بررسی عوامل موثر بر نوسانات قیمت رمز ارزها با تأکید بر سیاست‌های پولی و مالی ایالات متحده

Abolhasani, M. J., & Samadi, S. (2020). Analyzing the factors affecting the price of virtual currencies (Case study: Bitcoin and Ethereum). Journal of Monetary and Banking Research, 13(46), 591-629. SID. https://sid.ir/paper/1060154/fa

Bashiri, M., & Paryab, S. H. (2020). Predicting Bitcoin price using machine learning algorithms. Applied Economics, 10(34 & 35), 1-13. doi: 10.30495/JAE.2020.18114

Dashchi, A. (2021). Predicting Bitcoin cryptocurrency price using machine learning. [Master’s thesis, Campus of Science, Faculty of Mathematics, Statistics, and Computer Science]. doi: 10.30495/FED.2023.700133

Soleimanipour, M. M., Soltaninejad, H., & Pourmotahar, M. (2017). A jurisprudential (Fiqh) investigation of virtual money. Islamic Financial Researches, 6(2), 167-192. SID. https://sid.ir/paper/254891/fa

Sadeghian, M. K., Yavari, K., & Alavi Rad, A. (2021). Identifying variables affecting the price of Bitcoin cryptocurrency: Bayesian Model Averaging (BMA) and Weighted Average Least Squares (WALS) approach. Financial Engineering and Portfolio Management, 12(46), 517-539. doi: 20.1001.1.22519165.1400.12.46.22.8

Salehifar, M. (2019). Investigating the return and risk behavior of Bitcoin compared to gold, foreign exchange, and stock markets using GJR-GARCH and Threshold GARCH models. Financial Engineering and Portfolio Management, 10(40), 152-168. doi: 20.1001.1.22519165.1398.10.40.7.7

Mohammadsharifi, A., Khalili-Damghani, K., Abdi, F., & Sardar, S. (2021). Predicting Bitcoin price using a hybrid ARIMA and Deep Learning model. Industrial Management Studies, 19(61), 125-146. doi: 10.22054/jims.2021.52374.2488 doi: 10.22054/jims.2021.52374.2488

Mirzaei, K., Fotouhabadi, V., & Nosratabadi, J. (2018). Investigating factors affecting Bitcoin fluctuations and inherent financial risks in cryptocurrencies using the GARCH(1,1) model. Quarterly Journal of Defense Economics, 3(10), 131-148. doi: 20.1001.1.25382454.1397.3.10.6.6

Agnello, L., V. Castro, G. Dufrénot, F. Jawadi, and R. M. Sousa. (2019). “Unconventional Monetary Policy Reaction Functions: Evidence from the US.” Studies in Nonlinear Dynamics and Econometrics 24 (4).    DOI: 10.1515/snde-2018-0088

Albariqi, R., & Winarko, E. (2020). “Prediction of Bitcoin Price Change using Neural Networks”, In 2020 International Conference on Smart Technology and Applications (ICoSTA) (pp. 1-4). IEEE.    DOI: 10.1109/ICoSTA48221.2020.1570610936

Al-Yahyaee, K. H. Mensi, W. Yoon, a., (2018). Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets, Finance Research Letters Volume 27: 228-234.   https://doi.org/10.1016/j.frl.2018.03.017

Ampountolas, A. (2022). Cryptocurrencies Intraday High-Frequency Volatility Spillover Effects Using Univariate and Multivariate GARCH Models. Int. J. Financ, 10, 51.   https://doi.org/10.3390/ijfs10030051

Angelini, E; Banbura, M; Rünstler, G. (2010). Estimating and forecasting the euro area monthly national accounts from a dynamic factor model. OECD Journal: Journal of Business Cycle Measurement and Analysis, 2010(1), 1–22.  https://doi.org/10.1787/jbcma-2010-5kmmsxgf2qbs

Avdjiev, S., and G. Hale. (2019). “US Monetary Policy and Fluctuations of International Bank Lending.” Journal of International Money and Finance 95: 251–268.  https://doi.org/10.1016/j.jimonfin.2018.06.013

Aye, G . ; Gupta, R . ; Hammoudeh, Sh., & Kim, W. J. (2014). Forecasting the Price of Gold Using Dynamic Model Averaging . University of Pretoria, Department of Economics Working Paper Series.   DOI: 10.1016/j.irfa.2015.03.010

Balcilar, M. ; Gupta, R. ; Eyden, R. ; Thompson, K., & Majumdar, A. (2018). Comparing the forecasting ability of financial conditions indices:The case of South Africa. The Quarterly Review of Economics and Finance, 69(C): 245 - 259.  https://doi.org/10.1016/j.qref.2018.03.012

Belmonte, M; Koop, G. (2014). Model Switching and Model Averaging in Time-Varying Parameter Regression Models. Advances in Econometrics, 34, 45–69. DOI: 10.1108/S0731-905320140000034004

Bernanke, B. S., & Boivin, J. (2003). Monetary policy in a data -rich environment. Journal of Monetary Economics, 50 (3): 525 -546.  doi:10.1016/S0304-3932(03)00024-2

Blau BM, Griffith TG, Whitby RJ, (2021) Inflation and Bitcoin: A descriptive time-series analysis.Econ Lett 203: 109848.   https://doi.org/10.1016/j.econlet.2021.109848

Boivin, J., & Ng, S. (2006). Are more data always better for factor analysis? . Journal of Econometrics, 132 (1): 169 -194.  https://doi.org/10.1016/j.jeconom.2005.01.027

Briere, M. Oosterlinck, K. Szafarz, A., (2013). Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoin. Journal of Asset Management, 16, 6, 365-373.  DOI: 10.2139/ssrn.2324780

Buncic, D., & Moretto, C. (2015). Forecasting copper prices with dynamic averaging and selection models. North American Journal of Economics and Finance, 33: 1 -38.   https://doi.org/10.1016/j.najef.2015.03.002

Cheah E., Fry J., (2015), Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin, Economics Letters 130: 32– 36.  https://doi.org/10.1016/j.econlet.2015.02.029

Chen, Q., A. Filardo, D. He, and F. Zhu. (2016). “Financial Crisis, US Unconventional Monetary Policy and International Spillovers.” Journal of International Money and Finance 67: 62–81.  https://doi.org/10.1016/j.jimonfin.2015.06.011

Chohan UW ,(2021), Cryptocurrencies and Hyperinflation. Critical Blockchain Research Initiative (CBRI) Working Papers.  DOI: 10.2139/ssrn.3320702

Ciaian, P., Rajcaniova, M. & Kancs, D. A. (2018). Virtual relationships: Short- and long-run evidence from Bitcoin and altcoin markets. Journal of International Financial Markets, Institutions and Money, 52, 173-195.

Christopher, C. M. (2014). Whack - a -Mole: Why prosecuting digital currency exchanges won’t stop online laundering. Lewis & Clark Law Review, 18(1), 1–36.  http://hdl.handle.net/10601/2119

Conlon T, Corbet S, Mcgee RJ , (2021). Inflation and cryptocurrencies revisited: A time-scale analysis.  https://ssrn.com/abstract=3890938  DOI: 10.1016/j.econlet.2021.109996

Corbet, S., C. Larkin, B. Lucey, A. Meegan, and L. Yarovaya. (2020). “Cryptocurrency Reaction to FOMC Announcements: Evidence of Heterogeneity Based on Blockchain Stack Position.” Journal of Financial Stability 46: 100706.  https://doi.org/10.1016/j.jfs.2019.100706

Corradi F., Höfner P., (2018). The disenchantment of Bitcoin: unveiling the myth of a digital currency, International Review of Sociology, 28:1: 193-207.  https://doi.org/10.1080/03906701.2018.1430067

Drachal, K. (2016). Forecasting spot oil price in a dynamic model averaging framework - Have the determinants changed over time? Energy Economics, 60, 35–46.  DOI: 10.1016/j.eneco.2016.09.020

Dutta, A., Kumar, S., & Basu, M. (2020). “A Gated Recurrent Unit Approach to Bitcoin Price Prediction”, Journal of Risk and Financial Management, 13(2), 23.   https://doi.org/10.3390/jrfm13020023

Evans, D. S. (2014). Economic aspects of Bitcoin and other decentralized public -ledger currency platforms. Coase -Sandor Institute for Law and Economics Research Paper, (685).

Eyal, I., & Sirer, E. G. (2014). Majority is not enough: Bitcoin mining is vulnerable. In International conference on financial cryptography and data security (pp. 436 -454). Springer, Berlin, Heidelberg. https://doi.org/10.48550/arXiv.1311.0243

Feld, S., Schönfeld, M., & Werner, M. (2014). Analyzing the deployment of Bitcoin’s P2P network under an AS -level perspective. Procedia Computer Science, 32, 1121 –1126. doi:10.1016/j.procs.2014.05.542.

Ferreira, D; Palma, A. (2015). Forecasting Inflation with the Phillips Curve: A Dynamic Model Averaging Approach for Brazil. Revista Brasileira de Economia, 69(4).  https://doi.org/10.5935/0034-7140.20150021

Gupta, R . ; Hammoudeh, Sh. ; Kim, W.J., & Simo -Kengne, B.D. (2014). Forecasting China’s foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals, financial stress and economic uncertainty. North American Journal of Economics and Finance, 28: 170 -189  https://doi.org/10.1016/j.najef.2014.02.003

Jawadi, F., R. M. Sousa, and R. Traverso. (2017). “On the Macroeconomic and the Wealth Effects of Unconventional Monetary Policy.” Macroeconomic Dynamics 21 (5): 1189–1204.doi: 10.1017/S1365100515000292.

Ji, Q., Bouri E., Gupta, R., Roubaud, D. (2018). Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach , The Quarterly Review of Economics and Finance. DOI: 10.1016/j.qref.2018.05.016

Kim, T., (2017). On the transaction cost of Bitcoin, Finance Research Letters Volume 23, 2017: 300-305. https://doi.org/10.1016/j.frl.2017.07.014

Koop, G & Korobilis, D. (2013). A New Index of Financial Conditions. University of Glasgow, Adam Smith Business School, Gilbert Scott building, Glasgow. http://eprints.gla.ac.uk/95030/

Koop, G. (2012). Using VARs and TVP-VARs with Many Macroeconomic Variables. Central European Journal of Economic Modelling and Econometrics, 4(3), 143–167. DOI: RePEc:psc:journl:v:4:y:2012:i:3:p:143-167

Koop, G; McIntyre, S; Mitchell, J; Poon, A. (2020). Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970. Journal of Applied Econometrics, 35(2), 176–197. DOI: 10.1002/jae.2748

Korobilis, D. (2013). Assessing the transmission of monetary policy shocks using time -varying parameter dynamic factor models. Oxford Bulletin of Economics and Statistics 75:157-179. https://doi.org/10.1111/j.1468-0084.2011.00687.x

Kristoufek, l. (2013). On Bitcoin markets (in)efficiency and its evolution, Physica A: Statistical Mechanics and its Applications Volume 503, 257-262. https://doi.org/10.1016/j.physa.2018.02.161

Matta, M. Lunesu, I. Marchesi, M. (2015), Bitcoin Spread Prediction Using Social and Web Search Media Università degli Studi di Cagliari Piazza d’Armi, 09123 Cagliari, Italy. ISBN : 978-3-319-52757-4

Narayan PK, Narayan S, Rahman RE, et al. (2019), Bitcoin price growth and Indonesia’s monetary system. Emerg Mark Rev 38: 364–376. https://doi.org/10.1016/j.ememar.2018.11.005.

Naser, H. (2014). An Econometric Investigation of Forecasting GDP, Oil Prices, and Relationships among GDP and Energy Sources. Etheses.whiterose.ac.uk. https://etheses.whiterose.ac.uk/7041/1/Hanan_PhD_Thesis.pdf

Naser, H; Alaali, F. (2018). Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach. Empirical Economics, 55(4), 1757–1777. DOI: 10.1007/s00181-017-1323-5

Nicoletti, G., & Passaro, R. (2012). Sometimes it helps the evolving predictive power of spreads on GDP dynamics, Working Paper Series, European Central Bank, N o . 1447. Handle: RePEc:ecb:ecbwps:20121447

Raftery, A . ; Karny, M . ; Andrysek, J., & Ettler, P. (2007). Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill. Technical report, 525. Department of Statistics, University of Washington. https://stat.ufl.edu/wp-content/uploads/sites/120/rafteryTalk.pdf

Raftery, A. E; Kárný, M; Ettler, P. (2010). Online Prediction Under Model Uncertainty via Dynamic Model Averaging: Application to a Cold Rolling Mill. Technometrics, 52(1), 52–66. doi: 10.1198/TECH.2009.08104

Risse M., & Kern M. (2016). Forecasting house -price growth in the Euro area with dynamic model averaging. The North American Journal of Economics and Finance, 38: 70 -85 . https://doi.org/10.1016/j.najef.2016.08.001

Selmi, R., A. K. Tiwari, and S. Hammoudeh. (2018). “Efficiency or Speculation? A Dynamic Analysis of the Bitcoin Market.” Economics Bulletin 38 (4): 2037–2046. : https://www.researchgate.net/publication/329017099

Shengao, Z.; Mengze, L.; Chunxiao, Y. (2022). The Empirical Analysis of Bitcoin Price Prediction Based on Deep Learning Integration Method. Comput. Intell, 1265837. DOI:10.1155/2022/1265837

Stock, J., & Watson, M. (2005). An Empirical Comparison of Methods for Forecasting using Many Predictors. Manuscript, Princeton University. http://www.economics.harvard.edu/faculty/stock/files/beb_4.pdf

Tandon, Sanjana Revankar a, Hemant Palivela, (2021), How can we predict the impact of the social media messages on the value of cryptocurrency? Insights from big data analytics, International Journal of Information Management Data Insights, Volume 1, Issue 2. https://doi.org/10.1016/j.jjimei.2021.100035

Tillmann, P. (2016). Unconventional Monetary Policy and the Spillovers to Emerging Markets. Journal of International Money and Finance 66: 136–156. https://doi.org/10.1016/j.jimonfin.2015.12.010

Tropina, T. (2014). Fighting money laundering in the age of online banking, virtual currencies and internet gambling. ERA Forum, 15(1), 69 –84. Doi: 10.1007/s12027 -014 -0335 – 2

Troster, V., Tiwari A. K., Shahbaz M., Macedo D. N, (2019), Bitcoin returns and risk: A general GARCH and GAS analysis. Finance Research Letters Volume 30: 187-193. DOI: 10.1016/j.frl.2018.09.014

Urquhart, A., (2018). What causes the attention of Bitcoin? Economics Letters Volume 166: 40-44. DOI: 10.1016/j.econlet.2018.02.017

Vandezande, N. Virtual currencies under EU anti-money laundering law, Computer Law & Security Review Volume 33, Issue 3: 341-353,2017. DOI 10.1016/j.clsr.2017.03.011

Vidal-Tomas, D., and A. Ibañez. (2018). Semi-strong Efficiency of Bitcoin. Finance Research Letters 27: 259–265. DOI: 10.1016/j.frl.2018.03.013

Wang L, Sarker PK, Bouri E , (2022). Short-and Long-Term Interactions between Bitcoin and Economic Variables: Evidence from the US. Comput Econ, 1–26. https://doi.org/10.1007/s10614-022-10247-5

Yamak, P. T., Yujian, L., & Gadosey, P. K. (2019). A Comparison between ARIMA, LSTM, and GRU for Time Series Forecasting, In Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence (pp. 49-55). https://doi.org/10.1145/3377713.3377722

Yelowitz, A. Wilson, M. (2014). Characteristics of Bitcoin users: an analysis of Google search data Department of Economics, University of Kentucky, Lexington, Applied Economics Letters 22(13):1-7. http://dx.doi.org/10.1080/13504851.2014.995359

Tong, Z., Zhanbo Chen , Chen Zhu, (2022), Nonlinear Dynamics Analysis of Cryptocurrency Price Fluctuations Based on Bitcoin Finance Research Letters (IF 9.848) Pub Date: 2022-03-19.   https://doi.org/10.1016/j.frl.2022.102803

Abolhasani, M. J., & Samadi, S. (2020).**

Analyzing the factors affecting the price of virtual currencies (Case study: Bitcoin and Ethereum). *Journal of Monetary and Banking Research*, 13(46), 591-629. SID. https://sid.ir/paper/1060154/fa

Bashiri, M., & Paryab, S. H. (2020).**

Predicting Bitcoin price using machine learning algorithms. *Applied Economics*, 10(34 & 35), 1-13. doi: 10.30495/JAE.2020.18114

Dashchi, A. (2021).**

Predicting Bitcoin cryptocurrency price using machine learning. [Master's thesis, Campus of Science, Faculty of Mathematics, Statistics, and Computer Science]. doi: 10.30495/FED.2023.700133

Soleimanipour, M. M., Soltaninejad, H., & Pourmotahar, M. (2017).**

A jurisprudential (Fiqh) investigation of virtual money. *Islamic Financial Researches*, 6(2), 167-192. SID. https://sid.ir/paper/254891/fa

Sadeghian, M. K., Yavari, K., & Alavi Rad, A. (2021).**

Identifying variables affecting the price of Bitcoin cryptocurrency: Bayesian Model Averaging (BMA) and Weighted Average Least Squares (WALS) approach. *Financial Engineering and Portfolio Management*, 12(46), 517-539. doi: 20.1001.1.22519165.1400.12.46.22.8

Salehifar, M. (2019).**

Investigating the return and risk behavior of Bitcoin compared to gold, foreign exchange, and stock markets using GJR-GARCH and Threshold GARCH models. *Financial Engineering and Portfolio Management*, 10(40), 152-168. doi: 20.1001.1.22519165.1398.10.40.7.7

Mohammadsharifi, A., Khalili-Damghani, K., Abdi, F., & Sardar, S. (2021).**

Predicting Bitcoin price using a hybrid ARIMA and Deep Learning model. *Industrial Management Studies*, 19(61), 125-146. doi: 10.22054/jims.2021.52374.2488

Mirzaei, K., Fotouhabadi, V., & Nosratabadi, J. (2018).**

Investigating factors affecting Bitcoin fluctuations and inherent financial risks in cryptocurrencies using the GARCH(1,1) model. *Quarterly Journal of Defense Economics*, 3(10), 131-148.

Casino, F., Dasaklis, T. K., & Patsakis, C. (2019). A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and Informatics, 36, 55–81. DOI: https://doi.org/10.1016/j.tele.2018.11.006

Reijers, W., O’Brolcháin, F., & Haynes, P. (2021). Cryptocurrency. Internet Policy Review, 10(2).  DOI: 10.14763/2021.2.1561

Al Guindy, M. (2021). Cryptocurrency price volatility and investor attention. International Review of Economics & Finance, 76, 556–570. DOI: 10.1016/j.iref.2021.07.007

Cheah, E.‑T., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin.Economics Letters, 130, 32–36. DOI: https://doi.org/10.1016/j.econlet.2015.02.029

Mert, N., & Timur, M. C. (2023). Bitcoin and money supply relationship: An analysis of selected country economies. Quantitative Finance and Economics, 7(2), 224–245. https://doi.org/10.3934/QFE.2023012

Levin, R. B., O’Brien, A. A., & Zuberi, M. M. (2015). Real Regulation of Virtual Currencies : Handbook of Digital Currency: Bitcoin, Innovation, Financial Instruments, and Big Data Elsevier / Academic Press, Chapter 17, pp. 327–360. DOI 10.1016/B978-0-12-802117-0.00017-5

Saleh, F. (2021). Blockchain without Waste: Proof-of-Stake. Review of Financial Studies, 34(3), 1156–1190. DOI: https://doi.org/10.1093/rfs/hhaa075

Van Wijk, D. (2013). What can be expected from the bitcoin? Available as working paper / manuscript in many bibliographies

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Marcellino, M., Stock, J. H., & Watson, M. W. (2003). Macroeconomic Forecasting in the Euro Area: Country-Specific versus Area-Wide Information. European Economic Review, 47(1), 1–18. DOI:10.1016/S0014-2921(02)00206-4

Forni, M., Hallin, M., Lippi, M., & Reichlin, L. (2004). The generalized dynamic-factor model: One-sided estimation and forecasting. Journal of the American Statistical Association, 100(471), 830–840. DOI: 10.1198/016214504000002050

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