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Ioannis Karatzas

· Higgins Professor of Applied Probability Columbia UniversityMathematics Department

Columbia University · Mathematics

Active 1977–2026

h-index64
Citations41.5k
Papers31421 last 5y
Funding$2.1M
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About

Ioannis Karatzas is the Eugene Higgins Professor of Applied Probability at the Department of Mathematics and the Department of Statistics at Columbia University. His research interests encompass Probability and Mathematical Statistics, Random Processes, Stochastic Analysis, Optimization, and Mathematical Economics and Finance. He is involved in teaching a variety of courses related to probability, stochastic processes, and financial mathematics, reflecting his expertise in these areas. His academic work includes supervising doctoral students and contributing to the fields of applied probability and stochastic analysis, with a focus on their applications in finance and economics.

Research topics

  • Financial economics
  • Applied mathematics
  • Geometry
  • Statistical physics
  • Thermodynamics
  • Mathematical analysis
  • Physics
  • Statistics
  • Mathematics
  • Economics
  • Mathematical economics

Selected publications

  • Necessary and Sufficient Conditions for the Lacunary/Hereditary Laws of Large Numbers

    Open MIND · 2026-02-26

    preprint

    The celebrated theorem of Komlos asserts that L1-boundedness is sufficient for a given sequence of functions to contain a subsequence along which (in a "lacunary" manner), and along whose every further subsequence ("hereditarily"), a strong law of large numbers holds. We identify here slightly weaker, Egorov-type conditions, as not only sufficient in this context, but necessary as well. Necessary and sufficient conditions are developed also for the lacunary/hereditary version of the weak law of large numbers for general sequences, as well as for the weak law of large numbers in the context of exchangeable sequences, both long-open questions.

  • Necessary and Sufficient Conditions for the Lacunary/Hereditary Laws of Large Numbers

    arXiv (Cornell University) · 2026-02-26

    articleOpen access

    The celebrated theorem of Komlos asserts that L1-boundedness is sufficient for a given sequence of functions to contain a subsequence along which (in a "lacunary" manner), and along whose every further subsequence ("hereditarily"), a strong law of large numbers holds. We identify here slightly weaker, Egorov-type conditions, as not only sufficient in this context, but necessary as well. Necessary and sufficient conditions are developed also for the lacunary/hereditary version of the weak law of large numbers for general sequences, as well as for the weak law of large numbers in the context of exchangeable sequences, both long-open questions.

  • Drift control with discretionary stopping for a diffusion

    The Annals of Applied Probability · 2025-06-01 · 1 citations

    articleSenior author

    We consider stochastic control with discretionary stopping for the drift of a diffusion process over an infinite time horizon. The objective is to choose a control process and a stopping time to minimize the expectation of a convex terminal cost in the presence of a fixed operating cost and a control-dependent running cost per unit of elapsed time. Under appropriate conditions on the coefficients of the controlled diffusion, an optimal pair of control and stopping rules is shown to exist. Moreover, under the same assumptions, it is shown that the optimal control is a constant which can be computed fairly explicitly; and that it is optimal to stop the first time an appropriate interval is visited. We consider also a constrained version of the above problem, in which an upper bound on the expectation of available stopping times is imposed; we show that this constrained problem can be reduced to an unconstrained problem with some appropriate change of parameters and, as a result, solved by similar arguments.

  • Drift Control with Discretionary Stopping for a Diffusion

    arXiv (Cornell University) · 2024-01-18

    preprintOpen accessSenior author

    We consider stochastic control with discretionary stopping for the drift of a diffusion process over an infinite time horizon. The objective is to choose a control process and a stopping time to minimize the expectation of a convex terminal cost in the presence of a fixed operating cost and a control-dependent running cost per unit of elapsed time. Under appropriate conditions on the coefficients of the controlled diffusion, an optimal pair of control and stopping rules is shown to exist. Moreover, under the same assumptions, it is shown that the optimal control is a constant which can be computed fairly explicitly; and that it is optimal to stop the first time an appropriate interval is visited. We consider also a constrained version of the above problem, in which an upper bound on the expectation of available stopping times is imposed; we show that this constrained problem can be reduced to an unconstrained problem with some appropriate change of parameters and, as a result, solved by similar arguments.

  • Invariant measure of gaps in degenerate competing three-particle systems

    arXiv (Cornell University) · 2024-01-19

    preprintOpen access

    We study the gap processes in a degenerate system of three particles interacting through their ranks. We obtain the Laplace transform of the invariant measure of these gaps, and an explicit expression for the corresponding invariant density. To derive these results, we start from the basic adjoint relationship characterizing the invariant measure, and apply a combination of two approaches: first, the invariance methodology of W. Tutte, thanks to which we compute the Laplace transform in closed form; second, a recursive compensation approach which leads to the density of the invariant measure as an infinite convolution of exponential functions. As in the case of Brownian motion with reflection or killing at the endpoints of an interval, certain Jacobi theta functions play a crucial role in our computations.

  • A strong law of large numbers for positive random variables

    Illinois Journal of Mathematics · 2023-08-29 · 1 citations

    article1st authorCorresponding

    In the spirit of the famous Komlós (1967) theorem, every sequence of nonnegative, measurable functions {fn}n∈N on a probability space contains a subsequence which—along with all its subsequences—converges a.e. in Cesàro mean to some measurable f∗:Ω→[0,∞]. This result of von Weizsäcker (2004) is proved here using a new methodology and elementary tools; these sharpen also a theorem of Delbaen and Schachermayer (1994), replacing general convex combinations by Cesàro means.

  • A Weak Law of Large Numbers for Dependent Random Variables

    Theory of Probability and Its Applications · 2023-11-01 · 4 citations

    article1st authorCorresponding

    \bad Each sequence $f_1,f_2,\dots$ of random variables satisfying $\lim_{M\to \infty}(M\sup_{k\in \mathbf N}\mathbf{P}(|f_k|>M))=0$ contains a subsequence $f_{k_1},f_{k_2},\dots$ which, along with all its subsequences, satisfies the weak law of large numbers $\lim_{N\to\infty}\bigl((1/N) \sum^N_{n=1} f_{k_n}- D_N\bigr)=0$ in probability. Here, $D_N$ is a ``corrector” random variable with values in $[-N,N]$ for each $N\in\mathbf{N}$; these correctors are all equal to zero if, in addition, $\lim \inf_{n\to\infty}\mathbf{E}(f^2_n \mathbf{1}_{\{|f_n|\le M\}})=0$ for every $M\in(0,\infty)$.

  • A weak law of large numbers for dependent random variables

    Теория вероятностей и ее применения · 2023-01-01 · 1 citations

    article1st authorCorresponding

    Любая последовательность $f_1, f_2, …$ случайных величин, удовлетворяющая условию $\lim_{M\to\infty}(M \sup_{k\in \mathbf N} \mathbf{P}(|f_k|> M))=0$, содержит подпоследовательность $f_{k_1}, f_{k_2}, …$, которая вместе со всеми своими подпоследовательностями удовлетворяет слабому закону больших чисел $\lim_{N\to\infty} ((1/N) \sum^N_{n=1} f_{k_n}- D_N)=0$ по вероятности. Здесь $D_N$ является "корректирующей" случайной величиной со значениями в $[-N,N]$ для каждого $N\in\mathbf{N}$. Все корректоры равны нулю при условии, что $\lim \inf_{n\to\infty}\mathbf{E}(f^2_n \mathbf{1}_{\{|f_n|\le M\}})=0$ для каждого $M\in (0,\infty)$.

  • A sequential estimation problem with control and discretionary stopping

    Probability Uncertainty and Quantitative Risk · 2022-01-01 · 2 citations

    articleOpen accessSenior author

    <p style='text-indent:20px;'>We show that “full-bang” control is optimal in a problem which combines features of (i) sequential least-squares <i>estimation</i> with Bayesian updating, for a random quantity observed in a bath of white noise; (ii) bounded <i>control</i> of the rate at which observations are received, with a superquadratic cost per unit time; and (iii) “fast” discretionary <i>stopping</i>. We develop also the optimal filtering and stopping rules in this context.</p>

  • A Trajectorial Approach to the Gradient Flow Properties of Langevin--Smoluchowski Diffusions

    Theory of Probability and Its Applications · 2022 · 6 citations

    1st authorCorresponding
    • Mathematics
    • Statistical physics
    • Applied mathematics

    Article DataHistorySubmitted: 19 May 2021Published online: 03 February 2022Keywordsrelative entropy, Wasserstein distance, Fisher information, optimal transport, gradient flow, diffusion processes, time reversal, functional inequalitiesPublication DataISSN (print): 0040-585XISSN (online): 1095-7219Publisher: Society for Industrial and Applied MathematicsCODEN: tprbau

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Education

  • Ph.D., Probability, Mathematical Finance

    Columbia University

    1980
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