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

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

New Approaches to Robust Inference on Market (Non-)efficiency, Volatility Clustering and Nonlinear Dependence. / Ibragimov, Rustam; Pedersen, Rasmus Søndergaard; Skrobotov, Anton.

I: Journal of Financial Econometrics, 2024.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Ibragimov, R, Pedersen, RS & Skrobotov, A 2024, 'New Approaches to Robust Inference on Market (Non-)efficiency, Volatility Clustering and Nonlinear Dependence', Journal of Financial Econometrics. https://doi.org/10.1093/jjfinec/nbad020

APA

Ibragimov, R., Pedersen, R. S., & Skrobotov, A. (2024). New Approaches to Robust Inference on Market (Non-)efficiency, Volatility Clustering and Nonlinear Dependence. Journal of Financial Econometrics. https://doi.org/10.1093/jjfinec/nbad020

Vancouver

Ibragimov R, Pedersen RS, Skrobotov A. New Approaches to Robust Inference on Market (Non-)efficiency, Volatility Clustering and Nonlinear Dependence. Journal of Financial Econometrics. 2024. https://doi.org/10.1093/jjfinec/nbad020

Author

Ibragimov, Rustam ; Pedersen, Rasmus Søndergaard ; Skrobotov, Anton. / New Approaches to Robust Inference on Market (Non-)efficiency, Volatility Clustering and Nonlinear Dependence. I: Journal of Financial Econometrics. 2024.

Bibtex

@article{4a9051ddbe3241aaa43497aa49359388,
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 = "Rustam Ibragimov and Pedersen, {Rasmus S{\o}ndergaard} and Anton Skrobotov",
year = "2024",
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 - Ibragimov, Rustam

AU - Pedersen, Rasmus Søndergaard

AU - Skrobotov, Anton

PY - 2024

Y1 - 2024

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.

U2 - 10.1093/jjfinec/nbad020

DO - 10.1093/jjfinec/nbad020

M3 - Journal article

JO - Journal of Financial Econometrics

JF - Journal of Financial Econometrics

SN - 1479-8409

ER -

ID: 365964181