بررسی ویژگی‌های زمان ‌متغیر شبکۀ جریان اطلاعات میان شاخص‌های صنایع اصلی در بورس اوراق بهادار تهران

 doi: 10.52547/jfmp.10.31.9

 doi: 10.22075/jem.2022.28780.1771

Assaf, A., Mokni, K., & Youssef, M. (2023). COVID-19 and information flow between cryptocurrencies, and conventional financial assets. The Quarterly Review of Economics and Finance, 89, 73-81.

Behrendt, S., & Schmidt, A. (2021). Nonlinearity matters: The stock price–trading volume relation revisited. Economic Modelling, 98, 371-385.

Dimpfl, T., & Peter, F. J. (2014). The impact of the financial crisis on transatlantic information flows: An intraday analysis. Journal of International Financial Markets, Institutions and Money, 31, 1-13.

Dimpfl, T., & Peter, F. J. (2019). Group transfer entropy with an application to cryptocurrencies. Physica A: Statistical Mechanics and its Applications, 516, 543-551.

Elsayed, A. H., Naifar, N., Uddin, G. S., & Wang, G. J. (2023). Multilayer information spillover networks between oil shocks and banking sectors: Evidence from oil-rich countries. International Review of Financial Analysis, 87, 102602.

He, J., & Shang, P. (2017). Comparison of transfer entropy methods for financial time series. Physica A: Statistical Mechanics and its Applications, 482, 772-785.

Hung, N. T., Nguyen, L. T. M., & Vo, X. V. (2022). Exchange rate volatility connectedness during Covid-19 outbreak: DECO-GARCH and Transfer Entropy approaches. Journal of International Financial Markets, Institutions and Money, 81, 101628.

Jahangiri, K., & Hekmati Farid, S. (2015). Investigating the Effects of Volatility Spillover between Stock, Gold, Oil and Exchange Markets. Economics Research, 15(56), 161-194. [In Persian]

Jizba, P., Kleinert, H., & Shefaat, M. (2012). Rényi’s information transfer between financial time series. Physica A: Statistical Mechanics and its Applications, 391(10), 2971-2989.

Jurczyk, J., Rehberg, T., Eckrot, A., & Morgenstern, I. (2017). Measuring critical transitions in financial markets. Scientific Reports, 7(1), 11564.

Keshavarz Haddad, G., & Vahidi, H. (2022). Informational Asymmetry between Institutional and Individual Traders: Evidence from Tehran Stock Exchange. Economics Research, 22(86), 1-36. doi: 10.22054/joer.2023.69382.1086. [In Persian]

Long, H., Zhang, J., & Tang, N. (2017). Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market. PloS one, 12(7), e0180382.

Marschinski, R., & Kantz, H. (2002). Analysing the information flow between financial time series: An improved estimator for transfer entropy. The European Physical Journal B-Condensed Matter and Complex Systems, 30, 275-281.

 Mohajeri, P., & Taleblou, R. (2022). Investigating the Dynamics of Volatility Spillovers across Sectors’ Returns Utilizing a Time-Varying Parameter Vector Autoregressive Connectedness Approach; Evidence from Iranian Stock Market. Journal of Economic Research (Tahghighat- E- Eghtesadi), 57(2), 321-356. [In Persian]

Mohammadi, A., savari, Z., & Ahmadzadeh, K. (2016). Analyzing Price Discovery Function of Gold Coin Futures Contracts in Iran. Economics Research, 16(63), 25-60. [In Persian]

Münnix, M. C., Schäfer, R., & Grothe, O. (2014). Estimating correlation and covariance matrices by weighting of market similarity. Quantitative Finance, 14(5), 931-939.

Nie, C. X. (2020a). Correlation dynamics in the cryptocurrency market based on dimensionality reduction analysis. Physica A: Statistical Mechanics and its Applications, 554, 124702.

Nie, C. X. (2020b). A network-based method for detecting critical events of correlation dynamics in financial markets. Europhysics Letters, 131(5), 50001.

Nie, C. X. (2021). Dynamics of the price–volume information flow based on surrogate time series. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(1), 013106.

Nie, C. X. (2023). Time-varying characteristics of information flow networks in the Chinese market: An analysis based on sector indices. Finance Research Letters, 54, 103771.

Nie, C. X., & Song, F. T. (2023). Stable versus fragile community structures in the correlation dynamics of Chinese industry indices. Chaos, Solitons & Fractals, 167, 113044.

Nie, C. X., & Xiao, J. (2022). Dynamics of information flow between the Chinese A-Share market and the US stock market: from the 2008 crisis to the COVID-19 pandemic period. Entropy, 24(8), 1102.

Oh, G., Oh, T., Kim, H., & Kwon, O. (2014). An information flow among industry sectors in the Korean stock market. Journal of the Korean Physical Society, 65, 2140-2146.

Osoolian, M., & Koushki, A. (2020). Investigating the crisis forecasting ability of the cumulative residual entropy measure by using logistic map simulation data and Tehran stock exchange overall index. Journal of Financial Management Perspective, 10(31), 9-27. [In Persian]

Papana, A., Kyrtsou, C., Kugiumtzis, D., & Diks, C. (2016). Detecting causality in non-stationary time series using partial symbolic transfer entropy: Evidence in financial data. Computational Economics, 47, 341-365.

Peng, S., Han, W., & Jia, G. (2022). Pearson correlation and transfer entropy in the Chinese stock market with time delay. Data Science and Management, 5(3), 117-123.

Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461.

Škrinjarić, T., Quintino, D., & Ferreira, P. (2021). Transfer entropy approach for portfolio optimization: An empirical approach for CESEE markets. Journal of Risk and Financial Management, 14(8), 369.

Taleblou, R., & Mohajeri, P. (2023). Modeling the Daily Volatility of Oil, Gold, Dollar, Bitcoin and Iranian Stock Markets: An Empirical Application of a Nonlinear Space State Model. Iranian Economic Review, 27(3), 1033-1063. doi: 10.22059/ier.2023.328120.1007235[In persian]

Tse, C.K., Liu, J., & Lau, F.C.M. (2010). A network perspective of the stock market. Journal of Empirical Finance, 17(4), 659-667.

Yang, R., Li, X., & Zhang, T. (2014). Analysis of linkage effects among industry sectors in China’s stock market before and after the financial crisis. Physica A: Statistical Mechanics and its Applications, 411, 12-20.

Yang, Y., & Yang, H. (2008). Complex network-based time series analysis. Physica A: Statistical Mechanics and its Applications, 387(5-6), 1381-1386.

Yue, P., Cai, Q., Yan, W., & Zhou, W. X. (2020a). Information flow networks of Chinese stock market sectors. IEEE Access, 8, 13066-13077.

Yue, P., Fan, Y., Batten, J. A., & Zhou, W. X. (2020b). Information transfer between stock market sectors: A comparison between the USA and China. Entropy, 22(2), 194.