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Negative Volatility Spillovers In The Unrestricted Eccc-Garch Model

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  • Conrad, Christian
  • Karanasos, Menelaos

Abstract

This paper considers a formulation of the extended constant or time-varying conditional correlation GARCH model that allows for volatility feedback of either the positive or negative sign. In the previous literature, negative volatility spillovers were ruled out by the assumption that all the parameters of the model are nonnegative, which is a sufficient condition for ensuring the positive definiteness of the conditional covariance matrix. In order to allow for negative feedback, we show that the positive definiteness of the conditional covariance matrix can be guaranteed even if some of the parameters are negative. Thus, we extend the results of Nelson and Cao (1992) and Tsai and Chan (2008) to a multivariate setting. For the bivariate case of order one, we look into the consequences of adopting these less severe restrictions and find that the flexibility of the process is substantially increased. Our results are helpful for the model-builder, who can consider the unrestricted formulation as a tool for testing various economic theories.

Suggested Citation

  • Conrad, Christian & Karanasos, Menelaos, 2010. "Negative Volatility Spillovers In The Unrestricted Eccc-Garch Model," Econometric Theory, Cambridge University Press, vol. 26(03), pages 838-862, June.
  • Handle: RePEc:cup:etheor:v:26:y:2010:i:03:p:838-862_99
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    22. Stilianos Fountas & Menelaos Karanasos & Jinki Kim, 2006. "Inflation Uncertainty, Output Growth Uncertainty and Macroeconomic Performance," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(3), pages 319-343, June.
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    Cited by:

    1. Conrad, Christian & Karanasos, Menelaos & Zeng, Ning, 2010. "The link between macroeconomic performance and variability in the UK," Economics Letters, Elsevier, vol. 106(3), pages 154-157, March.
    2. Karanasos, Menelaos & Xu, Yongdeng, 2017. "Matrix Inequality Constraints for Vector (Asymmetric Power) GARCH/HEAVY Models and MEM with spillovers: some New (Mixture) Formulations," Cardiff Economics Working Papers E2017/14, Cardiff University, Cardiff Business School, Economics Section.
    3. Caporale, Guglielmo Maria & Hunter, John & Menla Ali, Faek, 2014. "On the linkages between stock prices and exchange rates: Evidence from the banking crisis of 2007–2010," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 87-103.
    4. Karanasos, Menelaos & Paraskevopoulos, Alexandros G. & Menla Ali, Faek & Karoglou, Michail & Yfanti, Stavroula, 2014. "Modelling stock volatilities during financial crises: A time varying coefficient approach," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 113-128.
    5. Palani-Rajan Kadapakkam & Timothy Krause & Yiuman Tse, 2015. "Exchange traded funds, size-based portfolios, and market efficiency," Review of Quantitative Finance and Accounting, Springer, vol. 45(1), pages 89-110, July.
    6. Conrad, Christian, 2010. "Non-negativity conditions for the hyperbolic GARCH model," Journal of Econometrics, Elsevier, vol. 157(2), pages 441-457, August.
    7. Conrad, Christian & Karanasos, Menelaos & Zeng, Ning, 2011. "Multivariate fractionally integrated APARCH modeling of stock market volatility: A multi-country study," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 147-159, January.
    8. Woźniak, Tomasz, 2015. "Testing causality between two vectors in multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 876-894.
    9. Eraslan, Sercan & Ali, Faek Menla, 2017. "Financial crises and the dynamic linkages between stock and bond returns," Discussion Papers 17/2017, Deutsche Bundesbank.
    10. Pedersen, Rasmus Søndergaard, 2017. "Inference and testing on the boundary in extended constant conditional correlation GARCH models," Journal of Econometrics, Elsevier, vol. 196(1), pages 23-36.
    11. repec:bor:bistre:v:17:y:2017:i:1:p:25-48 is not listed on IDEAS
    12. Nektarios Aslanidis & Isabel Casas, 2010. "Modelling asset correlations during the recent FInancial crisis: A semiparametric approach," CREATES Research Papers 2010-71, Department of Economics and Business Economics, Aarhus University.
    13. Conrad, Christian & Weber, Enzo, 2013. "Measuring Persistence in Volatility Spillovers," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79850, Verein für Socialpolitik / German Economic Association.
    14. Rittler, Daniel, 2012. "Price discovery and volatility spillovers in the European Union emissions trading scheme: A high-frequency analysis," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 774-785.
    15. Menelaos Karanasos & Alexandros Paraskevopoulos & Faek Menla Ali & Michail Karoglou & Stavroula Yfanti, 2014. "Modelling Returns and Volatilities During Financial Crises: a Time Varying Coefficient Approach," Papers 1403.7179, arXiv.org.
    16. Christian Francq & Jean-Michel Zakoïan, 2016. "Estimating multivariate volatility models equation by equation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 613-635, June.
    17. Carnero M. Angeles & Eratalay M. Hakan, 2014. "Estimating VAR-MGARCH models in multiple steps," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(3), pages 1-27, May.
    18. Noureddine Benlagha, 2014. "Volatility Linkage of Nominal and Index-linked Bond Returns: A Multivariate BEKK-GARCH Approach," Review of Economics & Finance, Better Advances Press, Canada, vol. 4, pages 49-60, November.
    19. repec:eee:intfor:v:34:y:2018:i:1:p:45-63 is not listed on IDEAS
    20. Tomasz Wozniak, 2012. "Granger-causal analysis of VARMA-GARCH models," Economics Working Papers ECO2012/19, European University Institute.
    21. Christian Conrad & Menelaos Karanasos, 2008. "Modeling Volatility Spillovers between the Variabilities of US Inflation and Output: the UECCC GARCH Model," Working Papers 0475, University of Heidelberg, Department of Economics, revised Sep 2008.
    22. Palani-Rajan Kadapakkam & Timothy Krause & Yiuman Tse, 2013. "Exchange Traded Funds, Size-Based Portfolios, And Market Efficiency," Working Papers 0214fin, College of Business, University of Texas at San Antonio.
    23. Menelaos Karanasos & Ning Zeng, 2013. "Conditional heteroskedasticity in macroeconomics data: UK inflation, output growth and their uncertainties," Chapters,in: Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 12, pages 266-288 Edward Elgar Publishing.
    24. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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

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