×

Networks of causal relationships in the U.S. stock market. (English) Zbl 1489.91304

Summary: We consider a network-based framework for studying causal relationships in financial markets and demonstrate this approach by applying it to the entire U.S. stock market. Directed networks (referred to as “causal market graphs”) are constructed based on publicly available stock prices time series data during 2001-2020, using Granger causality as a measure of pairwise causal relationships between all stocks. We consider the dynamics of structural properties of the constructed network snapshots, group stocks into network-based clusters, as well as identify the most “influential” market sectors via the PageRank algorithm. Interestingly, we observed drastic changes in the considered network characteristics in the years that corresponded to significant global-scale events, most notably, the financial crisis of 2008 and the COVID-19 pandemic of 2020.

MSC:

91G45 Financial networks (including contagion, systemic risk, regulation)
90B10 Deterministic network models in operations research
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Aiello, W., Chung, F., & Lu, L. (2000). A random graph model for power law graphs. Experimental Mathematics, 10, 53-66. · Zbl 0971.05100
[2] Aiello, W., Chung, F., & Lu, L. (2001). Random evolution in massive graphs. Annual Symposium on Foundations of Computer Science, 42, 510-521.
[3] Barabasi, A., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509-512. · Zbl 1226.05223
[4] Barabasi, A., Albert, R., & Jeong, H. (1999). Scale-free characteristics of random networks: The topology of the world wide web. Physica, A 272, 173-187.
[5] Boginski, V., Butenko, S., & Pardalos, P. (2006). Mining market data: A network approach. Computers and Operations Research, 33, 3171-3184. · Zbl 1113.90079
[6] Boginski, V., Butenko, S., & Pardalos, P. M. (2005). Statistical analysis of financial networks. Computational Statistics and Data Analysis, 48, 431-443. · Zbl 1429.62460
[7] Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks, 30, 107-117.
[8] Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., … Wiener, J. (2000). Graph structure in the web. Computer Networks, 33, 309-321.
[9] Brovelli, A., Ding, M., Ledberg, A., Chen, Y., Nakamura, R., & Bressler, S. L. (2004). Beta oscillations in a large-scale sensorimotor cortical network: Directional influences revealed by granger causality. Proceedings of the National Academy of Sciences of the United States of America, 101, 9849-9854.
[10] Bryan, K., & Leise, T. (2006). The \(25,000,000,000 eigenvector: The linear algebra behind Google. SIAM Review, 48, 569-581\) · Zbl 1115.15007
[11] Campbell, J., Lo, A., & MacKinlay, C. (1997). The econometrics of financial markets. Princeton, NJ: Princeton University Press. · Zbl 0927.62113
[12] Corsi, F., Lillo, F., Pirino, D., & Trapin, L. (2018). Measuring the propagation of financial distress with granger-causality tail risk networks. Journal of Financial Stability, 38, 18-36.
[13] Diks, C., & Panchenko, V. (2004, August). Modified Hiemstra-Jones test for Granger non-causality. Computing in Economics and Finance, 192. Society for Computational Economics.
[14] Giatsidis, C., Thilikos, D., & Vazirgiannis, M. (2011). D-cores: Measuring collaboration of directed graphs based on degeneracy. In IEEE 11th International Conference on Data Mining (ICDM) (pp. 201-210).
[15] Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37, 424-438. · Zbl 1366.91115
[16] Granger, C. W. J. (1980). Testing for causality: A personal viewpoint. Journal of Economic Dynamic and Control, 2, 329-352.
[17] Hecq, A., Margaritella, L., & Smeekes, S. (2021, Nov). Granger causality testing in high-dimensional VARs: A post-double-selection procedure. Journal of Financial Econometrics, 11, nbab023.
[18] Hull, J. (2008). Options, futures, and other derivatives (7th ed.). Lebanon, Indiana, USA: Prentice Hall. · Zbl 1087.91025
[19] Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45, 167-256. · Zbl 1029.68010
[20] Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71, 599-607. · Zbl 0564.62075
[21] Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464. · Zbl 0379.62005
[22] Seidman, S. B. (1983). Network structure and minimum degree. Social Networks, 5, 269-287.
[23] Shirokikh, O., Pastukhov, G., Boginski, V., & Butenko, S. (2013). Computational study of the us stock market evolution: A rank correlation-based network model. Computational Management Science, 10(2-3), 81-103. · Zbl 1282.91388
[24] Sowers, R., & Giesecke, K. (2011, October 18). Contagion. SIAM News.
[25] Tarjan, R. (1972). Depth-first search and linear graph algorithms. SIAM Journal on Computing, 2, 146-160. · Zbl 0251.05107
[26] Výrost, T., Lyócsa, Š., & Baumöhl, E. (2015). Granger causality stock market networks: Temporal proximity and preferential attachment. Physica A: Statistical Mechanics and its Applications, 427, 262-276.
[27] Wu, F., Zhang, D., & Zhang, Z. (2019). Connectedness and risk spillovers in china’s stock market: A sectoral analysis. Economic Systems, 43(3), 100718.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.