Nonparametric Estimation of Copulas for Time Series
AbstractWe consider a nonparametric method to estimate copulas, i.e. functions linking joint distributions to their univariate margins. We derive the asymptotic properties of kernel estimators of copulas and their derivatives in the context of a multivariate stationary process satisfactory strong mixing conditions. Monte Carlo results are reported for a stationary vector autoregressive process of order one with Gaussian innovations. An empirical illustration containing a comparison with the independent, comotonic and Gaussian copulas is given for European and US stock index returns.
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Bibliographic InfoPaper provided by International Center for Financial Asset Management and Engineering in its series FAME Research Paper Series with number rp57.
Date of creation: Feb 2003
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Nonparametric; Kernel; Time Series; Copulas; Dependence Measures; Risk Management; Loss Severity Distribution;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
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- Patton, Andrew J, 2001. "Estimation of Copula Models for Time Series of Possibly Different Length," University of California at San Diego, Economics Working Paper Series qt3fc1c8hw, Department of Economics, UC San Diego.
- Chollete, Loran & Ning, Cathy, 2012. "Asymmetric Dependence in the US Economy: Application to Money and the Phillips Curve," UiS Working Papers in Economics and Finance 2012/1, University of Stavanger.
- Beare, Brendan K. & Seo, Juwon, 2012. "Time irreversible copula-based Markov Models," University of California at San Diego, Economics Working Paper Series qt31f8500p, Department of Economics, UC San Diego.
- Karl Siburg & Pavel Stoimenov, 2011. "Symmetry of functions and exchangeability of random variables," Statistical Papers, Springer, vol. 52(1), pages 1-15, February.
- Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 22(2), pages 98-134.
- Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173.
- Chollete, Loran & Ning, Cathy, 2009.
"The Dependence Structure of Macroeconomic Variables in the US,"
UiS Working Papers in Economics and Finance
2009/31, University of Stavanger.
- Cathy Q. Ning & Loran Chollete, 2009. "The Dependence Structure of Macroeconomic Variables in the US," Working Papers 005, Ryerson University, Department of Economics.
- Necula, Ciprian, 2010. "Modeling the Dependency Structure of Stock Index Returns using a Copula Function Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 93-106, September.
- Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions. II," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 23(3), pages 98-132.
- Liang Peng & Yongcheng Qi & Ingrid Van Keilegom, 2012. "Jackknife empirical likelihood method for copulas," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 21(1), pages 74-92, March.
- Rob van den Goorbergh, 2004. "A Copula-Based Autoregressive Conditional Dependence Model of International Stock Markets," DNB Working Papers 022, Netherlands Central Bank, Research Department.
- Brodsky, Boris & Penikas, Henry & Safaryan, Irina, 2009. "Detection of Structural Breaks in Copula Models," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 16(4), pages 3-15.
- Joshua V. Rosenberg, 2003. "Nonparametric pricing of multivariate contingent claims," Staff Reports 162, Federal Reserve Bank of New York.
- Hall, Peter & Neumeyer, Natalie, 2005. "Estimating a bivariate density when there are extra data on one or both components," Technical Reports 2005,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
- Paul Doukhan & Jean-David Fermanian & Gabriel Lang, 2009. "An empirical central limit theorem with applications to copulas under weak dependence," Statistical Inference for Stochastic Processes, Springer, vol. 12(1), pages 65-87, February.
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