IDEAS home Printed from https://ideas.repec.org/p/hhs/lunewp/2014_037.html
   My bibliography  Save this paper

Macro-Finance Determinants of the Long-Run Stock-Bond Correlation: The DCC-MIDAS Specification

Author

Listed:
  • Asgharian, Hossein

    (Department of Economics, Lund University)

  • Christiansen, Charlotte

    (CREATES, Aarhus University)

  • Hou, Ai Jun

    (School of Business, Stockholm University)

Abstract

We investigate the long-run stock-bond correlation using a novel model that combines the dynamic conditional correlation model with the mixed-data sampling approach. The long-run correlation is affected by both macro-finance variables (historical and forecasts) and the lagged realized correlation itself. Macro-finance variables and the lagged realized correlation are simultaneously significant in forecasting the long-run stock-bond correlation. The behavior of the long-run stock-bond correlation is very different when estimated taking the macro-finance variables into account. Supporting the flight-to-quality phenomenon for the total stock-bond correlation, the long-run correlation tends to be small/negative when the economy is weak.

Suggested Citation

  • Asgharian, Hossein & Christiansen, Charlotte & Hou, Ai Jun, 2014. "Macro-Finance Determinants of the Long-Run Stock-Bond Correlation: The DCC-MIDAS Specification," Working Papers 2014:37, Lund University, Department of Economics.
    • Handle: RePEc:hhs:lunewp:2014_037
    as

    Download full text from publisher

    File URL: http://project.nek.lu.se/publications/workpap/papers/wp14_37.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Asgharian, Hossein & Hou, Ai Jun & Javed, Farrukh, 2013. "Importance of the macroeconomic variables for variance prediction A GARCH-MIDAS approach," Knut Wicksell Working Paper Series 2013/4, Lund University, Knut Wicksell Centre for Financial Studies.
    2. Campbell, John Y & Ammer, John, 1993. "What Moves the Stock and Bond Markets? A Variance Decomposition for Long-Term Asset Returns," Journal of Finance, American Finance Association, vol. 48(1), pages 3-37, March.
    3. P. Hartmann & S. Straetmans & C. G. de Vries, 2004. "Asset Market Linkages in Crisis Periods," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 313-326, February.
    4. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    5. 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.
    6. Bauwens Luc & Storti Giuseppe, 2009. "A Component GARCH Model with Time Varying Weights," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-33, May.
    7. Elena Andreou, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 290-318.
    8. 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.
    9. Amado, Cristina & Teräsvirta, Timo, 2013. "Modelling volatility by variance decomposition," Journal of Econometrics, Elsevier, vol. 175(2), pages 142-153.
    10. Jeff Fleming & Chris Kirby & Barbara Ostdiek, 2001. "The Economic Value of Volatility Timing," Journal of Finance, American Finance Association, vol. 56(1), pages 329-352, February.
    11. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 1998. "Information and volatility linkages in the stock, bond, and money markets," Journal of Financial Economics, Elsevier, vol. 49(1), pages 111-137, July.
    12. Granger, Clive W. J. & Ding, Zhuanxin, 1996. "Varieties of long memory models," Journal of Econometrics, Elsevier, vol. 73(1), pages 61-77, July.
    13. 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.
    14. 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.
    15. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
    16. John Y. Campbell & Carolin Pflueger & Luis M. Viceira, 2020. "Macroeconomic Drivers of Bond and Equity Risks," Journal of Political Economy, University of Chicago Press, vol. 128(8), pages 3148-3185.
    17. repec:bla:jfinan:v:44:y:1989:i:5:p:1115-53 is not listed on IDEAS
    18. Christian Conrad & Karin Loch, 2015. "Anticipating Long‐Term Stock Market Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1090-1114, November.
    19. Christodoulakis, George A. & Satchell, Stephen E., 2002. "Correlated ARCH (CorrARCH): Modelling the time-varying conditional correlation between financial asset returns," European Journal of Operational Research, Elsevier, vol. 139(2), pages 351-370, June.
    20. Naresh Bansal & Robert A. Connolly & Chris Stivers, 2010. "Regime‐switching in stock index and Treasury futures returns and measures of stock market stress," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(8), pages 753-779, August.
    21. Gençay, Ramazan & Gençay, Ramazan & Selçuk, Faruk & Whitcher, Brandon J., 2001. "An Introduction to Wavelets and Other Filtering Methods in Finance and Economics," Elsevier Monographs, Elsevier, edition 1, number 9780122796708.
    22. 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.
    23. 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.
    24. 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.
    25. Luis Viceira & Carolin Pflueger & John Campbell, 2014. "Monetary Policy Drivers of Bond and Equity Risks," 2014 Meeting Papers 137, Society for Economic Dynamics.
    26. Conrad, Christian & Loch, Karin & Rittler, Daniel, 2012. "On the Macroeconomic Determinants of the Long-Term Oil-Stock Correlation," Working Papers 0525, University of Heidelberg, Department of Economics.
    27. 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.
    28. Jaewon Choi & Matthew P. Richardson & Robert F. Whitelaw, 2014. "On the Fundamental Relation Between Equity Returns and Interest Rates," NBER Working Papers 20187, National Bureau of Economic Research, Inc.
    29. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 2003. "The economic value of volatility timing using "realized" volatility," Journal of Financial Economics, Elsevier, vol. 67(3), pages 473-509, March.
    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. Mobarek, Asma & Muradoglu, Gulnur & Mollah, Sabur & Hou, Ai Jun, 2016. "Determinants of time varying co-movements among international stock markets during crisis and non-crisis periods," Journal of Financial Stability, Elsevier, vol. 24(C), pages 1-11.
    2. 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.
    3. Harumi Ohmi & Tatsuyoshi Okimoto, 2016. "Trends in stock-bond correlations," Applied Economics, Taylor & Francis Journals, vol. 48(6), pages 536-552, February.
    4. 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.
    5. 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.
    6. McMillan, David G., 2019. "Cross-asset relations, correlations and economic implications," Global Finance Journal, Elsevier, vol. 41(C), pages 60-78.
    7. 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.
    8. Christian Leschinski & Michelle Voges & Philipp Sibbertsen, 2021. "Integration and Disintegration of EMU Government Bond Markets," Econometrics, MDPI, vol. 9(1), pages 1-17, March.
    9. 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).
    10. Wu, Chih-Chiang & Chiu, Junmao, 2017. "Economic evaluation of asymmetric and price range information in gold and general financial markets," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 53-68.
    11. 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.
    12. Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2019. "On the asymmetric impact of macro–variables on volatility," Economic Modelling, Elsevier, vol. 76(C), pages 135-152.
    13. Henryk Gurgul & Roland Mestel & Robert Syrek, 2017. "MIDAS models in banking sector – systemic risk comparison," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 18(2), pages 165-181.
    14. Wu, Chih-Chiang & Liang, Shin-Shun, 2011. "The economic value of range-based covariance between stock and bond returns with dynamic copulas," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 711-727, September.
    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. Yu‐Sheng Lai, 2022. "High‐frequency data and stock–bond investing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1623-1638, December.
    17. Hatice Gaye Gencer, 2015. "Flight-to-quality or contagion effect? An analysis from the Turkish and the US financial markets," Financial Theory and Practice, Institute of Public Finance, vol. 39(3), pages 325-340.
    18. Aslanidis, Nektarios & Christiansen, Charlotte & Cipollini, Andrea, 2019. "Predicting bond betas using macro-finance variables," Finance Research Letters, Elsevier, vol. 29(C), pages 193-199.
    19. Chen, Wei-Peng & Choudhry, Taufiq & Wu, Chih-Chiang, 2013. "The extreme value in crude oil and US dollar markets," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 191-210.
    20. 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.

    More about this item

    Keywords

    DCC-MIDAS model; Long-run correlation; Macro-finance variables; Stock-bond correlation;
    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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hhs:lunewp:2014_037. 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: Iker Arregui Alegria (email available below). General contact details of provider: https://edirc.repec.org/data/delunse.html .

    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.