---
title: "fGarch (R package): papers citing this CRAN package"
description: "6 papers in the tracked journals link to fGarch (R package) in their Crossref records, the first in 2017; most of them (2) in 2022."
url: https://scholar4ai.com/code/cran-fgarch
markdown_url: https://scholar4ai.com/code/cran-fgarch.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "6 papers in the tracked journals link to fGarch (R package) in their Crossref records, the first in 2017; most of them (2) in 2022. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# fGarch (R package): papers linking to it

Research papers in the 480 tracked journals that link to fGarch (R package) (https://CRAN.R-project.org/package=fGarch), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- CRAN
- https://CRAN.R-project.org/package=fGarch

All repositories: https://scholar4ai.com/code.md

## Key findings

- 6 papers in the tracked journals link to fGarch (R package) in their Crossref records, the first in 2017; most of them (2) in 2022.
- By category: Economics (2), Statistics (2) and Finance (1); the journals with the most are Insurance Mathematics and Economics (1), Computational Economics (1) and Data Mining and Knowledge Discovery (1).

## By year

*Papers linking to fGarch (R package) by publication year*

| Year | Papers |
|---|---:|
| 2017 | 1 |
| 2018 | 1 |
| 2022 | 2 |
| 2024 | 1 |
| 2026 | 1 |

## Journals

*Journals whose papers link to fGarch (R package) most*

| Journal | Papers |
|---|---:|
| [Insurance Mathematics and Economics](https://scholar4ai.com/journals/insurance-mathematics-and-economics.md) | 1 |
| [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | 1 |
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 1 |
| [Journal of Econometrics](https://scholar4ai.com/journals/journal-of-econometrics.md) | 1 |
| [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | 1 |

## Papers

*Papers linking to fGarch (R package), most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2017-11-01 | [Compound unimodal distributions for insurance losses](https://doi.org/10.1016/j.insmatheco.2017.10.007) | [Insurance Mathematics and Economics](https://scholar4ai.com/journals/insurance-mathematics-and-economics.md) | Antonio Punzo, Luca Bagnato, Antonello Maruotti | 73 | cites it |
| 2022-03-31 | [Novel features for time series analysis: a complex networks approach](https://doi.org/10.1007/s10618-022-00826-3) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Vanessa Freitas Silva, Maria Eduarda Silva, Pedro Ribeiro et al. | 29 | cites it |
| 2018-05-05 | [Inference on the tail process with application to financial time series modeling](https://doi.org/10.1016/j.jeconom.2018.01.009) | [Journal of Econometrics](https://scholar4ai.com/journals/journal-of-econometrics.md) | Richard A. Davis, Holger Drees, Johan Segers et al. | 24 | cites it |
| 2022-11-03 | [Forecasting Value at Risk and Expected Shortfall of Foreign Exchange Rate Volatility of Major African Currencies via GARCH and Dynamic Conditional Correlation Analysis](https://doi.org/10.1007/s10614-022-10340-9) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Emmanuel Afuecheta, Idika Eke OKORIE, Saralees Nadarajah et al. | 15 | cites it |
| 2024-03-23 | [An expectile computation cookbook](https://doi.org/10.1007/s11222-024-10403-x) | [Statistics and Computing](https://scholar4ai.com/journals/statistics-and-computing.md) | Abdelaati Daouia, Gilles Stupfler, Antoine Usseglio‐Carleve | 8 | cites it |
| 2026-05-26 | [Estimating the signal dimension in multivariate time series using augmented second order source separation methods](https://doi.org/10.1007/s00180-026-01762-2) | [Computational Statistics](https://scholar4ai.com/journals/computational-statistics.md) | Klaus Nordhausen, Una Radojičić | 0 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). fGarch (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-fgarch

```bibtex
@misc{scholar4ai-code-cran-fgarch,
  author = {{Scholar4AI}},
  title = {fGarch (R package): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/cran-fgarch}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).