2016-17
Term 3
25.04.2017 | Pham: Chapter 4.5 | Matthew Zeng |
02.05.2017 | Pham: Chapter 4.5 (continued) | Matthew Zeng |
09.05.2017 | Pham: Chapter 4.5 (continued) | Matthew Zeng |
16.05.2017 | Pham: Chapter 4.6 | Kevin Engelbrecht |
23.05.2017 | No seminar | |
30.05.2017 | Pham: Chapter 4.6 (continued) | Kevin Engelbrecht |
06.06.2017 | Pham: Chapters 5.1 and 5.2 | Jun Maeda |
13.06.2017 | No seminar | |
20.06.2017 | No seminar | |
27.06.2017 | Pham: Chapter 5.2 (continued) | Dominic Norgilas |
Term 2
24.01.2017 | Pham: Chapters 4.1 and 4.2 | Sebastian Armstrong |
31.01.2017 | No seminar | |
07.02.2017 | Pham: Chapter 4.3.1 | Jonathan Muscat |
14.02.2017 | Pham: Chapter 4.3.1 (continued) | Jonathan Muscat |
21.02.2017 | Pham: Chapter 4.3.2 | Jun Maeda |
28.02.2017 | Pham: Chapter 4.3.2 (continued) | Jun Maeda |
07.03.2017 | Pham: Chapter 4.4 | Dominic Norgilas |
14.03.2017 (9 - 11 in L5) | Pham: Chapter 4.4 (continued) | Dominic Norgilas |
Term 1
Additional talks listed below.
Week 6: Friday 18th November 2016, A1.01 2pm
Chuan Guo, Warwick
Title: Stochastic volatility Markov-functional models
Week 9: Friday 2nd December 2016, A1.01 2pm
Professor Saul Jacka, Warwick
Title: General Controlled Markov Processes and Optimal Stopping.
This talk will introduce abstract control via the weak (martingale problem) formulation and give two current examples-one involving general Markovian optimal stopping problems.
Week 10: Friday 9nd December 2016, A1.01 2pm
Dominic Norgilas, Warwick
Title: Dual of the Optimal stopping problem
It will be shown that the value function of an optimal stopping problem is equal to the value function of a particular stochastic control problem, where admissible controls are uniformly integrable martingales started at zero. In the Markovian setting, the gain process is of the form (G_t)=(g(X_t)), where X is a Markov process and g a payoff function. Then under an assumption that g belongs to the domain of an infinitesimal (martingale) generator of X we show that the dual can be viewed as a stochastic control problem of controlled Markov processes.