Affine Processes for Dynamic Mortality and Actuarial Valuations

By Enrico Biffis. 2005

382 citations recorded by the source corpus.

Subfields: Demography, Finance, General Health Professions

View Paper: https://www.semanticscholar.org/paper/466c5f721bc2102baa09c2ceb4765c81c1bd2eb2

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Citations in the corpus

An Optimal Product Mix for Hedging Longevity Risk in Life Insurance Companies: The Immunization Theory Approach (2009, positive): “Other developments in two-factor models (e.g., Mileysky and Promislow, 2001; Dahl, 2004; Dahl and Möller, 2005; Miltersen and Persson, 2005; Luciano and Vigna, 2005; Biffis, 2005; Schräger, 2006) employ a continuous-time framework and thus offer an important means to understand pricing of mortality-”
Mortality Risk Modeling: Applications to Insurance Securitization (2010, positive): “Biffis (2005) captures mortality random departures around a time-varying target with a longevity compound Poisson process, but that model cannot guarantee a nonnegative force of mortality.”
Non Mean Reverting Affine Processes for Stochastic Mortality (2005, methodology): “This inconvenient is also observed by Biffis (2005). However, in practical applications and calibrations the jump size and the frequency result so small that the probability of negative values can be considered negligible.”
Delta–Gamma hedging of mortality and interest rate risk (2012, methodology): “We take the view that their market is not liquid enough to permit such calibration (see also Biffis (2005), Cairns, Blake, Dowd, and MacMinn (2006), to mention a few).”
Longevity risk management for life and variable annuities: The effectiveness of static hedging using longevity bonds and derivatives (2011, methodology): “Affine mortality models were further developed by Biffis (2005) [3], who introduces the use of jump-diffusion, and Dahl and Møller (2006) [18], who extend the model to include a mortality improvement process using the CoxIngersoll-Ross model.”
Pricing Standardized Mortality Securitizations: A Two‐Population Model with Transitory Jump Effects (2013, positive): “In a continuous-time setting, such models have been developed by Biffis (2005), Cox et al. (2006) and Deng et al. (2012), and in a discrete-time setting, such models have been proposed by Chen and Cox (2009) and Cox et al. (2010).”
Stochastic mortality under measure changes (2010, neutral): “In the case of insurance contracts, useful information is provided by financial and reinsurance markets, or by suitably adjusted Embedded Value computations (see Biffis (2005)).”
A Two-Factor Model for Stochastic Mortality With Parameter Uncertainty: Theory and Calibration (2006, positive): “Other authors have developed models in a continuous-time framework (see, for example, Milevsky and Promislow, 2001; Dahl, 2004; Dahl and Møller, 2005; Miltersen and Persson, 2005; Biffis, 2005; Schrager, 2006). For further discussion and a review of previous work, the reader is referred to Cairns, B”
Modelling stochastic mortality for dependent lives (2008, neutral): “E [ e R T t −μ(u)du|Gt ] = eα(T−t)+β(T−t)μ(t), (3) where the coefficients α(·) and β(·) satisfy generalized Riccati ODEs.”
Understanding, modelling and managing longevity risk: key issues and main challenges (2012, background): “Yet, in the periodic approach of mortality modelling (spot mortality rates), some authors (Schrager (2006) and Dahl (2004) or Biffis (2005)) introduce the so-called intensity framework to stochastic mortality modelling, which is very popular both in interest rate and credit risk modelling. By doing ”
Mortality risk via affine stochastic intensities: calibration and empirical relevance (2008, methodology): “Dahl (2004), Biffis (2005), Denuit and Devolder (2006) and Schrager (2006) in modelling the stochastic force of mortality have applied the same mathematical tools used in the credit risk literature to model the time to default.”
Mortality surface by means of continuous time cohort models (2013, methodology): “Continuous-time stochastic mortality models for single generation were considered by a number of researchers, including Milevsky and Promislow (2001), Dahl (2004), Biffis (2005), Cairns et al. (2006a), Schrager (2006) and Luciano and Vigna (2008).”
Longevity Bond Premiums: The Extreme Value Approach and Risk Cubic Pricing (2010, positive): “The recent development of discrete-time or continuous-time jump models represents a main stream of research onmortality securitizationmodeling (see Biffis, 2005; Bauer and Kramer, 2007; Cox et al., 2008; Chen et al., 2009).”
On systematic mortality risk and risk-minimization with survivor swaps (2008, neutral): “In the literature, various models for the stochastic mortality have been proposed (see, eg, Marroco & Pitacco ( 1998 ), Milevsky & Promislow ( 2001 ), Dahl ( 2004 ), Biffis ( 2005 ), Cairns et al .”
Modeling Mortality With Jumps: Applications to Mortality Securitization (2009, positive): “Biffis (2005) uses affine jump diffusions to address the risk analysis and market valuation of life insurance contracts in the continuous time framework.”