Researcher

Dr Tom Stindl

Fields of Research (FoR)

Statistics, Applied Statistics, Econometric and Statistical Methods

Biography

Tom completed his PhD at the School of Mathematics and Statistics at UNSW Sydney under the supervision of Dr. Feng Chen.


My Research Activities

Research Aims

My core research is in point process models and their application in a range of disciplines. Currently, my research is focused on the following themes:

  • Statistical inference for self-exciting point processes 
  • Application of self-exciting point processes to finance and...view more

Tom completed his PhD at the School of Mathematics and Statistics at UNSW Sydney under the supervision of Dr. Feng Chen.


My Research Activities

Research Aims

My core research is in point process models and their application in a range of disciplines. Currently, my research is focused on the following themes:

  • Statistical inference for self-exciting point processes 
  • Application of self-exciting point processes to finance and seismology
  • Financial data modeling 

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.

Supervision

Modeling extreme negative returns for ultra-high-frequency financial data (2020)

Potential Projects:

Extreme return modeling: There exists an asymmetric self- and cross-excitation effect between the left- and right-tail extreme events for financial returns (losses and gains respectively). This project would attempt to model these extreme returns by applying a two-tailed peaks-over-threshold Hawkes model enhanced with exogenous renewals. However, these events are mutually exclusive and a single point process approach with a common intensity function would be more appropriate. This project will attempt to fit and compare these competing models using likelihood-based approaches. The model will be applied to daily log-returns from ASX stock for example.  

Crime modeling: Crime events such as burglaries and gang violence cluster in both time and space due to the crime-specific patterns of criminal behavior.  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. 


My Teaching

Courses recently taught:

  • MATH3821 - Statistical Modelling and Computing (T2 2019, T2 2020) 
  • ZZSC5905 - Statistical Inference for Data Scientists (H6 2019)
  • MATH5905 - Statistical Inference (T1 2020)
  • MATH2099 - Mathematics 2B (Statistics component, T2 2020)
  • MATH2859 - Probability, Statistics and Information (T2, 2020)
  • DATA3001 - Data Science and Decisions in Practice (T3, 2020)
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Location

School of Mathematics and Statistics
UNSW Sydney
NSW 2052
The Red Centre
Room 1037