Univariate and multivariate Pareto models
نویسنده
چکیده
The Pareto distribution has long been recognized as a suitable model for many non-negative socio-economic variables. Univariate and multivariate variations abound. Some unification is possible by representing the Pareto variables in terms of independent gamma distributed components. Further unification is sometimes possible since some of the frequently used multivariate Pareto models share the same copula. In some cases, inference strategies can be developed to take advantage of the stochastic representations in terms of gamma components.
منابع مشابه
Evaluation of Univariate, Multivariate and Combined Time Series Model to Prediction and Estimation the Mean Annual Sediment (Case Study: Sistan River)
Erosion, sediment transport and sediment estimate phenomenon with their damage in rivers is a one of the most importance point in river engineering. Correctly modeling and prediction of this parameter with involving the river flow discharge can be most useful in life of hydraulic structures and drainage networks. In fact, using the multivariate models and involving the effective other parameter...
متن کاملInformation and Covariance Matrices for Multivariate Pareto (IV), Burr, and Related Distributions
Main result of this paper is to derive the exact analytical expressions of information and covariance matrix for multivariate Pareto, Burr and related distributions. These distributions arise as tractable parametric models in reliability, actuarial science, economics, finance and telecommunications. We showed that all the calculations can be obtained from one main moment multidimensional integr...
متن کاملAsymmetric Univariate and Bivariate Laplace and Generalized Laplace Distributions
Alternative specifications of univariate asymmetric Laplace models are described and investigated. A more general mixture model is then introduced. Bivariate extensions of these models are discussed in some detail, with particular emphasis on associated parameter estimation strategies. Multivariate versions of the models are briefly introduced.
متن کاملMeasure of financial risk using conditional extreme value copulas with EVT margins
In this paper we propose a method to estimate the value-at-risk (VaR) of a portfolio based on a combination of time series, extreme value theory and copula fitting. Given multivariate financial data, we use a univariate ARMA-GARCH model for each return series. We then fit a generalized Pareto distribution to the tails of the residuals to model the distributions of marginal residuals, followed b...
متن کاملTime series forecasting of Bitcoin price based on ARIMA and machine learning approaches
Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine...
متن کامل