Neural Networks for quantile claim amount estimation: aq auntile regression approach

In this paper, we discuss the estimation of conditional quantiles of aggregate claim amounts for non-life insurance embedding the problem in a quantile regression framework using the neural network approach. As the first step, we consider the quantile regression neural networks (QRNN) procedure to compute quantiles for the insurance ratemaking framework. As the second step, we propose a new quantile regression combined actuarial neural network (Quantile-CANN) combining the traditional quantile regression approach with a QRNN. In both cases, we adopt a two-partmodel scheme where we fit a logistic regression to estimate the probability of positive claims and the QRNN model or the Quantile-CANN for the positive outcomes. Through a case study based on a health insurance dataset, we highlight the overall better performances of the proposed models with respect to the classical quantile regression one. We then use the estimated quantiles to calculate a loaded premium following the quantile premium principle, showing that the proposed models provide a better risk differentiation

Cross‑Country assessment of systemic risk in the European Stock Market: evidence from a CoVaR analysis

This work is intended to assess the contribution to systemic risk of major companies in the European stock market on a geographical basis. We use the EuroStoxx 50 Index as a proxy for the financial system and we rely on the CoVaR and Delta-CoVaR risk measures to estimate the contribution of each European country belonging to the index to systemic risk. We also conduct the significance and dominance test to evaluate whether the systemic relevance of considered countries is statistically significant and to determine which nation exerts the greatest influence on the spreading of negative spillover effects on the entire economy. Our empirical results show that, for the period ranging from 2008 to 2017, all countries contribute significantly to systemic risk, especially in times of crisis and high volatility in the markets. Moreover, it emerges that France is the systemically riskiest country, followed by Germany, Italy, Spain and Netherlands

Quantile Regression Neural Network for Quantile Claim Amount Estimation

Quantile Regression to estimate the conditional quantile of the claim amount for car insurance policies has already been by Heras et al. (2018) and others. In this paper, we explore two alternative approaches, the first involves Quantile Regression Neural Networks (QRNN), while the second is an extension of the Combined Actuarial Neural Network (CANN) by W uthrich et al. (2019) where we nest the Quantile Regression model into the structure of a neural network (Quantile-CANN). This technique captures additional information respect to the simple Quantile Regression, representing non linear relationship between the covariates and the dependent variable, and involving possible interactions between predictors. To compute the conditional quantile of the total claim amount for a generic car insurance policy, we adopt the two part model approach discussed by Heras et al. (2018). In a first step, we fit a logistic regression to estimate the probability of positive claim. Then, conditional on positive outcome, we use QRNN and Quantile-CANN to estimate the conditional quantile of the total claim amount. The simulation results show that QRNN and Quantile-CANN exhibit an overall better performance in terms of quantile loss function with respect to the classical Quantile Regression

Characterization of a Novel Yb:YLF Laser for Optical Frequency Metrology

none8A. Pesatori; M. Norgia; C. Svelto; N. Coluccelli; G. Galzerano; A. Di Lieto; M. Tonelli; P. LaportaPesatori, Alessandro; Norgia, Michele; Svelto, Cesare; Coluccelli, Nicola; Galzerano, Gianluca; A., Di Lieto; M., Tonelli; Laporta, Paol

Preliminary study on polycrystalline diamond films suitable for radiation detection

The microwave plasma enhanced chemical vapor deposition technique has been employed to grow polycrystalline diamond films on p-doped Si (100) substrates starting from highly diluted (1% CH 4 in H 2) gas mixtures. Coplanar interdigitated Cr/Au contacts have been thermally evaporated on two samples about 8 μm thick having different grain size. Dark current-voltage (I-V) measurements and impedance characterization have been found to be dependent on the grain size and on the quality of the examined samples

Characterization of polycrystalline diamond films grown by Microwave Plasma Enhanced Chemical Vapor Deposition (MWPECVD) for UV radiation detection

Photodetectors based on polycrystalline diamond (PCD) films are of great interest to many researchers for the attractive electronic, mechanical, optical and thermal properties. PCD films are grown using the Microwave Plasma Enhanced Chemical Vapor Deposition (MWPECVD) method. First, we characterized films by means of structural and morphological analysis (Raman spectroscopy and scanning electron microscopy), then we evaporated a pattern of coplanar interdigitated Cr/Au contacts with an inter-electrode spacing of 100 mu m in order to perform the electrical characterization. We carried out measurements of dark current and impedance spectroscopy to investigate the film properties and conduction mechanisms of films and the effects of post-growth treatments. Finally we developed a charge sensing pre-amplifier to read-out the signal produced by UV photons in the detector. (C) 2009 Elsevier B.V. All rights reserved