One World Actuarial Research Seminar

In these difficult times, when most departmental seminar streams are suspended and research conferences cancelled, we have thought that there is an opportunity to unite the community of researchers in the actuarial and related disciplines and organize a global seminar run remotely.



Date & Time Speaker Title Misc
22 April 2020 9am (GMT+1, London)
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Mario Wuethrich (ETH, Zurich) From Generalized Linear Models to Neural Networks, and Back
Abstract (click to expand)

We present how to enhance classical generalized linear models by neural network features. On the way there, we highlight the traps and pitfalls that need to be avoided to get good statistical models. This includes the non-uniqueness of sufficiently good regression models, the balance property, and representation learning, which brings us back to the concept of the good old generalized linear models.

06 May 2020 1am (GMT+1, London)
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Katja Hanewald (UNSW, Sydney) Long-term care insurance financing using home equity release: Evidence from an experimental study
Abstract (click to expand)

We explore new mechanisms to fund long-term care using housing wealth. We conduct and analyze an online experimental survey fielded to a representative sample of 1,200 Chinese homeowners aged 45-64 to assess the potential demand for new financial products that allow individuals to access their housing wealth to buy long-term care insurance. We find that the stated demand for long-term care insurance increases when individuals can use housing wealth in addition to savings to purchase long-term care insurance. Individuals prefer to access housing wealth through a reverse mortgage loan rather than home reversion (a partial sale of housing wealth). Our results inform current policy reforms in China which aim at developing the private market for health and long-term care insurance products.

20 May 2020 1pm (GMT+1, London)
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Moshe Milevsky (York University, Toronto) Is Covid-19 a parallel shift of the term structure of mortality? Implications for annuity pricing
Abstract (click to expand)

This presentation – which admittedly is rather speculative – examines the financial implications of a sudden shock to mortality on the pricing of pension & life annuities. Now, a textbook approach would suggest that holding interest rates constant, an increase in mortality reduces the discounted value of longevity-contingent claims. Stated simply, life insurance gets expensive and annuities become cheaper. However, if the shock to mortality actually weeds-out the frail and merely advances imminent deaths, then survivors will find that (counter-intuitively) annuities are suddenly dearer. Add plummeting interest rates and depressed equity markets to the mix and soon-to-be retirees might be facing an exceedingly higher "cost of retirement" post 2020. Technically speaking these matters tie into so-called compensation laws and the convergence of the term structure of (stochastic) mortality at very advanced ages. These matters also relate to the distinction between chronological age versus biological age and the relevant clock for measuring any shocks to mortality. In sum, this (conjectural) presentation speculates on how to “think” about covid-19 from the perspective of retirement income planning. One thing is for certain, the first-order independence between shocks to mortality (i.e. the insurance measure) and the economy (i.e. the financial measure) can no longer be assumed, even for textbook actuarial models.

03 June 2020 4pm (GMT+1, London)
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Ruodu Wang (University of Waterloo, Waterloo) PELVE: Probability Equivalent Level of VaR and ES
Abstract (click to expand)

Inspired by the recent transition from the 99% Value-at-Risk (VaR) to the 97.5% Expected Shortfall (ES) for internal market risk models in the Fundamental Review of the Trading Book (FRTB), we introduce a new distributional index, the probability equivalence level of VaR and ES (PELVE), which identifies the balancing point for the equivalence between VaR and ES. PELVE enjoys many desirable theoretical properties and it distinguishes heavy-tailed distributions from light-tailed ones via a threshold 2.72. Applying PELVE to financial asset and portfolio data leads to interesting observations that are not captured by VaR or ES alone. For instance, empirical PELVE exhibit unprecedented jumps during the COVID-19 period, which is not the case for VaR or ES. Moreover, the transition from VaR to ES in the FRTB yields an increase in risk capital for single-asset portfolios, but for well-diversified portfolios, the capital requirement remains almost unchanged. This leads to both a theoretical justification and an empirical evidence for that the use of ES rewards portfolio diversification more than the use of VaR. The talk is based on joint work with Hengxin Li (University of California, Berkeley).

17 June 2020 1pm (GMT+1, London)
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Katrien Antonio (KU Leuven, Belgium) Claims reserving in non-life insurance: old and new adventures
Abstract (click to expand)

Claims reserving deals with the prediction of the remaining development of reported, open claims (the reported but not settled reserve) and unreported claims (the incurred but not reported reserve). Micro-level or granular reserving approaches the reserving problem by using granular, detailed data on the development of individual claims. In this talk we briefly present recent work on modelling the number of incurred but not reported (ibnr) claims. We continue with presenting a modular, yet interpretable framework for including claim- and policy-specific covariates in reserving models for reported but not settled (rbns) claims. In this framework, reserving models are tailed to the portfolio at hand by adding building blocks for the events (e.g. settlement, payment, …) registered over the lifetime of a claim. This talk concentrates on a specific model structure with three events describing the development of a claim : the time to settlement, the number of payments and the size of each payment. We propose model selection techniques for predictive models adapted for censored data to select the relevant covariates in these models and demonstrate how the selected covariates determine the granularity of our reserving model. We illustrate the method with case studies on real life insurance data sets. These case studies provide new insights in the covariates driving the development of claims and demonstrate the accuracy and robustness of the reserving methodology over time. The talk is based on joint work with Jonas Crevecoeur, Roel Verbelen and Gerda Claeskens.

01 July 2020 9am (GMT+1, London)
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Han Lin Shang (Macquarie University, Australia) Coherent forecasting age distribution of death counts for multiple populations
Abstract (click to expand)

We consider a compositional data analysis approach to forecasting the age distribution of death counts for multiple populations. When modelling the age distribution of death counts for multiple populations, we need to consider at least two features: (1) how to incorporate any possible correlation among multiple populations to potentially improve point and interval forecast accuracy through multi-population joint modelling; (2) how to forecast multiple age distributions of death counts so that the forecasts are non-negative and have a constrained integral. To address these two issues, we introduce an extension of the compositional data analysis of Kokoszka, Miao, Petersen and Shang (2019, International Journal of Forecasting). Using the age-specific period life table death counts in Australia from 1921 to 2016 obtained from the Human Mortality Database (2020), we investigate 1-step-ahead to 20-step-ahead point and interval forecast accuracies of our models and make recommendations. The improved forecast accuracy of period life table death counts is of great interest to demographers for estimating survival probabilities and life expectancy, and to actuaries for determining temporary annuity prices for various ages and maturities.

15 July 2020 1pm (GMT+1, London)
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Rodrigo Targino (Getulio Vargas Foundation, Brazil) Avoiding zero probability events when computing Value at Risk contributions
Abstract (click to expand)

In this presentation I will discuss the process of risk allocation for a generic multivariate model when the risk measure is chosen as the Value-at-Risk (VaR). I will show how to recast the traditional Euler contributions from an expectation conditional to an event of zero probability to a ratio of conditional expectations, where both the numerator and the denominator's conditioning events have positive probability. For several different models we show empirically that the estimator using this novel representation has no perceivable bias and variance smaller than a standard estimator used in practice. Reference:

29 July 2020 4pm (GMT+1, London)
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Abstract (click to expand)


12 August 2020 9am (GMT+1, London)
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KC Cheung (The University of Hong Kong, Hong Kong) TBA
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  • Talk: 50min Q&A: 20min
  • Twice per month
  • Rotating time slots to accommodate researchers from all time zones
  • How to join

  • We will use Zoom.
  • Talk specific Zoom links will be advertised via the OWARS mailing list.
  • The Zoom link will also be available on this google doc link 15min prior to a scheduled talk.
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  • Organizers

  • Jennifer Alonso García (Department of Mathematics, Université Libre de Bruxelles)
  • Munir Hiabu (School of Mathematics and Statistics, University of Sydney)
  • Pietro Millossovich (Cass Business School, City, University of London; DEAMS, University of Trieste)
  • Silvana Pesenti (Department of Statistical Sciences, University of Toronto)
  • Andreas Tsanakas (Cass Business School, City, University of London)
  • Andrés Villegas (School of Risk and Actuarial Studies, University of New South Wales)
  • Scientific Advisory Board

    Arnold-Gaille, Séverine (Université de Lausanne)
    Avanzi, Benjamin (University of Melbourne)
    Bauer, Daniel (University of Wisconsin Madison)
    Bergel, Agnieszka (ISEG Lisboa)
    Bernard, Carole (Grenoble Ecole of Management)
    Bignozzi, Valeria (University of Milano-Bicocca)
    Bohnert, Alexander (Technical University of Munich)
    Boonen, Tim (University of Amsterdam)
    Chen, An (Ulm University)
    Chi, Yichun (Central University of Finance and Economics)
    Constantinescu Corina (Liverpool University)
    Dhaene, Jan (KU Leuven)
    Flici, Farid (Centre for Research in Applied Economics for Development, Algeria)
    Furman, Ed (York University)
    Ghossoub, Mario (University of Waterloo)
    Guillén, Montserrat (Universidad de Barcelona)
    Hillairet, Caroline (ENSAE)
    Kaakai, Sarah (Le Mans Université)
    Khosla, Shahib (Strathmore University)
    Kleinow, Thorsten (Heriot Watt University)
    Lindholm, Mathias (Stockholm University)
    Loisel, Stéphane (ISFA, Université Lyon 1)
    Londoño, Jaime (National University of Colombia)
    Ludkovski, Mike (University of California Santa Barbara)
    McKay, Anne (Université de Québec à Montreal)
    Richman, Ronald (QED Actuaries & Consultants)
    Shushi, Tomer (Ben-Gurion University of the Negev)
    Targino, Rodrigo S. (Getulio Vargas Foundation)
    Tzougas, George (London School of Economics)
    Vanduffel, Steven (Vrije Universiteit Brussel)
    Weke, Patrick (University of Nairobi)
    Wüthrich, Mario (ETH Zurich)

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