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Ioannis Karatzas
Researcher at Columbia University
Publications - 190
Citations - 26325
Ioannis Karatzas is an academic researcher from Columbia University. The author has contributed to research in topics: Stochastic control & Optimal stopping. The author has an hindex of 58, co-authored 189 publications receiving 25152 citations. Previous affiliations of Ioannis Karatzas include University of North Carolina at Chapel Hill & Princeton University.
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Utility Maximization with Habit Formation: Dynamic Programming and Stochastic PDEs
TL;DR: The effective state space of the corresponding optimal wealth and standard of living processes is described, the associated value function is identified as a generalized utility function, and the interplay between dynamic programming and Feynman-Kac results is exploited via the theory of random fields and stochastic partial differential equations.
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Diversity and relative arbitrage in equity markets
TL;DR: In this paper, it is shown that weakly-diverse financial markets contain relative arbitrage opportunities: it is possible to outperform (or underperform) such markets over sufficiently long time-horizons, and to under-perform them significantly over arbitrary timehorizons.
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On optimal arbitrage.
Daniel Fernholz,Ioannis Karatzas +1 more
TL;DR: In this article, the best arbitrage with respect to the market portfolio that can be achieved using non-anticipative investment strategies, in terms of the smallest positive solution to a parabolic partial difierential inequality, is determined entirely on the basis of the covariance structure of the model.
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Gittins Indices in the Dynamic Allocation Problem for Diffusion Processes
TL;DR: In this article, the problem of allocating effort among several competing projects, the states of which evolve according to one-dimensional diffusion processes, is discussed, and it is shown that the play-the-leader policy of continuing the project with the leading Gittins index is optimal, and very explicit computations of the index are offered.
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Adaptive Poisson disorder problem
TL;DR: The objective is to design an alarm time which is adapted to the history of the arrival process and detects the disorder time as soon as possible, and assumes in this paper that the new arrival rate after the disorder is a random variable.