A New Class of Stochastic Volatility Models with Jumps: Theory and Estimation

The purpose of this paper is to propose a new class of jump diffusions which feature both stochastic volatility and random intensity jumps. Previous studies have focussed primarily on pure jump processes with constant intensity and log-normal jumps or constant jump intensity combined with a one factor stochastic volatility model. We introduce several generalizations which can better accommodate several empirical features of returns data. In their most general form we introduce a class of processes which nests jump-diffusions previously considered in empirical work and includes the affine class of random intensity models studied by Bates (1998) and Duffie, Pan and Singleton (1998) but also allows for non-affine random intensity jump components. We attain the generality of our specification through a generic Lévy process characterization of the jump component. The processes we introduce share the desirable feature with the affine class that they yield analytically tractable and explicit option pricing formula. The non-affine class of processes we study include specifications where the random intensity jump component depends on the size of the previous jump which represent an alternative to affine random intensity jump processes which feature correlation between the stochastic volatility and jump component. We also allow for and experiment with different empirical specifications of the jump size distributions. We use two types of data sets. One involves the S&P500 and the other comprises of 100 years of daily Dow Jones index. The former is a return series often used in the literature and allows us to compare our results with previous studies. The latter has the advantage to provide a long time series and enhances the possibility of estimating the jump component more precisely. The non-affine random intensity jump processes are more parsimonious than the affine class and appear to fit the data much better. Nous présentons une nouvelle classe de processus à sauts avec volatilité stochastique. Cette nouvelle classe généralise les modèles affinés proposés par Duffie, Pan et Singleton (1998). La généralité se manifeste par une représentation générique des sauts par un processus de Lévy. La classe des processus que nous présentons nous fournit également des prix d'options. Une application empirique démontre la présence de sauts dans des séries financières telles le S&P500 et le Dow Jones. De plus, les processus n'ont pas une intensité constante. Nous analysons plusieurs spécifications empiriques.Efficient method of moments, Poisson processes, jump processes, stochastic volatility models, filtering, Processus à sauts, mesures de Lévy, modèles à volatilité stochastique

Modelling Mortality Using Multiple Stochastic Latent Factors

In this paper we develop a new model for stochastic mortality that considers the possibility of both positive and negative catastrophic mortality shocks. Specifically, we assume that the mortality intensity can be described by an affine function of a finite number of latent factors whose dynamics is represented by affine-jump diffusion processes. The model is then embedded into an affine-jump framework, widely used in the term structure literature, in order to derive closed-form solutions for the survival probability. This framework and model application to the classical Gompertz-Makeham mortality law provides a theoretical foundation for the pricing and hedging of longevity-linked derivatives.Stochastic mortality intensity; Longevity risk; Affine-jump models.

Stochastic Local Intensity Loss Models with Interacting Particle Systems

International audienceIt is well-known from the work of Schönbucher (2005) that the marginal laws of a loss process can be matched by a unit increasing time inhomogeneous Markov process, whose deterministic jump intensity is called local intensity. The Stochastic Local Intensity (SLI) models such as the one proposed by Arnsdorf and Halperin (2008) allow to get a stochastic jump intensity while keeping the same marginal laws. These models involve a non-linear SDE with jumps. The first contribution of this paper is to prove the existence and uniqueness of such processes. This is made by means of an interacting particle system, whose convergence rate towards the non-linear SDE is analyzed. Second, this approach provides a powerful way to compute pathwise expectations with the SLI model: we show that the computational cost is roughly the same as a crude Monte-Carlo algorithm for standard SDEs

Pricing Longevity Bonds Using Affine-Jump Diffusion Models

Historically, actuaries have been calculating premiums and mathematical reserves using a deterministic approach, by considering a deterministic mortality intensity, which is a function of the age only, extracted from available (static) life tables and by setting a flat ("best estimate") interest rate to discount cash flows over time. Since neither the mortality intensity nor interest rates are actually deterministic, life insurance companies and pension funds are exposed to both financial and mortality (systematic and unsystematic) risks when pricing and reserving for any kind of long-term living benefits, particularly on annuities and pensions. In this paper, we assume that an appropriate description of the demographic risks requires the use of stochastic models. In particular, we assume that the random evolution of the stochastic force of mortality of an individual can be modelled by using doubly stochastic processes. The model is then embedded into the well known affine-jump framework, widely used in the term structure literature, in order to derive closed-form solutions for the survival probability. We show that stochastic mortality models provide an adequate framework for the development of longevity risk hedging tools, namely mortality-linked contracts such as longevity bonds or mortality derivatives.Stochastic mortality intensity; Longevity risk; Affine models; Projected lifetables.

Survival Measures and Interacting Intensity Model: with Applications in Guaranteed Debt Pricing

This paper studies survival measures in credit risk models. Survival measure, which was first introduced by Schonbucher [12] in the framework of defaultable LMM, has the advantage of eliminating default indicator variable directly from the expectation by absorbing it into Randon-Nikodym density process. Survival measure approach was further extended by Collin-Duresne[4] to avoid calculating a troublesome jump in IBPR reduced-form model. This paper considers survival measure in "HBPR" model, i.e. default time is characterized by Cox construction, and studies the relevant drift changes and martingale representations. This paper also takes advantage of survival measure to solve the looping default problem in interacting intensity model with stochastic intensities. Guaranteed debt is priced under this model, as an application of survival measure and interacting intensity model. Detailed numerical analysis is performed in this paper to study influence of stochastic pre-default intensities and contagion on value of a two firms' bilateral guaranteed debt portfolio.Survival Measure, Interacting Intensity Model, Measure Change, Guaranteed Debt, Mitigation and Contagion.

In the core of longevity risk: hidden dependence in stochastic mortality models and cut-offs in prices of longevity swaps

In most stochastic mortality models, either one stochastic intensity process (for example a jump-diffusion process) or a collection of independent processes is used to model the stochastic evolution of survival probabilities. We propose and calibrate a new model that takes inter-age correlations into account. The so-called stochastic logit's Deltas model is based on the study of the multivariate time series of the differences of logits of yearly mortality rates. These correlations are important and we illustrate our study on a real-life portfolio. We determine their impact on the price of a longevity swap type reinsurance contract, in which most of the financial risk is taken by a third party. The hypotheses of our model are statistically tested and various measures of risk of the present value of liabilities are found to be significantly smaller in our model than in the case of one common underlying stochastic process.Longevity risk; longevity swap; inter-age correlations; stochastic mortality; multivariate process; logit; Lee-Carter