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

Abstract

Recent empirical evidence based on a linear framework tends to suggest that a Markovswitching version of the consumption-aggregate wealth ratio (cay MS ), 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 cay MS 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 cay MS which is a stronger predictor of not only stock returns, but also volatility.JEL Codes: C32, C58, G10, G17

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

“…To the best of our knowledge, this is the first paper that uses a nonparametric causality-in-quantiles framework to investigate the forecasting power of cay and cay MS for excess and real stock and housing returns, as well as their volatility. Yet, our study is related to the works of Ludvigson and Ng (2007) and Bekiros and Gupta (2015). While the former analyses finds in favour of predictive ability for cay, for both excess returns and their volatility using a linear predictive regression framework, the latter investigates the predictability of real stock returns and its volatility emanating from cay and cay MS using the k th -order nonparametric causality test of Nishiyama et al (2011).…”

“…While the former analyses finds in favour of predictive ability for cay, for both excess returns and their volatility using a linear predictive regression framework, the latter investigates the predictability of real stock returns and its volatility emanating from cay and cay MS using the k th -order nonparametric causality test of Nishiyama et al (2011). Note, the causality-inquantiles test that we employ in this paper is more general than the Nishiyama et al (2011) test used by Bekiros and Gupta (2015), since our approach allows us to study the entire conditional distribution of returns and volatility. In addition, unlike Ludvigson and Ng (2007) and Bekiros and Gupta (2015), we also analyse housing returns and volatility over and above stock returns and volatility.…”

Incorporating economic policy uncertainty in US equity premium models: A nonlinear predictability analysis

“…Primarily in-sample empirical evidence in this regard can be found in Antonakakis et al, (2013), Kang and Ratti (2013), Gupta et al,(2014), Bekiros et al, (2015), Chang et al, (2015) and Jurado et al, (2015). 1 Against this backdrop, and under the widely held view that predictive models require out-of-sample validation (Rapach and Zhou, 2013), the objective of this paper is to investigate whether the news-based measure of economic policy uncertainty (EPU) introduced by Baker et al (2013) could help in forecasting the S&P500-based equity premium.…”

“…Based on the recent contribution by Bekiros and Gupta (2015) who proved the relationship between returns and predictors not being linear, we consider a quantile predictive regression model over and above the standard linear modelling. The quantile-based approach is clearly more informative relative to any linear model, as it investigates the ability of the EPU to forecast the entire conditional distribution of the equity premium, rather than being restricted just to the conditional-mean.…”

Explanatory Power of Selected Proxies in Predicting Stock Returns of Large U.K. Companies

“…Some earlier studies used the techniques of time-series or cross section analysis to explain market beta in expected returns (Fama and French, 2004). Such techniques can also be applied, as proposed by Bekiros and Gupta (2015), using a linear predictive regression model with different indicators to predict stock market returns in monthly sequences. Difficulties arose, however, when attempts were made to implement this model to predict volatility.…”