Welcome to the Department of Statistics and Actuarial Science

The Department of Statistics and Actuarial Science is a top tier academic unit among statistical and actuarial science globally. Our students and faculty explore topics such as Actuarial Science, Biostatistics, Data Science, Quantitative Finance, Statistics, & Statistics-Computing. Our department is home to:

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full-time faculty researching diverse and exciting areas

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undergraduate students from around the world

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 graduate students in Master, Doctoral, and professional programs

Interested in graduate studies with Statistics and Actuarial Science? Meet Mingyu (Bruce) Feng, a PhD student in actuarial science. Bruce is a pioneer researcher in the field of sustainable investment and the impact of climate change. Learn more about furthering your education on the Future Graduate page on the Math site.

  1. Sep. 19, 2022Department of Statistics and Actuarial Science is pleased to welcome Mikko Pakkanen as an Associate Professor
    Mikko Pakkanen

    PAKKANEN, Mikko (MSc (Mathematics), 2006, University of Helsinki; PhD (Applied Mathematics), 2010, University of Helsinki) will be joining the Department of Statistics and Actuarial Science on September 19, 2022 as an Associate Professor. Before coming to Waterloo, Mikko held academic appointments at Imperial College London, most recently as a Senior Lecturer. At Imperial, he served also as Co-Director of the MSc in Mathematics and Finance and as Co-Director of the EPSRC Centre of Doctoral Training in Financial Computing & Analytics. Mikko's research is focused on data science, stochastic processes, and quantitative finance. His specific research interests include statistical modeling of high frequency financial data, volatility modeling, limit theorems for stochastic processes, and machine learning in finance. Apart from financial applications, Mikko has recently started working on stochastic modeling in epidemiology and is keen to expand his research activity in this area.

  2. Aug. 29, 2022Fangya Mao awarded Student Conference Award at the 43rd Meeting of the International Society of Clinical Biostatistics
    Fangya Mao

    Congratulations Dr. Fangya Mao for being recognized with a Student Conference Award at the 43rd Meeting of the International Society of Clinical Biostatistics Meeting in Newcastle, UK. Fangya's presentation on Spatial Dependence Modeling of Latent Susceptibility and Time to Joint Damage in Psoriatic Arthritis on August 23rd involved the use of mixture models for fitting clustered failure time processes under intermittent observation. Fangya completed her doctoral program in the spring of 2022 under the supervision of Richard Cook. In September she starts a Research Fellowship at the Biostatistics Branch of the National Cancer Institute,  National Institutes of Health.

    Learn more about the ISCB on their website.

  3. Aug. 25, 2022Math Alum Joy Jiang receives 40 under 40 Public Health Catalyst Award
    Joy Jiang

    Mathematics Statistics alumni Joy Jiang (PhD ‘18) has been named as one of the inaugural 40 Under 40 Public Health Catalyst Award recipients. The Boston Congress of Public Health recognized these 40 individuals as representatives of the next generation of leaders, entrepreneurs, researchers, scientists, activists, intellectual provocateurs, authors, and directors who inspire and catalyze a more just and equitable world.

    Jiang was excited to be recognized, saying, “I’m extremely honoured to have received the award this year. This is a huge recognition for my work on bridging statistical methods with breast cancer research and big support to advancing method developments in women’s health. I look forward to leveraging this platform to develop and disseminate innovative and rigorous statistical methods leading to new pathways that can be targeted in breast cancer prevention and intervention trials to expand capacity for precision prevention.”

    Learn more on the Math Faculty website.

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  1. Oct. 7, 2022Seminar by Zhiwei Tong

    Please Note: This seminar will be given in-person.

    Actuarial Science and Financial Mathematics seminar series

    Zhiwei Tong
    University of Iowa

    Room: M3 3127

    The Gradient Allocation Principle based on the Higher Moment Risk Measure

  2. Oct. 14, 2022David Sprott Distinguished Lecture by Viktor Todorov
    Viktor Todorov

    Please Note: This seminar will be given in-person.

    Distinguished Lecture Series

    Viktor Todorov 
    Kellogg School of Management at Northwestern University

    Room: EIT 1015

    Recalcitrant Betas: Intraday Cross-Sectional Distributions of Systematic Risk


    High-frequency financial data allows for efficient estimation of assets’ exposures to systematic risk, provided these exposures do not vary significantly at high frequencies.  We develop a test for deciding whether this is the case. The test is constructed for a panel of high-frequency asset returns, with the size of the cross-section and the sampling frequency increasing simultaneously. It is based on a comparison of the empirical characteristic functions of estimates of the assets' factor loadings at different parts of the trading day, formed from local blocks of asset returns and the corresponding factor realizations. The limiting behavior of the test statistic is governed by unobservable latent factors in the asset prices. Empirical implementation of the test to stocks in the S&P 500 index and the five Fama-French factors, as well as the momentum factor, reveals different intraday behavior of the factor loadings: assets' exposure to size, market and value risks vary systematically over the trading day while the three remaining factors do not exhibit statistically significant intraday variation. Moreover, we find diverse, and for some factors large, reactions in the assets' factor loadings to major economic or firm specific news releases. Finally, we document that time-varying correlations between the observable risk factors drive a wedge between the time-of-day pattern of market betas, estimated with and without control for the other observable risk factors.


  3. Oct. 20, 2022Distinguished Lecture by Claudia Klüppelberg
    Claudia Klüppelberg

    Please Note: This seminar will be given in-person.

    Distinguished Lecture

    Claudia Klüppelberg  
    Technical University of Munich

    Room: M3 3127

    Max-linear Graphical Models for Extreme Risk Modelling


    Graphical models can represent multivariate distributions in an intuitive way and, hence, facilitate statistical analysis of high-dimensional data. Such models are usually modular so that high-dimensional distributions can be described and handled by careful combination of lower dimensional factors. Furthermore, graphs are natural data structures for algorithmic treatment. Moreover, graphical models can allow for causal interpretation, often provided through a recursive system on a directed acyclic graph (DAG) and the max-linear Bayesian network we introduced in [1] is a specific example. This talk contributes to the recently emerged topic of graphical models for extremes, in particular to max-linear Bayesian networks, which are max-linear graphical models on DAGs. 

    In this context, the Latent River Problem has emerged as a flagship problem for causal discovery in extreme value statistics. In [2] we provide a simple and efficient algorithm QTree to solve the Latent River Problem. QTree returns a directed graph and achieves almost perfect recovery on the Upper Danube, the existing benchmark dataset, as well as on new data from the Lower Colorado River in Texas. It can handle missing data, and has an automated parameter tuning procedure. In our paper, we also show that, under a max-linear Bayesian network model for extreme values with propagating noise, the QTree algorithm returns asymptotically a.s. the correct tree. Here we use the fact that the non-noisy model has a left-sided atom for every bivariate marginal distribution, when there is a directed edge between the the nodes.

    For linear graphical models, algorithms are often based on Markov properties and conditional independence properties. In [3] we characterise conditional independence properties of max-linear Bayesian networks and in my talk I will present some of these results and exemplify the difference to linear networks. 

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Meet our people

Mario Ghossoub

Mario Ghossoub

Assistant Professor

My research is mainly concerned with model uncertainty and the modern theory of choice under uncertainty and ambiguity, and with their use and applications in insurance, risk measurement and management, quantitative behavioral finance, and the theory of risk sharing.

More information on my personal webpage.