Using quantile regression analysis, the authors show how changes in select measures of systemic risk in the United States, United Kingdom, and Europe are predictive of both negative macroeconomic shocks and the ensuing monetary policy response.
The authors investigate how a buildup of systemic risk in the financial markets increases risk in the real economy. Using the existing measures of financial sector distress, the authors quantify how fluctuations in systemic risk affect the probability of a macroeconomic downturn. Aggregating recession-relevant information across the gamut of individual measures, the authors propose a new systemic risk index, increases in which are associated with an increase in probability of downward shock to the economic activity.
How Is This Research Useful to Practitioners?
The authors argue that systemic risk measures should be not just theoretically appealing but also relevant to and informative of negative changes to real economic activity—particularly when used in support of public policy. The authors evaluate the empirical validity of each measure by testing its ability to capture robustly conditional quantiles of real economic shocks. Their conditional quantile factor model permits, in accordance with the literature, the possible asymmetric effect that changes in systemic risk have on macroeconomic outcomes.
Although only a few individual systemic risk measures provide any out-of-sample left-tail forecasting ability, when the data are aggregated into indexes using dimension reduction methods, the authors find evidence of a strong statistical dependence between the systemic risk indexes and the distribution of future adverse shocks.
A set of new, stylized facts emerges from the authors’ empirical study. First, the systemic measures are more informative about the downside risk of macroeconomic shocks than about their central tendency or right tail. Second, the measures of financial sector equity volatility are the most useful predictors of macroeconomic downturns, and the equity volatility in the nonfinancial sector appears to have insignificant predictive power. Finally, the systemic risk indicators forecast policy decisions; a rise in the systemic risk indicator predicts an increased probability of a large drop in the federal funds rate. The authors, however, find that such preventive action fails to fully dispel increased downside macroeconomic risks.
The authors, based on the results of their empirical tests, reach a positive conclusion that systemic risk measures, when taken together, contain useful information about the probability of future macroeconomic downturns.
How Did the Authors Conduct This Research?
The authors examine 19 proposed measures of systemic risk in the United States and 10 measures for the United Kingdom and Europe and use the longest possible data history. Some of the measures constructed capture information on institution-specific risks, comovement and contagion, volatility and instability, and liquidity and credit.
They focus on real macroeconomic shocks measured by innovations to industrial production growth in the United States, the United Kingdom, and the EU. The authors derive the data for the United States from the Federal Reserve Board and the data for the United Kingdom and EU from the Organisation for Economic Co-operation and Development (OECD).
Each systemic risk measure is studied individually, and its predictive power regarding future macroeconomic shocks is evaluated. The authors find the approach of including all the measures as separate right-hand-side variables in a multiple quantile regression to have virtually no forecasting power because of overfit problems.
Two procedures for aggregating information in the cross section of systemic risk measures are proposed. The authors use principal components quantile regression (PCQR) and partial quantile regression (PQR) to produce consistent forecasts for the true conditional quantiles of a macroeconomic target variable and find that PQR produces consistent quantile forecasts with fewer factors than PCQR.
The authors perform an interesting study to empirically show the impact of movement in systemic risk measures and financial market distress on certain macroeconomic variables. These empirical findings can potentially serve as guideposts for macroeconomic models of systemic risk going forward, which can demonstrate the impact on specific sectors of the economy.