The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression
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Citations
Cites
Engle, R. F. (1982) · Econometrica · doi:10.2307/1912773 · Cited by 20,795
Urquhart, A. (2016) · Economics Letters · doi:10.1016/j.econlet.2016.09.019 · Cited by 1,338
Bridging the divide in financial market forecasting: machine learners vs. financial economists
Hsu, M.-W., Lessmann, S., Sung, M.-C., Ma, T., & Johnson, J. E. V. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.05.033 · Cited by 195
New Private Monies: A Bit-Part Player?
Dowd, K. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.3903964 · Cited by 68
Cited by
RETRACTED: Cryptocurrencies as a financial asset: A systematic analysis
Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019) · International Review of Financial Analysis · doi:10.1016/j.irfa.2018.09.003 · Cited by 1,172
RetractedMallqui, D. C. A., & Fernandes, R. A. S. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2018.11.038 · Cited by 253
Metadata
- Authors
- Peng, Y., Albuquerque, P. H. M., Camboim de Sá, J. M., Padula, A. J. A., & Montenegro, M. R.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 97
- Pages
- 177-192
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2771814524
- Cited by
- 257
- References
- 4
- FWCI
- 36.03
- Type
- journal article
- Language
- en
- Open access
- closed
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- This page
- CoChat. (2026). Citation check: The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression. https://app.cochat.ai/papers/10.1016/j.eswa.2017.12.004
- The paper
- Peng, Y., Albuquerque, P. H. M., Camboim de Sá, J. M., Padula, A. J. A., & Montenegro, M. R. (2018). The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2017.12.004
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
