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Smooth Transition Patterns in the Realized Stock- Bond Correlation

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  • Aslanidis, Nektarios
  • Christiansen, Charlotte

Abstract

Abstract: We analyze the realized stock-bond correlation. Gradual transitions between negative and positive stock-bond correlation is accommodated by the smooth transition regression (STR) model. The changes in regime are de…ned by economic and …financial transition variables. Both in sample and out-of- sample results document that STR models with multiple transition variables outperform STR models with a single transition variable. The most important transition variables are the short rate, the yield spread, and the VIX volatility index. Keywords: realized correlation; smooth transition regressions; stock-bond correlation; VIX index JEL Classifi…cations: C22; G11; G12; G17

Suggested Citation

  • Aslanidis, Nektarios & Christiansen, Charlotte, 2011. "Smooth Transition Patterns in the Realized Stock- Bond Correlation," Working Papers 2072/152138, Universitat Rovira i Virgili, Department of Economics.
  • Handle: RePEc:urv:wpaper:2072/152138
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    1. Annastiina Silvennoinen & Timo Teräsvirta, 2009. "Modeling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(4), pages 373-411, Fall.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. 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(04), pages 1107-1125, September.
    7. 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.
    8. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
    9. 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.
    10. Audrino, Francesco & Corsi, Fulvio, 2010. "Modeling tick-by-tick realized correlations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2372-2382, November.
    11. Allan Timmermann & Massimo Guidolin, 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.
    12. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    13. Lieven Baele, 2010. "The Determinants of Stock and Bond Return Comovements," Review of Financial Studies, Society for Financial Studies, vol. 23(6), pages 2374-2428, June.
    14. Campbell, John Y. & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Journal of Financial Economics, Elsevier, vol. 81(1), pages 27-60, July.
    15. 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.
    16. 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.
    17. 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(01), pages 161-194, March.
    18. Lieven Baele & Geert Bekaert & Koen Inghelbrecht, 2007. "The determinants of stock and bond return comovements," Working Paper Research 119, National Bank of Belgium.
    19. 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.
    20. 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.
    21. 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.).
    22. 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.
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    Citations

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    Cited by:

    1. Stein, Michael & Islami, Mevlud & Lindemann, Jens, 2012. "Identifying time variability in stock and interest rate dependence," Discussion Papers 24/2012, Deutsche Bundesbank.
    2. 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.
    3. repec:eee:reveco:v:55:y:2018:i:c:p:285-294 is not listed on IDEAS
    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, Society for Financial Econometrics, vol. 14(3), pages 617-642.
    5. Harumi Ohmi & Tatsuyoshi Okimoto, 2016. "Trends in stock-bond correlations," Applied Economics, Taylor & Francis Journals, vol. 48(6), pages 536-552, February.
    6. repec:eee:finana:v:52:y:2017:i:c:p:260-280 is not listed on IDEAS
    7. Aloui, Chaker & Hammoudeh, Shawkat & Hamida, Hela Ben, 2015. "Price discovery and regime shift behavior in the relationship between sharia stocks and sukuk: A two-state Markov switching analysis," Pacific-Basin Finance Journal, Elsevier, vol. 34(C), pages 121-135.
    8. repec:eee:reveco:v:53:y:2018:i:c:p:25-38 is not listed on IDEAS
    9. Skintzi, Vasiliki, 2017. "Determinants of stock-bond market comovement in the Eurozone under model uncertainty," MPRA Paper 78278, University Library of Munich, Germany.

    More about this item

    Keywords

    Correlació (Estadística); Actius financers -- Preus; Cartera de valors -- Gestió; 336 - Finances. Banca. Moneda. Borsa;

    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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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