Quasi-Maximum Exponential Likelihood Estimation of Conditional Quantiles for GARCH Models Based on High-Frequency Augmented Data.

Zhang Z, Zhao S, Cheng J, Wang A

DOI: 10.3390/e28030326
Published: 2026 Mar 13
Container: Entropy (Basel, Switzerland)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/e28030326,
  title = {Quasi-Maximum Exponential Likelihood Estimation of Conditional Quantiles for GARCH Models Based on High-Frequency Augmented Data.},
  author = {Zhang Z and Zhao S and Cheng J and Wang A},
  year = {2026},
  journal = {Entropy (Basel, Switzerland)},
  doi = {10.3390/e28030326},
  url = {https://doi.org/10.3390/e28030326}
}

RIS

TY  - JOUR
TI  - Quasi-Maximum Exponential Likelihood Estimation of Conditional Quantiles for GARCH Models Based on High-Frequency Augmented Data.
AU  - Zhang Z
AU  - Zhao S
AU  - Cheng J
AU  - Wang A
PY  - 2026
JO  - Entropy (Basel, Switzerland)
DO  - 10.3390/e28030326
UR  - https://doi.org/10.3390/e28030326
ER  -

APA

Z, Z., S, Z., J, C., & A, W. (2026). Quasi-Maximum Exponential Likelihood Estimation of Conditional Quantiles for GARCH Models Based on High-Frequency Augmented Data.. Entropy (Basel, Switzerland). https://doi.org/10.3390/e28030326

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pubmed · retrieved 2026-09-25T03:19:08.983Z