World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
41
Citations
10115
World Ranking
1868
National Ranking
105

Wai Keung Li 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 Wai Keung Li 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: 203 publications — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Wai Keung Li 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 Wai Keung Li 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: 41 D-Index — 49th percentile

49% 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

  • 2003 - Fellow of the American Statistical Association (ASA)

Overview

Wai Keung Li is affiliated with the University of Hong Kong in China and has contributed extensively to the fields of economics, econometrics, and finance, with additional work in mathematics. Their research primarily centers on financial risk and volatility modeling, as well as statistical and Bayesian methods applied to economics and finance.

Li's recent publications cover a variety of topics within these fields. Notable papers include:

  • Hybrid quantile estimation for asymmetric power GARCH models, 2020, Journal of Econometrics
  • Variable screening for survival data in the presence of heterogeneous censoring, 2020, Scandinavian Journal of Statistics
  • Evaluation methods for portfolio management, 2020, Applied Stochastic Models in Business and Industry
  • Time series models for realized covariance matrices based on the matrix-F distribution, 2020, Statistica Sinica
  • An empirical evaluation of large dynamic covariance models in portfolio value-at-risk estimation, 2020, The Journal of Risk Model Validation

The frequent co-authors collaborating with Li include:

  • Philip L. H. Yu
  • Ke Zhu
  • Keith Law
  • Feiyu Jiang
  • Guochang Wang

Li's work has appeared repeatedly in prominent academic venues such as:

  • Statistica Sinica
  • Journal of Econometrics
  • Scandinavian Journal of Statistics
  • Applied Stochastic Models in Business and Industry
  • Journal of Time Series Analysis

The main fields of study for Li comprise:

  • Economics, Econometrics and Finance
  • Mathematics

Within these areas, the scientist's subfields include:

  • Finance
  • Statistics and Probability
  • General Economics, Econometrics and Finance
  • Economics and Econometrics
  • Artificial Intelligence

Research topics frequently addressed in Li's work include:

  • Financial Risk and Volatility Modeling
  • Monetary Policy and Economic Impact
  • Statistical Methods and Inference
  • Statistical Methods and Bayesian Inference
  • Financial Markets and Investment Strategies
  • Market Dynamics and Volatility
  • Bayesian Methods and Mixture Models

Li was recognized as a Fellow of the American Statistical Association in 2003.

Best Publications

  • DIAGNOSTIC CHECKING ARMA TIME SERIES MODELS USING SQUARED‐RESIDUAL AUTOCORRELATIONS

    A. I. McLeod;Wai Keung Li

  • An adaptive estimation of dimension reduction space

    Yingcun Xia;Yingcun Xia;Howell Tong;Howell Tong;Wai Keung Li;Li-Xing Zhu;Li-Xing Zhu

  • On a mixture autoregressive model

    Chun Shan Wong;Wai Keung Li

  • Recent Theoretical Results for Time Series Models with GARCH Errors

    W. K. Li;Shiqing Ling;Michael McAleer

  • On Fractionally Integrated Autoregressive Moving-Average Time Series Models with Conditional Heteroscedasticity

    Shiqing Ling;W. K. Li

  • On a double‐threshold autoregressive heteroscedastic time series model

    C. W. Li;Wai Keung Li

  • Distribution of the Residual Autocorrelations in Multivariate Arma Time Series Models

    Unknown

  • On the squared residual autocorrelations in non-linear time series with conditional heteroskedasticity

    Wai Keung Li;T. K. Mak

  • A Stochastic Volatility Model With Markov Switching

    Mike K. P. So;K. Lam;Wai Keung Li

  • Fractional time series modelling

    W. K. Li;A. I. Mcleod

  • Diagnostic Checks in Time Series

    Wai Keung Li

  • On a mixture autoregressive conditional heteroscedastic model

    Chun Shan Wong;Wai Keung Li

  • On Single-Index Coefficient Regression Models

    Yingcun Xia;W. K. Li

  • Time series models based on generalized linear models: some further results.

    Wai Keung Li

  • A threshold stochastic volatility model

    Mike K. P. So;Wai Keung Li;K. Lam

  • On extended partially linear single-index models

    Yingcun Xia;Howell Tong;Wai Keung Li

  • Limiting distributions of maximum likelihood estimators for unstable autoregressive moving-average time series with general autoregressive heteroscedastic errors

    Shiqing Ling;Wai Keung Li

  • Diagnostic checking of nonlinear multivariate time series with multivariate ARCH errors

    Shiqing Ling;Wai Keung Li

  • On a logistic mixture autoregressive model

    C. S. Wong;W. K. Li

  • ON THE ESTIMATION AND TESTING OF FUNCTIONAL-COEFFICIENT LINEAR MODELS

    Yingcun Xia;Wai Keung Li

  • A Survey of Recent Theoretical Results for Time Series Models with GARCH Errors

    W. K. Li;Shiqing Ling;Michael McAleer

Frequent Co-Authors

Shiqing Ling
Shiqing Ling Hong Kong University of Science and Technology
Yingcun Xia
Yingcun Xia National University of Singapore
Howell Tong
Howell Tong London School of Economics and Political Science
Wai-Ki Ching
Wai-Ki Ching University of Hong Kong
Tak Kuen Siu
Tak Kuen Siu Macquarie University
Michael McAleer
Michael McAleer Erasmus University Rotterdam
Kenneth M.Y. Leung
Kenneth M.Y. Leung City University of Hong Kong
Richard A. Davis
Richard A. Davis Columbia University
Lixing Zhu
Lixing Zhu Beijing Normal University
Bellie Sivakumar
Bellie Sivakumar Indian Institute of Technology Bombay

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