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| Author | : Johannes Schupp |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
| Author | : Severine Arnold (-Gaille) |
| Publisher | : |
| Total Pages | : 16 |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
Longevity risk is amongst the most important factors to consider for pricing and risk management of longevity products. Past improvements in mortality over many years, and the uncertainty of these improvements, have attracted the attention of experts, both practitioners and academics. Since aggregate mortality rates reflect underlying trends in causes of death, insurers and demographers are increasingly considering cause-of-death data to better understand risks in their mortality assumptions. The relative importance of causes of death has changed over many years. As one cause reduces, others increase or decrease. The dependence between mortality for different causes of death is important when projecting future mortality. However, for scenario analysis based on causes of death, the assumption usually made is that causes of death are independent. Recent models, in the form of Vector Error Correction Models (VECM), have been developed for multivariate dynamic systems and capture time dependency with common stochastic trends. These models include long-run stationary relations between the variables, and thus allow a better understanding of the nature of this dependence. This paper applies VECM to cause-of-death mortality rates in order to assess the dependence between these competing risks. We analyze the five main causes of death in Switzerland. Our analysis confirms the existence of a long-run stationary relationship between these five causes. This estimated relationship is then used to forecast mortality rates, which are shown to be an improvement over forecasts from more traditional ARIMA processes, that do not allow for cause-of-death dependencies.
| Author | : Michael Sherris |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2011 |
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| ISBN | : |
Modeling mortality and longevity risk presents challenges because of the impact of improvements at different ages and the existence of common trends. Modeling cause of death mortality rates is even more challenging since trends and age effects are more diverse. Despite this, successfully modeling these mortality rates is critical to assessing risk for insurers issuing longevity risk products including life annuities. Longevity trends are often forecasted using a Lee-Carter model. A common stochastic trend determines age-based improvements. Other approaches fit an age-based parametric model with a time series or vector autoregression for the parameters. Vector Error Correction Models (VECM), developed recently in econometrics, include common stochastic long-run trends. This paper uses a stochastic parameter VECM form of the Heligman-Pollard model for mortality rates, estimated using data for circulatory disease deaths in the United States over a period of 50 years. The model is then compared with a version of the Lee-Carter model and a stochastic parameter ARIMA Heligman-Pollard model. The VECM approach proves to be an improvement over the Lee-Carter and ARIMA models as it includes common stochastic long-run trends.
| Author | : Ermanno Pitacco |
| Publisher | : OUP Oxford |
| Total Pages | : 416 |
| Release | : 2009-01-29 |
| Genre | : Business & Economics |
| ISBN | : 0191609420 |
Mortality improvements, uncertainty in future mortality trends and the relevant impact on life annuities and pension plans constitute important topics in the field of actuarial mathematics and life insurance techniques. In particular, actuarial calculations concerning pensions, life annuities and other living benefits (provided, for example, by long-term care insurance products and whole life sickness covers) are based on survival probabilities which necessarily extend over a long time horizon. In order to avoid underestimation of the related liabilities, the insurance company (or the pension plan) must adopt an appropriate forecast of future mortality. Great attention is currently being devoted to the management of life annuity portfolios, both from a theoretical and a practical point of view, because of the growing importance of annuity benefits paid by private pension schemes. In particular, the progressive shift from defined benefit to defined contribution pension schemes has increased the interest in life annuities with a guaranteed annual amount. This book provides a comprehensive and detailed description of methods for projecting mortality, and an extensive introduction to some important issues concerning longevity risk in the area of life annuities and pension benefits. It relies on research work carried out by the authors, as well as on a wide teaching experience and in CPD (Continuing Professional Development) initiatives. The following topics are dealt with: life annuities in the framework of post-retirement income strategies; the basic mortality model; recent mortality trends that have been experienced; general features of projection models; discussion of stochastic projection models, with numerical illustrations; measuring and managing longevity risk.
| Author | : Michael Sherris |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
Longevity risk and the modeling of trends and volatility for mortality improvement has attracted increased attention driven by ageing populations around the world and the expected financial implications. The original Lee-Carter model that was used for longevity risk assessment included a single improvement factor with differential impacts by age. Financial models that allow for risk pricing and risk management have attracted increasing attention along with multiple factor models. This paper investigates trends, including common trends through co-integration, and the factors driving the volatility of mortality using principal components analysis for a number of developed countries including Australia, England, Japan, Norway and USA. The results demonstrate the need for multiple factors for modeling mortality rates across all these countries. The basic structure of the Lee-Carter model can not adequately model the random variation and the full risk structure of mortality changes. Trends by country are found to be stochastic. Common trends and co-integrating relationships are found across ages highlighting the benefits from modeling mortality rates as a system in a Vector-Autoregressive (VAR) model and capturing long run equilibrium relationships in a Vector Error-Correction Model (VECM) framework.
| Author | : Yinglu Deng |
| Publisher | : |
| Total Pages | : 216 |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
This dissertation studies the adverse financial implications of "longevity risk" and "mortality risk", which have attracted the growing attention of insurance companies, annuity providers, pension funds, public policy decision-makers, and investment banks. Securitization of longevity/mortality risk provides insurers and pension funds an effective, low-cost approach to transferring the longevity/mortality risk from their balance sheets to capital markets. The modeling and forecasting of the mortality rate is the key point in pricing mortality-linked securities that facilitates the emergence of liquid markets. First, this dissertation introduces the discrete models proposed in previous literature. The models include: the Lee-Carter Model, the Renshaw Haberman Model, The Currie Model, the Cairns-Blake-Dowd (CBD) Model, the Cox-Lin-Wang (CLW) Model and the Chen-Cox Model. The different models have captured different features of the historical mortality time series and each one has their own advantages. Second, this dissertation introduces a stochastic diffusion model with a double exponential jump diffusion (DEJD) process for mortality time-series and is the first to capture both asymmetric jump features and cohort effect as the underlying reasons for the mortality trends. The DEJD model has the advantage of easy calibration and mathematical tractability. The form of the DEJD model is neat, concise and practical. The DEJD model fits the actual data better than previous stochastic models with or without jumps. To apply the model, the implied risk premium is calculated based on the Swiss Re mortality bond price. The DEJD model is the first to provide a closed-form solution to price the q-forward, which is the standard financial derivative product contingent on the LifeMetrics index for hedging longevity or mortality risk. Finally, the DEJD model is applied in modeling and pricing of life settlement products. A life settlement is a financial transaction in which the owner of a life insurance policy sells an unneeded policy to a third party for more than its cash value and less than its face value. The value of the life settlement product is the expected discounted value of the benefit discounted from the time of death. Since the discount function is convex, it follows by Jensen's Inequality that the expected value of the function of the discounted benefit till random time of death is always greater than the benefit discounted by the expected time of death. So, the pricing method based on only the life expectancy has the negative bias for pricing the life settlement products. I apply the DEJD mortality model using the Whole Life Time Distribution Dynamic Pricing (WLTDDP) method. The WLTDDP method generates a complete life table with the whole distribution of life times instead of using only the expected life time (life expectancy). When a life settlement underwriter's gives an expected life time for the insured, information theory can be used to adjust the DEJD mortality table to obtain a distribution that is consistent with the underwriter projected life expectancy that is as close as possible to the DEJD mortality model. The WLTDDP method, incorporating the underwriter information, provides a more accurate projection and evaluation for the life settlement products. Another advantage of WLTDDP is that it incorporates the effect of dynamic longevity risk changes by using an original life table generated from the DEJD mortality model table.
| Author | : Sixian Tang |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023 |
| Genre | : |
| ISBN | : |
Over the past several decades, human life expectancy has been increasing rather consistently, driving concerns about longevity risk for pension plans and annuity providers. While extrapolative methods assume continued mortality declines, recent years have seen stagnation or even declines in life expectancy in some developed countries due to decaying transient mortality reductions. In response, a heat wave mortality model was developed to divide mortality improvements into a background level captured by classical mortality models and temporary improvements described by a heat wave component. We propose two multi-population heat wave models to investigate the impact of stalling mortality improvements on longevity risk management. These models provide a parsimonious way to depict the latest mortality developments and produce forecasts that are more in line with recent observations. A hedging exercise using country-level mortality data reveals that our proposed models suggest a tolerance for higher risk premium embedded in longevity swaps before the hedge becomes financially infeasible. However, hedge effectiveness can decrease by 15% when the reference and book populations involved in a longevity swap do not share the same path of transient mortality rates. Overall, our study emphasises the importance of accounting for stalling mortality improvements when managing longevity risk and provides a practical framework for doing so using multi-population heat wave models.
| Author | : A. Hunt |
| Publisher | : |
| Total Pages | : |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
| Author | : Andrew J. G. Cairns |
| Publisher | : Princeton University Press |
| Total Pages | : 289 |
| Release | : 2018-06-05 |
| Genre | : Business & Economics |
| ISBN | : 0691187428 |
The field of financial mathematics has developed tremendously over the past thirty years, and the underlying models that have taken shape in interest rate markets and bond markets, being much richer in structure than equity-derivative models, are particularly fascinating and complex. This book introduces the tools required for the arbitrage-free modelling of the dynamics of these markets. Andrew Cairns addresses not only seminal works but also modern developments. Refreshingly broad in scope, covering numerical methods, credit risk, and descriptive models, and with an approachable sequence of opening chapters, Interest Rate Models will make readers--be they graduate students, academics, or practitioners--confident enough to develop their own interest rate models or to price nonstandard derivatives using existing models. The mathematical chapters begin with the simple binomial model that introduces many core ideas. But the main chapters work their way systematically through all of the main developments in continuous-time interest rate modelling. The book describes fully the broad range of approaches to interest rate modelling: short-rate models, no-arbitrage models, the Heath-Jarrow-Morton framework, multifactor models, forward measures, positive-interest models, and market models. Later chapters cover some related topics, including numerical methods, credit risk, and model calibration. Significantly, the book develops the martingale approach to bond pricing in detail, concentrating on risk-neutral pricing, before later exploring recent advances in interest rate modelling where different pricing measures are important.
| Author | : Severine Arnold (-Gaille) |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
Modelling mortality and longevity risk is critical to assessing risk for insurers issuing longevity risk products. It has challenged practitioners and academics alike because of first the existence of common stochastic trends and second the unpredictability of an eventual mortality improvement in some age groups. When considering cause-of-death mortality rates, both aforementioned trends are additionally affected by the cause of death. Longevity trends are usually forecasted using a Lee-Carter model with a single stochastic time series for period improvements, or using an age-based parametric model with univariate time series for the parameters. We assess a multivariate time series model for the parameters of the Heligman-Pollard function, through Vector Error Correction Models which include the common stochastic long-run trends. The model is applied to circulatory disease deaths in U.S. over a 50-year period and is shown to be an improvement over both the Lee-Carter model and the stochastic parameter ARIMA Heligman-Pollard model.
