Academic journal article Economic Perspectives

Monitoring Financial Stability: A Financial Conditions Index Approach

Academic journal article Economic Perspectives

Monitoring Financial Stability: A Financial Conditions Index Approach

Article excerpt

Introduction and summary

One of the key observations to come out of the recent crisis is that financial innovation has made it difficult to capture broad financial conditions in a small number of variables covering just a few traditional financial markets. The network of financial firms outside the traditional commercial banking system--that is, the so-called shadow banking system--was at the forefront of many of the major events of the crisis, as were newer financial markets for derivatives and asset-backed securities.

In the wake of the crisis, policymakers, regulators, financial market participants, and researchers have all affirmed the importance of the interconnections between traditional and newly developed financial markets, as well as their linkages to the nonfinancial sectors of the economy. The Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 sets forth a financial stability mandate built on this widespread affirmation.

Monitoring financial stability, thus, now explicitly requires an understanding of both how traditional and evolving financial markets relate to each other and how they relate to economic conditions. Indexes of financial conditions are an attempt to quantify these relationships. Here, we describe two new indexes that expand on the work of Illing and Liu (2006), Nelson and Perli (2007), Hakkio and Keeton (2009), and Hatzius et al. (2010).

In what follows, we first describe our method of index construction. The novel contribution of our method is that it takes into account both the cross-correlations of a large number of financial variables and the historical evolution of the index to derive a set of weights for each element of the index. We also develop an alternative index that separates the influence of economic conditions from financial conditions. We then highlight the contribution of different sectors of the financial system to our indexes, as well as the systemically important indicators among them.

Next, we show that the indexes of financial conditions we produce are useful tools in gauging financial stability. Major events in U.S. financial history are well captured by the history of our indexes, as is the interdependence of financial and economic conditions. To further demonstrate the latter, we establish that it is possible to use our indexes to improve upon forecasts of measures of economic activity over short and medium forecast horizons.

Measuring financial conditions

Indexes of financial conditions are typically constructed as weighted averages of a number of indicators of the financial system's health. Commonly, a statistical method called principal component analysis, or PCA, is used to estimate the weight given each indicator (see box 1 for details). The benefit of PCA is its ability to determine the individual importance of a large number of indicators so that the weight each receives is consistent with its historical importance to fluctuations in the broader financial system.

Indexes of this sort have the advantage of capturing the interconnectedness of financial markets--a desirable feature allowing for an interpretation of the systemic importance of each indicator. The more correlated an indicator is with its peers, the higher the weight it receives. This allows for the possibility that a small deterioration in a heavily weighted indicator may mean more for financial stability than a large deterioration in an indicator of little weight.

BOX 1

What is principal component analysis?

Here, we explain the mathematics behind PCA. (1)
In our explanation, x denotes the 1 x N element row
vector of data at time t. The first step is to form the
stacked matrix of data vectors [X.sub.T], where each column
of this vector contains T observations of a financial
indicator normalized to have a mean of zero and a
standard deviation of one. The eigenvector-eigenvalue
decomposition of the variance-covariance matrix
[X. … 
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