Volume 20 , Issue 3 , PP: 65-71, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Noura Metawa 1 * , Rhada Boujlil 2 , Maha Metawea 3
Doi: https://doi.org/10.54216/IJNS.200306
The media, legislators, investors, scholars, and regulatory agencies have all shown increased interest in the Cryptocurrency sector recently. Using different criteria in furthermore to return and risk in the cryptocurrency issue utilizing the multi-criteria decision-making (MCDM) methodologies makes it more practical in the real world. A model for predicting the volatility of cryptocurrencies is proposed in this study, and it is based on the TOPSIS approach. The model uses five criteria and six cryptocurrencies. Using a multi-valued neutrosophic set, also known as MVNS, helps to reduce the amount of uncertainty associated with the problem. MVNS was used to express the criteria and alternatives, and the model might possibly represent the Cryptocurrency with varying degrees of truth, indeterminacy, and falsity values.
TOPSIS , Multi-valued neutrosophic sets , Forecasting cryptocurrency volatility , MCDM
[1] F. Ma, C. Liang, Y. Ma, and M. I. M. Wahab, “Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach,” Journal of Forecasting, vol. 39, no. 8, pp. 1277–1290, 2020.
[2] Z. Ftiti, W. Louhichi, and H. Ben Ameur, “Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?,” Annals of Operations Research, pp. 1–26, 2021.
[3] L. Catania and S. Grassi, “Forecasting cryptocurrency volatility,” International Journal of Forecasting, vol. 38, no. 3, pp. 878–894, 2022.
[4] T. Walther, T. Klein, and E. Bouri, “Exogenous drivers of Bitcoin and Cryptocurrency volatility–A mixed data sampling approach to forecasting,” Journal of International Financial Markets, Institutions and Money, vol. 63, p. 101133, 2019.
[5] J.-M. Kim, C. Jun, and J. Lee, “Forecasting the volatility of the cryptocurrency market by GARCH and Stochastic Volatility,” Mathematics, vol. 9, no. 14, p. 1614, 2021.
[6] L. Catania, S. Grassi, and F. Ravazzolo, “Predicting the volatility of cryptocurrency time-series,” Mathematical and Statistical Methods for Actuarial Sciences and Finance: MAF 2018, pp. 203–207, 2018.
[7] J. Almeida and T. C. Gonçalves, “A systematic literature review of volatility and risk management on cryptocurrency investment: A methodological point of view,” Risks, vol. 10, no. 5, p. 107, 2022.
[8] N. Abu Bakar and S. Rosbi, “Autoregressive integrated moving average (ARIMA) model for forecasting cryptocurrency exchange rate in high volatility environment: A new insight of bitcoin transaction,” International Journal of Advanced Engineering Research and Science, vol. 4, no. 11, pp. 130–137, 2017.
[9] Y. Peng, P. H. M. Albuquerque, J. M. C. de Sá, A. J. A. Padula, and M. R. Montenegro, “The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression,” Expert Systems with Applications, vol. 97, pp. 177–192, 2018.
[10] H. Peng, H. Zhang, and J. Wang, “Probability multi-valued neutrosophic sets and its application in multi-criteria group decision-making problems,” Neural Computing and Applications, vol. 30, pp. 563–583, 2018.
[11] J. Peng, J. Wang, X. Wu, J. Wang, and X. Chen, “Multi-valued neutrosophic sets and power aggregation operators with their applications in multi-criteria group decision-making problems,” International Journal of Computational Intelligence Systems, vol. 8, no. 2, pp. 345–363, 2015.
[12] P. Liu, S. Cheng, and Y. Zhang, “An extended multi-criteria group decision-making PROMETHEE method based on probability multi-valued neutrosophic sets,” International journal of fuzzy systems, vol. 21, pp. 388–406, 2019.
[13] P. Liu and S. Cheng, “An extension of ARAS methodology for multi-criteria group decision-making problems within probability multi-valued neutrosophic sets,” International Journal of Fuzzy Systems, vol. 21, pp. 2472–2489, 2019.
[14] J. Ye, J. Song, and S. Du, “Correlation coefficients of consistency neutrosophic sets regarding neutrosophic multi-valued sets and their multi-attribute decision-making method,” International Journal of Fuzzy Systems, pp. 1–8, 2022.
[15] J. Ye, S. Du, and R. Yong, “Correlation coefficients of credibility interval-valued neutrosophic sets and their group decision-making method in single-and interval-valued hybrid neutrosophic multi-valued environment,” Complex & Intelligent Systems, vol. 7, pp. 3225–3239, 2021.
[16] H. Guan, J. He, A. Zhao, Z. Dai, and S. Guan, “A forecasting model based on multi-valued neutrosophic sets and two-factor, third-order fuzzy fluctuation logical relationships,” Symmetry, vol. 10, no. 7, p. 245, 2018.
[17] W. Yang and Y. Pang, “New multiple attribute decision making method based on DEMATEL and TOPSIS for multi-valued interval neutrosophic sets,” Symmetry, vol. 10, no. 4, p. 115, 2018.
[18] J. Peng, J. Wang, and W.-E. Yang, “A multi-valued neutrosophic qualitative flexible approach based on likelihood for multi-criteria decision-making problems,” International Journal of Systems Science, vol. 48, no. 2, pp. 425–435, 2017.
[19] A. Mahmood, M. Abbas, and G. Murtaza, “Multi-Valued Multi-Polar Neutrosophic Sets with an application in Multi-Criteria Decision-Making,” Neutrosophic Sets and Systems, vol. 53, no. 1, p. 32, 2023.
[20] L. Yang, B. Li, and H. Xu, “Multi-valued neutrosophic linguistic power operators and their applications,” Engineering Letters, vol. 26, no. 4, pp. 518–525, 2018.
[21] J. Peng, J. Wang, and X. Wu, “An extension of the ELECTRE approach with multi-valued neutrosophic information,” Neural Computing and Applications, vol. 28, pp. 1011–1022, 2017.
[22] X. Libo, L. Xingsen, and C. Honglei, “Novel Stable Approach with Probability Distribution for Multi-Criteria Decision-Making Problems of Multi-Valued Neutrosophic Sets,” International Journal of Information Technology & Decision Making, vol. 19, no. 05, pp. 1271–1292, 2020.
[23] J. Ye, E. Türkarslan, M. Ünver, and M. Olgun, “Algebraic and Einstein weighted operators of neutrosophic enthalpy values for multi-criteria decision making in neutrosophic multi-valued set settings,” Granular Computing, vol. 7, no. 3, pp. 479–487, 2022.
