Titlebook: Mathematical and Statistical Methods for Actuarial Sciences and Finance; Marco Corazza,Claudio Pizzi Book 2014 Springer International Publ

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,Firm’s Volatility Risk Under Microstructure Noise,idea proposed in [20] we use high-frequency equity prices in order to estimate the volatility risk component of a firm within a structural credit risk modeling approach. Differently from [20] we consider a more general framework by introducing market microstructure noise as a direct effect of using

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Socially Responsible Mutual Funds: An Efficiency Comparison Among the European Countries,ean countries with three different data envelopment analysis (DEA) models. Secondly, with a series of statistical tests we compare the performance of SRI and non SRI mutual funds in the various countries, to determine if SRI mutual funds have to sacrifice something in terms of financial performance;

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Fitting Financial Returns Distributions: A Mixture Normality Approach,e series returns. In this paper we propose the use of mixtures of truncated normal distributions in modelling returns. An optimization algorithm has been developed to obtain the best fit by using the minimum distance approach. Empirical results show evidence of the capability of the method to fit re

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傀儡

Bifactorial Pricing Models: Light and Shadows in Correlation Role,[5] model. Distinguished in this field are models allowing for stochastic interest rates, as suggested for the first time by Merton [20]. Afterwards, many stochastic interest rate models to evaluate the price of hybrid securities have been proposed in literature. Most of these are equilibrium pricin

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Particle Swarm Optimization for Preference Disaggregation in Multicriteria Credit Scoring Problems,determine the values of the parameters that characterize the preference model of the decision maker, we adopt Particle Swarm Optimization, which is a biologically-inspired heuristics based on swarm intelligence. We test the ability of PSO to find the optimal values of the parameters on a real data s

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Time Series Clustering on Lower Tail Dependence for Portfolio Selection,l returns in groups being homogeneous in the sense that their joint bivariate distributions exhibit high association in the lower tail. The dissimilarity measure used for such clustering is based on tail dependence coefficients estimated using copula functions. We carry out the clustering using an a

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