Time Series and Stochastic Processes ada 21 22
- 1 General course info
- 2 Teachers and assistants
- 3 Week progress
General course info
- Boring official web page
- teams group: all class videos are there :)
Teachers and assistants
Lecturer: Peter Lukianchenko
Class teacher: Boris Demeshev
Class: First step analysis, expected time to get HTH.
Class: Markov chain states classification
Class: Poisson process.
Class: Conditional expected value. Conditional variance.
Class: Sigma-algebras, measurability. Conditional expected value with respect to sigma-algebra.
Class: Probability limit, Moment generating function
The long-awaited midterm will be on 28 October, 10:00 - 12:00.
Duration: 120 minutes. No proctoring.
- First step analysis
- Classification of states and classes of MC.
- Conditional expected value (two views).
- Poisson process.
- Sigma algebras.
- Probability limit
- Moment generating function
Class: Martingales in discrete time
Class: Wiener process definition, basic properties, inversion
Class: Stochastic integral, intuition, limit in L2
Class: Stochastic integral properties, Ito's lemma
Class: BS model, Girsanov theorem, pricing
Class: more pricing examples in BS model
Class: Recap on martingales, Ito, etc
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!)
- Zastawniak, Basic Stochastic Processes
- Van der Vaart, Time Series
- Harvey Jaeger, Detrending, Stylized Facts and the Business Cycle
- João Tovar Jalles, Structural Time Series Models and the Kalman Filter