1,721,013 research outputs found
Factor Copula through a vine structure
No full textCopula 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
Tail diversification strategy. An application to MSCI World Sector Indeces
No full textIn the theory of asset allocation and in the practice of portfolio management the diversification strategy is generally thought of in terms of market capitalization and investment style, yet sector diversification is equally important. As demonstrated empirically in recent years, pursuing a growth investment style via internet stocks leads to substantially different portfolios-and results- than pursuing growth via healthcare stocks. In addition, the severe recent crisis induces to consider extreme values dependence, preferring in the selecting procedure assets with low dependence between negative extreme returns. In this point of view, this paper provides a way to compose a portfolio choosing MSCI (Morgan Stanley Capital International) sector weighted indeces, designed to measure the equity performance of Industry, by the analysis of multivariate lower tail dependence. The selection procedure is based on modelling marginal behaviour of each stock index returns via a GARCH type model and, after, using a copula function to join the margins into a multivariate distribution. In this paper a particular familiy of copula functions is proposed to model the multivariate distribution of MSCI stock index returns, a Family of Multivariate Biparametric (MB) Copulae, a multivariate extension of BB family (Joe, 1997). In particular, the MB1 and MB7 copula functions are selected, because they allow to estimate both tail dependence in a asymmetric way. Due to computational complexity of high-dimensional copulae, the selection of stock index returns in portfolio is sequentially executed. In the first, two assets are chosen, privileging those with the minimum lower tail dependence coefficient measured on the joint distribution. Then, the selection of a third asset in portfolio, given the previous couple, follows the same path, choosing the triple with the minimum trivariate lower tail dependence and so on to add the remaining assets. At each step, the joint multivariate distribution is obtained by one of the selected copula functions with the best performance to the data, which is measured by the application of Kolmogorov test
Factor Copulas through a vine structure
No full textCopula 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, in
high dimensional contexts they are not particularly attractive. A factor model which involves the copula
functions has proved to be a powerful tool in credit risk applications, e.g. for the pricing of CDO, due to
its capability in describing, in a both flexible and tractable way, the joint default for a large number of
names within a semi-analytical framework. Many of the factor copula models proposed in theoretical and
empirical application are embedded into a stochastic correlations framework or in analysing simulation
and pricing, not considering the estimation of copula parameters. We exploit a new approach to obtain
a factor copula model based on a vine structure for the asset returns, which enables to modeling the
dependence and conditional dependence of variables through a representation of a cascade of arbitrary
bivariate copulas. According to the Inference for Margins (IFM) method, we have computed, separately,
the margins and the copula parameters via maximum likelihood estimation. In the first, GARCH models
for margins are applied and then, given the conditional independence of the transformed standardized
residuals with respect to common factors, vine copulas are estimated, providing the parameters of an
“implied copula” for the asset returns. Finally, a tail dependence measure is given for the implied copula
estimated
Lo sviluppo turistico dei comuni del Mezzogiorno: l'interpretazione dei risultati dell'analisi statistica
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Parte I, capitolo II: Localizzazione dei contesti turistici museali ed archelologici nel Mezzogiorno
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