Marco Minozzo

Zen and well-being at the workplace

Purpose – The purpose of this paper is to evaluate connections between the practice of mindfulness meditation and individual and organisational well-being. Design/methodology/approach – A direct randomised study conducted on a groups of persons involved in various work activities through a programme of Zen meditation courses and a comparison between the situation of well-being found before and after taking part in the courses, assessed in the light of results obtained from a control group that had not taken part in the courses. Findings – The comparison and analysis of results showed that the group of participants taking part in the meditation training obtained a significant increase in certain indicators relating in particular to subjectively perceived well-being, as regards attention and concentration as well as in a physiological indicator measuring stress reduction. Originality/value – The study brought to the place of business a tool traditionally used almost exclusively in relation to the personal s...

A Latent Variable Approach to Modelling Multivariate Geostatistical Skew-Normal Data

In this paper we propose a spatial latent factor model to deal with multivariate geostatistical skew-normal data. In this model we assume that the unobserved latent structure, responsible for the correlation among different variables as well as for the spatial autocorrelation among different sites is Gaussian, and that the observed variables are skew-normal. For this model we provide some of its properties like its spatial autocorrelation structure and its finite dimensional marginal distributions. Estimation of the unknown parameters of the model is carried out by employing a Monte Carlo Expectation Maximization algorithm, whereas prediction at unobserved sites is performed by using closed form formulas and Markov chain Monte Carlo algorithms. Simulation studies have been performed to evaluate the soundness of the proposed procedures.

MONTE CARLO DERIVATIVE PRICING WITH PARTIAL INFORMATION IN A CLASS OF DOUBLY STOCHASTIC POISSON PROCESSES WITH MARKS

To model intraday stock price movements we propose a class of marked doubly stochastic Poisson processes, whose intensity process can be interpreted in terms of the effect of information release on market activity. Assuming a partial information setting in which market agents are restricted to observe only the price process, a filtering algorithm is applied to compute, by Monte Carlo approximation, contingent claim prices, when the dynamics of the price process is given under a martingale measure. In particular, conditions for the existence of the minimal martingale measure Q are derived, and properties of the model under Q are studied.