yan Maximov
Home assignments
Log-book
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
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
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
- Charles Geyer, MCMC lecture notes (with a little bit of kernels!)
Stochastic Calculus
- Zastawniak, Basic Stochastic Processes
Time Series
- Van der Vaart, Time Series
UCM
- Harvey Jaeger, Detrending, Stylized Facts and the Business Cycle
- João Tovar Jalles, Structural Time Series Models and the Kalman Filter
