Analyzing Financial Time-Series Using Random Matrix Theory

Random matrix theory (RMT) is a useful tool for noise reduction in the sample covariance matrix in financial time-series analysis. Duan Wang, a quantitative analyst on the Derivatives and Quantitative Strategies team, demonstrates how SLC Management implemented RMT in MATLAB^® to produce an improved estimator for the sample covariance variance. He also shows a couple of examples in portfolio optimization and asset allocation.