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Wealth‐to‐Income Ratio and Stock Market Movements: Evidence from a Nonparametric Causality Test

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

Abstract

We use a nonparametric causality‐in‐quantile test to analyze the predictive ability of the wealth‐to‐income ratio (wy) for excess stock returns and their volatility. Our results reveal that the wy is nonlinearly related with excess stock returns, and hence, results from linear Granger causality tests cannot be deemed robust. When we apply the nonparametric causality‐in‐quantile test, we find that the wy can predict excess stock returns over the majority of the conditional distribution, with the exception being the extreme ends, that is, when the market is in deep bear or bull phases. However, the wy has no predictability for the volatility of excess stock returns.

Suggested Citation

  • Mehmet Balcilar & Rangan Gupta & Ricardo M. Sousa & Mark E. Wohar, 2018. "Wealth‐to‐Income Ratio and Stock Market Movements: Evidence from a Nonparametric Causality Test," International Review of Finance, International Review of Finance Ltd., vol. 18(3), pages 495-506, September.
    • Handle: RePEc:bla:irvfin:v:18:y:2018:i:3:p:495-506
    DOI: 10.1111/irfi.12136
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    Cited by:

    1. 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.
    2. 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.
    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.

    More about this item

    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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