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

Zhenming Zhang, Shishun Zhao, Jianhua Cheng, Anze Wang

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DOI: 10.3390/e28030326
Published: 2026-03-13
Container: Entropy
Publisher: MDPI AG
Open access: unknown

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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 = {Zhenming Zhang and Shishun Zhao and Jianhua Cheng and Anze Wang},
  year = {2026},
  journal = {Entropy},
  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  - Zhenming Zhang
AU  - Shishun Zhao
AU  - Jianhua Cheng
AU  - Anze Wang
PY  - 2026
JO  - Entropy
DO  - 10.3390/e28030326
UR  - https://doi.org/10.3390/e28030326
ER  -

APA

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

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crossref · retrieved 2026-11-10T18:49:07.550Z