RANDOM MATRIX THEORY AND FINANCIAL CORRELATIONS

By L. Laloux, P. Cizeau, Marc Potters, Jean-Philippe Bouchaud. 2000

446 citations recorded by the source corpus.

Subfields: Condensed Matter Physics, Economics and Econometrics, Finance

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Citations in the corpus

Autoregressive Convolutional Neural Networks for Asynchronous Time Series (2017, methodology): “applications of Random Matrix Theory in econophysics (Laloux et al., 2000; Bun et al., 2017)) and heavy-tailed distributions (Cont, 2001).”
Cluster analysis for portfolio optimization (2005, methodology): “The correlation matrices obtained by this filtering procedure has been used in portfolio optimization in some studies (Laloux et al., 2000; Rosenow et al., 2002), which have shown that under the assumption of perfect forecasting of future returns and volatilities the distance V. Tola et al. /…”
A methodology for index tracking based on time-series clustering (2004, methodology): “An elegant illustration of this fact was provided recently by Laloux et al (1999a), Ormerod and Mounfield (2000), and Plerou et al (1999) using the random matrix theory, a mathematical theory developed in the 1950s in the context of quantum mechanics.”
A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory (2015, background): “data-driven tool for numerous phenomena, such as quantum systems [7], financial systems [8], [9], biological systems [10],”
Random Matrix Theory and Wireless Communications (2004, methodology): “• Data mining and multivariate time series modelling and analysis [155, 139].”
Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice (2016, methodology): “The first use of this approach we have seen in finance is due to Laloux et al. (2000). Eigenvalues less than λmax are replaced by the average value of the eigenvalue less than this threshold,”
Concentration of measure and spectra of random matrices: Applications to correlation matrices, elliptical distributions and beyond (2009, background): “We refer the reader who is particularly interested in these questions to [31] for an early and interesting application of random matrix ideas to portfolio optimization problems.”
Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, unknown): “However, the limitations of the method in these applications became apparent, because background corrections proved troublesome and for the more complicated cases, the intervention of skilled operators during the fitting procedure was usually required.”
Noise sensitivity of portfolio selection under various risk measures (2006, methodology): “These filtering procedures include single- and multifactor models, (for a review, see e.g. Elton and Gruber, 1995), Bayesian estimators (e.g. Jorion, 1986; Frost and Savarino, 1986; Ledoit and Wolf, 2003), and more recently, tools taken over from random matrix theory (Laloux et al., 2000).”
Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices (2004, background): “Other examples of similar phenomena include mathematical finance [25, 26, 32], wireless communication [37], physics of mixture [34] and data analysis and statistical learning [18].”
High-dimensionality effects in the Markowitz problem and other quadratic programs with linear constraints: Risk underestimation (2010, background): “We note in particular the paper Laloux et al. (2000) , where a random matrix-inspired (shrinkage) approach to improved estimation of the sample covariance matrix is proposed in the context of the Markowitz problem.”
Random Matrix Theory and Fund of Funds Portfolio Optimisation (2007, methodology): “The spectral properties ofCmay be compared to those of a “random”Wishart correlation matrix [8,11],”
Do High-Frequency Data Improve High-Dimensional Portfolio Allocations? (2013, methodology): “…we regularize Ct by the eigenvalue cleaning procedure that is applied to BRK estimates in Section 3.1 and discussed in more detail in Appendix B. Laloux et al. (2000) demonstrate that sample covariance matrices regularized by this technique yield considerably lower portfolio volatilities than…”
Cross-Correlation Dynamics in Financial Time Series (2009, background): “03 21 v1 [ qfi n. ST ] 1 F eb The dynamics of the equal-time cross-correlation matrix of multivariate financial time series is explored by examination of the eigenvalue spectrum over sliding time windows.”