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Dr Tom Stindl

Dr Tom Stindl

Lecturer
Science
School of Mathematics & Statistics

Tom Stindl is a statistician and Lecturer in the School of Mathematics and Statistics. He is interested in statistics and computing. His recent research topics include Hawkes Processes, Statistical Inference, and Computational Statistics. Tom's Ph.D. was on statistical inference for self-exciting point processes under the supervision of Dr. Feng Chen at UNSW Sydney.
E-mail
t.stindl@unsw.edu.au
Location
School of Mathematics and Statistics UNSW Sydney NSW 2052 The Red Centre Room 2086
  • Journal articles | 2023
    Stindl T; Chen F, 2023, 'STOCHASTIC DECLUSTERING OF EARTHQUAKES WITH THE SPATIOTEMPORAL RENEWAL ETAS MODEL', Annals of Applied Statistics, 17, pp. 3173 - 3194,
    Journal articles | 2023
    Stindl T; Chen F, 2023, 'Stochastic declustering of earthquakes with the spatiotemporal renewal ETAS model', Annals of Applied Statistics,
    Journal articles | 2023
    Stindl T, 2023, 'Forecasting intraday market risk: A marked self-exciting point process with exogenous renewals', Journal of Empirical Finance, 70, pp. 182 - 198,
    Journal articles | 2022
    Stindl T; Chen F, 2022, 'Spatiotemporal ETAS model with a renewal main-shock arrival process', Journal of the Royal Statistical Society Series C: Applied Statistics, 71, pp. 1356 - 1380,
    Journal articles | 2021
    Stindl T; Chen F, 2021, 'Accelerating the estimation of renewal Hawkes self-exciting point processes', Statistics and Computing,
    Journal articles | 2019
    Stindl T; Chen F, 2019, 'Modeling extreme negative returns using marked renewal Hawkes processes', Extremes, 22, pp. 705 - 728,
    Journal articles | 2018
    stindl ; Chen F, 2018, 'Likelihood Based Inference for the Multivariate Renewal Hawkes Process', Computational Statistics and Data Analysis, 123, pp. 131 - 145,
    Journal articles | 2017
    Chen F; Stindl T, 2017, 'Direct Likelihood Evaluation for the Renewal Hawkes Process', Journal of Computational and Graphical Statistics, 27, pp. 119 - 131,
  • Software / Code | 2018
    Stindl T, 2018, Multivariate Renewal Hawkes Process, CRAN, R Package, Published: 15 August 2018, Software / Code,
    Software / Code | 2017
    Stindl T, 2017, Renewal Hawkes Process, CRAN, R Package, Published: 16 November 2017, Software / Code,

  • Feng Chen, Tom Stindl, William Dunsmuir, Jiancang Zhuang. "Inference for Hawkes processes with challenging data", Australian Research Council, Discovery Project. 2024 - 2026, $463,452.Ìý

Early Career Teaching Excellence - Faculty of Science

Research Aims

My core research is in point process modelsÌýand their application in a range of disciplines. Currently, my research is focused onÌýthe following themes:

  • Statistical inference for self-exciting point processesÌý
  • Application of self-exciting point processes to finance, seismology, crime, and bushfires
  • Financial data modelingÌý

Research in Detail

My research focuses on methods to perform efficient statistical inferences for point process models, with a particular focus on the renewal Hawkes process and its marked and multivariate variants.

Potential Projects:

Limit Order Book Modeling:ÌýDevelop a point process model for order flows in limit order books. More specifically, a Hawkes process with a state-dependent factor with potential state observations including observed imbalance or spread among other economic variables. The project would develop computationally efficient methods for estimation using direct MLE or EM algorithms including methods for goodness-of-fit assessment. These methods would then serve to conduct an empirical study on different ASX stocks.

Bayesian estimation for ETAS model with application to seismology: The ETAS model has been successfully modeled using likelihood-based algorithms such as MLE and EM algorithms. However, forecasts based on frequentist approaches fail to account for the uncertainty in the estimates. By employing a Bayesian approach the parameter uncertainties can be explicitly accounted for in the forecast. This project will continue the development of the Bayesian framework for estimating the ETAS model and developing the appropriate software to implement the methods. These methods would be used to fit an earthquake catalog and perform forecasts of future seismicity.Ìý

Non-parametric estimation for ETAS model with application to crime:ÌýCrime events such as burglaries and gang violence cluster in both time and space due to the crime-specific patterns of criminal behavior.Ìý A self-exciting space-time point process is well suited to model this clustering behavior. In this project, we will apply a non-parametric space-time point process with a temporal background process that renews on each background event.Ìý

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My Research Supervision

PhD Student(s):

  • Jason Lambe (2024 - ; joint with Feng Chen)

MRes Student(s):

  • Zhe Han (2020 - 2022 ; joint with Feng Chen):

Honours Students:

  • Miah Panovrakos (2024)
  • Oden Petersen (2024)
  • Claire Xue (2023 - ; joint with David Khoury)
  • Thomas Lee (2021, joint with Feng Chen): Ergodicity of renewal Hawkes processes

Masters Students:

  • Xiaowen Liang (2020): Modeling intraday extreme negative financial returns using renewal Hawkes processes

  • Zhipeng Dai (2021): Approximate Bayesian computation for self-exciting point processes

  • Yuxin Song (2021): Modeling Left- and Right-tailed extreme return using point processes

  • Ke Zhu (2021):ÌýPoint processes with gamma distributed self-excitation

  • Sarah Akari (2022):ÌýTail risk estimation using an extreme value approach with covariates

  • Zhuoyu Zhou (2022): Using Pearson IV and Generalized Hyperbolic distributions to estimate tail risk related measures

  • Dongyue Lu (2022 - ): Self-exciting point processes with inhibition

  • Daniel Ng (2022 - ):ÌýMachine learning approach to predicting the mid-point/fair value of BTC-perpetual swap

My Teaching

Courses recently taught:

  • MATH3821 - Statistical Modelling and Computing (T2 2019, T2 2020)Ìý
  • ZZSC5905 - Statistical Inference for Data Scientists (H6 2019)
  • MATH5905 - Statistical Inference (T1 2020)
  • MATH2099 - Mathematics 2B (Statistics component, T2 2020)
  • MATH2859 -ÌýProbability, Statistics, and Information (T2, 2020)
  • DATA3001 - Data Science and Decisions in Practice (T3, 2020)
  • MATH3811/MATH3911 - Statistical Inference (T1, 2022)
  • MATH3841 - StatisticalÌýAnalysis of Dependent Data (T2, 2023)