Leandro dos Santos Maciel

Personal Details

First Name:	Leandro
Middle Name:	dos Santos
Last Name:	Maciel
Suffix:
RePEc Short-ID:	pma3473
	[This author has chosen not to make the email address public]
	https://www.fea.usp.br/usuarios/docente/leandromaciel
	+5515988103677
Terminal Degree:	(from RePEc Genealogy)

Affiliation

Faculdade de Economia, Administração e Contabilidade
Universidade de São Paulo

São Paulo, Brazil
http://www.fea.usp.br/
RePEc:edi:feuspbr (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

Sorry, no citations of working papers recorded.

Articles

1. Renan Diniz & Diogo de Prince & Leandro Maciel, 2022. "Bubble detection in Bitcoin and Ethereum and its relationship with volatility regimes," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 50(3), pages 429-447, March.

   Cited by:

   1. Ben Osman, Myriam & Galariotis, Emilios & Guesmi, Khaled & Hamdi, Haykel & Naoui, Kamel, 2024. "Are markets sentiment driving the price bubbles in the virtual?," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 272-285.

2. Maciel, Leandro, 2021. "A new approach to portfolio management in the Brazilian equity market: Does assets efficiency level improve performance?," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 38-56.

   Cited by:

   1. Oliveira, Alexandre Silva de & Ceretta, Paulo Sergio & Albrecht, Peter, 2023. "Performance comparison of multifractal techniques and artificial neural networks in the construction of investment portfolios," Finance Research Letters, Elsevier, vol. 55(PA).

3. Leandro Maciel & Rosangela Ballini, 2021. "Functional Fuzzy Rule-Based Modeling for Interval-Valued Data: An Empirical Application for Exchange Rates Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 743-771, February.

   Cited by:

   1. Ha Che-Ngoc & Nga Do-Thi & Thao Nguyen-Trang, 2023. "Profitability of Ichimoku-Based Trading Rule in Vietnam Stock Market in the Context of the COVID-19 Outbreak," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1781-1799, December.

4. Leandro Maciel, 2021. "Cryptocurrencies value‐at‐risk and expected shortfall: Do regime‐switching volatility models improve forecasting?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4840-4855, July.

   Cited by:

   1. Fantazzini, Dean, 2022. "Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death," MPRA Paper 113744, University Library of Munich, Germany.
      • Dean Fantazzini, 2022. "Crypto-Coins and Credit Risk: Modelling and Forecasting Their Probability of Death," JRFM, MDPI, vol. 15(7), pages 1-34, July.
   2. Panagiotidis, Theodore & Papapanagiotou, Georgios & Stengos, Thanasis, 2022. "On the volatility of cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 62(C).
      • Thanasis Stengos & Theodore Panagiotidis & Georgios Papapanagiotou, 2022. "On the volatility of cryptocurrencies," Working Papers 2202, University of Guelph, Department of Economics and Finance.
   3. Mercik, Aleksander & Słoński, Tomasz & Karaś, Marta, 2024. "Understanding crypto-asset exposure: An investigation of its impact on performance and stock sensitivity among listed companies," International Review of Financial Analysis, Elsevier, vol. 92(C).
   4. Nicolás Magner & Nicolás Hardy, 2022. "Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle," Mathematics, MDPI, vol. 10(13), pages 1-27, July.
   5. Ke, Rui & Yang, Luyao & Tan, Changchun, 2022. "Forecasting tail risk for Bitcoin: A dynamic peak over threshold approach," Finance Research Letters, Elsevier, vol. 49(C).
   6. Amaro, Raphael & Pinho, Carlos & Madaleno, Mara, 2022. "Forecasting the Value-at-Risk of energy commodities: A comparison of models and alternative distribution functions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 77-101.
   7. Amaro, Raphael & Pinho, Carlos, 2022. "Energy commodities: A study on model selection for estimating Value-at-Risk," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 5-27.
   8. Cheng, Jiyang & Tiwari, Sunil & Khaled, Djebbouri & Mahendru, Mandeep & Shahzad, Umer, 2024. "Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
   9. Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).
   10. Müller, Fernanda Maria & Santos, Samuel Solgon & Gössling, Thalles Weber & Righi, Marcelo Brutti, 2022. "Comparison of risk forecasts for cryptocurrencies: A focus on Range Value at Risk," Finance Research Letters, Elsevier, vol. 48(C).
   11. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.
   12. Gao, Lingbo & Ye, Wuyi & Guo, Ranran, 2022. "Jointly forecasting the value-at-risk and expected shortfall of Bitcoin with a regime-switching CAViaR model," Finance Research Letters, Elsevier, vol. 48(C).

5. Leandro Maciel, 2020. "Technical analysis based on high and low stock prices forecasts: evidence for Brazil using a fractionally cointegrated VAR model," Empirical Economics, Springer, vol. 58(4), pages 1513-1540, April.

   Cited by:

   1. Kangyi Li & Yang Zhou, 2024. "Improved Financial Predicting Method Based on Time Series Long Short-Term Memory Algorithm," Mathematics, MDPI, vol. 12(7), pages 1-17, April.
   2. Ciniro A. L. Nametala & Jonas Villela de Souza & Alexandre Pimenta & Eduardo Gontijo Carrano, 2023. "Use of Econometric Predictors and Artificial Neural Networks for the Construction of Stock Market Investment Bots," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 743-773, February.
   3. Monge, Manuel & Romero Rojo, María Fátima & Gil-Alana, Luis Alberiko, 2023. "The impact of geopolitical risk on the behavior of oil prices and freight rates," Energy, Elsevier, vol. 269(C).
   4. Monge, Manuel & Lazcano, Ana & Parada, José Luis, 2023. "Growth vs value investing: Persistence and time trend before and after COVID-19," Research in International Business and Finance, Elsevier, vol. 65(C).

6. Leandro Maciel & Rosangela Ballini & Fernando Gomide, 2018. "Evolving fuzzy modelling for yield curve forecasting," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 15(3), pages 290-311.

   Cited by:

   1. Helena Gaspars-Wieloch, 2020. "A New Application for the Goal Programming—The Target Decision Rule for Uncertain Problems," JRFM, MDPI, vol. 13(11), pages 1-14, November.
   2. Helena Gaspars-Wieloch, 2024. "AHP based on scenarios and the optimism coefficient for new and risky projects: case of independent criteria," Annals of Operations Research, Springer, vol. 341(2), pages 937-961, October.
   3. Helena Gaspars-Wieloch & Dominik Gawroński, 2024. "How can one improve SAW and max-min multi-criteria rankings based on uncertain decision rules?," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(1), pages 131-148.
   4. Gaspars-Wieloch Helena, 2021. "On some analogies between one-criterion decision making under uncertainty and multi-criteria decision making under certainty," Economics and Business Review, Sciendo, vol. 7(2), pages 17-36, June.

7. Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2016. "Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 379-398, October.

   Cited by:

   1. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
      • Bucci, Andrea, 2019. "Realized Volatility Forecasting with Neural Networks," MPRA Paper 95443, University Library of Munich, Germany.
      • Andrea Bucci, 0. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
   2. Colubi, Ana & Ramos-Guajardo, Ana Belén, 2023. "Fuzzy sets and (fuzzy) random sets in Econometrics and Statistics," Econometrics and Statistics, Elsevier, vol. 26(C), pages 84-98.
   3. Das, Sudeepa & Sahu, Tirath Prasad & Janghel, Rekh Ram, 2022. "Oil and gold price prediction using optimized fuzzy inference system based extreme learning machine," Resources Policy, Elsevier, vol. 79(C).
   4. Ehsan Hajizadeh & Masoud Mahootchi, 2019. "Developing a Risk-Based Approach for American Basket Option Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1593-1612, April.
   5. Seyed Mehrzad Asaad Sajadi & Pouya Khodaee & Ehsan Hajizadeh & Sabri Farhadi & Sohaib Dastgoshade & Bo Du, 2022. "Deep Learning-Based Methods for Forecasting Brent Crude Oil Return Considering COVID-19 Pandemic Effect," Energies, MDPI, vol. 15(21), pages 1-23, October.

8. Leandro Maciel, 2012. "A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting," Brazilian Review of Finance, Brazilian Society of Finance, vol. 10(3), pages 337-367.

   Cited by:

   1. Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2016. "Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 379-398, October.

Chapters

1. Leandro Maciel, 2013. "A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting," Palgrave Macmillan Books, in: Jonathan A. Batten & Peter MacKay & Niklas Wagner (ed.), Advances in Financial Risk Management, chapter 11, pages 253-283, Palgrave Macmillan.

   Cited by:

   1. Fahad Mostafa & Pritam Saha & Mohammad Rafiqul Islam & Nguyet Nguyen, 2021. "GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies," JRFM, MDPI, vol. 14(9), pages 1-22, September.
   2. Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2016. "Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 379-398, October.

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-FOR: Forecasting (1) 2014-03-15

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