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Predicting stock market movements with a time-varying consumption-aggregate wealth ratio

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  • Chang, Tsangyao
  • Gupta, Rangan
  • Majumdar, Anandamayee
  • Pierdzioch, Christian

Abstract

We develop a time-varying measure of cay (cayTVP) using time-varying cointegration, and then compare the predictive ability of cayTVP with cay and a Markov-switching cay (cayMS) for excess stock returns and volatility in the US over the period 1952:Q2-2015:Q3, using a k-th order nonparametric causality-in-quantiles test. We find that time-varying cointegration exists between consumption, asset wealth, and labor income. In addition, while there is no evidence of predictability of volatility of excess returns from cay, cayMS, or cayTVP, they tend to act as strong predictors of stock returns, with cayTVP being important during the bearish phases of the equity market.

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  • 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.
    • Handle: RePEc:eee:reveco:v:59:y:2019:i:c:p:458-467
    DOI: 10.1016/j.iref.2018.10.009
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    1. Park, Joon Y, 1992. "Canonical Cointegrating Regressions," Econometrica, Econometric Society, vol. 60(1), pages 119-143, January.
    2. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch & Mark E. Wohar, 2018. "Terror attacks and stock-market fluctuations: evidence based on a nonparametric causality-in-quantiles test for the G7 countries," The European Journal of Finance, Taylor & Francis Journals, vol. 24(4), pages 333-346, March.
    3. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2017. "On exchange-rate movements and gold-price fluctuations: evidence for gold-producing countries from a nonparametric causality-in-quantiles test," International Economics and Economic Policy, Springer, vol. 14(4), pages 691-700, October.
    4. Kapetanios, George & Shin, Yongcheol & Snell, Andy, 2003. "Testing for a unit root in the nonlinear STAR framework," Journal of Econometrics, Elsevier, vol. 112(2), pages 359-379, February.
    5. 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.
    6. Ludwig Alexander & Sløk Torsten, 2004. "The Relationship between Stock Prices, House Prices and Consumption in OECD Countries," The B.E. Journal of Macroeconomics, De Gruyter, vol. 4(1), pages 1-28, March.
    7. 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.
    8. Sousa, Ricardo M., 2010. "Consumption, (dis)aggregate wealth, and asset returns," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 606-622, September.
    9. 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.
    10. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    11. 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.
    12. Martin Lettau & Sydney C. Ludvigson, 2004. "Understanding Trend and Cycle in Asset Values: Reevaluating the Wealth Effect on Consumption," American Economic Review, American Economic Association, vol. 94(1), pages 276-299, March.
    13. 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.
    14. Park, Joon Y. & Hahn, Sang B., 1999. "Cointegrating Regressions With Time Varying Coefficients," Econometric Theory, Cambridge University Press, vol. 15(5), pages 664-703, October.
    15. Beatrice Simo-Kengne & Stephen Miller & Rangan Gupta & Goodness Aye, 2015. "Time-Varying Effects of Housing and Stock Returns on U.S. Consumption," The Journal of Real Estate Finance and Economics, Springer, vol. 50(3), pages 339-354, April.
    16. 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.
    17. Ludwig, Alexander & Sløk, Torsten, 2004. "The relationship between stock prices, house prices and consumption in OECD," Sonderforschungsbereich 504 Publications 04-12, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    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. 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.
    20. 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.
    21. Francesco Bianchi & Martin Lettau & Sydney C. Ludvigson, 2016. "Monetary Policy and Asset Valuation," NBER Working Papers 22572, National Bureau of Economic Research, Inc.
    22. 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.
    23. 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.
    24. Balcilar, Mehmet & Gupta, Rangan & Pierdzioch, Christian, 2016. "Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test," Resources Policy, Elsevier, vol. 49(C), pages 74-80.
    25. 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.
    26. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    27. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    28. 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.
    29. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch & Mark Wohar, 2016. "Do Terror Attacks Affect the Dollar-Pound Exchange Rate? A Nonparametric Causality-in-Quantiles Analysis," Working Papers 201615, University of Pretoria, Department of Economics.
    30. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    31. Mehmet Balcilar & Rangan Gupta & Ricardo M. Sousa & Mark E. Wohar, 2015. "The Predictability of cay and cayMS for Stock and Housing Returns: A Nonparametric Causality in Quantile Test," Working Papers 201577, University of Pretoria, Department of Economics.
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    Cited by:

    1. Marina Kolosnitsyna & Anna Philippova, 2017. "Family Benefits and Poverty: The Case of Russia," HSE Working papers WP BRP 03/PSP/2017, National Research University Higher School of Economics.
    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. 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.
    4. Hui Hong & Zhicun Bian & Chien-Chiang Lee, 2021. "COVID-19 and instability of stock market performance: evidence from the U.S," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-18, December.
    5. 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.
    6. 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.

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    More about this item

    Keywords

    Consumption-aggregate wealth ratio; Time-varying cointegration; Stock returns; Volatility; Nonparametric causality-in-quantiles test;
    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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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