--- title: "rugarch (R package): papers citing this CRAN package" description: "14 papers in the tracked journals link to rugarch (R package) in their Crossref records, the first in 2017; most of them (3) in 2018." url: https://scholar4ai.com/code/cran-rugarch markdown_url: https://scholar4ai.com/code/cran-rugarch.md license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/) share_text: "14 papers in the tracked journals link to rugarch (R package) in their Crossref records, the first in 2017; most of them (3) in 2018. (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) --- # rugarch (R package): papers linking to it Research papers in the 480 tracked journals that link to rugarch (R package) (https://CRAN.R-project.org/package=rugarch), 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=rugarch All repositories: https://scholar4ai.com/code.md ## Key findings - 14 papers in the tracked journals link to rugarch (R package) in their Crossref records, the first in 2017; most of them (3) in 2018. - By category: Economics (7), Finance (4) and Statistics (2); the journals with the most are Computational Economics (4), International Journal of Forecasting (2) and Insurance Mathematics and Economics (1). ## By year *Papers linking to rugarch (R package) by publication year* | Year | Papers | |---|---:| | 2017 | 3 | | 2018 | 3 | | 2019 | 2 | | 2021 | 1 | | 2022 | 2 | | 2023 | 1 | | 2024 | 1 | | 2026 | 1 | ## Journals *Journals whose papers link to rugarch (R package) most* | Journal | Papers | |---|---:| | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | 4 | | [International Journal of Forecasting](https://scholar4ai.com/journals/international-journal-of-forecasting.md) | 2 | | [Insurance Mathematics and Economics](https://scholar4ai.com/journals/insurance-mathematics-and-economics.md) | 1 | | [Journal of Economics and Finance](https://scholar4ai.com/journals/journal-of-economics-and-finance.md) | 1 | | [Finance Research Open](https://scholar4ai.com/journals/finance-research-open.md) | 1 | ## Papers *Papers linking to rugarch (R package), most cited first* | Published | Title | Journal | Authors | Citations | Link | |---|---|---|---|---:|---| | 2018-05-04 | [Portfolio optimization based on GARCH-EVT-Copula forecasting models](https://doi.org/10.1016/j.ijforecast.2018.02.004) | [International Journal of Forecasting](https://scholar4ai.com/journals/international-journal-of-forecasting.md) | Maziar Sahamkhadam, Andreas Stephan, Ralf Östermark | 84 | cites it | | 2017-04-21 | [Stock return prediction under GARCH — An empirical assessment](https://doi.org/10.1016/j.ijforecast.2017.01.002) | [International Journal of Forecasting](https://scholar4ai.com/journals/international-journal-of-forecasting.md) | Helmut Herwartz | 78 | cites it | | 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 | | 2019-05-13 | [Forecasting Financial Returns Volatility: A GARCH-SVR Model](https://doi.org/10.1007/s10614-019-09896-w) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Hao Sun, Bo Yu | 54 | cites it | | 2018-05-19 | [Quantifying uncertainty in short-term traffic prediction and its application to optimal staffing plan development](https://doi.org/10.1016/j.trc.2018.05.012) | [Transportation Research Part C Emerging Technologies](https://scholar4ai.com/journals/transportation-research-part-c-emerging-technologies.md) | Lei Lin, John C. Handley, Yiming Gu et al. | 53 | cites it | | 2021-11-09 | [More to cryptos than bitcoin: A GARCH modelling of heterogeneous cryptocurrencies](https://doi.org/10.1016/j.frl.2021.102544) | [Finance research letters](https://scholar4ai.com/journals/finance-research-letters.md) | Kennard Fung, Jiin Jeong, Javier Pereira | 41 | cites it | | 2019-09-27 | [Exploring the driving forces of the Bitcoin currency exchange rate dynamics: an EGARCH approach](https://doi.org/10.1007/s00181-019-01776-4) | [Empirical Economics](https://scholar4ai.com/journals/empirical-economics.md) | Siwen Zhou | 31 | cites it | | 2023-08-02 | [Volatility spillovers between oil and financial markets during economic and financial crises: A dynamic approach](https://doi.org/10.1007/s12197-023-09634-x) | [Journal of Economics and Finance](https://scholar4ai.com/journals/journal-of-economics-and-finance.md) | Javier Sánchez García, Salvador Cruz Rambaud | 26 | cites it | | 2022-11-10 | [Comparison of Value at Risk (VaR) Multivariate Forecast Models](https://doi.org/10.1007/s10614-022-10330-x) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Fernanda Maria Müller, Marcelo Brutti Righi | 18 | cites it | | 2017-04-07 | [Visualizing dependence in high-dimensional data: An application to S&P 500 constituent data](https://doi.org/10.1016/j.ecosta.2017.03.007) | [Econometrics and Statistics](https://scholar4ai.com/journals/econometrics-and-statistics.md) | Marius Hofert, Wayne Oldford | 17 | 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 | | 2018-03-17 | [Risk: An R Package for Financial Risk Measures](https://doi.org/10.1007/s10614-018-9806-9) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Stephen Chan, Saralees Nadarajah | 8 | 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 | | 2026-08-22 | [Forecasting exchange rate volatility in Ghana: A GARCH family and facebook prophet approach](https://doi.org/10.1016/j.finr.2026.100166) | [Finance Research Open](https://scholar4ai.com/journals/finance-research-open.md) | Emmanuel Ayitey, Jonathan Sackey, Charles Kwame Appiah et al. | 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). rugarch (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-rugarch ```bibtex @misc{scholar4ai-code-cran-rugarch, author = {{Scholar4AI}}, title = {rugarch (R package): papers linking to it}, year = {2026}, howpublished = {\url{https://scholar4ai.com/code/cran-rugarch}}, 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/).