World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
44
Citations
20510
World Ranking
1523
National Ranking
27

Michael Wolf publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Michael Wolf sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 105 publications — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Michael Wolf D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Michael Wolf sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 44 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Research.com Recognitions

  • 2013 - Fellow of the American Mathematical Society

Overview

Michael Wolf is affiliated with the University of Zurich in Switzerland and has contributed extensively to the field of Economics, Econometrics, and Finance. Their research encompasses a variety of subfields including Statistics and Probability, Finance, Economics and Econometrics, Management Science and Operations Research, and Signal Processing.

Wolf's recent publications focus mainly on statistical and financial methodologies. Key papers include:

  • "The Romano-Wolf multiple-hypothesis correction in Stata" (2020) published in The Stata Journal Promoting communications on statistics and Stata
  • "Analytical nonlinear shrinkage of large-dimensional covariance matrices" (2020) published in The Annals of Statistics
  • "Quadratic shrinkage for large covariance matrices" (2022) published in Bernoulli
  • "The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation" (2020) published in Journal of Financial Econometrics
  • "Large dynamic covariance matrices: Enhancements based on intraday data" (2022) published in Journal of Banking & Finance

The main topics addressed in Wolf's work include:

  • Random Matrices and Applications
  • Complex Systems and Time Series Analysis
  • Financial Risk and Volatility Modeling
  • Financial Markets and Investment Strategies
  • Statistical Methods and Bayesian Inference
  • Stock Market Forecasting Methods
  • Statistical Methods in Clinical Trials

Wolf often collaborates with multiple co-authors, most frequently with Olivier Ledoit, followed by Gianluca De Nard, Elliot Beck, Robert F. Engle, and Damian Clarke.

Their work has been published primarily in venues such as the SSRN Electronic Journal, Zurich Open Repository and Archive (University of Zurich), Journal of Financial Econometrics, The Stata Journal Promoting communications on statistics and Stata, and The Annals of Statistics.

In recognition of their contributions, Michael Wolf was named a Fellow of the American Mathematical Society in 2013.

Best Publications

  • A well-conditioned estimator for large-dimensional covariance matrices

    Olivier Ledoit;Michael Wolf

  • Improved Estimation of the Covariance Matrix of Stock Returns With an Application to Portfolio Selection

    Olivier Ledoit;Olivier Ledoit;Michael Wolf

  • A data locality optimizing algorithm

    Michael E. Wolf;Monica S. Lam

  • Honey, I shrunk the sample covariance matrix

    Olivier Ledoit;Michael Wolf

  • Weak convergence of dependent empirical measures with application to subsampling in function spaces

    Dimitris Politis;Joseph P. Romano;Michael Wolf

  • Stepwise multiple testing as formalized data snooping

    Joseph P. Romano;Michael Wolf

  • Robust Performance Hypothesis Testing with the Sharpe Ratio

    Olivier Ledoit;Michael Wolf

  • A loop transformation theory and an algorithm to maximize parallelism

    M.E. Wolf;M.S. Lam

  • Exact and approximate stepdown methods for multiple hypothesis testing

    Joseph P Romano;Michael Wolf

  • Nonlinear Shrinkage Estimation of Large-Dimensional Covariance Matrices

    Olivier Ledoit;Michael Wolf

  • Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks

    Olivier Ledoit;Michael Wolf

  • Flexible multivariate GARCH modeling with an application to international stock markets

    Olivier Ledoit;Pedro Santa-Clara;Michael Wolf

  • Efficient computation of adjusted p-values for resampling-based stepdown multiple testing

    Joseph P. Romano;Michael Wolf

  • The Romano–Wolf multiple-hypothesis correction in Stata:

    Damian Clarke;Joseph P. Romano;Michael Wolf

  • Large Dynamic Covariance Matrices

    Robert F. Engle;Olivier Ledoit;Michael Wolf

  • Stepwise Multiple Testing as Formalized Data Snooping

    Michael Wolf;Joseph P. Romano

  • Formalized data snooping based on generalized error rates

    Joseph P. Romano;Azeem M. Shaikh;Michael Wolf

  • Control of generalized error rates in multiple testing

    Joseph P. Romano;Michael Wolf

  • Analytical nonlinear shrinkage of large-dimensional covariance matrices

    Olivier Ledoit;Michael Wolf

  • Control of the False Discovery Rate under Dependence using the Bootstrap and Subsampling

    Joseph P. Romano;Azeem M. Shaikh;Michael Wolf

  • Robust Performance Hypothesis Testing with the Sharpe Ratio

    Michael Wolf

  • Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size

    Olivier Ledoit;Michael Wolf

Frequent Co-Authors

Joseph P. Romano
Joseph P. Romano Stanford University
Dimitris N. Politis
Dimitris N. Politis University of California, San Diego
Monica S. Lam
Monica S. Lam Stanford University
Rebecca Schüle
Rebecca Schüle University of Tübingen
Thomas Klopstock
Thomas Klopstock Ludwig-Maximilians-Universität München
Alfredo Ramirez
Alfredo Ramirez University of Cologne
Stefan Herms
Stefan Herms University of Basel
Patrick F. Chinnery
Patrick F. Chinnery University of Cambridge
Garth A. Nicholson
Garth A. Nicholson University of Sydney
Ludger Schöls
Ludger Schöls University of Tübingen

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