Statistical analysis of financial networks

By Vladimir Boginski, Sergiy Butenko, P. Pardalos. Computational Statistics & Data Analysis, 2005

428 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b903a88b62bb22d2284eee66583346f09a4261e8

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Citations in the corpus

Extreme risk spillover network: application to financial institutions (2017, methodology): “(2017) and Brownlees and Engle (2017) develop SRISK to measure the capital shortfall of a financial institution conditional on a prolonged market decline, which can quantify a financial institution’s systematic risk contribution. Engle et al. (2015) apply the SRISK measure to investigate the systemi”
Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics (2011, positive): “Specifically, the inference and modeling of large financial networks (Mantegna and Stanley, 2000; Boginski et al., 2005) requires methods akin to those for gene relevance and association networks.”
Correlation structure and dynamics of international real estate securities markets: A network perspective (2015, methodology): “That is to say, a price change of one financial asset in a clique is easy to cause the price fluctuations of other financial assets in this clique [27].”
Mining market data: A network approach (2006, methodology): “Another contribution of Boginski et al. [8] [9] is in a suggestion to relate some correlation-based properties of the market to certain combinatorial properties of the corresponding market graph.”
Quantum computing with neutral atoms (2020, background): “This problem, which has various direct applications in network design [76] or finance [77], becomes hard to solve on a classical computer when the size of the graph grows [78].”
Clique Relaxations in Social Network Analysis: The Maximum k-Plex Problem (2011, background): “Graph based data mining [19] has been used in studying structural properties of social networks [40] and stock markets [12,11], unraveling molecular structures to facilitate drug discovery and compound synthesis [24,16], and for identifying frequently occurring patterns in data sets (modeled as grap”
Random matrix theory analysis of cross-correlations in the US stock market: Evidence from Pearson’s correlation coefficient and detrended cross-correlation coefficient (2013, methodology): “[47] cleaned the cross-correlation matrix of the Tehran stock market via removing the market factor and then combined the filtered cross-correlation matrix with the correlation threshold method [48,49] to construct the market network and investigate its topological structure.”