Maximum Market Price of Longevity Risk under Solvency Regimes: The Case of Solvency II.

Longevity risk constitutes an important risk factor for life insurance companies, and it can be managed through longevity-linked securities. The market of longevity-linked securities is at present far from being complete and does not allow finding a unique pricing measure. We propose a method to estimate the maximum market price of longevity risk depending on the risk margin implicit within the calculation of the technical provisions as defined by Solvency II. The maximum price of longevity risk is determined for a survivor forward (S-forward), an agreement between two counterparties to exchange at maturity a fixed survival-dependent payment for a payment depending on the realized survival of a given cohort of individuals. The maximum prices determined for the S-forwards can be used to price other longevity-linked securities, such as q-forwards. The Cairns–Blake–Dowd model is used to represent the evolution of mortality over time that combined with the information on the risk margin, enables us to calculate upper limits for the risk-adjusted survival probabilities, the market price of longevity risk and the S-forward prices. Numerical results can be extended for the pricing of other longevity-linked securities

Maximum Market Price of Longevity Risk under Solvency Regimes: The Case of Solvency II.

Longevity risk constitutes an important risk factor for life insurance companies, and it can be managed through longevity-linked securities. The market of longevity-linked securities is at present far from being complete and does not allow finding a unique pricing measure. We propose a method to estimate the maximum market price of longevity risk depending on the risk margin implicit within the calculation of the technical provisions as defined by Solvency II. The maximum price of longevity risk is determined for a survivor forward (S-forward), an agreement between two counterparties to exchange at maturity a fixed survival-dependent payment for a payment depending on the realized survival of a given cohort of individuals. The maximum prices determined for the S-forwards can be used to price other longevity-linked securities, such as q-forwards. The Cairns–Blake–Dowd model is used to represent the evolution of mortality over time that combined with the information on the risk margin, enables us to calculate upper limits for the risk-adjusted survival probabilities, the market price of longevity risk and the S-forward prices. Numerical results can be extended for the pricing of other longevity-linked securities

The mortality of the Italian population: Smoothing techniques on the Lee--Carter model

Several approaches have been developed for forecasting mortality using the stochastic model. In particular, the Lee-Carter model has become widely used and there have been various extensions and modifications proposed to attain a broader interpretation and to capture the main features of the dynamics of the mortality intensity. Hyndman-Ullah show a particular version of the Lee-Carter methodology, the so-called Functional Demographic Model, which is one of the most accurate approaches as regards some mortality data, particularly for longer forecast horizons where the benefit of a damped trend forecast is greater. The paper objective is properly to single out the most suitable model between the basic Lee-Carter and the Functional Demographic Model to the Italian mortality data. A comparative assessment is made and the empirical results are presented using a range of graphical analyses.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS394 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Application of Machine Learning to Mortality Modeling and Forecasting

Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by extrapolating one or more latent factors. The abundance of proposed models shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine learning estimator is then forecasted according to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard stochastic models. Out-of sample forecasts are provided to verify the model accuracy

Does Fiscal Discipline towards Sub-national Governments Affect Citizens’ Well-being? Evidence on Health

This paper aims at assessing the impact of fiscal discipline towards sub-national governments on citizens’ well-being. We model fiscal discipline by considering the expectations of deficit bailouts by Central Government, and focus on a particular dimension of well-being, namely health outcomes at the regional level. We study then how bailout expectations affect the expenditure for health care policies carried out by Regional Governments: in the presence of opportunistic behaviours by local governments – induced by soft budget constraints – bailout expectations should affect only spending inefficiency, and should not have any real effects on citizens’ health. To investigate this issue, we model the efficient use of public resources for health care delivery as an input requirement frontier, and assess the effects of bailout expectations on both the structural component of health spending and its deviations from the best practice. The evidence from a sample of 15 Italian Regions observed from 1993 to 2006 highlights that bailout expectations do not significantly influence the position of the frontier, thus do not affect citizens’ health. However, they appear to exert a remarkable impact on excess spending.Intergovernmental relationships, Soft budget constraint, Bailout expectations, Health care policy, Spending efficiency

The job creation effect of R&D expenditures

In this study we use a unique database covering 25 manufacturing and service sectors for 16 European countries over the period 1996-2005, for a total of 2,295 observations, and apply GMM-SYS panel estimations of a demand-for-labour equation augmented with technology. We find that R&D expenditures have a job-creating effect, in accordance with the previous theoretical and empirical literature discussed in the paper. Interestingly enough, the labour-friendly nature of R&D emerges in both the flow and the stock specifications. These findings provide further justification for the European Lisbon-Barcelona targets.Technological change, corporate R&D, employment, product innovation, GMMSYS