Time-Varying Short-Horizon Predictability

Henkel, Sam James; Martin, J. Spencer; Nardari, Federico

doi:10.2139/ssrn.1177375

Cited by 126 publications

(146 citation statements)

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“…The out-of- 7 We thank Allan Timmermann for suggesting this test. These results are consistent with Henkel, Martin, and Nardari (2011), who find that the forecasting ability of common market return predictors is higher during recessions.…”

supporting

confidence: 92%

Davids, Goliaths, and Business Cycles

Duarte

Kapadia

2012

SSRN Journal

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We show that a simple, intuitive variable,   (Goliath versus David) reflects timevariation in discount rates related to changes in aggregate business conditions.   is the annual change in the weight of the largest 250 firms in the aggregate stock market, and is motivated by research that shows that small firms are more severely impacted than large firms by economic shocks due to differences in access to external finance. We find that   is the best single predictor of market returns out-of-sample among traditional predictors, predicting quarterly market returns with an out-of-sample  2 of 6.3% in the 1976-2011 evaluation period. Consistent with its motivation,   forecasts lower future  and aggregate investment growth.   also forecasts returns on treasury bonds and the 25 size and book-to-market sorted portfolios (and consequently  as well as ), with assets we ex-ante expect to be more sensitive to discount rate variation displaying greater sensitivity to  . We show that   is the only variable among traditional predictors that forecasts stock returns, bond returns, and macroeconomic variables. We also show that  's predictive ability is robust, and not due to information contained in traditional variables, such as  and net payout.

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confidence: 92%

Davids, Goliaths, and Business Cycles

Duarte

Kapadia

2012

SSRN Journal

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“…In terms of economic evaluation, DMMA shows its unique power to generate considerable returns especially in recessions, with CER gains during recessions approximately 41 times larger than that during expansions. The fact that predictability will rise during recessions is in line with the empirical evidence provided by Rapach et al (2010), Henkel et al (2011) and Dangl and Halling (2012). Researchers argue that this is because HM model overestimates the equity premium, therefore, suffers from huge losses particularly in recessions.…”

Section: Link Predictability To the Business Cyclesupporting

confidence: 54%

“…Moreover, local maxima of the equity risk premia often appears to be near business cycles troughs, whereas, local minima occurs near business cycles peaks (Fama and French, 1989;Campbell and Cochrane, 1995;Cochrane, 1999). In this framework, DMMA's predictability would rise if it could capture the business cycle (Rapach et al, 2010;Henkel et al, 2011;Dangl and Halling, 2012). Rapach et al (2010) systematically and empirically study the link between prediction improvements and business cycle.…”

Section: Link Predictability To the Business Cyclementioning

confidence: 99%

Stock Return Prediction with Fully Flexible Models and Coefficients

Byrne

2017

SSRN Journal

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We evaluate stock return predictability using a fully flexible Bayesian framework, which explicitly allows for different degrees of time-variation in coefficients and in forecasting models. We believe that asset return predictability can evolve quickly or slowly, based upon market conditions, and we should account for this. Our approach has superior out-of-sample predictive performance compared to the historical mean, from a statistical and economic perspective. We also find that our model statistically dominates its nested models, including models in which parameters evolve at a constant rate. By decomposing sources of prediction uncertainty into five parts, we find that our fully flexible approach more precisely identifies time-variation in coefficients and in forecasting models, leading to mitigation of estimation risk and forecasting improvements. Finally, we relate predictability to the business cycle.JEL classification: C11,G11, G12, G17

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“…The evidence presented suggests that it is worth investing in knowledge of volatility dynamics -though this result is highly temporal and depends on investor risk preferences and economic conditions (including volatility levels); see Henkel et al (2011) for similar conditional evidence within the asset return predictability literature. 26 Of the methods considered there is overwhelming support for use of forecasts based on hybridtype methods and those in which the underling model parameters are estimated by minimising the economic criterion considered in this paper; however, simple moving average and random walk-type methods also have value depending on the nature of the sample of data used and the investment environment adopted.…”

Section: Discussionmentioning

confidence: 73%

The Economic Value of Volatility Forecasts: A Conditional Approach

Taylor

2013

Journal of Financial Econometrics

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This paper provides an investigation of the economic value of multivariate volatility forecasting ability using a testing framework in which the quality of competing methods is assessed from a conditional investment perspective. In doing this we are able to provide a novel means by which the benefits of using a particular set of volatility forecasts can be assessed. The results associated with an application to US bond and stock futures markets indicate that investors are willing to pay a significant premium for knowledge of the dynamics of volatility, though the magnitude of this premium varies over time, and depends on risk preferences and economic conditions. The latter variation implies that selection of appropriate dynamics (this is, forecasting method choice) should be a conditional exercise.

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Time-Varying Short-Horizon Predictability

Cited by 126 publications

References 0 publications

Davids, Goliaths, and Business Cycles

Davids, Goliaths, and Business Cycles

Stock Return Prediction with Fully Flexible Models and Coefficients

The Economic Value of Volatility Forecasts: A Conditional Approach

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