Robust Econometric Inference for Stock Return Predictability

Kostakis, Alexandros; Magdalinos, Tassos; Stamatogiannis, Michalis P.

doi:10.1093/rfs/hhu139

Cited by 212 publications

(85 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The table also shows that the predictive power of cyclical consumption applies to both real returns (Panel B) and actual returns (Panel C), although evidence of predictability for actual returns is not quite as pronounced. Kostakis, Magdalinos, and Stamatogiannis (2015) develop a test that is robust to the regressor's degree of persistence (including unit root, local-to-unit root, near-stationary, or stationary persistence classes) and that has good size and power properties. This approach alleviates concerns about the quality of inference under possible misspecification of the (generally unobservable) time-series properties of the regressor in long-horizon predictive regressions.…”

Section: Benchmark Predictive Regressionsmentioning

confidence: 99%

“…This table reports IVX-Wald statistics of Kostakis, Magdalinos, and Stamatogiannis (2015) for the OLS regressions summarized in Table I. *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively.…”

Section: Ivx-wald Statisticsmentioning

confidence: 99%

“…We perform a battery of robustness checks and address a number of econometric concerns surrounding predictive regressions with persistent predictors (Nelson and Kim (1993), Stambaugh (1999)). Both the IVX testing approach of Kostakis, Magdalinos, and Stamatogiannis (2015) that accounts for the degree of regressor persistence and an advanced bootstrap procedure that accounts for the regressor's time-series properties provide strong evidence of predictability at the one-quarter horizon that extends to horizons of up to five years. Moreover, this predictability does not vanish during the post-oil-crisis period, a period in which standard business cycle indicators have proven dismal as predictive variables (Welch and Goyal (2008)).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Consumption Fluctuations and Expected Returns

Atanasov¹,

Møller²,

Priestley³

2020

The Journal of Finance

View full text Add to dashboard Cite

This paper introduces a novel consumption-based variable, cyclical consumption, and examines its predictive properties for stock returns. Future expected stock returns are high (low) when aggregate consumption falls (rises) relative to its trend and marginal utility from current consumption is high (low). We show that the empirical evidence ties consumption decisions of agents to time variation in returns in a manner consistent with asset pricing models based on external habit formation. The predictive power of cyclical consumption is not confined to bad times and subsumes the predictability of many popular forecasting variables. 1 The detrending procedure of Hamilton (2018) allows us to remove the nonstationary component of c t without modeling the nonstationarity, as the decomposition in equation (1) implies a stationary process ω t if either the k th difference of c t or the deviation of c t from a k th -order deterministic time polynomial is stationary for some k as the sample size becomes large. See Hamilton (2018) for a formal proof.2 In this respect, Hamilton (2018) argues that in contrast to the Hodrick-Prescott (1997) cyclical series, which can be readily forecasted from its own lagged values and past values of other variables, by construction, the realizations of ω are difficult to predict.

show abstract

Section: Benchmark Predictive Regressionsmentioning

confidence: 99%

Section: Ivx-wald Statisticsmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Consumption Fluctuations and Expected Returns

Atanasov¹,

Møller²,

Priestley³

2020

The Journal of Finance

View full text Add to dashboard Cite

show abstract

“…Letting φ T = (1−c z /T δ ) for some c z > 0 (say c z = 1 as discussed in Phillips and Magdalinos (2009) and Kostakis, Magdalinos and Stamatogiannis (2014)) and δ ∈ (0, 1) the IV variable is constructed…”

Section: Testingmentioning

confidence: 99%

“…The econometric difficulties that arise due to the combination of a persistent regressor and possible endogeneity have generated an enormous literature aiming to improve inferences in such settings. Common examples include the predictability of stock returns with valuation ratios, the predictability of GDP growth with interest rates amongst numerous others (see for instance Valkanov (2003), Lewellen (2004), Campbell and Yogo (2006), Jansson and Moreira (2006), Rossi (2007), Bandi and Perron (2008), Ang and Bekaert (2008), Wei and Wright (2013) and more recently Kostakis, Magdalinos and Stamatogiannis (2014)). …”

Section: Introductionmentioning

confidence: 99%

Inferring the Predictability Induced by a Persistent Regressor in a Predictive Threshold Model

Gonzalo

Pitarakis

2017

Journal of Business & Economic Statistics

View full text Add to dashboard Cite

We develop tests for detecting possibly episodic predictability induced by a persistent predictor. Our framework is that of a predictive regression model with threshold effects and our goal is to develop operational and easily implementable inferences when one does not wish to imposeà priori restrictions on the parameters of the model other than the slopes corresponding to the persistent predictor. Differently put our tests for the null hypothesis of no predictability against threshold predictability remain valid without the need to know whether the remaining parameters of the model are characterised by threshold effects or not (e.g. shifting versus non-shifting intercepts). One interesting feature of our setting is that our test statistics remain unaffected by whether some nuisance parameters are identified or not. We subsequently apply our methodology to the predictability of aggregate stock returns with valuation ratios and document a robust countercyclicality in the ability of some valuation ratios to predict returns.

show abstract

Mixed‐frequency predictive regressions with parameter learning

Leippold

Yang

2023

Journal of Forecasting

View full text Add to dashboard Cite

We explore the performance of mixed‐frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed‐frequency models improve predictability, not only because of the combination of predictors with different frequencies but also due to the preservation of high‐frequency features such as time‐varying volatility. Temporally aggregated models misspecify the evolution frequency of the volatility dynamics, resulting in poor volatility timing and worse portfolio performance than the mixed‐frequency specification. These results highlight the importance of preserving the potential mixed‐frequency nature of predictors and volatility in predictive regressions.

show abstract

Robust Econometric Inference for Stock Return Predictability

Cited by 212 publications

References 61 publications

Consumption Fluctuations and Expected Returns

Consumption Fluctuations and Expected Returns

Inferring the Predictability Induced by a Persistent Regressor in a Predictive Threshold Model

Mixed‐frequency predictive regressions with parameter learning

Contact Info

Product

Resources

About