IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v76y2014icp43-60.html
   My bibliography  Save this article

Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks

Author

Listed:
  • Audrino, Francesco

Abstract

The predictive power of recently introduced components affecting correlations is investigated. The focus is on models allowing for a flexible specification of the short-run component of correlations as well as the long-run component. Moreover, models allowing the correlation dynamics to be subjected to regime-shift caused by threshold-based structural breaks of a different nature are also considered. The results indicate that in some cases there may be a superimposition of the long-term and short-term movements in correlations. Therefore, care is called for in interpretations when estimating the two components. Testing the forecasting accuracy of correlations during the late-2000s financial crisis yields mixed results. In general, component models allowing for a richer correlation specification possess an increased predictive accuracy. Economically speaking, no relevant gains are found by allowing for more flexibility in the correlation dynamics.

Suggested Citation

  • Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
  • Handle: RePEc:eee:csdana:v:76:y:2014:i:c:p:43-60
    DOI: 10.1016/j.csda.2013.06.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947313002144
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2013.06.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Annastiina Silvennoinen & Timo Teräsvirta, 2009. "Modeling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model," Journal of Financial Econometrics, Oxford University Press, vol. 7(4), pages 373-411, Fall.
    2. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models: The Model Confidence Set Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 839-861, December.
    3. Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012. "On the forecasting accuracy of multivariate GARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
    4. Giovanni Barone Adesi & Robert F. Engle & Loriano Mancini, 2014. "A GARCH Option Pricing Model with Filtered Historical Simulation," Palgrave Macmillan Books, in: Giovanni Barone Adesi (ed.), Simulating Security Returns: A Filtered Historical Simulation Approach, chapter 4, pages 66-108, Palgrave Macmillan.
    5. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    6. Fulvio Corsi & Stefano Peluso & Francesco Audrino, 2015. "Missing in Asynchronicity: A Kalman‐em Approach for Multivariate Realized Covariance Estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(3), pages 377-397, April.
    7. Boudt, Kris & Croux, Christophe, 2010. "Robust M-estimation of multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2459-2469, November.
    8. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    9. Laurent, Sébastien & Rombouts, Jeroen V.K. & Violante, Francesco, 2013. "On loss functions and ranking forecasting performances of multivariate volatility models," Journal of Econometrics, Elsevier, vol. 173(1), pages 1-10.
    10. Otranto, Edoardo, 2010. "Identifying financial time series with similar dynamic conditional correlation," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 1-15, January.
    11. 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.
    12. Caporin, Massimiliano & McAleer, Michael, 2014. "Robust ranking of multivariate GARCH models by problem dimension," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 172-185.
    13. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    14. Fulvio Corsi & Francesco Audrino, 2012. "Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects," Journal of Financial Econometrics, Oxford University Press, vol. 10(4), pages 591-616, September.
    15. José Rangel & Robert Engle, 2012. "The Factor–Spline–GARCH Model for High and Low Frequency Correlations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 109-124.
    16. Francesco Audrino & Fabio Trojani, 2011. "A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 138-149, January.
    17. Olivier Ledoit & Pedro Santa-Clara & Michael Wolf, 2003. "Flexible Multivariate GARCH Modeling with an Application to International Stock Markets," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 735-747, August.
    18. Rossi, E. & Spazzini, F., 2010. "Model and distribution uncertainty in multivariate GARCH estimation: A Monte Carlo analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2786-2800, November.
    19. Francesco Audrino & Fabio Trojani, 2006. "Estimating and predicting multivariate volatility thresholds in global stock markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 345-369, April.
    20. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    21. Fabio Trojani, 2007. "Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent," Journal of Financial Econometrics, Oxford University Press, vol. 5(4), pages 591-623, Fall.
    22. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    23. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    24. Boudt, Kris & Cornelissen, Jonathan & Croux, Christophe, 2012. "Jump robust daily covariance estimation by disentangling variance and correlation components," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 2993-3005.
    25. Hafner, Christian M. & Reznikova, Olga, 2012. "On the estimation of dynamic conditional correlation models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3533-3545.
    26. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    27. LAURENT, Sebastien & ROMBOUTS, Jeroen V.K. & VIOLANTE, FRANCESCO, 2009. "Consistent ranking of multivariate volatility models," LIDAM Discussion Papers CORE 2009002, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    28. Pelletier, Denis, 2006. "Regime switching for dynamic correlations," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 445-473.
    29. Audrino, Francesco & Barone-Adesi, Giovanni, 2005. "Functional gradient descent for financial time series with an application to the measurement of market risk," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 959-977, April.
    30. Federico Bandi & Jeffrey Russell & Yinghua Zhu, 2008. "Using High-Frequency Data in Dynamic Portfolio Choice," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 163-198.
    31. repec:hal:journl:peer-00732539 is not listed on IDEAS
    32. Audrino, Francesco, 2006. "The impact of general non-parametric volatility functions in multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3032-3052, July.
    33. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    34. Alp, Tansel & Demetrescu, Matei, 2010. "Joint forecasts of Dow Jones stocks under general multivariate loss function," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2360-2371, November.
    35. Lars Forsberg & Eric Ghysels, 2007. "Why Do Absolute Returns Predict Volatility So Well?," Journal of Financial Econometrics, Oxford University Press, vol. 5(1), pages 31-67.
    36. Engle, Robert & Colacito, Riccardo, 2006. "Testing and Valuing Dynamic Correlations for Asset Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 238-253, April.
    37. Cavit Pakel & Neil Shephard & Kevin Sheppard & Robert F. Engle, 2021. "Fitting Vast Dimensional Time-Varying Covariance Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 652-668, July.
    38. Fulvio Corsi & Francesco Audrino, 2007. "Realized Correlation Tick-by-Tick," University of St. Gallen Department of Economics working paper series 2007 2007-02, Department of Economics, University of St. Gallen.
    39. Chen, Cathy W.S. & So, Mike K.P., 2006. "On a threshold heteroscedastic model," International Journal of Forecasting, Elsevier, vol. 22(1), pages 73-89.
    40. Santos, André A.P. & Moura, Guilherme V., 2014. "Dynamic factor multivariate GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 606-617.
    41. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
    3. Rombouts, Jeroen & Stentoft, Lars & Violante, Franceso, 2014. "The value of multivariate model sophistication: An application to pricing Dow Jones Industrial Average options," International Journal of Forecasting, Elsevier, vol. 30(1), pages 78-98.
    4. Shang, Han Lin & Kearney, Fearghal, 2022. "Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1025-1049.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Caporin, Massimiliano & McAleer, Michael, 2014. "Robust ranking of multivariate GARCH models by problem dimension," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 172-185.
    3. João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 15(2), pages 247-285.
    4. Caporin, M. & McAleer, M.J., 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Econometric Institute Research Papers EI 2011-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Becker, R. & Clements, A.E. & Doolan, M.B. & Hurn, A.S., 2015. "Selecting volatility forecasting models for portfolio allocation purposes," International Journal of Forecasting, Elsevier, vol. 31(3), pages 849-861.
    6. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    7. Geert Dhaene & Piet Sercu & Jianbin Wu, 2022. "Volatility spillovers: A sparse multivariate GARCH approach with an application to commodity markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(5), pages 868-887, May.
    8. Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," CARF F-Series CARF-F-219, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    9. Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
    10. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    11. Rombouts, Jeroen & Stentoft, Lars & Violante, Franceso, 2014. "The value of multivariate model sophistication: An application to pricing Dow Jones Industrial Average options," International Journal of Forecasting, Elsevier, vol. 30(1), pages 78-98.
    12. Varneskov, Rasmus & Voev, Valeri, 2013. "The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 83-95.
    13. Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.
    14. Laurent, Sébastien & Rombouts, Jeroen V.K. & Violante, Francesco, 2013. "On loss functions and ranking forecasting performances of multivariate volatility models," Journal of Econometrics, Elsevier, vol. 173(1), pages 1-10.
    15. Harris, Richard D.F. & Nguyen, Anh, 2013. "Long memory conditional volatility and asset allocation," International Journal of Forecasting, Elsevier, vol. 29(2), pages 258-273.
    16. Marchese, Malvina & Kyriakou, Ioannis & Tamvakis, Michael & Di Iorio, Francesca, 2020. "Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models," Energy Economics, Elsevier, vol. 88(C).
    17. Timo Dimitriadis & Yannick Hoga, 2022. "Dynamic CoVaR Modeling," Papers 2206.14275, arXiv.org, revised Feb 2024.
    18. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    19. Yudong Wang & Chongfeng Wu & Li Yang, 2015. "Hedging with Futures: Does Anything Beat the Naïve Hedging Strategy?," Management Science, INFORMS, vol. 61(12), pages 2870-2889, December.
    20. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.

    More about this item

    Keywords

    Correlation forecasting; Component models; Threshold regime-switching models; Mixed data sampling; Performance evaluation;
    All these keywords.

    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
    • 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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:76:y:2014:i:c:p:43-60. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.