Reduced-order autoregressive dynamics of a complex financial system: a PCA-based approach
This provides an incremental method for analyzing cross-asset dependencies in financial systems, useful for quantitative analysts and economists.
The study tackled modeling dynamic interactions among NASDAQ, crude oil, gold, and the US dollar using a reduced-order approach, finding that a limited number of principal components captured dominant dynamics with varying complexity across markets, as measured by R².
This study analyzes the dynamic interactions among the NASDAQ index, crude oil, gold, and the US dollar using a reduced-order modeling approach. Time-delay embedding and principal component analysis are employed to encode high-dimensional financial dynamics, followed by linear regression in the reduced space. Correlation and lagged regression analyses reveal heterogeneous cross-asset dependencies. Model performance, evaluated using the coefficient of determination ($R^2$), demonstrates that a limited number of principal components is sufficient to capture the dominant dynamics of each asset, with varying complexity across markets.