A Markov-switching COGARCH approach to cryptocurrency portfolio selection and optimization
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DOI: 10.1007/s11408-020-00346-4
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Cited by:
- Mario I. Contreras-Valdez & José Antonio Núñez & Guillermo Benavides Perales, 2022. "Bitcoin in Portfolio Selection: A Multivariate Distribution Approach," SAGE Open, , vol. 12(2), pages 21582440221, May.
- John Weirstrass Muteba Mwamba & Sutene Mwambetania Mwambi, 2021. "Assessing Market Risk in BRICS and Oil Markets: An Application of Markov Switching and Vine Copula," IJFS, MDPI, vol. 9(2), pages 1-22, May.
- 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).
- Osman, Myriam Ben & Galariotis, Emilios & Guesmi, Khaled & Hamdi, Haykel & Naoui, Kamel, 2023. "Diversification in financial and crypto markets," International Review of Financial Analysis, Elsevier, vol. 89(C).
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More about this item
Keywords
Long range dependence; Lévy processes; Differential evolution; R-vine copula; Portfolio optimization;All these keywords.
JEL classification:
- C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
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