New Approaches to Robust Inference on Market (Non-)efficiency, Volatility Clustering and Nonlinear Dependence

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New Approaches to Robust Inference on Market (Non-)efficiency, Volatility Clustering and Nonlinear Dependence. / Ибрагимов, Рустам Маратович; Pedersen, Rasmus Søndergaard; Скроботов, Антон Андреевич.

In: Journal of Financial Econometrics, 08.08.2023.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{535d6513d1a546c0b450d9a390c8ef4b,

title = "New Approaches to Robust Inference on Market (Non-)efficiency, Volatility Clustering and Nonlinear Dependence",

abstract = "We present novel, robust methods for inference on market (non-)efficiency, volatility clustering, and nonlinear dependence in financial return series. In contrast to existing methodology, our proposed methods are robust against nonlinear dynamics and tail-heaviness of returns. Specifically, our methods only rely on return processes being stationary and weakly dependent (mixing) with finite moments of a suitable order. This includes robustness against power-law distributions associated with nonlinear dynamic models such as GARCH and stochastic volatility. The methods are easy to implement and perform well in realistic settings. We revisit a recent study by Baltussen, van Bekkum, and Da (2019, J. Financ. Econ., 132, 26–48) on autocorrelation in major stock indexes. Using our robust methods, we document that the evidence of the presence of negative autocorrelation is weaker, compared with the conclusions of the original study.",

author = "Ибрагимов, {Рустам Маратович} and Pedersen, {Rasmus S{\o}ndergaard} and Скроботов, {Антон Андреевич}",

year = "2023",

month = aug,

day = "8",

doi = "10.1093/jjfinec/nbad020",

language = "English",

journal = "Journal of Financial Econometrics",

issn = "1479-8409",

publisher = "Oxford University Press",

}

RIS

TY - JOUR

T1 - New Approaches to Robust Inference on Market (Non-)efficiency, Volatility Clustering and Nonlinear Dependence

AU - Ибрагимов, Рустам Маратович

AU - Pedersen, Rasmus Søndergaard

AU - Скроботов, Антон Андреевич

PY - 2023/8/8

Y1 - 2023/8/8

N2 - We present novel, robust methods for inference on market (non-)efficiency, volatility clustering, and nonlinear dependence in financial return series. In contrast to existing methodology, our proposed methods are robust against nonlinear dynamics and tail-heaviness of returns. Specifically, our methods only rely on return processes being stationary and weakly dependent (mixing) with finite moments of a suitable order. This includes robustness against power-law distributions associated with nonlinear dynamic models such as GARCH and stochastic volatility. The methods are easy to implement and perform well in realistic settings. We revisit a recent study by Baltussen, van Bekkum, and Da (2019, J. Financ. Econ., 132, 26–48) on autocorrelation in major stock indexes. Using our robust methods, we document that the evidence of the presence of negative autocorrelation is weaker, compared with the conclusions of the original study.

AB - We present novel, robust methods for inference on market (non-)efficiency, volatility clustering, and nonlinear dependence in financial return series. In contrast to existing methodology, our proposed methods are robust against nonlinear dynamics and tail-heaviness of returns. Specifically, our methods only rely on return processes being stationary and weakly dependent (mixing) with finite moments of a suitable order. This includes robustness against power-law distributions associated with nonlinear dynamic models such as GARCH and stochastic volatility. The methods are easy to implement and perform well in realistic settings. We revisit a recent study by Baltussen, van Bekkum, and Da (2019, J. Financ. Econ., 132, 26–48) on autocorrelation in major stock indexes. Using our robust methods, we document that the evidence of the presence of negative autocorrelation is weaker, compared with the conclusions of the original study.

UR - https://www.mendeley.com/catalogue/6a4c7c81-fdac-3935-a0cf-970d13482c12/

U2 - 10.1093/jjfinec/nbad020

DO - 10.1093/jjfinec/nbad020

M3 - Article

JO - Journal of Financial Econometrics

JF - Journal of Financial Econometrics

SN - 1479-8409

ER -

ID: 113384226