The quantilogram: With an application to evaluating directional predictability

Linton, Oliver; Whang, Yoon-Jae

doi:10.1016/j.jeconom.2007.01.004

Cited by 174 publications

(122 citation statements)

References 31 publications

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“…These last two papers can be classi…ed as part of the predictive QR literature since they focus on the prediction of stock return quantiles from lagged …nancial variables. Another piece of related research is Han et al (2014) which studies the quantile dependence between stock return and a predictor, wherein the new analysis becomes possible by extending quantilogram theory (Linton and Whang, 2007) to the cross-quantilogram. In the mean predictive regression literature, Gonzalo and Pitarakis (2012) and Kostakis et al (2012) are close to this paper, since they have also applied the IVX methodology to the stock return regression.…”

Section: Introductionmentioning

confidence: 99%

Predictive quantile regression with persistent covariates: IVX-QR approach

Lee

2016

Journal of Econometrics

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This paper develops econometric methods for inference and prediction in quantile regression (QR) allowing for persistent predictors. Conventional QR econometric techniques lose their validity when predictors are highly persistent. I adopt and extend a methodology called IVX …ltering (Magdalinos and Phillips, 2009) that is designed to handle predictor variables with various degrees of persistence. The proposed IVX-QR methods correct the distortion arising from persistent multivariate predictors while preserving discriminatory power. Simulations con…rm that IVX-QR methods inherit the robust properties of QR. These methods are employed to examine the predictability of US stock returns at various quantile levels.

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Section: Introductionmentioning

confidence: 99%

Predictive quantile regression with persistent covariates: IVX-QR approach

Lee

2016

Journal of Econometrics

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“…Linton and Whang (2004) have recently proposed related "quantilogram" inference methods for exploring linear dependence in time series at various quantiles. In this paper, we propose a new quantile autoregression (QAR) model whose autoregressive coefficient may take different values (possibly unity) over different quantiles of the innovation process.…”

Section: Introductionmentioning

confidence: 99%

Quantile Autoregression

Koenker

Zhang

2006

Journal of the American Statistical Association

533

366

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Abstract. We consider quantile autoregression (QAR) models in which the autoregressive coefficients can be expressed as monotone functions of a single, scalar random variable. The models can capture systematic influences of conditioning variables on the location, scale and shape of the conditional distribution of the response, and therefore constitute a significant extension of classical constant coefficient linear time series models in which the effect of conditioning is confined to a location shift. The models may be interpreted as a special case of the general random coefficient autoregression model with strongly dependent coefficients. Statistical properties of the proposed model and associated estimators are studied. The limiting distributions of the autoregression quantile process are derived. Quantile autoregression inference methods are also investigated. Empirical applications of the model to the U.S. unemployment rate and U.S. gasoline prices highlight the potential of the model.

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“…Christo¤ersen and Diebold (2006), Linton and Whang (2007) and Anatolyev and Gospodinov (2009) …nd convincing evidence of sign predictability of US stock returns, while Anatolyev (2008) discovers signi…cant directional predictability in some Eastern European stock markets. Christoffersen and Diebold (2006) show that directional predictability may be induced by volatility dynamics alone even when the returns are conditionally mean independent.…”

Section: Adding Dynamicsmentioning

confidence: 86%

Multi-Market Direction-of-Change Modeling Using Dependence Ratios

Anatolyev¹

2009

Studies in Nonlinear Dynamics &Amp; Econometrics

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We consider a multivariate dynamic model for the joint distribution of binary outcomes associated with directions-of-change for several markets or assets. The marginal distribution of each binary outcome follows a dynamic binary choice model, while the association structure is parameterized via possibly time varying dependence ratios. We illustrate the technique using daily stock index returns from three European markets, from three Baltic markets, and from two Chinese exchanges.

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The quantilogram: With an application to evaluating directional predictability

Cited by 174 publications

References 31 publications

Predictive quantile regression with persistent covariates: IVX-QR approach

Predictive quantile regression with persistent covariates: IVX-QR approach

Quantile Autoregression

Multi-Market Direction-of-Change Modeling Using Dependence Ratios

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