Automated variable selection in vector multiplicative error models
AbstractMultiplicative Error Models (MEM) can be used to trace the dynamics of non-negative valued processes. Interactions between several such processes are accommodated by the vector MEM (vMEM) in the form of parametric (estimated by Maximum Likelihood) or semiparametric specifications (estimated by Generalized Method of Moments). In choosing the relevant variables an automated procedure can be followed where the full specification is successively pruned in a general-to-specific approach. An efficient and fast algorithm is presented and evaluated by means of simulations. The empirical application shows the interdependence across European markets and the relative strength of volatility spillovers.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 54 (2010)
Issue (Month): 11 (November)
Contact details of provider:
Web page: http://www.elsevier.com/locate/csda
Other versions of this item:
- Fabrizio Cipollini & Giampiero M. Gallo, 2009. "Automated Variable Selection in Vector Multiplicative Error Models," Econometrics Working Papers Archive wp2009_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2013.
"Semiparametric Vector Mem,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 28(7), pages 1067-1086, November.
- Manganelli, Simone, 2005.
"Duration, volume and volatility impact of trades,"
Journal of Financial Markets,
Elsevier, vol. 8(4), pages 377-399, November.
- Peter Xue-Kun Song, 2000. "Multivariate Dispersion Models Generated From Gaussian Copula," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 27(2), pages 305-320.
- Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
- Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2007. "A Model for Multivariate Non-negative Valued Processes in Financial Econometrics," Econometrics Working Papers Archive wp2007_16, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
- Strickland, Chris M. & Forbes, Catherine S. & Martin, Gael M., 2006.
"Bayesian analysis of the stochastic conditional duration model,"
Computational Statistics & Data Analysis,
Elsevier, vol. 50(9), pages 2247-2267, May.
- Chris M. Strickland & Catherine S. Forbes & Gael M. Martin, 2003. "Bayesian Analysis of the Stochastic Conditional Duration Model," Monash Econometrics and Business Statistics Working Papers 14/03, Monash University, Department of Econometrics and Business Statistics.
- Ahoniemi, Katja & Lanne, Markku, 2007.
"Joint Modeling of Call and Put Implied Volatility,"
6318, University Library of Munich, Germany.
- Robert F. Engle & Giampiero M. Gallo, 2003.
"A Multiple Indicators Model For Volatility Using Intra-Daily Data,"
Econometrics Working Papers Archive
wp2003_07, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
- Engle, Robert F. & Gallo, Giampiero M., 2006. "A multiple indicators model for volatility using intra-daily data," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 3-27.
- Robert F. Engle & Giampiero M. Gallo, 2003. "A Multiple Indicators Model for Volatility Using Intra-Daily Data," NBER Working Papers 10117, National Bureau of Economic Research, Inc.
- Chou, Ray Yeutien, 2005. "Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 561-82, June.
- Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, 06.
- Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
- Frahm, Gabriel & Junker, Markus & Szimayer, Alexander, 2003. "Elliptical copulas: applicability and limitations," Statistics & Probability Letters, Elsevier, vol. 63(3), pages 275-286, July.
- Christian T. Brownlees & Giampiero M. Gallo, 2008. "On Variable Selection for Volatility Forecasting: The Role of Focused Selection Criteria," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(4), pages 513-539, Fall.
- Frijns, Bart & Schotman, Peter C., 2006. "Nonlinear dynamics in Nasdaq dealer quotes," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2246-2266, December.
- Nikolaus Hautsch & Julia Schuamburg & Melanie Schienle, 2012. "Modeling Time-Varying Dependencies between Positive-Valued High-Frequency Time Series," SFB 649 Discussion Papers SFB649DP2012-054, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Shulin Zhang, & Ostap Okhrin, & Qian M. Zhou & Peter X.-K. Song, 2013. "Goodness-of-fit Test for Specification of Semiparametric Copula Dependence Models," SFB 649 Discussion Papers SFB649DP2013-041, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.