Optimal stopping

Wednesday, October 3, 2018 - 2:45pm - 3:30pm
Velibor Misic (University of California, Los Angeles)
Optimal stopping is the problem of deciding when to stop a stochastic system to obtain the greatest reward; this arises in numerous application areas, such as finance, healthcare and marketing. State-of-the-art methods for high-dimensional optimal stopping involve determining an approximation to the value function or to the continuation value, and then using that approximation within a greedy policy.
Wednesday, June 13, 2018 - 9:00am - 9:50am
Xunyu Zhou (Columbia University)
We consider the problem of stopping a diffusion process with a payoff functional that renders the problem time inconsistent. We study stopping decisions of naive agents who reoptimize continuously in time, as well as equilibrium strategies of sophisticated agents who anticipate but lack control over their future selves' behaviors. When the state process is one dimensional and the payoff functional satisfies some regularity conditions, we prove that any equilibrium can be obtained as a fixed point of an operator.
Wednesday, May 9, 2018 - 10:00am - 10:50am
Jose Menaldi (Wayne State University)
First, an optimal stopping problem of a Markov-Feller process is considered when the controller is allowed to stop the evolution only at the arrival times of a signal. A complete setting and resolution of this problem is discussed, e.g., when the inter-arrival times of the signal are independent identically distributed random variables, and then several extensions to other signals and to other cases of state spaces are also mentioned.
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