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Predicting Stock Returns and Volatility Using Consumption-Aggregate Wealth Ratios: A Nonlinear Approach

Author

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  • Stelios Bekiros

    () (IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France; Department of Economics, Via della Piazzuola; 43, I-50133, Florence, Italy; Department of Finance, 76 Patission str, GR104 34, Athens)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

Abstract

Recent empirical evidence based on a linear framework tends to suggest that a Markov-switching version of the consumption-aggregate wealth ratio (cayMS), developed to account for structural breaks, is a better predictor of stock returns than the conventional measure (cay) – a finding we confirm as well. Using quarterly data over 1952:Q1-2013:Q3, we however provide statistical evidence that the relationship between stock returns and cay or cayMS is in fact nonlinear. Then, given this evidence of nonlinearity, using a nonparametric Granger causality test, we show that it is in fact cay and not cayMS which is a stronger predictor of not only stock returns, but also volatility.

Suggested Citation

  • Stelios Bekiros & Rangan Gupta, 2015. "Predicting Stock Returns and Volatility Using Consumption-Aggregate Wealth Ratios: A Nonlinear Approach," Working Papers 201505, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201505
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    References listed on IDEAS

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    1. 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.
    2. Martin Lettau, 2001. "Consumption, Aggregate Wealth, and Expected Stock Returns," Journal of Finance, American Finance Association, vol. 56(3), pages 815-849, June.
    3. 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.
    4. 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.
    5. Gençay, Ramazan & Signori, Daniele, 2015. "Multi-scale tests for serial correlation," Journal of Econometrics, Elsevier, vol. 184(1), pages 62-80.
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    Cited by:

    1. Bekiros, Stelios & Gupta, Rangan & Majumdar, Anandamayee, 2016. "Incorporating economic policy uncertainty in US equity premium models: A nonlinear predictability analysis," Finance Research Letters, Elsevier, vol. 18(C), pages 291-296.
    2. 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.
    3. 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.
    4. Rangan Gupta & Anandamayee Majumdar & Mark E. Wohar, 2017. "The Role of Current Account Balance in Forecasting the US Equity Premium: Evidence From a Quantile Predictive Regression Approach," Open Economies Review, Springer, vol. 28(1), pages 47-59, February.
    5. repec:eee:ecofin:v:47:y:2019:i:c:p:65-84 is not listed on IDEAS

    More about this item

    Keywords

    cay; Stock markets; Volatility; Nonlinear causality;

    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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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