10 Correlations, Hierarchies, Networks and Clustering
271
77. Kullmann, L., Kertesz, J., Mantegna, R.N.: Identification of clusters of companies in stock
indices via potts super-paramagnetic transitions. Phys. A: Stat. Mech. Appl. 287(3), 412–419
(2000)
78. Lautier, D., Raynaud, F.: Systemic risk in energy derivative markets: a graph-theory analysis.
Available at SSRN 1579629, (2011)
79. Lee, G.S., Djauhari, M.A.: Multidimensional stock network analysis: An Escoufier’s RV
coefficient approach. AIP Conference Proceedings 1, 550–555 (2013)
80. Lee, J., Youn, J., Chang, W.: Intraday volatility and network topological properties in the
Korean stock market. Phys. A: Stat. Mech. Appl. 391(4), 1354–1360 (2012)
81. Lemieux, V., Rahmdel, P.S., Walker, R., Wong, B.L., Flood, M.: Clustering techniques and
their effect on portfolio formation and risk analysis. In: Proceedings of the International
Workshop on Data Science for Macro-Modeling, pp. 1–6. ACM (2014)
82. León, D., Aragón, A., Sandoval, J., Hernández, G., Arévalo, A., Niño, J.: Clustering algorithms
for risk-adjusted portfolio construction. Procedia Comput. Sci. 108, 1334–1343 (2017)
83. Letizia, E., Lillo, F.: Corporate Payments Networks and Credit Risk Rating (2018)
84. Lohre, H., Rother, C., Schäfer, K.A.: Hierarchical risk parity: Accounting for tail dependencies
in multi-asset multi-factor allocations. Mach. Learn, Asset Manag (2020)
85. de Prado, M.L.: Building Diversified Portfolios that Outperform Out-Of-Sample (2016)
86. de Prado, M.L.: Estimation of theory-implied correlation matrices. Available at SSRN (2019)
87. de Prado, M.L., Lewis, M.J.: Detection of False Investment Strategies Using Unsupervised
Learning Methods (2018)
88. MacMahon, M., Garlaschelli, D.: Community detection for correlation matrices. Phys. Rev.
X 5, 021006 (2015)
89. Mantegna, R.N.: Hierarchical structure in financial markets. Eur. Phys. J. B-Condensed Matter
Complex Syst. 11(1), 193–197 (1999)
90. Mantegna, R.N., Stanley, H.E.: Introduction to Econophysics: Correlations and Complexity
in Finance. Cambridge University Press, Cambridge (1999)
91. Marti, G.: Corrgan: Sampling Realistic Financial Correlation Matrices Using Generative
Adversarial Networks (2019)
92. Marti, G., Andler, S., Nielsen, F., Donnat, P.: Clustering financial time series: How long
is enough? In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial
Intelligence, IJCAI 2016, pp. 2583–2589. New York, NY, USA (2016)
93. Marti, G., Andler, S., Nielsen, F., Donnat, P.: Optimal transport vs. fisher-rao distance between
copulas for clustering multivariate time series. IEEE Statistical Signal Processing Workshop.
SSP 2016, pp. 1–5. Palma de Mallorca, Spain (2016)
94. Marti, G., Nielsen, F., Donnat, P.: Optimal copula transport for clustering multivariate time
series. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), pp. 2379–2383. IEEE (2016)
95. Marti, G., Very, P., Donnat, P., Nielsen, F.: A proposal of a methodological framework with
experimental guidelines to investigate clustering stability on financial time series. 14th IEEE
International Conference on Machine Learning and Applications. ICMLA 2015, pp. 32–37.
FL, USA, Miami (2015)
96. Massara, G.P., Di Matteo, T., Aste, T.: Network filtering for big data: triangulated maximally
filtered graph. J. Complex Netw. 5(2), 161–178 (2016)
97. Matesanz, D., Ortega, G.J.: Sovereign public debt crisis in europe. a network analysis. Phys.
A: Stat. Mech. Appl. 436, 756–766 (2015)
98. Meng, H., Xie, W.-J., Jiang, Z.-Q., Podobnik, B., Zhou, W.-X., Stanley, H.E.: Systemic risk
and spatiotemporal dynamics of the US housing market. Sci. Rep. 4, 3655 (2014)
99. Morales, R., Di Matteo, T., Aste, T.: Dependency structure and scaling properties of financial
time series are related. Sci. Rep. 4(4589) (2014)
100. Münnix, M.C., Shimada, T., Schäfer, R., Leyvraz, F., Seligman, T.H., Guhr, T., Stanley, H.E.:
Identifying states of a financial market. Sci. Rep. 2, 644 (2012)
101. Musciotto, F., Marotta, L., Miccichè, S., Mantegna, R.N.: Bootstrap validation of links of a
minimum spanning tree (2018). arXiv:1802.03395
271
77. Kullmann, L., Kertesz, J., Mantegna, R.N.: Identification of clusters of companies in stock
indices via potts super-paramagnetic transitions. Phys. A: Stat. Mech. Appl. 287(3), 412–419
(2000)
78. Lautier, D., Raynaud, F.: Systemic risk in energy derivative markets: a graph-theory analysis.
Available at SSRN 1579629, (2011)
79. Lee, G.S., Djauhari, M.A.: Multidimensional stock network analysis: An Escoufier’s RV
coefficient approach. AIP Conference Proceedings 1, 550–555 (2013)
80. Lee, J., Youn, J., Chang, W.: Intraday volatility and network topological properties in the
Korean stock market. Phys. A: Stat. Mech. Appl. 391(4), 1354–1360 (2012)
81. Lemieux, V., Rahmdel, P.S., Walker, R., Wong, B.L., Flood, M.: Clustering techniques and
their effect on portfolio formation and risk analysis. In: Proceedings of the International
Workshop on Data Science for Macro-Modeling, pp. 1–6. ACM (2014)
82. León, D., Aragón, A., Sandoval, J., Hernández, G., Arévalo, A., Niño, J.: Clustering algorithms
for risk-adjusted portfolio construction. Procedia Comput. Sci. 108, 1334–1343 (2017)
83. Letizia, E., Lillo, F.: Corporate Payments Networks and Credit Risk Rating (2018)
84. Lohre, H., Rother, C., Schäfer, K.A.: Hierarchical risk parity: Accounting for tail dependencies
in multi-asset multi-factor allocations. Mach. Learn, Asset Manag (2020)
85. de Prado, M.L.: Building Diversified Portfolios that Outperform Out-Of-Sample (2016)
86. de Prado, M.L.: Estimation of theory-implied correlation matrices. Available at SSRN (2019)
87. de Prado, M.L., Lewis, M.J.: Detection of False Investment Strategies Using Unsupervised
Learning Methods (2018)
88. MacMahon, M., Garlaschelli, D.: Community detection for correlation matrices. Phys. Rev.
X 5, 021006 (2015)
89. Mantegna, R.N.: Hierarchical structure in financial markets. Eur. Phys. J. B-Condensed Matter
Complex Syst. 11(1), 193–197 (1999)
90. Mantegna, R.N., Stanley, H.E.: Introduction to Econophysics: Correlations and Complexity
in Finance. Cambridge University Press, Cambridge (1999)
91. Marti, G.: Corrgan: Sampling Realistic Financial Correlation Matrices Using Generative
Adversarial Networks (2019)
92. Marti, G., Andler, S., Nielsen, F., Donnat, P.: Clustering financial time series: How long
is enough? In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial
Intelligence, IJCAI 2016, pp. 2583–2589. New York, NY, USA (2016)
93. Marti, G., Andler, S., Nielsen, F., Donnat, P.: Optimal transport vs. fisher-rao distance between
copulas for clustering multivariate time series. IEEE Statistical Signal Processing Workshop.
SSP 2016, pp. 1–5. Palma de Mallorca, Spain (2016)
94. Marti, G., Nielsen, F., Donnat, P.: Optimal copula transport for clustering multivariate time
series. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), pp. 2379–2383. IEEE (2016)
95. Marti, G., Very, P., Donnat, P., Nielsen, F.: A proposal of a methodological framework with
experimental guidelines to investigate clustering stability on financial time series. 14th IEEE
International Conference on Machine Learning and Applications. ICMLA 2015, pp. 32–37.
FL, USA, Miami (2015)
96. Massara, G.P., Di Matteo, T., Aste, T.: Network filtering for big data: triangulated maximally
filtered graph. J. Complex Netw. 5(2), 161–178 (2016)
97. Matesanz, D., Ortega, G.J.: Sovereign public debt crisis in europe. a network analysis. Phys.
A: Stat. Mech. Appl. 436, 756–766 (2015)
98. Meng, H., Xie, W.-J., Jiang, Z.-Q., Podobnik, B., Zhou, W.-X., Stanley, H.E.: Systemic risk
and spatiotemporal dynamics of the US housing market. Sci. Rep. 4, 3655 (2014)
99. Morales, R., Di Matteo, T., Aste, T.: Dependency structure and scaling properties of financial
time series are related. Sci. Rep. 4(4589) (2014)
100. Münnix, M.C., Shimada, T., Schäfer, R., Leyvraz, F., Seligman, T.H., Guhr, T., Stanley, H.E.:
Identifying states of a financial market. Sci. Rep. 2, 644 (2012)
101. Musciotto, F., Marotta, L., Miccichè, S., Mantegna, R.N.: Bootstrap validation of links of a
minimum spanning tree (2018). arXiv:1802.03395