An Exploration to the Correlation Structure and Clustering of Macroeconomic Variables

Paper: arXiv 2401.10162

Authors: Garvit Arora, Shubhangi Tiwari, Ying Wu, Xuan Mei

Abstract

As a quantitative characterization of the complicated economy, Macroeconomic Variables (MEVs), including GDP, inflation, unemployment, income, spending, interest rate, etc., are playing a crucial role in banks’ portfolio management and stress testing exercise. In recent years, especially during the COVID-19 period and the current high inflation environment, people are frequently talking about the changing “correlation structure” of MEVs. In this paper, we use a principal component based algorithm to perform unsupervised clustering on MEVs so we can quantify and better understand MEVs’ correlation structure in any given period. We also demonstrate how this method can be used to visualize historical MEVs pattern changes between 2000 and 2022. Further, we use this method to compare different hypothetical and/or historical macroeconomic scenarios and present our key findings. One of these interesting observations is that, for a list of 132 transformations derived from 44 targeted MEVs that cover 5 different aspects of the U.S. economy (which takes as a subset the 10+ key MEVs published by FRB), compared to benign years where there are typically 20-25 clusters, during the great financial crisis (GFC), i.e., 2007-2010, they exhibited a more synchronized and less diversified pattern of movement, forming roughly 15 clusters. We also see this contrast in the hypothetical CCAR2023 FRB scenarios where the Severely Adverse scenario has 15 clusters and the Baseline scenario has 21 clusters. We provide our interpretation to this observation and hope this research can inspire and benefit researchers from different domains all over the world.

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper provides a practical, empirical application of PCA-based unsupervised clustering to macroeconomic variables and CCAR stress testing scenarios. While the mathematical framework relies on established linear algebra and clustering techniques rather than novel theoretical derivations, the empirical observations regarding scenario dimensionality during crisis periods offer valuable insights for risk management.

Research Flowchart

  flowchart TD
    A[Research Goal: Quantify & Understand Changing Correlation Structure of MEVs] --> B[Data Input: 132 Transformations from 44 US MEVs 2000-2022]
    B --> C[Methodology: PCA-based Unsupervised Clustering Algorithm]
    C --> D[Analysis: Compare Historical Eras & CCAR 2023 Scenarios]
    D --> E[Outcome 1: Benign Years & Baseline Scenario Show High Diversity 20-25 Clusters]
    D --> F[Outcome 2: GFC & Severely Adverse Scenario Show Synchronized Movement ~15 Clusters]

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0 of 22 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.