Efficient estimation of a multivariate multiplicative volatility model
AbstractWe propose a multivariate generalization of the multiplicative volatility model of Engle and Rangel (2008), which has a nonparametric long run component and a unit multivariate GARCH short run dynamic component. We suggest various kernel-based estimation procedures for the parametric and nonparametric components, and derive the asymptotic properties thereof. For the parametric part of the model, we obtain the semiparametric efficiency bound. Our method is applied to a bivariate stock index series. We find that the univariate model of Engle and Rangel (2008) appears to be violated in the data whereas our multivariate model is more consistent with the data.
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Bibliographic InfoPaper provided by HAL in its series Post-Print with number peer-00732539.
Date of creation: 15 Sep 2010
Date of revision:
Publication status: Published, Journal of Econometrics, 2010, 159, 1, 55
Note: View the original document on HAL open archive server: http://peer.ccsd.cnrs.fr/peer-00732539
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C12; C13; C14; GARCH; Kernel estimation; Local stationarity; Semiparametric;
Other versions of this item:
- Hafner, Christian M. & Linton, Oliver, 2010. "Efficient estimation of a multivariate multiplicative volatility model," Journal of Econometrics, Elsevier, vol. 159(1), pages 55-73, November.
- Christian M. Hafner & Oliver Linton, 2009. "Efficient Estimation of a Multivariate Multiplicative Volatility Model," STICERD - Econometrics Paper Series /2009/541, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- BAUWENS, Luc & LAURENT, Sébastien & ROMBOUTS, Jeroen, 2003.
"Multivariate GARCH models: a survey,"
CORE Discussion Papers
2003031, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Lanne, Markku & Saikkonen, Pentti, 2007.
"A Multivariate Generalized Orthogonal Factor GARCH Model,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 25, pages 61-75, January.
- Lanne, Markku & Saikkonen, Pentti, 2005. "A Multivariate Generalized Orthogonal Factor GARCH Model," MPRA Paper 23714, University Library of Munich, Germany.
- Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006.
"Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns,"
Journal of Financial Econometrics,
Society for Financial Econometrics, vol. 4(4), pages 537-572.
- Sheppard, Kevin & Cappiello, Lorenzo & Engle, Robert F., 2003. "Asymmetric dynamics in the correlations of global equity and bond returns," Working Paper Series 0204, European Central Bank.
- Robert F. Engle & Kevin Sheppard, 2001.
"Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH,"
NBER Working Papers
8554, National Bureau of Economic Research, Inc.
- Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
- 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.
- H. Peter Boswijk & Roy van der Weide, 2006.
"Wake me up before you GO-GARCH,"
Tinbergen Institute Discussion Papers
06-079/4, Tinbergen Institute, revised 21 Sep 2006.
- Comte, F. & Lieberman, O., 2003. "Asymptotic theory for multivariate GARCH processes," Journal of Multivariate Analysis, Elsevier, vol. 84(1), pages 61-84, January.
- Andrew Patton, 2004.
"Modelling Asymmetric Exchange Rate Dependence,"
wp04-04, Warwick Business School, Finance Group.
- Pesaran, M. Hashem & Timmermann, Allan, 2004.
"How costly is it to ignore breaks when forecasting the direction of a time series?,"
International Journal of Forecasting,
Elsevier, vol. 20(3), pages 411-425.
- Pesaran, H.M. & Timmermann, A., 2003. "How Costly is it to Ignore Breaks when Forecasting the Direction of a Time Series?," Cambridge Working Papers in Economics 0306, Faculty of Economics, University of Cambridge.
- Allan Timmermann & M. Hashem Pesaran, 2003. "How Costly is it to Ignore Breaks when Forecasting the Direction of a Time Series?," CESifo Working Paper Series 875, CESifo Group Munich.
- Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-62, July.
- Jondeau, Eric & Rockinger, Michael, 2006. "The Copula-GARCH model of conditional dependencies: An international stock market application," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 827-853, August.
- Denis Pelletier, 2004.
"Regime Switching for Dynamic Correlations,"
Econometric Society 2004 North American Summer Meetings
230, Econometric Society.
- Kawakatsu, Hiroyuki, 2006. "Matrix exponential GARCH," Journal of Econometrics, Elsevier, vol. 134(1), pages 95-128, September.
- Acatrinei, Marius & Gorun, Adrian & Marcu, Nicu, 2013. "A DCC-GARCH Model To Estimate the Risk to the Capital Market in Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 136-148, March.
- Michael Vogt, 2012. "Nonparametric regression for locally stationary time series," CeMMAP working papers CWP22/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Luc Bauwens & Christian M. Hafner & Diane Pierret, 2011.
"Multivariate Volatility Modeling of Electricity Futures,"
SFB 649 Discussion Papers
SFB649DP2011-063, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Luc Bauwens & Christian M. Hafner & Diane Pierret, 2013. "Multivariate Volatility Modeling Of Electricity Futures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 743-761, 08.
- bauwens, Luc & hafner, Christian & pierret, Diane, 2011. "Multivariate volatility modeling of electricity futures," CORE Discussion Papers 2011011, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- VAN BELLEGEM, Sébastien, 2011. "Locally stationary volatility modelling," CORE Discussion Papers 2011041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2012. "The conditional autoregressive Wishart model for multivariate stock market volatility," Journal of Econometrics, Elsevier, vol. 167(1), pages 211-223.
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