IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-01531234.html
   My bibliography  Save this paper

Cross-market index with Factor-DCC

Author

Listed:
  • Julien Chevallier

    (UCP - Université de Cergy Pontoise - Université Paris-Seine)

  • Sofiane Aboura

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper proposes a new empirical methodology for computing a cross-market index – coined CMI – based on the Factor DCC-model. This approach solves both problems of treating high-dimensional data and estimating time-varying conditional correlations. We provide an application to a multi-asset market data composed of equities, bonds, foreign exchange rates and commodities during 1983–2013. This new methodology may be attractive to asset managers, since it provides a simple way of constructing passive portfolios customized on any asset class

Suggested Citation

  • Julien Chevallier & Sofiane Aboura, 2014. "Cross-market index with Factor-DCC," Post-Print hal-01531234, HAL.
  • Handle: RePEc:hal:journl:hal-01531234
    DOI: 10.1016/j.econmod.2014.04.001
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(6), pages 1113-1141, December.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    4. Connor, Gregory & Korajczyk, Robert A, 1993. "A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-1291, September.
    5. Alexei Onatski, 2009. "Testing Hypotheses About the Number of Factors in Large Factor Models," Econometrica, Econometric Society, vol. 77(5), pages 1447-1479, September.
    6. 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.
    7. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
    8. Gary Gorton & K. Geert Rouwenhorst, 2004. "Facts and Fantasies about Commodity Futures," NBER Working Papers 10595, National Bureau of Economic Research, Inc.
    9. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    10. Carol Alexander, 2000. "Orthogonal Methods for Generating Large Positive Semi-Definite Covariance Matrices," ICMA Centre Discussion Papers in Finance icma-dp2000-06, Henley Business School, University of Reading.
    11. repec:dau:papers:123456789/13625 is not listed on IDEAS
    12. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
    13. Miffre, Joelle & Rallis, Georgios, 2007. "Momentum strategies in commodity futures markets," Journal of Banking & Finance, Elsevier, vol. 31(6), pages 1863-1886, June.
    14. Kun Zhang & Laiwan Chan, 2009. "Efficient factor GARCH models and factor-DCC models," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 71-91.
    15. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    16. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    17. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2010. "Improved penalization for determining the number of factors in approximate factor models," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1806-1813, December.
    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. Tsuji, Chikashi, 2018. "New DCC analyses of return transmission, volatility spillovers, and optimal hedging among oil futures and oil equities in oil-producing countries," Applied Energy, Elsevier, vol. 229(C), pages 1202-1217.
    2. Antonakakis, Nikolaos & Kizys, Renatas, 2015. "Dynamic spillovers between commodity and currency markets," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 303-319.
    3. Tsuji, Chikashi, 2018. "Return transmission and asymmetric volatility spillovers between oil futures and oil equities: New DCC-MEGARCH analyses," Economic Modelling, Elsevier, vol. 74(C), pages 167-185.

    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. Aboura, Sofiane & Chevallier, Julien, 2015. "Cross-market volatility index with Factor-DCC," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 132-140.
    2. Aboura, Sofiane & Chevallier, Julien, 2015. "A cross-volatility index for hedging the country risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 25-41.
    3. Aboura, Sofiane & Chevallier, Julien, 2015. "Geographical diversification with a World Volatility Index," Journal of Multinational Financial Management, Elsevier, vol. 30(C), pages 62-82.
    4. Matteo Luciani, 2015. "Monetary Policy and the Housing Market: A Structural Factor Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 199-218, March.
    5. Sofiane Aboura & Julien Chevallier, 2014. "The cross-market index for volatility surprise," Journal of Asset Management, Palgrave Macmillan, vol. 15(1), pages 7-23, February.
    6. Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2015. "Dynamic factor models with infinite-dimensional factor spaces: One-sided representations," Journal of Econometrics, Elsevier, vol. 185(2), pages 359-371.
    7. Matteo Barigozzi & Antonio M. Conti & Matteo Luciani, 2014. "Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(5), pages 693-714, October.
    8. Aboura, Sofiane & Chevallier, Julien, 2017. "A new weighting-scheme for equity indexes," International Review of Financial Analysis, Elsevier, vol. 54(C), pages 159-175.
    9. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    10. Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2017. "Dynamic factor models with infinite-dimensional factor space: Asymptotic analysis," Journal of Econometrics, Elsevier, vol. 199(1), pages 74-92.
    11. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    12. Barigozzi, Matteo & Hallin, Marc, 2017. "Generalized dynamic factor models and volatilities: estimation and forecasting," Journal of Econometrics, Elsevier, vol. 201(2), pages 307-321.
    13. Juho Koistinen & Bernd Funovits, 2022. "Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization," Papers 2202.00310, arXiv.org, revised Feb 2022.
    14. Forni, Mario & Cavicchioli, Maddalena & Lippi, Marco & Zaffaroni, Paolo, 2016. "Eigenvalue Ratio Estimators for the Number of Common Factors," CEPR Discussion Papers 11440, C.E.P.R. Discussion Papers.
    15. Lütkepohl, Helmut, 2014. "Structural vector autoregressive analysis in a data rich environment: A survey," SFB 649 Discussion Papers 2014-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2014. "Dynamic factor models: A review of the literature," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 73-107.
    17. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
    18. Jianqing Fan & Yuan Liao & Martina Mincheva, 2013. "Large covariance estimation by thresholding principal orthogonal complements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 603-680, September.
    19. Philipp Gersing & Christoph Rust & Manfred Deistler, 2023. "Weak Factors are Everywhere," Papers 2307.10067, arXiv.org, revised Jan 2024.
    20. Mario Forni & Luca Gambetti & Luca Sala, 2014. "No News in Business Cycles," Economic Journal, Royal Economic Society, vol. 124(581), pages 1168-1191, December.

    More about this item

    Keywords

    Cross-market index; Factor-DCC; Asset management;
    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
    • G01 - Financial Economics - - General - - - Financial Crises
    • F15 - International Economics - - Trade - - - Economic Integration

    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:hal:journl:hal-01531234. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.