Linkages Among Asset Markets in the United States: Tests in a Bivariate GARCH Framework

IMF Working Papers, November 1, 1999

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Develops a bivariate GARCH model that allows for time-varying conditional correlations.
Enables simultaneous testing of two Granger-causal linkages:
- The impact of return volatility in a market on intermarket correlation.
- The impact of return volatility in one market on the volatility of another.
Uses daily data from stock, bond, currency, and commodity markets in the United States.
Employs technical terminology and model features as in the source: conditional correlation, covariance matrix, cross-asset correlation, Granger causality, Logistic Exponential GARCH, spillover coefficient, volatility spillover.

Finds evidence of both forms of linkage (volatility → intermarket correlation; volatility in one market → volatility in another).
Conditional correlations are time-varying and exhibit considerable persistence.
The estimated time-varying conditional correlations provide insight into the nature of the stock market crash of 1987.

Demonstrates that volatility in one asset market can influence both cross-market correlations and volatility dynamics in other markets.
Time-varying conditional correlations are important for understanding episodes of market stress (example cited: stock market crash of 1987).
Results are relevant to analyses of spillovers across Currency markets, Securities markets, and Stock markets.

Linkages Among Asset Markets in the United States: Tests in a Bivariate GARCH Framework - WP/99/158
- Linkages Among Asset Markets in the United States: Tests in a Bivariate GARCH Framework - WP/99/158 (Markdown version)
- Linkages Among Asset Markets in the United States: Tests in a Bivariate GARCH Framework - WP/99/158 (PDF)