World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
32
Citations
5795
World Ranking
3146
National Ranking
157

Shiqing Ling 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 Shiqing Ling 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: 115 publications — 18th percentile

18% of scientists in this discipline score the same or lower.

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

Shiqing Ling 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 Shiqing Ling 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: 32 D-Index — 14th percentile

14% of scientists in this discipline score the same or lower.

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

Overview

Shiqing Ling is affiliated with the Hong Kong University of Science and Technology in China and has contributed extensively to the fields of economics, econometrics, and finance through a variety of research topics and publications. Their work primarily focuses on financial risk and volatility modeling, monetary policy and economic impact, and statistical methods and inference.

Their recent research covers topics such as financial risk and volatility modeling, monetary policy and economic impact, statistical distribution estimation and applications, fault detection and control systems, and stochastic processes related to financial applications.

Ling's publications have appeared in established venues including the Journal of Econometrics, Statistica Sinica, SSRN Electronic Journal, Journal of Business and Economic Statistics, and Statistics and Its Interface. Some of the most recent papers include:

  • LADE-based inferences for autoregressive models with heavy-tailed G-GARCH(1, 1) noise (2020, Journal of Econometrics)
  • Whittle parameter estimation for vector ARMA models with heavy-tailed noises (2021, Journal of Statistical Planning and Inference)
  • Automated Estimation of Heavy-Tailed Vector Error Correction Models (2021, Statistica Sinica)
  • Self-Weighted LSE and Residual-Based QMLE of ARMA-GARCH Models (2022, Journal of risk and financial management)
  • Statistical Inference for Heavy-tailed and Partially Nonstationary Vector ARMA Models (2023, Statistica Sinica)

The scientist has collaborated frequently with co-authors such as Zichuan Mi, Feifei Guo, Shixuan Wang, Yaosong Zhan, and Rongmao Zhang. These collaborations have contributed to a range of research outputs spanning topics of econometrics and finance.

Ling's research spans several subfields including finance, statistics and probability, control and systems engineering, and general economics, econometrics and finance. Specific areas of work include:

  • Financial Risk and Volatility Modeling
  • Monetary Policy and Economic Impact
  • Statistical Methods and Inference
  • Statistical Distribution Estimation and Applications
  • Fault Detection and Control Systems
  • Stochastic Processes and Financial Applications
  • Market Dynamics and Volatility

Best Publications

  • Asymptotic Theory for a Vector ARMA-GARCH Model

    Shiqing Ling;Michael McAleer

  • Stationarity and the existence of moments of a family of GARCH processes

    Shiqing Ling;Michael McAleer

  • Recent Theoretical Results for Time Series Models with GARCH Errors

    W. K. Li;Shiqing Ling;Michael McAleer

  • Necessary and Sufficient Moment Conditions for the GARCH(r,s) and Asymmetric Power GARCH(r,s) Models

    Shiqing Ling;Michael McAleer

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

    Shiqing Ling;W. K. Li

  • Self-weighted and local quasi-maximum likelihood estimators for ARMA-GARCH/IGARCH models

    Shiqing Ling

  • On Adaptive Estimation in Nonstationary Arma Models with Garch Errors

    Shiqing Ling;Michael McAleer

  • On the Probabilistic Properties of a Double Threshold ARMA Conditional Heteroskedastic Model

    Shiqing Ling

  • Estimation and testing stationarity for double‐autoregressive models

    Shiqing Ling

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

    Shiqing Ling;Wai Keung Li

  • Global self-weighted and local quasi-maximum exponential likelihood estimators for ARMA-GARCH/IGARCH models

    Ke Zhu;Shiqing Ling

  • Self‐weighted least absolute deviation estimation for infinite variance autoregressive models

    Shiqing Ling

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

    Shiqing Ling;Wai Keung Li

  • On the least squares estimation of multiple-regime threshold autoregressive models

    Dong Li;Shiqing Ling

  • A Double AR(p) Model: Structure and Estimation

    Shiqing Ling

  • Estimation and Testing for Unit Root Processes with GARCH (1, 1) Errors: Theory and Monte Carlo Evidence

    Shiqing Ling;W. K. Li;Michael McAleer

  • Fitting an Error Distribution in Some Heteroscedastic Time Series Models

    Hira L. Koul;Shiqing Ling

  • Empirical Likelihood for GARCH Models

    Ngai Hang Chan;Shiqing Ling

  • Asymptotic inference for unit root processes with GARCH(1,1) errors

    Shiqing Ling;Wai Keung Li

  • TESTING FOR A LINEAR MA MODEL AGAINST THRESHOLD MA MODELS

    Shiqing Ling;Howell Tong

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

    W. K. Li;Shiqing Ling;Michael McAleer

  • Ergodicity and invertibility of threshold moving-average models

    Shiqing Ling;Howell Tong;Dong Li

Frequent Co-Authors

Michael McAleer
Michael McAleer Erasmus University Rotterdam
Wai Keung Li
Wai Keung Li University of Hong Kong
Howell Tong
Howell Tong London School of Economics and Political Science
Lajos Horváth
Lajos Horváth University of Utah
Liang Peng
Liang Peng Georgia State University
Jean-Michel Zakoian
Jean-Michel Zakoian École Nationale de la Statistique et de l'Administration Économique
Ruey S. Tsay
Ruey S. Tsay University of Chicago
Hira L. Koul
Hira L. Koul Michigan State University
Qi-Man Shao
Qi-Man Shao Chinese University of Hong Kong

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