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. Nov. 5, 2019Tony Wirjanto appointed as the Curator in Insurance and Asset Management for the World Economic Forum
    Professor Tony Wirjanto

    On October 4, 2019, the World Economic Forum (WEF) and University of Waterloo appointed Professor Tony Wirjanto as the Curator in Insurance and Asset Management for the WEF.

  2. Oct. 24, 2019Students shine at the first Waterloo Student Conference in Statistics, Actuarial Science, and Finance
    Conference Banner

    On Friday October 18 and Saturday October 19, 2019 the first Waterloo Student Conference in Statistics, Actuarial Science, and Finance took place.  While this event was hosted by the Department of Statistics and Actuarial Science, it was the students who brought it to life.  This two-day conference was organized by students, for students.

    The agenda for the conference featured keynote presentations by leading researchers, Xiao-Li Meng (Harvard University), Sebastian Jaimungal (University of Toronto), and Mary Thompson (University of Waterloo), as well as 40 research presentations by students from a variety of universities. 

    The conference also included presentation awards for the two most outstanding talks in each field:

    In the fields of Actuarial Science and Finance, the winners were:

    • Xiyue Han (University of Waterloo) for the talk: On the Extrema of Functions in the Takagi Class
    • Francois Micheal Boire (University of Western) for the talk: Distributional Response to Fiscal Stimulus

    In the fields of Statistics and Biostatisics, the winners were:

    • Christopher Salahub (University of Waterloo) for the talk: Seen to Be Done: A Graphical Investigation of Peremptory Challenge
    • Gabriela Gonzalez Martinez (York University) for the talk: Bandwidth selection for the effective dose problem

    To view information on other talks presented at the conference, including abstracts, please view the conference program.

  3. Oct. 18, 2019Maclean’s features READI
    READI


    Maclean’s wrote an article about how the University of Waterloo and funding from the Canadian government is helping Indonesia prepare for climate change. Through the Risk Management, Economic Sustainability and Actuarial Science in Indonesia (READI)project in the Department of Statistics and Actuarial Science, Waterloo is working to establish Indonesia as a regional centre of actuarial science.

    Indonesia’s universities did not have a top-ranked actuarial science program, nor a cooperative program. With funding from Global Affairs, Manulife Indonesia, and Sun Life, the dedication of staff and faculty members from the University of Waterloo, and eager partners in Indonesia, nine out of 12 Indonesian universities are working with READI.

    The success continues to grow. At the most recent co-op event, more than 80 companies showed up at one university. To learn more about the success of the program, see the full article in Maclean’s

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  1. Nov. 15, 2019Department seminar by Arthur Charpentier, Université du Québec à Montréal

    Insurance Pricing in a Competitive Market


    Insurance is usually defined as "the contribution of the many to the misfortune of the few". This idea of pooling risks together using the law of large number legitimates the use of the expected value as actuarial "fair" premium. In the context of heterogeneous risks, nevertheless, it is possible to legitimate price segmentation based on observable characteristics. But in the context of "Big Data", intensive segmentation can be observed, with a much wider range of offered premium, on a given portfolio. In this talk, we will briefly get back on economical, actuarial and philosophical approaches of insurance pricing, trying to link a fair unique premium on a given population and a highly segmented one. We will then get back on recent experiments (so-called "actuarial pricing game") organized since 2015, where (real) actuaries were playing in competitive (artificial) market, that mimic real insurance market. We will get back on conclusions obtained on two editions, the first one, and the most recent one, where a dynamic version of the game was launched.

  2. Nov. 21, 2019Department seminar by Linglong Kong, University of Alberta

    A General Framework for Quantile Estimation with Incomplete Data


    Quantile estimation has attracted significant research interests in recent years. However, there has been only a limited literature on quantile estimation in the presence of incomplete data. In this paper, we propose a general framework to address this problem. Our framework combines the two widely adopted approaches for missing data analysis, the imputation approach and the inverse probability weighting approach, via the empirical likelihood method. The proposed method is capable of dealing with many different missingness settings. We mainly study three of them: (i) estimating the marginal quantile of a response that is subject to missingness while there are fully observed covariates; (ii) estimating the conditional quantile of a fully observed response while the covariates are partially available; and (iii) estimating the conditional quantile of a response that is subject to missingness with fully observed covariates and extra auxiliary variables. The proposed method allows multiple models for both the missingness probability and the data distribution. The resulting estimators are multiply robust in the sense that they are consistent if any one of these models is correctly specified. The asymptotic distributions are established using the empirical process theory.

     

    Joint work with Peisong Han, Jiwei Zhao and Xingcai Zhou.

  3. Nov. 22, 2019Department seminar by Mathieu Boudreault, Université du Québec à Montréal

    Do Jumps Matter in the Long Run? A Tale of Two Horizons


    Economic scenario generators (ESGs) for equities are important components of the valuation and risk management process of life insurance and pension plans. As the resulting liabilities are very long-lived, it is a desired feature of an ESG to replicate equity returns over such horizons. However, the short-term performance of the assets backing these liabilities may also trigger significant losses and in turn, affect the financial stability of the insurer or plan. For example, a line of GLWBs with frequent withdrawals may trigger losses when subaccounts suddenly lose after a stock market crash or pension contributions may also need to be revised after a long-lasting economic slump. Therefore, the ESG must replicate both short- and long-term stock price dynamics in a consistent manner, which is a critical problem in actuarial finance. Popular features of financial models include stochastic volatility and jumps, and as such, we would like to investigate how these features matter for typical long-term actuarial applications.

    For a model to be useful in actuarial finance, it should at least replicate the dynamics of daily, monthly and annual returns (and any frequency in between). A crucial characteristic of returns at these scales is that the kurtosis tends to be very high on a daily basis (25-30) but close to 4-5 on an annual basis. We show that jump-diffusion models, featuring both stochastic volatility and jumps, cannot replicate such features if estimated with the maximum likelihood. Using the generalized method of moments, we find that simple jump-diffusion models or regime-switching models (with at least three regimes) have an excellent fit for various moments observed at different time scales. Finally, we investigate three typical actuarial applications: $1 accumulated in the long run with no intermediate monitoring, a long-term solvency analysis with frequent monitoring and a portfolio rebalancing problem, also with frequent monitoring and updates. Overall, we find that a stochastic volatility model with independent jumps or a regime-switching lognormal model with three regimes, both fitted with the GMM, yield the best fit to moments at different scales and also provide the most conservative figures in actuarial applications, especially when there is intermediate monitoring.

    So yes, jumps or jump-like features are essential in the long run. This also illustrates how typical actuarial models fitted with the maximum likelihood may be inadequate for reserving, economic capital and solvency analyses.

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Meet our people

Mary Thompson

Mary Thompson

Distinguished Professor Emerita

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Mary Thompson

Research interests

Professor Thompson works primarily in survey methodology and sampling theory. Her book Theory of Sample Surveys describes the mathematical and foundational theory in detail; it also contains a systematic approach to using estimating functions in surveys, and a thorough discussion (with examples) of the role of the sampling design when survey data are used for analytic purposes.

Estimation for stochastic processes has been another theme of her research. These twothemes come together in aspects of inference from complex longitudinal surveys. Issues in the design of longitudinal surveys to support causal inference are central to work on the International Tobacco Control Survey, with which Professor Thompson has been involved since 2002. She studies the application of multilevel models and longitudinal models with time-varying covariates to complex survey data, including the best ways to adapt the estimating functions systems for use with survey weights, and the use of resampling techniques to provide accurate interval estimates.

She is also currently collaborating on projects in survival and multistate models and the design of behavioural interventions on random networks.