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Ken Seng Tan


Ken Seng TanSun Life Fellow in International Actuarial Science, Chief Actuarial Advisor,  Risk Management, Economic Sustainability, and Actuarial Science Development in Indonesia (READI)

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Ken Seng Tan

Research interests

Professor Tan held a Canada Research Chair in Quantitative Risk Management. His research interests lie at the intersection of actuarial science, finance, mathematics, and statistics. Much of his work relates to the development and implementation of innovative approaches to risk management, scientific computation, and optimal reinsurance.

The modern financial industry---comprising the banking and investment sectors, as well as insurance companies and pension funds---relies heavily on modern, computer-based risk analysis and management. As the array of financial products grows in variety and complexity, accurate and reliable risk management has become both more complex and more essential. Several tragic failures in recent years---such as the rapid demise of Confederation Life, Enron, Barings Bank, and Long Term Capital---have caused millions of investors to lose their money, and have provided ample evidence of the consequences of inappropriate risk management.

One of Professor Tan's research areas is in developing a framework for modelling and analyzing the risks involved in long-term insurance contracts with embedded financial guarantees such as segregated funds and variable annuities. These products are extremely popular in Canada and the U.S., accounting for some $113.7 billion in sales (in the U.S. and for the variable annuities) in 2002. Reliable risk mitigating and risk managing of these complex securities is critical for the viability of a financial institution. It is in this important area that Professor Tan has made significant contributions.

Professor Tan has also provided state-of-the-art algorithms for solving high-dimensional computational problems. These are precisely the types of problems that are ubiquitous in the risk management of large financial institutions. The Monte Carlo method is the workhorse for many of these applications but crude Monte Carlo is often inefficient. The Quasi-Monte Carlo method as developed by Professor Tan and others has an immediate benefit here because it makes these computations more efficient. Professor Tan's work on Quasi-Monte Carlo has been honoured as one of the seven most important contributions in investment research in the last 50 years, as judged by the investment council of the Society of Actuaries (SOA).


  • 1998 PhD (Statistics) University of Waterloo
  • 1993 MMath (Actuarial Science) University of Waterloo
  • 1992 BMath (Honours Actuarial Science) University of Waterloo Professor

Tan collaborates on a regular basis with a number of academic researchers at the University of Waterloo as well as researchers in other universities. Professor Tan is also actively involved with many professional organizations. He has attained Associateship of the SOA. He was a founding member of the SOA Risk Management section and is currently serving as an elected council member for this section. He has been a committee member of the Investment section of the Canadian Institute of Actuaries (CIA). He also served on the CIA's task force on Liaison with Banks and Trusts.

In addition to these research activities and professional affiliations, Professor Tan has collaborated on a number of projects with industry in the areas of pricing, hedging, and risk management.

Selected publications

  • Cong, J. K.S. Tan and C. Weng (2012). “CVaR-based optimal partial hedging.” To appear in Journal of Risk.
  • Cong, J. K.S. Tan and C. Weng. “VaR-based optimal partial hedging.” To appear in Astin Bulletin.
  • Zhou, R., J.S.H. Li and K.S. Tan “Pricing mortality risk: A two-population model with transitory jump effects,” to appear in Journal of Risk and Insurance.
  • Zhou, R., J.S.H. Li and K.S. Tan “Economic pricing of mortality-linked securities: a Tâtonnement approach,” to appear in Journal of Risk and Insurance.
  • Porth, L., K.S. Tan, and C.Weng “Optimal Reinsurance Analysis from a Crop Insurer’s Perspective”, to appear in Agricultural Finance Review.
  • Boyle, P.P., A.W. Kolkiewicz and K.S. Tan. (2013) “Pricing Bermudan options using low discrepancy mesh method.” Quantitative Finance 13(6):841-860.
  • Chi, Y. and K.S. Tan. “Optimal reinsurance with general premium principles.” Insurance: Mathematics and Economics, 52(2):180-189.
  • Wang, X. and K.S. Tan. (2013) “Pricing and hedging with discontinuous functions:Quasi-Monte Carlo methods and dimension reduction.” Management Science, 59(2):376-389.
  • Weng, C., Y. Zhang and K.S. Tan. “Tail Behavior of Poisson Shot Noise Processes under Heavy-tailed Shocks and Actuarial Applications” to appear in Methodology and Computing in Applied Probability. Online First (January 2012), 28 pages.
  • Tan, K.S. and C. Weng. (2012) “Enhancing insurer value using reinsurance and value at risk criterion.” The Geneva Risk and Insurance Review, 37:109-140.
  • Wang, X. and K.S. Tan. (2012)“How do path generation methods affect the accuracy of Quasi-Monte Carlo methods for problems in finance?” Journal of Complexity, 28(2):250-277.
  • Chi, Y. and K.S. Tan. (2011) “Optimal reinsurance under VaR and CVaR risk measures: A simplified approach.” ASTIN Bulletin, 41(2):487-509.
  • Zhou, R., J.S.H. Li and K.S. Tan. (2011) “Economic pricing of mortality-linked securities in the presence of population basis risk.” The Geneva Papers, 36:544-546.
  • Tan, K.S., C.Weng, and Y. Zhang (2011). “Optimality of general reinsurance contracts under CTE risk measure.” Insurance: Mathematics and Economics, 49(2): 175–187.
  • Li, S., M.R. Hardy, and K.S. Tan. (2010) “Developing mortality improvement formulae: The Canadian insured lives case study.” North American Actuarial Journal, 14(4):381–399.
  • Zhou, M., K.S. Tan and H. Dong (2010). “Optimal reinsurance strategy under RORAC criteria” (in Chinese). System Engineering Theory and Practice, 30(11):1931–1937.
  • Li, S., M.R. Hardy, and K.S. Tan (2010). “On pricing and hedging the no-negativeequity-guarantee in equity release mechanisms.” Journal of Risk and Insurance, 77(2):499-522.
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