A Bayesian Quantile Time Series Model for Asset Returns

Griffin, Jim E.; Mitrodima, Gelly

doi:10.1080/07350015.2020.1766470

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…They provide tools for inference that are robust to the existence of moments and to the form of weak cross-sectional dependence in the idiosyncratic error term. Griffin and Mitrodima (2022) consider jointly modeling a finite collection of quantiles over time. They propose a flexible Bayesian time-varying transformation model, which allows the likelihood and the quantile function to be directly calculated.…”

Section: Quantile Regressionbased Asset Pricingmentioning

confidence: 99%

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On tree-structured linear and quantile regression-based asset pricing

Galakis¹,

Vrontos

Xidonas

2022

RAF

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Purpose This study aims to introduce a tree-structured linear and quantile regression framework to the analysis and modeling of equity returns, within the context of asset pricing. Design/Methodology/Approach The approach is based on the idea of a binary tree, where every terminal node parameterizes a local regression model for a specific partition of the data. A Bayesian stochastic method is developed including model selection and estimation of the tree structure parameters. The framework is applied on numerous U.S. asset pricing models, using alternative mimicking factor portfolios, frequency of data, market indices, and equity portfolios. Findings The findings reveal strong evidence that asset returns exhibit asymmetric effects and non- linear patterns to different common factors, but, more importantly, that there are multiple thresholds that create several partitions in the common factor space. Originality/Value To the best of the authors' knowledge, this paper is the first to explore and apply a tree-structured and quantile regression framework in an asset pricing context.

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Section: Quantile Regressionbased Asset Pricingmentioning

confidence: 99%

“…Griffin and Mitrodima (2022) consider jointly modeling a finite collection of quantiles over time. They propose a flexible Bayesian time-varying transformation model, which allows the likelihood and the quantile function to be directly calculated.…”

Section: Literature Reviewmentioning

confidence: 99%

On tree-structured linear and quantile regression-based asset pricing

Galakis¹,

Vrontos

Xidonas

2022

RAF

View full text Add to dashboard Cite

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“…Summarising the results of a comprehensive comparison of different models, data and transformations,Faust and Wright (2013) argue that a basic principle in forecasting inflation is to allow for its local mean to be smoothly varying over time, and an obvious way of doing this is via time-varying parameters(Stock and Watson, 2007).2 For related Bayesian modeling approaches, seeGerlach et al (2011),Griffin andMitrodima (2020) andPfarrhofer (2021).ECB Working Paper Series No 2600 / October 2021…”

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confidence: 99%

The Time-Varying Evolution of Inflation Risks

et al. 2021

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This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting inflation with the ability of quantile regression to model flexibly the whole distribution of inflation. In order to make our approach accessible and empirically relevant for forecasting, we derive an efficient Gibbs sampler by transforming the state-space form of the TVP quantile regression into an equivalent high-dimensional regression form. An application of this methodology points to a good forecasting performance of quantile regressions with TVPs augmented with specific credit and money-based indicators for the prediction of the conditional distribution of inflation in the euro area, both in the short and longer run, and specifically for tail risks.

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“…A variety of quantile regression applications can be found inBuchinsky (1994),Koenker and Hallock (2001),Engle and Manganelli (2004),Koenker (2005),White et al (2015),Koenker (2017),Kiley (2018), Escanciano and Hualde (2020),Griffin and Mitrodima (2020) andSong and Taamouti (2020).ECB Working Paper Series No 2436 / July 2020…”

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confidence: 99%