Forecasting the next crisis

The occurrence and intensity of the 2007-10 financial crisis surprised most of the world’s economists and policymakers. The unanticipated nature of the crisis highlighted the difficulty that academics and policymakers face in forecasting economic downturns. Academic scholars and policymakers have struggled since then to identify indicators that can flag a brewing recession on a consistent basis. Such an exercise can be extremely useful for policymakers to take preventive action. Recent work by researchers at the University of Chicago’s Booth School of Business (Systemic Risk and the Macroeconomy: An Empirical Evaluation by Stefano Giglio, Bryan Kelly, Seth Pruitt and Xiao Qiao) may provide some light at the end of this economic forecasting tunnel.

The researchers examine 18 measures of systemic risk for the US and 12 measures for the UK and Europe. In building these measures, they use the longest possible data history, which in most cases allows them to extend the measures to the 1960s or earlier for the US, and to the 1970s for the UK and Europe. To the extent that systemically risky episodes are rarely observed phenomena, their longer time series helps provide new insights gathered over several business cycles and over multiple decades. The analysis spread over a long time period contrasts with the emphasis on systemic risk behaviour in the past five years, which itself has been motivated by the recent financial crisis. These indicators include standard regulatory measurements, such as levels of debt, risky assets and equity, liquidity, and default and credit spreads. The researchers also measured equity volatility, or the price swings in financial-sector stocks. The data focuses on the 20 largest financial institutions in each region. The researchers study the extent to which different measures co-move and evaluate which measures behave as contemporaneous indicators of distress in the financial sector and which may be viewed as leading indicators.

The study reports the following findings. First, by combining indicators that are used to measure risk in the financial sector, the study creates a more sensitive warning signal for impending economic downturns. The signal is based on an index of systemic risk. The statistical model developed by the study can predict economic downturns much better than any of its individual components. Their systemic-risk index predicted downturns in industrial production three months into the future, in every region and every time period. For example, the index predicted a 20% chance that quarterly growth in industrial production would drop by at least 3% ahead of the recent worldwide recession. Second, the study finds that volatility in the financial sector is highly predictive of macroeconomic tail risk. However, volatility of non-financial firms does a poor job of predicting macroeconomic tail risk.

This second finding is incredibly important for building knowledge in forecasting the macroeconomy. Earlier scholars have argued that volatility of the aggregate equity market plays an important role in predicting the business cycle. However, this study makes a strong case that the financial sector is quite special in any economy. Therefore, volatility in the financial sector can be a leading indicator for predicting macroeconomic activity. In contrast, equity volatility in non-financial sectors results from the tail risk events in the macroeconomy. This finding shows the importance of distinguishing economic uncertainty in the financial sector from turbulence in other industries and sectors.

Lastly, the study identifies the role of monetary policy easing, as measured by the response of the Federal Funds rate to various systemic risk measures. The researchers explore whether policymakers respond to the systemic risk indices by investigating whether the systemic risk index predicts changes in the Federal Funds rate. The researchers focus their analysis on three predictor variables: financial sector volatility, a variable termed ‘turbulence’ that aims to measure excess financial sector volatility, and their aggregate measure for the systemic risk. Quite pertinently, the study finds that either of the three measures—financial sector volatility, turbulence or the systemic risk index—predicts much better the change in the federal funds rate when this rate is low than when this rate is normal. Because monetary easing is typically undertaken during times of economic distress, this analysis shows that measures identifying distress in the financial sector relate not only to brewing economic downturns but also to the responses from the monetary authorities.

The study thus provides the important implication that aggregate systemic risk measure is not just related to any macroeconomic outcome, but specifically related to downside macroeconomic risks. Therefore, implementing this study in the Indian context would be important to enhance our ability to predict future financial calamities. The research departments at the Reserve Bank of India as well as the finance ministry should attempt to replicate this study for the Indian setting. Given the regime shift in the Indian economy since 1991, the data prior to the 1990s may not be relevant in attempting this exercise. Given the differences in the Indian context, it may be useful to segregate volatility in the public sector banks from volatility in the private sector banks while attempting to generate the systemic risk index and using it to predict future economic downturns.

Krishnamurthy Subramanian is an associate professor of finance at the Indian School of Business.

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