IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v48y2017icp269-279.html
   My bibliography  Save this article

Do cay and cayMS predict stock and housing returns? Evidence from a nonparametric causality test

Author

Listed:
  • Balcilar, Mehmet
  • Gupta, Rangan
  • Sousa, Ricardo M.
  • Wohar, Mark E.

Abstract

We use a nonparametric causality-in-quantiles test to compare the predictive ability of the consumption-wealth ratio (cay) and the Markov Switching version (cayMS) for excess and real stock and housing returns and their volatility. Our results reveal strong evidence of nonlinearity and regime changes in the relationship between asset returns and cay or cayMS, which corroborates the relevance of this econometric framework. Moreover, both cay or cayMS are found to predict only excess stock returns over its entire conditional distribution, with the latter being a strong predictor only at certain quantiles. As for the housing market, these two consumption-wealth ratios only predict the volatility of real housing returns, with cayMS outperforming cay over the majority of the conditional distribution.

Suggested Citation

  • Balcilar, Mehmet & Gupta, Rangan & Sousa, Ricardo M. & Wohar, Mark E., 2017. "Do cay and cayMS predict stock and housing returns? Evidence from a nonparametric causality test," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 269-279.
    • Handle: RePEc:eee:reveco:v:48:y:2017:i:c:p:269-279
    DOI: 10.1016/j.iref.2016.12.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056016301344
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2016.12.007?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Francesco Bianchi & Martin Lettau & Sydney C. Ludvigson, 2022. "Monetary Policy and Asset Valuation," Journal of Finance, American Finance Association, vol. 77(2), pages 967-1017, April.
    2. Pakoš, Michal, 2011. "Estimating Intertemporal and Intratemporal Substitutions When Both Income and Substitution Effects Are Present: The Role of Durable Goods," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 439-454.
    3. Case Karl E. & Quigley John M. & Shiller Robert J., 2005. "Comparing Wealth Effects: The Stock Market versus the Housing Market," The B.E. Journal of Macroeconomics, De Gruyter, vol. 5(1), pages 1-34, May.
    4. Rocha Armada, Manuel J. & Sousa, Ricardo M. & Wohar, Mark E., 2015. "Consumption growth, preference for smoothing, changes in expectations and risk premium," The Quarterly Review of Economics and Finance, Elsevier, vol. 56(C), pages 80-97.
    5. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2014. "Sticky prices or economically-linked economies: The case of forecasting the Chinese stock market," Journal of International Money and Finance, Elsevier, vol. 41(C), pages 95-109.
    6. Hanno N. Lustig & Stijn G. Van Nieuwerburgh, 2005. "Housing Collateral, Consumption Insurance, and Risk Premia: An Empirical Perspective," Journal of Finance, American Finance Association, vol. 60(3), pages 1167-1219, June.
    7. Piazzesi, Monika & Schneider, Martin & Tuzel, Selale, 2007. "Housing, consumption and asset pricing," Journal of Financial Economics, Elsevier, vol. 83(3), pages 531-569, March.
    8. Ludvigson, Sydney C. & Ng, Serena, 2007. "The empirical risk-return relation: A factor analysis approach," Journal of Financial Economics, Elsevier, vol. 83(1), pages 171-222, January.
    9. Karl E. Case & John M. Quigley & Robert J. Shiller, 2011. "Wealth Effects Revisited 1978-2009," Cowles Foundation Discussion Papers 1784, Cowles Foundation for Research in Economics, Yale University.
    10. Martin Lettau & Sydney Ludvigson, 2001. "Consumption, Aggregate Wealth, and Expected Stock Returns," Journal of Finance, American Finance Association, vol. 56(3), pages 815-849, June.
    11. Sousa, Ricardo M., 2010. "Consumption, (dis)aggregate wealth, and asset returns," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 606-622, September.
    12. Ricardo M. Sousa, 2015. "Linking wealth and labour income with stock returns and government bond yields," The European Journal of Finance, Taylor & Francis Journals, vol. 21(10-11), pages 806-825, August.
    13. António Afonso & Ricardo M. Sousa, 2011. "Consumption, Wealth, Stock And Government Bond Returns: International Evidence," Manchester School, University of Manchester, vol. 79(6), pages 1294-1232, December.
    14. Leung, Charles, 2004. "Macroeconomics and housing: a review of the literature," Journal of Housing Economics, Elsevier, vol. 13(4), pages 249-267, December.
    15. Bonaccolto, G. & Caporin, M. & Gupta, R., 2018. "The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 446-469.
    16. Mehmet Balcilar & Stelios Bekiros & Rangan Gupta, 2017. "The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method," Empirical Economics, Springer, vol. 53(3), pages 879-889, November.
    17. Caporale, Guglielmo Maria & Sousa, Ricardo M., 2016. "Consumption, wealth, stock and housing returns: Evidence from emerging markets," Research in International Business and Finance, Elsevier, vol. 36(C), pages 562-578.
    18. Bekiros, Stelios & Gupta, Rangan, 2015. "Predicting stock returns and volatility using consumption-aggregate wealth ratios: A nonlinear approach," Economics Letters, Elsevier, vol. 131(C), pages 83-85.
    19. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    20. Motohiro Yogo, 2006. "A Consumption‐Based Explanation of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 61(2), pages 539-580, April.
    21. Jeong, Kiho & Härdle, Wolfgang K. & Song, Song, 2012. "A Consistent Nonparametric Test For Causality In Quantile," Econometric Theory, Cambridge University Press, vol. 28(4), pages 861-887, August.
    22. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    23. Nishiyama, Yoshihiko & Hitomi, Kohtaro & Kawasaki, Yoshinori & Jeong, Kiho, 2011. "A consistent nonparametric test for nonlinear causality—Specification in time series regression," Journal of Econometrics, Elsevier, vol. 165(1), pages 112-127.
    24. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    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. Balcilar, Mehmet & Gupta, Rangan & Kim, Won Joong & Kyei, Clement, 2019. "The role of economic policy uncertainties in predicting stock returns and their volatility for Hong Kong, Malaysia and South Korea," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 150-163.
    2. Oguzhan Cepni & Rangan Gupta & Mark E. Wohar, 2019. "Variants of Consumption-Wealth Ratios and Predictability of U.S. Government Bond Risk Premia: Old is still Gold," Working Papers 201912, University of Pretoria, Department of Economics.
    3. Maghyereh, Aktham & Abdoh, Hussein, 2020. "The tail dependence structure between investor sentiment and commodity markets," Resources Policy, Elsevier, vol. 68(C).
    4. Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2021. "Variants of consumption‐wealth ratios and predictability of U.S. government bond risk premia," International Review of Finance, International Review of Finance Ltd., vol. 21(2), pages 661-674, June.

    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. Chang, Tsangyao & Gupta, Rangan & Majumdar, Anandamayee & Pierdzioch, Christian, 2019. "Predicting stock market movements with a time-varying consumption-aggregate wealth ratio," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 458-467.
    2. Rangan Gupta & Hardik A. Marfatia & Eric Olson, 2020. "Effect of uncertainty on U.S. stock returns and volatility: evidence from over eighty years of high-frequency data," Applied Economics Letters, Taylor & Francis Journals, vol. 27(16), pages 1305-1311, September.
    3. Balcilar, Mehmet & Gupta, Rangan & Sousa, Ricardo M. & Wohar, Mark E., 2021. "Linking U.S. State-level housing market returns, and the consumption-(Dis)Aggregate wealth ratio," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 779-810.
    4. Mehmet Balcilar & Rangan Gupta & Ricardo M. Sousa & Mark E. Wohar, 2021. "What Can Fifty-Two Collateralizable Wealth Measures Tell Us About Future Housing Market Returns? Evidence from U.S. State-Level Data," The Journal of Real Estate Finance and Economics, Springer, vol. 62(1), pages 81-107, January.
    5. Mauro Costantini & Ricardo M. Sousa, 2020. "Consumption, asset wealth, equity premium, term spread, and flight to quality," European Financial Management, European Financial Management Association, vol. 26(3), pages 778-807, June.
    6. Rangan Gupta & Tahir Suleman & Mark E. Wohar, 2019. "The role of time‐varying rare disaster risks in predicting bond returns and volatility," Review of Financial Economics, John Wiley & Sons, vol. 37(3), pages 327-340, July.
    7. João M. Sousa & Ricardo M. Sousa, 2019. "Asset Returns Under Model Uncertainty: Evidence from the Euro Area, the US and the UK," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 139-176, June.
    8. Gupta, Rangan & Risse, Marian & Volkman, David A. & Wohar, Mark E., 2019. "The role of term spread and pattern changes in predicting stock returns and volatility of the United Kingdom: Evidence from a nonparametric causality-in-quantiles test using over 250 years of data," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 391-405.
    9. Bahloul, Walid & Balcilar, Mehmet & Cunado, Juncal & Gupta, Rangan, 2018. "The role of economic and financial uncertainties in predicting commodity futures returns and volatility: Evidence from a nonparametric causality-in-quantiles test," Journal of Multinational Financial Management, Elsevier, vol. 45(C), pages 52-71.
    10. Christou, Christina & Gupta, Rangan & Jawadi, Fredj, 2021. "Does inequality help in forecasting equity premium in a panel of G7 countries?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    11. Rangan Gupta & Chi Keung Marco Lau & Wendy Nyakabawo, 2018. "Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment," Working Papers 201866, University of Pretoria, Department of Economics.
    12. Bekiros, Stelios & Gupta, Rangan, 2015. "Predicting stock returns and volatility using consumption-aggregate wealth ratios: A nonlinear approach," Economics Letters, Elsevier, vol. 131(C), pages 83-85.
    13. Caporale, Guglielmo Maria & Sousa, Ricardo M., 2016. "Consumption, wealth, stock and housing returns: Evidence from emerging markets," Research in International Business and Finance, Elsevier, vol. 36(C), pages 562-578.
    14. Guglielmo Maria Caporale & Ricardo M. Sousa, 2011. "Are Stock and Housing Returns Complements or Substitutes? Evidence from OECD Countries," NIPE Working Papers 33/2011, NIPE - Universidade do Minho.
    15. Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2021. "Variants of consumption‐wealth ratios and predictability of U.S. government bond risk premia," International Review of Finance, International Review of Finance Ltd., vol. 21(2), pages 661-674, June.
    16. Ren, Yu & Yuan, Yufei & Zhang, Yang, 2014. "Human capital, household capital and asset returns," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 11-22.
    17. Rocha Armada, Manuel J. & Sousa, Ricardo M. & Wohar, Mark E., 2015. "Consumption growth, preference for smoothing, changes in expectations and risk premium," The Quarterly Review of Economics and Finance, Elsevier, vol. 56(C), pages 80-97.
    18. Sousa, João & Sousa, Ricardo M., 2017. "Predicting risk premium under changes in the conditional distribution of stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 204-218.
    19. Balcilar, Mehmet & Gupta, Rangan & Kim, Won Joong & Kyei, Clement, 2019. "The role of economic policy uncertainties in predicting stock returns and their volatility for Hong Kong, Malaysia and South Korea," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 150-163.
    20. Ghysels, Eric & Plazzi, Alberto & Valkanov, Rossen & Torous, Walter, 2013. "Forecasting Real Estate Prices," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 509-580, Elsevier.

    More about this item

    Keywords

    C22; C32; C53; Q41; Stock returns; Housing returns; Causality-in-quantiles test; Nonparametric;
    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
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

    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:reveco:v:48:y:2017:i:c:p:269-279. 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/inca/620165 .

    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.