Is Connectedness a Leading Indicator of Financial Crises?

Thomas M. Menino Convention & Exhibition Center 

The methods of econometric connectedness-which involve estimating a vector autoregression (VAR) and decomposing the forecast error variance-are popular techniques for measuring financial market integration and shock spillovers between banks, markets, or assets. Previous papers that employ these tools often motivate their results by saying that their connectedness indices can help monitor volatility contagions and serve as an early warning sign of financial crises. In this paper, we test that statement. Namely, can connectedness indices help forecast high volatility and thus serve as an early warning sign? Using a 2-step procedure, we first estimate a VAR over rolling windows using stock market volatility data of 16 countries, which yields a sequence of connectedness indices. In the 2nd step, we re-estimate the VAR but include a connectedness index sequence in the model. Granger causality tests indicate that, yes, connectedness indices can help forecast market volatility. But the improvement in forecast accuracy by including these connectedness indices is relatively minor. Thus, the predictive power of these indices is statistically significant but economically small.

Crisis Forecastability

Volatility Prediction

Vector Autoregression

Connectedness

Market Integration

Forecast Error Variance Decomposition 

Business and Economic Statistics Section