# 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:

50+

full-time faculty researching diverse and exciting areas

1000+

undergraduate students from around the world

175+

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. 20, 2019Congratulations to Richard J Cook for being awarded the Math Faculty Research Chair position

The Department of Statistics and Actuarial Science would like to congratulate Professor Richard J Cook for being awarded the five-year Faculty of Mathematics Research Chair position in recognition of his outstanding research contributions. The Faculty of Mathematics recognizes Richard's exceptional scholarly achievements and pre-eminence in the field of Statistics. Richard will receive a $250,000 research grant and a teaching reduction of one course per year. 2. Nov. 5, 2019Tony Wirjanto appointed as the Curator in Insurance and Asset Management for the World Economic Forum 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. 3. Oct. 24, 2019Students shine at the first Waterloo Student Conference in Statistics, Actuarial Science, and Finance 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. Read all news 1. 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. 2. 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.

3. Nov. 28, 2019Department seminar by Shihao Yang, Georgia Institute of Technology

Bayesian inference of dynamic systems via constrained Gaussian processes

Ordinary differential equations are an important tool for modeling behaviors in science, such as gene regulation, epidemics, etc.  An important statistical problem is to infer and characterize the uncertainty of parameters that govern the equations.  We present a fast Bayesian inference method using constrained Gaussian processes, such that the derivatives of the Gaussian process must satisfy the dynamics of the differential equations.  Our method completely avoids the numerical solver and is thus practically fast to compute. Our construction is cleanly embedded in a rigorous Bayesian framework, and is demonstrated to yield fast and reliable inference in a variety of practical scenarios.

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## Diana Skrzydlo

Lecturer

Contact Information:
Diana Skrzydlo

Diana Skrzydlo is a Continuing Lecturer in the Department of Statistics and Actuarial Science, and the current Director of the MActSc program. She has been teaching at UW since 2007 and has spoken widely on innovative teaching and assessment techniques. Over the last year and a half she developed a faculty mentorship program for the department, and started a monthly teaching discussion group. She has a BMath (2006) and MMath (2007) from UW, and achieved her ASA designation from the Society of Actuaries in 2018, where she is a member of the Education & Research Section council.