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

Engaged Learning Internship at FEC

Describing my experience interning at the Family Empowerment Center and my learning project that I created for the students and the PSYC390 internship course

The Importance of Economic Variables on London Real Estate Market: A Random Forest Approach

This paper follows the recent literature on real estate price prediction and proposes to take advantage of machine learning techniques to better explain which variables are more important in describing the real estate market evolution. We apply the random forest algorithm on London real estate data and analyze the local variables that influence the interaction between housing demand, supply and price. The variables choice is based on an urban point of view, where the main force driving the market is the interaction between local factors like population growth, net migration, new buildings and net supply

Italian deposits time series forecasting via functional data analysis

none1Piscopo G.Piscopo, Gabriell

The ‘Dark Power’ of Instagram: Prospects and Threats for Tourism Organisations

The key to understand and analyse the dynamic relationship between territories, organisations and tourists is currently undergoing significant changes. Due to both their endogenous and exogenous factors, territories should be read as complex adaptive systems (CAS), i.e. systems structurally composed of different sub-systems which interact with each other and help to improve the central systems thanks to the interconnections established among themselves. Thus, in this scenario, territories evolve into potential tourism destinations if these changes make them particularly attractive and capable of setting a profitable dialogue with new emerging tourists profiles. As a matter of fact, contexts and in which these actors communicate between each other nowadays are unconventional and ‘bottom-up oriented’: social media represent the main source for territories and organisation of tourist experience to receive feedback. Nevertheless, the established relationship is not always qualitatively relevant nor reliable. Therefore, by utilising both a data and content analysis approach, the authors will analyse users’ reactions to Instagram posts by destinations to evaluate their engagement process and their emerging profile

Longevity risk management through Machine Learning: state of the art

Longevity risk management is an area of the life insurance business where the use of Artificial Intelligence is still underdeveloped. The paper retraces the main results of the recent actuarial literature on the topic to draw attention to the potential of Machine Learning in predicting mortality and consequently improving the longevity risk quantification and management, with practical implication on the pricing of life products with long-term duration and lifelong guaranteed options embedded in pension contracts or health insurance products. The application of AI methodologies to mortality forecasts improves both fitting and forecasting of the models traditionally used. In particular, the paper presents the Classification and the Regression Tree framework and the Neural Network algorithm applied to mortality data. The literature results are discussed, focusing on the forecasting performance of the Machine Learning techniques concerning the classical model. Finally, a reflection on both the great potentials of using Machine Learning in longevity management and its drawbacks is offered

Lee–Carter model: assessing the potential to capture gender-related mortality dynamics

We investigate the ability of the Lee–Carter model to effectively estimate the gender gap ratio (GGR), the ratio between themale death rates over the female ones, by using a Cox–Ingersoll–Ross (CIR) process to provide a stochastic representation of the fitting errors. The novelty consists in the fact that we use the parameters characterizing the CIR process itself (long-term mean and volatility), in their intrinsic meanings, as quantitative measures of the long-term fitting attitude of the Lee–Carter model and synthetic indicators of the overall risk of this model. The analysis encompasses 25 European countries, to provide evidence-based indications about the goodness of fit of the Lee–Carter model in describing the GGR evolution.We highlight some stylized facts, namely systematic evidence about the fitting bias and the risk of the model across ages and countries. Furthermore, we perform a functional cluster analysis, allowing to capture similarities in the fitting performance of the Lee–Carter model among countries