Fernando Antonio Lucena Aiube

Personal Details

First Name:	Fernando Antonio
Middle Name:	Lucena
Last Name:	Aiube
Suffix:
RePEc Short-ID:	pai41
	[This author has chosen not to make the email address public]
	http://blogdolivro-aiube.blogspot.com.br/

Affiliation

Faculdade de Ciências Econômicas
Universidade do Estado do Rio de Janeiro

Rio de Janeiro, Brazil
http://www.fce.uerj.br/
RePEc:edi:fcerjbr (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

1. Márcio Poletti Laurini & Roberto Baltieri Mauad & Fernando Antonio Lucena Aiube, 2016. "Multivariate Stochastic Volatility-Double Jump Model: an application for oil assets," Working Papers Series 415, Central Bank of Brazil, Research Department.

   Cited by:

   1. Chaim, Pedro & Laurini, Márcio P., 2018. "Volatility and return jumps in bitcoin," Economics Letters, Elsevier, vol. 173(C), pages 158-163.
   2. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
      • Aurelio F. Bariviera & Ignasi Merediz-Sol`a, 2020. "Where do we stand in cryptocurrencies economic research? A survey based on hybrid analysis," Papers 2003.09723, arXiv.org.
   3. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
   4. Rodolfo C. Moura & Márcio P. Laurini, 2021. "Spillovers and jumps in global markets: A comparative analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5997-6013, October.
   5. Chaim, Pedro & Laurini, Márcio P., 2019. "Nonlinear dependence in cryptocurrency markets," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 32-47.

Articles

1. Fernando Antonio Lucena Aiube & Carlos Patricio Samanez, 2014. "On the comparison of Schwartz and Smith's two- and three-factor models on commodity prices," Applied Economics, Taylor & Francis Journals, vol. 46(30), pages 3736-3749, October.

   Cited by:

   1. Carolina Effio Saldivar & José Herskovits & Juan Pablo Luna & Claudia Sagastizábal, 2019. "Multidimensional Calibration Of Crude Oil And Refined Products Via Semidefinite Programming Techniques," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-31, February.
   2. Mario Figueiredo & Yuri F. Saporito, 2023. "Forecasting the term structure of commodities future prices using machine learning," Digital Finance, Springer, vol. 5(1), pages 57-90, March.
   3. Fernando Antonio Lucena Aiube & Ariel Levy, 2019. "Recent movement of oil prices and future scenarios [Movimentos recentes dos preços do petróleo e os cenários futuros]," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 29(1), pages 223-248, January-A.

2. Aiube, Fernando Antonio Lucena & Baidya, Tara Keshar Nanda & Tito, Edison Americo Huarsaya, 2008. "Analysis of commodity prices with the particle filter," Energy Economics, Elsevier, vol. 30(2), pages 597-605, March.

   Cited by:

   1. Date, Paresh & Mamon, Rogemar & Tenyakov, Anton, 2013. "Filtering and forecasting commodity futures prices under an HMM framework," Energy Economics, Elsevier, vol. 40(C), pages 1001-1013.
   2. Taylor-de-Lima, Reynaldo L.N. & Gerbasi da Silva, Arthur José & Legey, Luiz F.L. & Szklo, Alexandre, 2018. "Evaluation of economic feasibility under uncertainty of a thermochemical route for ethanol production in Brazil," Energy, Elsevier, vol. 150(C), pages 363-376.
   3. Chris Brooks & Marcel Prokopczuk, 2011. "The Dynamics of Commodity Prices," ICMA Centre Discussion Papers in Finance icma-dp2011-09, Henley Business School, University of Reading.
      • Chris Brooks & Marcel Prokopczuk, 2013. "The dynamics of commodity prices," Quantitative Finance, Taylor & Francis Journals, vol. 13(4), pages 527-542, March.
   4. Moreno, Manuel & Novales, Alfonso & Platania, Federico, 2019. "Long-term swings and seasonality in energy markets," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1011-1023.
      • Manuel Moreno & Alfonso Novales & Federico Platania, 2019. "Long-term swings and seasonality in energy markets," Documentos de Trabajo del ICAE 2019-29, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
   5. Mario Figueiredo & Yuri F. Saporito, 2023. "Forecasting the term structure of commodities future prices using machine learning," Digital Finance, Springer, vol. 5(1), pages 57-90, March.
   6. Aiube, Fernando Antonio Lucena & Faquieri, Winicius Botelho, 2019. "Can Gaussian factor models of commodity prices capture the financialization phenomenon?," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
   7. V. Guigues & C. Sagastizábal & J. P. Zubelli, 2014. "Robust Management and Pricing of Liquefied Natural Gas Contracts with Cancelation Options," Journal of Optimization Theory and Applications, Springer, vol. 161(1), pages 179-198, April.
   8. Fernando Antonio Lucena Aiube & Ariel Levy, 2019. "Recent movement of oil prices and future scenarios [Movimentos recentes dos preços do petróleo e os cenários futuros]," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 29(1), pages 223-248, January-A.
   9. Islyaev, Suren & Date, Paresh, 2015. "Electricity futures price models: Calibration and forecasting," European Journal of Operational Research, Elsevier, vol. 247(1), pages 144-154.
   10. Heydari, Somayeh & Siddiqui, Afzal, 2010. "Valuing a gas-fired power plant: A comparison of ordinary linear models, regime-switching approaches, and models with stochastic volatility," Energy Economics, Elsevier, vol. 32(3), pages 709-725, May.

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