IDEAS home Printed from https://ideas.repec.org/a/eee/empfin/v19y2012i4p454-464.html
   My bibliography  Save this article

Smooth transition patterns in the realized stock–bond correlation

Author

Listed:
  • Aslanidis, Nektarios
  • Christiansen, Charlotte

Abstract

This paper explores the time variation in the stock–bond correlation using high-frequency data. Gradual transitions between regimes of negative and positive stock–bond correlation are well accommodated by the smooth transition regression (STR) model. We find that the regimes are systematically related to movements in financial and to a minor extent macroeconomic transition variables. In particular, the most informative transition variables are the short rate, the yield spread, and the VIX volatility index. Importantly, both in-sample and out-of-sample evaluation criteria show that multiple transition variable STR specifications considerably outperform single transition variable STR models. Our results are robust to different forecast horizons.

Suggested Citation

  • Aslanidis, Nektarios & Christiansen, Charlotte, 2012. "Smooth transition patterns in the realized stock–bond correlation," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 454-464.
  • Handle: RePEc:eee:empfin:v:19:y:2012:i:4:p:454-464
    DOI: 10.1016/j.jempfin.2012.04.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927539812000254
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jempfin.2012.04.005?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. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 119-136, Suppl. De.
    2. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
    3. Ana Beatriz Galvão & Michael Artis & Massimiliano Marcellino, 2007. "The transmission mechanism in a changing world," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 39-61.
    4. 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.
    5. Connolly, Robert & Stivers, Chris & Sun, Licheng, 2005. "Stock Market Uncertainty and the Stock-Bond Return Relation," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 40(1), pages 161-194, March.
    6. T. G. Andersen & T. Bollerslev, 1998. "Towards a unified framework for high and low frequency return volatility modeling," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 52(3), pages 273-302, November.
    7. Campbell, John Y. & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Journal of Financial Economics, Elsevier, vol. 81(1), pages 27-60, July.
    8. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    9. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    10. Christiansen, Charlotte & Ranaldo, Angelo & Söderlind, Paul, 2011. "The Time-Varying Systematic Risk of Carry Trade Strategies," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 46(4), pages 1107-1125, August.
    11. Viceira, Luis M., 2012. "Bond risk, bond return volatility, and the term structure of interest rates," International Journal of Forecasting, Elsevier, vol. 28(1), pages 97-117.
    12. Charlotte Christiansen & Angelo Ranaldo, 2007. "Realized bond—stock correlation: Macroeconomic announcement effects," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(5), pages 439-469, May.
    13. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836.
    14. Audrino, Francesco & Corsi, Fulvio, 2010. "Modeling tick-by-tick realized correlations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2372-2382, November.
    15. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    16. Connolly, Robert A. & Stivers, Chris & Sun, Licheng, 2007. "Commonality in the time-variation of stock-stock and stock-bond return comovements," Journal of Financial Markets, Elsevier, vol. 10(2), pages 192-218, May.
    17. Refet S. Gürkaynak & Brian Sack & Jonathan H. Wright, 2010. "The TIPS Yield Curve and Inflation Compensation," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(1), pages 70-92, January.
    18. Lieven Baele, 2010. "The Determinants of Stock and Bond Return Comovements," The Review of Financial Studies, Society for Financial Studies, vol. 23(6), pages 2374-2428, June.
    19. Nadir Ocal & Denise R. Osborn, 2000. "Business cycle non-linearities in UK consumption and production," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 27-43.
    20. Massimo Guidolin & Allan Timmermann, 2006. "An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 1-22, January.
    21. Teräsvirta, Timo, 1996. "Smooth Transition Models," SSE/EFI Working Paper Series in Economics and Finance 132, Stockholm School of Economics.
    22. Arturo Estrella & Mary R. Trubin, 2006. "The yield curve as a leading indicator: some practical issues," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 12(Jul).
    23. Jonathan H. Wright, 2006. "The yield curve and predicting recessions," Finance and Economics Discussion Series 2006-07, Board of Governors of the Federal Reserve System (U.S.).
    24. Yang, Jian & Zhou, Yinggang & Wang, Zijun, 2009. "The stock-bond correlation and macroeconomic conditions: One and a half centuries of evidence," Journal of Banking & Finance, Elsevier, vol. 33(4), pages 670-680, April.
    Full references (including those not matched with items on IDEAS)

    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. Harumi Ohmi & Tatsuyoshi Okimoto, 2016. "Trends in stock-bond correlations," Applied Economics, Taylor & Francis Journals, vol. 48(6), pages 536-552, February.
    2. Allard, Anne-Florence & Iania, Leonardo & Smedts, Kristien, 2020. "Stock-bond return correlations: Moving away from “one-frequency-fits-all” by extending the DCC-MIDAS approach," International Review of Financial Analysis, Elsevier, vol. 71(C).
    3. Ermolov, Andrey, 2022. "Time-varying risk of nominal bonds: How important are macroeconomic shocks?," Journal of Financial Economics, Elsevier, vol. 145(1), pages 1-28.
    4. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou, 2016. "Macro-Finance Determinants of the Long-Run Stock–Bond Correlation: The DCC-MIDAS Specification," Journal of Financial Econometrics, Oxford University Press, vol. 14(3), pages 617-642.
    5. Skintzi, Vasiliki D., 2019. "Determinants of stock-bond market comovement in the Eurozone under model uncertainty," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 20-28.
    6. Jammazi, Rania & Ferrer, Román & Jareño, Francisco & Hammoudeh, Shawkat M., 2017. "Main driving factors of the interest rate-stock market Granger causality," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 260-280.
    7. Nguyen, Hoang & Javed, Farrukh, 2021. "Dynamic relationship between Stock and Bond returns: A GAS MIDAS copula approach," Working Papers 2021:15, Örebro University, School of Business.
    8. Gupta, Rangan & Kollias, Christos & Papadamou, Stephanos & Wohar, Mark E., 2018. "News implied volatility and the stock-bond nexus: Evidence from historical data for the USA and the UK markets," Journal of Multinational Financial Management, Elsevier, vol. 47, pages 76-90.
    9. Thomas Chiang & Jiandong Li & Sheng-Yung Yang, 2015. "Dynamic stock–bond return correlations and financial market uncertainty," Review of Quantitative Finance and Accounting, Springer, vol. 45(1), pages 59-88, July.
    10. Nguyen, Hoang & Javed, Farrukh, 2023. "Dynamic relationship between Stock and Bond returns: A GAS MIDAS copula approach," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 272-292.
    11. Thomas C. Chiang & Lanjun Lao & Qingfeng Xue, 2016. "Comovements between Chinese and global stock markets: evidence from aggregate and sectoral data," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1003-1042, November.
    12. Mohammad Alomari & Abdel Razzaq Al rababa’a & Ghaith El-Nader & Ahmad Alkhataybeh, 2021. "Who’s behind the wheel? The role of social and media news in driving the stock–bond correlation," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 959-1007, October.
    13. Coudert, Virginie & Mignon, Valérie, 2013. "The “forward premium puzzle” and the sovereign default risk," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 491-511.
    14. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2015. "The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US," Applied Economics, Taylor & Francis Journals, vol. 47(22), pages 2259-2277, May.
    15. Refk Selmi & Christos Kollias & Stephanos Papadamou & Rangan Gupta, 2017. "A Copula-Based Quantile-on-Quantile Regression Approach to Modeling Dependence Structure between Stock and Bond Returns: Evidence from Historical Data of India, South Africa, UK and US," Working Papers 201747, University of Pretoria, Department of Economics.
    16. Audrino, Francesco & Corsi, Fulvio, 2010. "Modeling tick-by-tick realized correlations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2372-2382, November.
    17. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    18. Emilio Zanetti Chini, 2013. "Generalizing smooth transition autoregressions," CREATES Research Papers 2013-32, Department of Economics and Business Economics, Aarhus University.
    19. Aslanidis, Nektarios & Christiansen, Charlotte, 2014. "Quantiles of the realized stock–bond correlation and links to the macroeconomy," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 321-331.
    20. Aslanidis, Nektarios & Christiansen, Charlotte & Cipollini, Andrea, 2019. "Predicting bond betas using macro-finance variables," Finance Research Letters, Elsevier, vol. 29(C), pages 193-199.

    More about this item

    Keywords

    Realized stock–bond correlation; Smooth transition regressions; Correlation regimes; VIX index;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:empfin:v:19:y:2012:i:4:p:454-464. 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/jempfin .

    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.