Factor copulas through a vine structure

Copula functions have been widely used in actuarial science, nance andeconometrics. Though multivariate copulas allow for a flexible specication of the dependence structure of economic variables, they are not particularly tempting in high dimensional contexts. A factor model which involves copula functions has already proved to be a powerful tool in credit risk applications.We exploit a recent approach to obtain a factor copula model based on a vine structure, which enables to model the dependence and conditional dependence of variables through a representation of a cascade of arbitrary bivariate copulas. The contribution of this paper consists into applying the vine copula model in order to derive a non linear three factor model. In particular, we draw the three factor model of Fama and French (1992). According to the Inference for Margins (IFM) method, we have computed, separately, the margins and the copula parameters via maximum likelihood estimation. Finally, tail dependence measures are given for the implied estimated copula

Contribution to the ecology of the Italian hare (Lepus corsicanus)

the italian hare (Lepus corsicanus) is endemic to Central-Southern Italy and Sicily, classified as vulnerable due to habitat alterations, low density and fragmented populations and ecological competition with the sympatric european hare (Lepus europaeus). Despite this status, only few and local studies have explored its ecological features. We provided some key traits of the ecological niche of the italian hare as well as its potential distribution in the italian peninsula. All data derived from genetically validated presences. We generated a habitat suitability model using maximum entropy distribution model for the italian hare and its main competitor, the european hare. the dietary habits were obtained for the italian hare with DnA metabarcoding and High-throughput Sequencing on faecal pellets. The most relevant environmental variables affecting the potential distribution of the italian hare are shared with the european hare, suggesting a potential competition. the variation in the observed altitudinal distribution is statistically significant between the two species.The diet of the Italian hare all year around includes 344 plant taxa accounted by 62 families. The Fagaceae, Fabaceae, Poaceae, Rosaceae and Solanaceae (counts > 20,000) represented the 90.22% of the total diet. Fabaceae (60.70%) and Fagaceae (67.47%) were the most abundant plant items occurring in the Spring/Summer and Autumn/Winter diets, respectively. the Spring/Summer diet showed richness (N = 266) and diversity index values (Shannon: 2.329, Evenness: 0.03858, Equitability: 0.4169) higher than the Autumn/Winter diet (N = 199, Shannon: 1.818, Evenness: 0.03096, Equitability: 0.3435). Our contribution adds important information to broaden the knowledge on the environmental (spatial and trophic) requirements of the Italian hare, representing effective support for fitting management actions in conservation planning

Factor Copula through a vine structure

Copula functions have been widely used in actuarial science, finance and econometrics. Though multivariate copulas allow for a flexible specification of the dependence structure of economic variables, they are not particularly tempting in high dimensional contexts. A factor model which involves copula functions has already proved to be a powerful tool in credit risk applications. We exploit a recent approach to obtain a factor copula model based on a vine structure, which enables to model the dependence and conditional dependence of variables through a representation of a cascade of arbitrary bivariate copulas. The contribution of this paper consists into applying the vine copula model in order to derive a non linear three-factor model. In particular, we draw the three-factor model of Fama and French (1992). According to the Inference for Margins (IFM) method, we have computed, separately, the margins and the copula parameters via maximum likelihood estimation. Finally, tail dependence measures are given for the implied estimated copul

Copula quantile dependence for the analysis of multiple time series

In financial researches and among risk management practitioners the analysis of multiple time-series is often conducted in a non-linear context. In addition, capturing the quantile conditional dependence structure could prove of interest in order to measure financial contagion risk. We propose a 3-stage estimation copula-based method applied to non-linear quantile dependence analysis of timeseries vectors. This method aims to analyze the serial and cross-section dependence of time-series given specified quantiles, reducing the computational complexity. To the best of our knowledge, this is the first approach that combines the conditional quantile dependence analysis of multiple time-series with non-linear modeling by means of copula functions. Finally, we examine the conditional quantile behavior of real financial time-series with a non-linear copula quantile VAR model

Multivariate tail dependence coefficients for Archimedean Copulae

We analyze the multivariate upper and lower tail dependence coefficients,obtained extending the existing definitions in the bivariate case. We provide their expressions for a popular class of copula functions, the Archimedean one. Finally, we apply the formulae to some well known copula functions used in many financial analyses

Modeling The Conditional Dependence between Stock Market Returns with a Copula-GARCH Approach

Recenti ricerche si sono indirizzate verso lo studio delle dinamiche che caratterizzano i legami tra i mercati azionari. Nell’ambito di tali analisi, emerge che la correlazione presentata dai rendimenti dei maggiori indici di mercato azionario non è costante nel tempo e risulta condizionata alle informazioni passate. Il presente lavoro indaga tali dinamiche mediante un approccio Copula-GARCH. I vantaggi dell’adozione di tale approccio risiedono nell’utilizzo di strumenti flessibili di analisi della distribuzione congiunta dei rendimenti (funzioni copula) caratterizzati da un parametro time-varying condizionatamente al passato, con una separata specificazione della forma delle marginali mediante l’applicazione di modelli T-GARCH

A Copula-VAR approach for the analysis of serial dependence in stock returns

The description of thedynamic behavior of multiple time series represents an important point of departure to obtain accurate forecasts both in economic and financial analysis. The aim of this work isthe comparison of methods whichexploit serial dependence in stock returns to improve out-of-sample portfolio performance.For multivariate time series, the popular and easy-to-use Vector AutoRegressive (VAR) model is compared to some copula models which allow for a non-linear and/or asymmetric dependencestructure among the variables.After deriving theVAR-based and copula-based conditional expected returns, we construct different portfolios and compare them in terms of Sharpe ratio