Characteristics of the transmission of autoregressive sub-patterns in financial time series

Scientific Reports

Volumn 4, Issue , 2014, Pages

  Gao, Xiangyun ^a   An, Haizhong ^a,b   Fang, Wei ^c   Huang, Xuan ^d   Li, Huajiao ^b   Zhong, Weiqiong ^a  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

^b China University of Geosciences Beijing)   (China)

^c China University of Geosciences   (China)

^d UNIVERSITY OF WATERLOO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84923359795     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep06290     Document Type: Article

References (36)

1. Munnix, M. C. et al. Identifying States of a Financial Market. Sci. Rep. 2, 644 (2012)
2. Box, G. E. & Pierce, D. A. Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Am. Stati. Assoc. 65, 1509-1526 (1970)
3. Granger, C. W. J. & Andersen, A. P. An introduction to bilinear time series models. (Vandenhoeck und Ruprecht Gottingen, 1978)
4. Engle, R. F. Autoregressive conditional heteroscedasticity with estimates of the variance of united-kingdom inflation. Econometrica 50, 987-1007 (1982)
5. Bollerslev, T. Generalized autoregressive conditional heteroskedasticity. J. Econometrics 31, 307-327 (1986)
6. Tong, H. & Lim, K. S. Threshold autoregression, limit-cycles and cyclical data. J. Roy. Stat. Soc. B 42, 245-292 (1980)
7. Zhang, G. P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159-175 (2003)
8. Hansen, J. V. & Nelson, R. D. Neural networks and traditional time series methods: A synergistic combination in state economic forecasts. Ieee T. Neural Networ. 8, 863-873 (1997)
9. Ivanov, P. C. et al. Scaling behaviour of heartbeat intervals obtained by waveletbased time-series analysis. Nature 383, 323-327 (1996)
10. Schulte-Frohlinde, V. et al. Noise effects on the complex patterns of abnormal heartbeats. Phys. Rev. Lett. 87 (2001)
11. Costa, M., Goldberger, A. L. & Peng, C. K. Multiscale entropy analysis of complex physiologic time series. Phys. Rev. Lett. 89 (2002)
12. Kantelhardt, J. W. et al. Multifractal detrended fluctuation analysis of nonstationary time series. Physica A 316, 87-114 (2002)
13. Amaral, L. A. N. et al. Behavioral-independence features of complex heartbeat dynamics. Phys. Rev. Lett. 86, 6026-6029 (2001)
14. Zbilut, J. P. &Webber, C. L. Embeddings and delays as derived from quantification of recurrence plots. Phys. Lett. A 171, 199-203 (1992)
15. Strozzi, F., Zaldivar, J. M. & Zbilut, J. P. Recurrence quantification analysis and state space divergence reconstruction for financial time series analysis. Physica A 376 (2007)
16. Zbilut, J. P. & Webber, C. L. Recurrence quantification analysis: Introduction and historical context. Int. J. Bifurcat. Chaos 17, 3477-3481 (2007)
17. Barabasi, A.-L. & Albert, R. Emergence of scaling in random networks. Science 286, 509-512 (1999)
18. Watts, D. J. & Strogatz, S. H. Collective dynamics of 'small-worldnetworks. Nature 393, 440-442 (1998)
19. Newman, M. E. & Watts, D. J. Renormalization group analysis of the small-world network model. Phys. Lett. A 263, 341-346 (1999)
20. Zhang, J. & Small, M. Complex network from pseudoperiodic time series: Topology versus dynamics. Phys. Rev. Lett. 96, 238701 (2006)
21. Xu, X., Zhang, J. & Small, M. Superfamily phenomena and motifs of networks induced from time series. Proc. Natl. Acad. Sci. USA 105, 19601-19605 (2008)
22. Yang, Y. & Yang, H. Complex network-based time series analysis. Physica A 387, 1381-1386 (2008)
23. Jiang, Z., Yang, H. & Wang, J. Complexities of human promoter sequences. Physica A 388, 1299-1302 (2009)
24. Donner, R. V., Zou, Y., Donges, J. F., Marwan, N. & Kurths, J. Recurrence networks-A novel paradigm for nonlinear time series analysis. New J. Phys. 12, 033025 (2010)
25. Lacasa, L., Luque, B., Ballesteros, F., Luque, J. & Nuno, J. C. From time series to complex networks: The visibility graph. Proc. Natl. Acad. Sci. USA 105, 4972-4975 (2008)
26. Lacasa, L., Luque, B., Luque, J. & Nuno, J. C. The visibility graph: A new method for estimating the Hurst exponent of fractional Brownian motion. EPL (Europhysics Letters) 86, 30001 (2009)
27. Qian, M.-C., Jiang, Z.-Q. & Zhou, W.-X. Universal and nonuniversal allometric scaling behaviors in the visibility graphs of world stock market indices. J. Phys. A -Math. Theor. 43, 335002 (2010)
28. Yang, Y., Wang, J., Yang, H. & Mang, J. Visibility graph approach to exchange rate series. Physica A 388, 4431-4437 (2009)
29. Gao, X. Y., An, H. Z. &Fang, W. Research on fluctuation of bivariate correlation of time series based on complex networks theory. Acta Phys. Sin. 61, 098902 (2012)
30. Gao, X. Y., An, H. Z., Liu, H. H. & Ding, Y. H. Analysis on the topological properties of the linkage complex network between crude oil future price and spot price. Acta Phys. Sin. 60, 068902 (2011)
31. An, H., Gao, X., Fang, W., Huang, X. & Ding, Y. The role of fluctuating modes of autocorrelation in crude oil prices. Physica A 393, 382-390 (2014)
32. Gujarati, D. N. & Porter, D. C. Basic Econometrics. 972-973 (McGraw Hill Higher Education, 2009)
33. Mucha, P. J., Richardson, T., Macon, K., Porter, M. A. & Onnela, J.-P. Community structure in time-dependent, multiscale, and multiplex networks. Science 328, 876-878 (2010)
34. Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech -Theory E. 2008, P10008 (2008)
35. Freeman, L. C. Centrality in social networks conceptual clarification. Soc. Network. 1, 215-239 (1979)
36. Goh, K.-I., Oh, E., Kahng, B. & Kim, D. Betweenness centrality correlation in social networks. Phys. Rev. E 67, 017101 (2003)