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Predicting stock returns and volatility using consumption-aggregate wealth ratios: A nonlinear approach

Stelios Bekiros and Rangan Gupta ()

Economics Letters, 2015, vol. 131, issue C, 83-85

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.

Keywords: Cay; Stock markets; Volatility; Nonlinear causality (search for similar items in EconPapers)
JEL-codes: C32 C58 G10 G17 (search for similar items in EconPapers)
Date: 2015
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Working Paper: Predicting Stock Returns and Volatility Using Consumption-Aggregate Wealth Ratios: A Nonlinear Approach (2015)
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DOI: 10.1016/j.econlet.2015.03.019

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