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De copulis non est disputandum Copulae : an overview
Härdle, Wolfgang; Okhrin, Ostap - 2009
Normal distribution of the residuals is the traditional assumption in the classical multivariate time series models. Nevertheless it is not very often consistent with the real data. Copulae allows for an extension of the classical time series models to nonelliptically distributed residuals. In...
Persistent link: https://www.econbiz.de/10003850706
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Time varying hierarchical archimedean copulae
Härdle, Wolfgang; Okhrin, Ostap; Okhrin, Yarema - 2010
There is increasing demand for models of time-varying and non-Gaussian dependencies for mul- tivariate time-series. Available models suffer from the curse of dimensionality or restrictive assumptions on the parameters and the distribution. A promising class of models are the hierarchical...
Persistent link: https://www.econbiz.de/10003953027
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Fitting high-dimensional copulae to data
Okhrin, Ostap - 2010
This paper make an overview of the copula theory from a practical side. We consider different methods of copula estimation and different Goodness-of-Fit tests for model selection. In the GoF section we apply Kolmogorov-Smirnov and Cramer-von-Mises type tests and calculate power of these tests...
Persistent link: https://www.econbiz.de/10003953039
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Nonparametric estimates for conditional quantiles of time series
Franke, Jürgen E.; Mwita, Peter; Wang, Weining - 2014
We consider the problem of estimating the conditional quantile of a time series fYtg at time t given covariates Xt, where Xt can ei- ther exogenous variables or lagged variables of Yt . The conditional quantile is estimated by inverting a kernel estimate of the conditional distribution function,...
Persistent link: https://www.econbiz.de/10010238365
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Realized copula
Fengler, Matthias R.; Okhrin, Ostap - 2012
We introduce the notion of realized copula. Based on assumptions of the marginal distributions of daily stock returns and a copula family, realized copula is defined as the copula structure materialized in realized covariance estimated from within-day high-frequency data. Copula parameters are...
Persistent link: https://www.econbiz.de/10009537332
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The One-Trading-Day-Ahead Forecast Errors of Intra-Day Realized Volatility
Degiannakis, Stavros Antonios - 2018
Two volatility forecasting evaluation measures are considered; the squared one-day ahead forecast error and its … standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility …. The exploration of forecast errors is paving the way for rethinking the evaluation of ultra-high frequency realized …
Persistent link: https://www.econbiz.de/10012910114
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Intra-Day Realized Volatility for European and USA Stock Indices
Degiannakis, Stavros Antonios - 2019
The paper constructs measures of intra-day realized volatility for 17 European and USA stock indices. We utilize a model-free de-noising method by assembling the realized volatility in sampling frequency selected according to the volatility signature plot which minimizes the micro-structure...
Persistent link: https://www.econbiz.de/10012897936
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De Copulis Non Est Disputandum - Copulae : An Overview
Härdle, Wolfgang K. - 2017
Normal distribution of the residuals is the traditional assumption in the classical multivariate time series models. Nevertheless it is not very often consistent with the real data. Copulae allows for an extension of the classical time series models to nonelliptically distributed residuals. In...
Persistent link: https://www.econbiz.de/10012966281
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Time Varying Hierarchical Archimedean Copulae
Härdle, Wolfgang K. - 2017
There is increasing demand for models of time-varying and non-Gaussian dependencies for multivariate time-series. Available models suffer from the curse of dimensionality or restrictive assumptions on the parameters and the distribution. A promising class of models are the hierarchical...
Persistent link: https://www.econbiz.de/10012966304
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Copula-Based Dependence Characteriztions and Modeling for Time Series
Ibragimov, Rustam - 2005
This paper develops a new unified approach to copula-based modeling and characterizations for time series and stochastic processes. We obtain complete characterizations of many time series dependence structures in terms of copulas corresponding to their finite-dimensional distributions. In...
Persistent link: https://www.econbiz.de/10014062258