Focus
Quantitative Finance, Risk Modeling
Motivation
Market Stability, Noise Reduction, Volatility Forecasting
About the project
This research explores how Random Matrix Theory (RMT), originally developed in nuclear physics, can be applied to modern financial data to separate genuine market structures from random anomalies in asset correlations. By replicating the foundational study by Laloux et al. (1999) with an updated dataset of S&P 500 opening prices from 2013 to 2018, the author tests the enduring relevance of RMT in understanding today’s complex financial systems. The study employs standardized log returns to construct correlation matrices and compares their eigenvalue spectra against the theoretical Marčenko–Pastur distribution, identifying which correlations reflect real economic patterns and which stem from noise.
The analysis finds that most eigenvalues fall within the random noise band predicted by RMT, confirming that a large portion of observed correlations are statistically insignificant. However, a small number of large eigenvalue outliers correspond to meaningful collective market movements — such as broad market trends and sector-level behaviors. By applying spectral filtering to remove these noisy components, the paper demonstrates improved accuracy in identifying systematic structures within financial markets.
Ultimately, the findings support the continuing value of Random Matrix Theory as a tool for denoising correlation matrices, enhancing portfolio stability, and improving volatility forecasting. The study reaffirms that while much of market correlation data may be noise-dominated, RMT provides a robust mathematical lens for distinguishing true market dynamics — a principle increasingly vital in today’s high-dimensional, algorithm-driven trading environment.
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