Monitoring financial stability: a financial conditions index approach.

Brave, Scott ; Butters, R. Andrew

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.sub.T][X.sub.T] then produces a set of weights referenced by
the N x 1 vector W corresponding to the eigenvector
associated with the largest eigenvalue of this matrix. (2)
These weights are used to construct a weighted sum
of the x at each point in time such that the resulting
index is given by [I.sub.t] = [X.sub.T]W.

In a general setting, variation in the frequency or
availability of data makes PCA infeasible. To circumvent
this issue, many indexes restrict the set of financial
indicators and the time period examined at the cost of
losing coverage of more recently developed financial
markets and longer historical comparisons. Alternatively,
Stock and Watson (2002) show how this issue can be
addressed by an iterative estimation strategy that relies
on the incomplete data methods of the expectation-maximization
(EM) algorithm of Watson and Engle
(1983). As the number of indicators becomes large,
this strategy produces an index estimate with the same
desirable statistical properties as PCA.

The EM algorithm uses the information from the
complete, or "balanced," panel of indicators to make
the best possible prediction of the incomplete, or
"unbalanced," panel of indicators. Stock and Watson's
(2002) EM algorithm begins with estimation by PCA
on a balanced subset of the data to obtain an initial
estimate of the index. Data for each of the financial
indicators are then regressed on this estimate of the
index, and the results of each regression are used to
predict missing data. The index is then re-estimated
by PCA on both the actual and predicted data. This
process continues until the difference in the sum of
the squared prediction errors between iterations
reaches a desired level of convergence.

Stock and Watson's (2002) EM algorithm is,
however, a purely static estimation method and does
not incorporate information along the time dimension
into the construction of the index. In addition, it, too,
is restricted by the need for an initial balanced panel
of the highest-frequency indicators, given its reliance
on PCA. Because most high-frequency financial indicators
are not readily available prior to the mid-1980s,
this constraint is not trivial. We, instead, use this
method as a starting point, but then rely on the alternative
estimation procedure of Doz, Giannone, and
Reichlin (2006). Their method allows us to also incorporate
information along the time dimension into
our index, and is a form of what is referred to as
dynamic factor analysis.

(1) For further details on PCA, see Theil (1971), pp. 46-48.

(2) Underlying the normalization of the data is the concept of
"stationarity," or the notion that the mean and variance of
each indicator do not vary over time. For this to be true, some
indicators must first be altered with a stationarity-inducing
transformation prior to estimation. The stationarity-inducing
transformations we used can be found in table A1 in the
appendix.

The PCA method also has its limitations, however. For instance, often the choice of which financial indicators to include is restricted by the frequency of data availability, as well as the length of time for which data are available. Work by Stock and Watson (2002) and others have shown how to relax some of these constraints, and we pursue this direction further so as to construct a richer and longer time series for our indexes.

Our goal is to be able to construct high-frequency indexes with broad coverage of measures of risk, liquidity, and leverage. By risk, we mean both the premium placed on risky assets embedded in their returns and the volatility of asset prices. In terms of liquidity, our measures capture the willingness to both borrow and lend at prevailing prices. Measures of leverage, in turn, provide a reference point for financial debt relative to equity.

To allow for historical comparisons and financial innovation, our method must also be able to incorporate time series of varying lengths and different frequencies. To do so, we apply the methods of Doz, Giannone, and Reichlin (2006) and Aruoba, Diebold, and Scotti (2009) (see box 2 for details). This framework allows us to make use of weekly, monthly, and quarterly financial indicators with histories that potentially begin and end at different times.

To briefly describe our method, we add a second dimension to the averaging process--namely, the time-series dimension of the index. At each point in time, all of the available indicators are used to construct the index, ignoring those that are unavailable. The historical time-series dynamics of the index are then used to smooth its history; and when these indicators again become available, the history is updated to reflect the information gained.

BOX 2

Estimating our financial conditions indexes

Our FCI is constructed in a similar fashion to many
coincident indicator models where the variation in
a panel of time series is governed linearly by an unknown
common source and an idiosyncratic error
term. The static measurement equation these models
all have in common is of the following form:

[x.sub.t ]=[GAMMA][f.sub.T] + [[epsilon].sub.t],

where [F.sub.t] represents a 1 x T latent factor capturing a
time-varying common source of variation in the N x T
matrix of standardized and stationary financial indicators
[X.sub.t] and [GAMMA] represents its N x 1 loadings onto this
factor. A defining characteristic of [X.sub.t] for our FCI is
its large size in both the cross section (N) and time
domain (T).

Adding dynamics of some finite order to the
latent factor moves the model into the large approximate
dynamic factor framework of Doz, Giannone,
and Reichlin (2006). The state-space representation
of this model is given by:

[X.sub.t] = [GAMMA][F.sub.t] + [[epsilon].sub.t]

[F.sub.t] = [AF.sub.t-1] + [v.sub.t],

where [GAMMA] are factor loadings estimated off the cross
section of financial indicators and A is the transition
matrix describing the evolution of the latent factor
over time. The latent factor's dynamics, p, as expressed
in the transition matrix A are assumed to be
of finite order: p = 15 weeks. Fifteen lags correspond
roughly with one quarter's worth of data.

With the model in state-space form and initial
estimates of the system matrices, the EM algorithm
outlined by Shumway and Stoffer (1982) can be used
to estimate the latent factor [F.sub.t]. At each iteration of
the algorithm, one pass of the data through the Kalman
filter and smoother is made, followed by re-estimation
of the system matrices by linear regression. (1) The
log-likelihood function that results is nondecreasing,
and convergence is governed by its stability.

We use the PCA-based EM algorithm of Stock
and Watson (2002) to provide consistent initial estimates
of [GAMMA] and [[epsilon].sup.'.sub.t][[epsilon].sub.t]/N, and we
use linear regression on the
subsequent estimate of [F.sub.t] to obtain consistent initial
estimates of A and [v.sup.'.sub.t][v.sub.t]/T. It is worth
emphasizing,
however, that these initializations are more restrictive
than necessary and serve in this framework only to
considerably reduce the required number of iterations
of the EM algorithm. For instance, PCA normalizes
the factor loadings to satisfy [[GAMMA].sup.'][GAMMA]/N = I and
assumes
that [[epsilon].sup.'.sub.t][[epsilon].sub.t]/N = [[sigma].sup.2]I.
The large approximate dynamic factor
model framework relaxes this assumption, instead using
the normalization that [v.sup.'.sub.t][v.sub.t]/T and accommodating
cross-sectional heteroskedasticity, that is,
[[epsilon].sup.'.sub.t][[epsilon].sub.t]/N = [[sigma].sup.2]I.

Because of the varying frequencies of observation
of the data in our FCI, we must also make two
extensions to the EM algorithm prior to estimation.
The first involves setting up the Kalman filter to
deal with missing values as discussed by Durbin and
Koopman (2001). The second modification involves
including additional state variables that evolve deterministically
to adjust for the temporal aggregation
issues caused by the varying frequencies of data
observation. Here, we follow Aruoba, Diebold, and
Scotti (2009) in their application of Harvey (1989)
to data of varying frequencies of observation to
augment the transition dynamics of the state-space
model accordingly.

Our adjusted FCI requires pretreatment of the data
before application of the routine we just described. Each
of the 100 financial variables is first regressed on current
and lagged values of a measure of the business cycle-that
is, the three-month moving average of the Chicago
Fed National Activity Index (CFNAI-MA3)--and inflation--that
is, three-month total inflation as measured
by the Personal Consumption Expenditures (PCE)
Price Index--with the number of current and lagged
values in each regression chosen for each variable using
the Bayesian Information Criterion. The independent
variables of these regressions were transformed so as
to match the frequency of observation of the dependent
variable. For weekly variables, we assumed only lagged
values enter the regression and that these values are
constant over the weeks of the month because of the
monthly frequency of observation for the CFNAI-MA3
and total PCE inflation. The standardized residuals
from these regressions are then used to construct our
adjusted FCI.

Our 100 financial indicators consist of 47 weekly,
29 monthly, and 24 quarterly variables. The longest
time series extends back to 1971, while the shortest
begins in 2008. We estimate the EM algorithm on the
unbalanced panel from the first week of 1971 through
2010. However, we only consider the estimates from
the first week of 1973 onward. At this point, over
25 percent of the financial indicators we examine
have complete time series. Because of the number
of high-frequency indicators we examine, it is not
until 1987 that 50 percent have complete time series.

(1) In addition, a small alteration in the least squares step is
required
to account for the fact that the unobserved components
of the model must first be estimated. See Brave and Butters
(2010a) for more information on the construction of the index.

Using this method, we construct our weekly financial conditions index (FCI) that takes into account both the cross-correlations of the indicators and the historical evolution of the index itself in determining the appropriate weights. The latter serves to smooth changes to the index over time, leaving behind more persistent contributions from the indicators. This feature is desirable, particularly in real time, because it avoids putting too much emphasis on potentially temporary factors influencing financial conditions.

Following Hatzius et al. (2010), we also consider adjusting the indicators for current and past economic activity and inflation prior to construction of the index. Our "adjusted" FCI, described in box 2, is motivated by the observation that financial and economic conditions are highly correlated. Removing the variation explained by the latter addresses potential asymmetries in the response of one to the other. For instance, a deterioration in financial conditions when economic growth is high and inflation low may have different effects on the real economy than a deterioration in financial conditions when economic growth is low and inflation high.

Our adjusted FCI is, thus, likely relevant for isolating the source of the shock to financial conditions. (1) That said, our FCI is a broader metric of financial stability because it captures the interaction of financial conditions and economic conditions. Combined, the two indexes could serve as useful policy tools by providing a sense of how tight or loose financial markets are operating relative to historical norms.

Figure 1 plots our FCI and adjusted FCI. Interpreting the level of both requires a reference to some historical norm. The norm considered in figure 1 is the sample mean of each index, which provides a sense of the average state of financial conditions, or its long-term historical trend. In this sense, a zero value for our FCI in figure 1 corresponds with a financial system operating at the historical average levels of risk, liquidity, and leverage. For our adjusted FCI, a zero value indicates a financial system operating at the historical average levels of risk, liquidity, and leverage consistent with economic conditions.

In general, risk measures receive positive weights in each index, whereas liquidity and leverage measures tend to have negative weights. This pattern of increasing risk premiums and declining liquidity and leverage is consistent with tightening financial conditions, and provides us a basis for interpreting both indexes: Positive index values indicate tighter conditions than on average, and negative index values indicate looser conditions than on average.

In addition, it is common for financial conditions indexes to be expressed relative to their sample standard deviations. We follow this approach to establish a scale for our FCI and adjusted FCI in figure I. Measured in this way, an index value of 1.0 is associated with financial conditions that are tighter than on average by one standard deviation. Similarly, an index value of -l.0 indicates that financial conditions are looser than on average by one standard deviation.

It is important to note, however, that given the transformations described previously, direct comparisons across the two indexes are not valid. Instead, comparisons must be made with respect to how each captures financial conditions over time. For instance, our adjusted FCI is much less persistent, moving above and below its average value more frequently than our FCI. It is also the case that our adjusted FCI gives more emphasis to recent financial market disruptions, often putting them on par with the more volatile 1970s and 1980s.

Instances can occur where adjusting for economic conditions produces a different interpretation of financial conditions than our FCI. Periods of high economic growth, such as the mid-1980s and late 1990s, often lead to an above-average adjusted FCI when our FCI is below average. Conversely, periods of high inflation, such as the 1970s and early 1980s, often lead to a below-average adjusted FCI when our FCI is above average.

[FIGURE 1 OMITTED]

Systemically important indicators

There are two ways to view the systemic relationship expressed in each indicator's weight: by its sign and by its magnitude. Risk measures with their generally positive weights and liquidity and leverage measures with their generally negative weights imply that increasingly positive values of the index capture periods of above-average risk and below-average liquidity and leverage. Conversely, increasingly negative values of the index capture periods where risk premiums are below average and liquidity and leverage are above average.

The way in which leverage enters our indexes is in line with Adrian and Shin (2010), who find leverage is often procyclical (that is, it is positively correlated with the overall state of the economy). In this way, the process of deleveraging appears in the indexes as an indicator of deteriorating financial conditions. Unlike other methods, however, our estimation framework can potentially take into account that a buildup of leverage generates a tendency to reverse itself that depends on the degree of mean reversion that our FCI and adjusted FCI have shown over time.

Taking into account the financial markets represented, we have segmented the financial indicators in our FCI and adjusted FCI into three categories: money markets (28 indicators), debt and equity markets (27), and the banking system (45). Table A1 in the appendix summarizes all 100 financial indicators in the form they enter both indexes; the indicators are listed in this order--from those with the largest positive weights to those with the largest negative weights within each category for our FCI. Because in our estimation method the weights are only identified up to scale, we have scaled them to have a unit variance in the table for ease of comparison.

The money markets category is made up mostly of interest rate spreads that form the basis of most other financial conditions indexes.2 However, unlike for many of these indexes, we also include in this category measures of implied volatility and trading volumes of several money market financial products. Interest rate spreads and measures of implied volatility tend to receive positive weights, whereas trading volumes tend to receive negative weights. The implication of this pattern is that widening spreads, increasing volatility, and declining volumes all constitute a tightening in money market conditions.

Some of the interest rate spreads given the greatest positive weights in our FCI include the one-month nonfinancial A2P2/AA commercial paper credit spread, as well as the two-year interest rate swap and the three-month Libor spreads relative to Treasuries. The first captures the risk premium for issuing short-term commercial paper to less creditworthy borrowers. The remaining two indicators capture elements of liquidity and credit risk in the interest rate derivative and interbank lending markets, respectively. The Merrill Lynch implied volatility measures for options and swaptions (MOVE and SMOVE) also receive large positive weights, whereas open interest in money market derivatives and repo market volume receive sizable negative weights. The former two indicators are, in a sense, measures of risk, while the latter two can be viewed as measures of liquidity and leverage.

The debt and equity markets category comprises mostly equity and bond price measures capturing volatility and risk premiums in their various forms. In addition to stock and bond market prices, we include in this category residential and commercial real estate prices, as well as municipal and corporate bond, stock, asset-backed security, and credit derivative market volumes. The latter measures capture elements of both market liquidity and leverage. In general, the indicators in this category follow the same pattern as the money market category, so that widening credit spreads, increasing volatility, and declining volumes denote tightening debt and equity market conditions.

In terms of equities, the largest positive weight in our FCI is given to the Chicago Board Options Exchange (CBOE) Market Volatility Index, commonly referred to as the VIX, which measures the implied volatility of the Standard & Poor's (S&P) 500; the largest negative weight is given to the relative valuation of financial stocks in the S&P 500 (S&P Financials/ S&P 500). In terms of bonds, credit spreads such as the high yield/Baa corporate and financial/corporate enter strongly here with large positive weights; so do spreads relative to Treasuries or swaps for nonmortgage asset-backed securities (ABS), mortgage-backed securities (MBS), and commercial-mortgage-backed securities (CMBS). Swap spreads on credit derivatives for investment grade and high-yield corporate bonds-or credit default swaps (CDS), a measure of insurance protection tied to default--are also given sizable positive weights.

The banking system category contains mainly survey-based measures of credit availability as well as accounting-based measures for commercial banks and so-called shadow banks, but a few interest rate spreads also appear in this category. The former indicators are primarily measures of liquidity and leverage, but they also capture the risk tied to deteriorations in credit quality. Of the interest rate spreads, the difference between the 30-year jumbo and conforming fixed-rate mortgages receives the largest positive weight, followed by the 30-year conforming mortgage/10-year Treasury yield spread.

The Federal Reserve Board's Senior Loan Officer Opinion Survey questions on loan spreads and lending standards all enter strongly into our FCI (mostly with large positive weights so that widening spreads and tighter standards reflect tighter conditions in the banking system), as do several other survey measures of business and consumer credit availability. Depending on how these survey measures are expressed, some receive large negative weights; but in each case, declining availability coincides with tighter banking system conditions.

The Credit Derivatives Research Counterparty Risk Index, measured as the average of the CDS spreads of the largest 14 issuers of CDS contracts, also receives a large positive weight, with the remaining weight split roughly evenly between measures of credit quality and commercial and shadow bank lending and leverage. All of these measures capture the inherent risks to the stability of the financial system posed by the potential collapse of commercial and shadow bank entities.

Differences arise in the relative systemic importance of several indicators when considering the impact of economic conditions in the estimation of the indicator weights. Figure 2 helps to explain these differences. Measures of the health of the banking system capture 41 percent of the variation explained by our FCI, followed by money market measures at 30 percent and debt and equity market measures at 29 percent. After performing the same calculation on our adjusted FCI, we note that money market measures now dominate at 54 percent, with debt and equity market measures accounting for 26 percent and the banking system measures accounting for 20 percent.

Thus, the primary effect of adjusting for economic conditions appears to be the reduction in importance of banking system measures. The survey-based indicators within the banking system category, in particular, show the largest declines in weight. A lower weight in this case indicates that much of the variation in these indicators can be explained by changes in either economic activity or inflation over time. A secondary effect, visible in table A1 in the appendix, is the addition of weight to certain measures of liquidity and leverage--that is, corporate bond and asset-backed security issuance, the net notional value of credit derivatives, and several commercial and shadow bank leverage measures.

It is likely that some of this result, shown in figure 2, stems from the fact that most of the previously mentioned measures are available at a weekly frequency. Our adjustment for economic conditions is more likely to account for medium-frequency rather than high-frequency variation. However, an examination of the weights in table A1 suggests that this cannot be the sole explanation. Several weekly money market measures receive greater weight--for example, the three-month London interbank bid (Eurodollar) and offered (TED) rate spreads; but there are also a number of weekly debt and equity market measures that receive less--for example, the high yield/Baa corporate bond, CMBS, and various credit derivative swap spreads, as well as the VIX.

Gauging financial stability

One way to judge the validity of our indexes as measures of financial stability is to follow the narrative approach and link their values to significant events in U.S. financial history. To illustrate this point, we plot our FCI and adjusted FCI in figure 3, highlighting prominent historical events. (3) Each panel of figure 3 depicts a decade of the index. Events are labeled with text boxes and arrows directed toward a specific week of both indexes denoted by a circle marker.

Overall, significant periods of crisis in financial history are well captured by both indexes, as are periods of relative calm. There are subtle differences, however, between the indexes around the time of several of the major events marked in figure 3. The first is clearly seen in panel A of figure 3 during the 1973-75 period that saw disruptions in equity markets and the failures of several large banks. In general, our adjusted FCI is quicker than our FCI to note both the onset and end of pressures--as financial conditions began to deteriorate prior to the 1973-75 recession and as they began to recover sooner than the real economy.

For most of the rest of the 1970s, both indexes indicate very similar financial conditions. However, by the end of the decade and into the early 1980s, as shown in panels A and B of figure 3, differences again emerge. The large swings in economic activity and inflation during these periods lead the adjusted FCI to be much more volatile, often swinging from well below zero to well above it very quickly. At their peak levels, both indexes are still very similar, capturing very well the major events of this period.

From the mid-1980s through the end of the decade, differences between the two indexes are much smaller (panel B of figure 3). Two events, however, stand out during this period of strong growth and disinflation: the resolution of Continental Illinois National Bank and Trust Company and the "Black Monday" stock market crash of 1987; the adjusted FCI puts more weight relative to earlier events on each compared with the FCI. The adjusted FCI is also quicker to note above-average tightness in response to the U.S. savings and loan crisis and quicker to recover from the crisis after accounting for the 1990-91 recession (see panels B and C of figure 3).

From the mid-1990s through the end of the decade (panel C of figure 3), the adjusted FCI consistently indicates financial conditions relative to economic conditions either about average or tighter than on average. In contrast, only after the Russian debt default, the subsequent collapse of Long-Term Capital Management, and the run-up to Y2K (the year 2000 software problem) does the FCI indicate financial conditions that are tighter than on average. During this period, the adjusted FCI additionally picks up the relative tightening in financial markets surrounding the Mexican peso devaluation and Asian financial crisis (around the time of the devaluation of the Thai baht).

Despite small differences surrounding the crash of the NASDAQ Stock Market and the corporate accounting scandals of the early 2000s (panel D of figure 3), both indexes generally indicated conditions looser than on average through the early part of the previous decade. Beginning in late 2005, the adjusted FCI moved closer to its average, while the FCI remained well below its average. The recent financial crisis appears at about the same time in both indexes, from mid-2007 through mid-2009, while the recovery registers a little later in the adjusted FCI.

More recently, as seen in figure 1 (p. 26), both indexes demonstrate that the financial system has healed significantly. Financial conditions by either measure, however, remain tighter than they were before the crisis. They have also been responsive to the European sovereign debt concerns that began in the spring of 2010 and the slowdown in economic activity throughout the summer months of 2010. In fact, our adjusted FCI breached its average level in the summer of 2010 before easing again during the rest of 2010.

Our historical analysis shows that persistent deviations in the interpretation of our two indexes contain useful information. The adjusted FCI is, in some sense, a forward-looking indicator of the FCI. When financial conditions are out of balance with economic conditions for an extended period, a correction in the FCI tends to result. Whether or not this result is due to the influence of the policy actions taken during these periods or other economic forces is beyond the scope of the analysis here. However, we refer the reader to Brave and Butters (2010a) and Brave and Genay (2011) for more rigorous analyses of the FCI and adjusted FCI.

[FIGURE 3 OMITTED]

Forecasting economic conditions

Another test of our indexes is their ability to predict the impact of changes in financial conditions on future economic activity. We follow the forecasting framework of Hatzius et al. (2010); but we refine their approach in two ways: 1) by concentrating on the portion of our FCI that cannot be explained by its historical dynamics and 2) by including as explanatory variables high-frequency nonfinancial measures of economic activity, such as the Chicago Fed National Activity Index (CFNAI). (4)

We refer to the portion of our FCI that cannot be predicted based on its historical dynamics as the FCI residual. The FCI residual corresponds with the error term, v, from the transition equation of our dynamic factor model (described in detail in box 2), where we follow the convention described previously for our FCI and scale it by its sample standard deviation. Because the FCI captures an element of financial conditions that also depends on economic conditions, systematic changes in the FCI over time reflect the historical response of financial conditions to past changes in financial and economic conditions. The FCI residual, therefore, reflects the deviation of financial conditions from this historical pattern.

It is this aspect of the FCI residual that we find appealing as an explanatory variable for future economic activity; in this regard, we prefer the FCI residual over the adjusted FCI, which captures only the deviation of financial conditions from economic conditions. Hatzius et al. (2010) frame the use of their adjusted index as a method of focusing purely on the impact of financial shocks on economic activity. We, instead, use our FCI because it also contains information on economic shocks. We then control for whether this information is in addition to that found in high-frequency nonfinancial measures of economic activity.

To demonstrate the ability of the FCI residual to predict future economic conditions and for the sake of comparison with the adjusted FCI, we conducted a pseudo out-of-sample forecasting exercise. Our mixed-frequency forecasting regressions incorporated lagged values of quarterly forecast variables taken from the U.S. Bureau of Economic Analysis's national income and product accounts (NIPA), as well as current and lagged values of the three-month moving average of the CFNAI alone or in combination with the 13-week moving average of one of the following sampled at the end of each month: the FCI residual, adjusted FCI, or adjusted FCI residual (which is the portion of the adjusted FCI unexplained by its historical dynamics). (5)

The CFNAI's three-month moving average serves as our reference point in evaluating the marginal information content of our measures of financial conditions over high-frequency nonfinancial measures of economic activity. It is a summary measure of 85 indicators constructed using PCA on data for production and income; employment, unemployment, and hours; personal consumption and housing; and sales, orders, and inventories. (6) The CFNAI has been used in the past to forecast economic growth and inflation by Stock and Watson (1999) and Brave and Butters (2010b), among others.

Our forecasting regression takes the following form:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where Y refers to the natural log of a particular NIPA data series, CFNAI indicates the three-month moving average of the CFNAI, and FCI is the 13-week moving average of either the FCI residual, adjusted FCI, or adjusted FCI residual. The explanatory variables were aligned with the NIPA data in the last month of each quarter (t) to match frequencies so that the index i represents a quarter (or three months) and the indexes j and k both represent months.

To construct forecasts, we began with data from 1973:Q1 through 1984:Q4. (7) One quarter's worth of data was then added on a recursive basis and forecasts made at a horizon (h) of one, two, four, and six quarter(s) ahead until the end of our data in 2010:Q2. The advantage of this framework is that it mimics the production of forecasts in real time (minus the impact of data revisions). In this way, we can account for model uncertainty. To allow for the further possibility of a change in lag structure over time, we had each recursive regression incorporate the Bayesian Information Criterion lag selection method. (8)

For an evaluation criterion, we used the mean-squared forecast error (MSFE) statistic computed from our sample of forecasts from 1985:Q1 through 2010:Q2 expressed relative to the similar statistic based on forecasts computed using only lagged quarterly growth rates of the NIPA variables. This ratio provides a test of model fit, so that a value less than 1 indicates an improvement in forecast accuracy relative to an autoregressive baseline for each NIPA variable. The MSFE statistic summarizes two elements in our pseudo out-of-sample context: the improvement in fit from incorporating the CFNAI alone or from incorporating the CFNAI along with the FCI residual, adjusted FCI, or adjusted FCI residual to the forecasting regression, balanced against the added parameter uncertainty from estimating additional regression coefficients.

Table 1 summarizes the results for nine NIPA variables all expressed in real, or constant price, terms. Gross domestic product (GDP) in panel A is the broadest measure we consider, but we also examine several of its components. Gross domestic purchases (panel B) exclude exports, and thus solely capture domestic demand. Final sales (panel C) remove the influence of changes in inventories. Nonfarm private inventories, nonresidential investment, and residential investment (panels D, E, and F) form the basis of the investment component of GDP we consider, and personal expenditures on durables, nondurables, and services (panels G, H, and I) account for consumption. We do not directly consider government spending or exports.

A few observations are readily apparent from this table. First, including the CFNAI in our forecasting regressions on NIPA data results in a substantial improvement in forecast accuracy (MSFE ratios less than 1) for GDP and measures of business investment, particularly at shorter horizons. Adding the FCI residual improves upon these initial forecasts at every horizon and for every variable, with the magnitude of improvement ranging from just less than 1 percent to 22 percent? In contrast, adding the adjusted FCI rarely improves on the forecasts based on the CFNAI alone; and the forecasts augmented with the adjusted FCI are less accurate than the forecasts augmented with the FCI residual in nearly every case.

[FIGURE 4 OMITTED]

The FCI-residual forecasts are also superior when compared with the adjusted-FCI-residual forecasts in nearly every case. However, the adjusted-FCI-residual forecasts are often superior to the forecasts based on the CFNAI alone and those augmented with the adjusted FCI. In this respect, our results suggest how to improve the ability of the adjusted FCI to forecast future economic activity--the key is to focus on the portion of the adjusted FCI that is not explained by its historical dynamics. This potential improvement is made by our extension of the index construction methodology of Hatzius et al. (2010) to a dynamic framework.

The results in table 1 also suggest that the FCI residual contains information on future economic activity in addition to that found in high-frequency nonfinancial measures of economic activity. There is, however, considerable variation in the forecasting performance of the FCI residual over time not shown in table 1. Much of the gains in forecast accuracy are concentrated in the recent period. Despite this fact, the inclusion of the FCI residual in our forecasting regressions rarely significantly worsens the forecast based on the CFNAI alone, so that it comes with little cost but potentially large benefits.

Figure 4 captures an instance of the small cost, large reward nature of including the FCI residual in our forecasting regression. It depicts actual real GDP growth at a two-quarter horizon and the forecasts for this measure based on the CFNAI's three-month moving average, as well as these forecasts including the 13-week moving average of the FCI residual or adjusted FCI residual. Differences prior to the recent crisis tend to be small. During these periods, sometimes the forecast including the FCI residual is marginally superior and sometimes it is not.

The forecast series begin to consistently deviate from one another in the second half of 2007, when the crisis started to unfold. Throughout the recent recession and recovery, the forecast including the FCI residual has more consistently tracked actual real GDP growth than any of the other forecasts we consider. At times during this period, however, the adjusted-FCI-residual forecast has been superior. The FCI-residual forecast's dominance over the adjusted-FCI-residual forecast over the entire period is due in large part to it more quickly picking up the beginning of the recent recession and the magnitude of the subsequent recovery.

Conclusion

Our newly constructed financial conditions indexes can serve as tools for both policymakers and financial market participants in gauging the current state of financial markets. Computed over a long time horizon and from a large sample of financial indicators of different frequencies, these indexes provide a timely assessment of how tightly or loosely financial markets are operating relative to historical financial and economic conditions.

As a measure of financial stability, our indexes exhibit several essential characteristics. Known periods of financial crisis correspond closely with peak periods of tightness in each index, and the turning points of each index coincide with well-known events in U.S. financial history. Furthermore, our indexes contain information on future economic activity beyond that found in nonfinancial measures of economic activity.

Our indexes are also unique in that they derive from an estimation method that captures both the systemic importance of traditional and new financial markets and the dynamic evolution of overall financial conditions. In the future, we plan to develop this framework further in order to better understand the channels through which changes in financial conditions affect economic activity.

APPENDIX

TABLE A1
Financial indicators in the financial conditions indexes (FCI and
adjusted FCI)

Financial indicator              Transformation   Frequency

1-month Nonfinancial CP
  A2P2/AA credit spread                LV             W
2-year Swap/Treasury yield
  spread                               LV             W
3-month TED spread
  (Libor-Treasury)                     LV             W
1-month Merrill Lynch Options
  Volatility Expectations
  (MOVE)                               LV             W
3-month Merrill Lynch Swaption
  Volatility Expectations
  (SMOVE)                              LV             W
3-month/1-week AA Financial
  CP spread                            LV             W
1-month Asset-backed/Financial
  CP credit spread                     LV             W
3-month Eurodollar spread
  (LIBID-Treasury)                     LV             W
On-the-run vs. Off-the-run
  10-year Treasury liquidity
  premium                              LV             W
10-year Swap/Treasury yield
  spread                               LV             W
3-month Financial CP/Treasury
  bill spread                          LV             W
Fed Funds/Overnight Treasury
  Repo rate spread                     LV             W
3-month OIS/Treasury yield
  spread                               LV             W
Agency MBS Repo Delivery
  Failures Rate                       DLNQ            W
1-year/1-month Libor spread            LV             W
Treasury Repo Delivery
  Failures Rate                       DLNQ            W
Agency Repo Delivery Failures
  Rate                                DLNQ            W
Fed Funds/Overnight Agency
  Repo rate spread                     LV             W
Corporate Securities Repo
  Delivery Failures Rate              DLNQ            W
Fed Funds/Overnight MBS Repo
  rate spread                          LV             W
10-year Constant Maturity
  Treasury yield                      DLV             W
Broker-dealer Debit Balances
  in Margin Accounts                  DLN             M
3-month/1-week Treasury Repo
  spread                               LV             W
2-year/3-month Treasury yield
  spread                               LV             W
Commercial Paper Outstanding          DLN             W
10-year/2-year Treasury yield
  spread                               LV             W
3-month Eurodollar, 10-year/
  3-month swap, 2-year and
  10-year Treasury Options and
  Futures Open Interest               DLNQ            W
Total Repo Market Volume
  (Repurchases+Reverse
  Repurchases)                        DLNQ            W

Citigroup Global Markets
  ABS/5-year Treasury yield
  spread                               LV             M
Bloomberg 5-year AAA CMBS
  spread to Treasuries                 LV             W
Merrill Lynch High Yield/Moody
  5 Baa corporate bond yield
  spread                               LV             W
CBOE S&P 500 Volatility Index
  (VIX)                                LV             W
Credit Derivatives Research
  North America Investment
  Grade Index                          LV             W
Credit Derivatives Research
  North America High Yield
  Index                                LV             W
Citigroup Global Markets
  Financial/Corporate Credit
  bond spread                          LV             M
Citigroup Global Markets
  MBS/10-year Treasury yield
  spread                               LV             M
Bond Market Association
  Municipal Swap/20-year
  Treasury yield spread                LV             W
20-year Treasury/State & Local
  Government 20-year General
  Obligation Bond yield spread         LV             W
Moody's Baa corporate
  bond/10-year Treasury yield
  spread                               LV             W
Total Money Market Mutual Fund
  Assets/Total Long-term Fund
  Assets                               LV             M
Nonfinancial business debt
  outstanding/GDP                     DLN             Q
Federal, state, and local
  debt outstanding/GDP                DLN             Q
Total MBS Issuance (Relative
  to 12-month MA)                     LVMA            M
S&P 500, NASDAQ, and NYSE
  Market Capitalization/GDP           DLN             Q
New US Corporate Equity
  Issuance (Relative to
  12-month MA)                        LVMA            M
Wilshire 5000 Stock Price
  Index                               DLN             M
Loan Performance Home Price
  Index                               DLN             M
New State & Local Government
  Debt Issues (Relative to
  12-month MA)                         LV             M
MIT Center for Real Estate
  Transactions-Based
  Commercial Property Price
  Index                               DLN             Q
Nonmortgage ABS Issuance
  (Relative to 12-month MA)           LVMA            M
S&P 500, S&P 500 mini, NASDAQ
  100, NASDAQ mini Options and
  Futures Open Interest               DLNQ            W
CMBS Issuance (Relative to
  12-month MA)                        LVMA            M
New US Corporate Debt Issuance
  (Relative to 12-month MA)           LVMA            M
Net Notional Value of Credit
  Derivatives                         DLN             W
S&P 500 Financials/S&P 500
  Price Index (Relative to
  2-year MA)                          LVMA            W

Sr Loan Officer Opinion
  Survey: Tightening standards
  on Small C&I Loans                   LV             Q
Sr Loan Officer Opinion                LV             Q
  Survey: Increasing spreads
  on Small C&I Loans
Sr Loan Officer Opinion                LV             Q
  Survey: Tightening standards
  on CRE Loans
Sr Loan Officer Opinion                LV             Q
  Survey: Tightening standards
  on Large C&I Loans
Sr Loan Officer Opinion                LV             Q
  Survey: Increasing spreads
  on Large C&I Loans
30-year Jumbo/Conforming               LV             W
  fixed-rate mortgage spread
Credit Derivatives Research            LV             W
  Counterparty Risk Index
National Federation of                 LV             M
  Independent Business Survey:
  Credit Harder to Get
30-year Conforming Mortgage/           LV             W
  10-year Treasury yield
  spread
American Bankers Association
  Value of Delinquent Home            DLV             M
  Equity Loans/Total Loans
American Bankers Association
 Value of Delinquent Consumer         DLV             M
  Loans/Total Loans
American Bankers Association
  Value of Delinquent Credit
  Card Loans/Total Loans              DLV             M
S&P US Credit Card Quality
  Index 3-month Delinquency
  Rate                                DLV             M
Noncurrent/Total Loans at
  Commercial Banks                    DLN             Q
American Bankers Association
  Value of Delinquent Non-card
  Revolving Credit Loans/Total
  Loans                               DLV             M
Commercial Bank C&I
  Loans/Total Assets                  DLNQ            W
Mortgage Bankers Association
  Serious Delinquencies               DLV             Q
Total Assets of Funding
  Corporations/GDP                    DUN             Q
Mortgage Bankers Association
  Mortgage Applications
  Volume Market Index                 DUN             W
Total Assets of Agency and
  GSE backed mortgage
  pools/GDP                           DLN             Q
Total Assets of ABS
  issuers/GDP                         DLN             Q
FDIC Volatile Bank Liabilities        DLN             Q

Commercial Bank Deposits/
  Total Assets                        DLNQ            W
Fed funds and Reverse
  Repurchase Agreements w/
  nonbanks and Interbank
  Loans/Total Assets                  DLNQ            W
Total Assets of Finance
  Companies/GDP                       DLN             Q
Total Unused C&I Loan
  Commitments/Total Assets            DLN             Q
Total REIT Assets/GDP                 DLN             Q
Total Assets of Broker-
  dealers/GDP                         DLN             Q
Commercial Bank Real Estate
  Loans/Total Assets                  DLNQ            W
Total Assets of Pension
  Funds/GDP                           DLN             Q
MZM Money Supply                      DLN             M
Total Assets of Insurance
  Companies/GDP                       DLN             Q
Commercial Bank 48-month New
  Car Loan/2-year Treasury
  yield spread                         LV             Q
Consumer Credit Outstanding           DLN             M
Commercial Bank Securities in
  Bank Credit/Total Assets            DLNQ            W
Commercial Bank 24-month
  Personal Loan/2-year
  Treasury yield spread                LV             Q
S&P US Credit Card Quality
  Index Receivables
  Outstanding                         DUN             M
S&P US Credit Card Quality
  Index Excess Rate Spread             LV             M
Finance Company Receivables
  Outstanding                         DUN             M
Finance Company New Car Loan
  interest rate/2-year
  Treasury yield spread
Sr Loan Officer Opinion                LV             M
  Survey: Willingness to Lend
  to Consumers                         LV             Q
UM Household Survey: Auto
  Credit Conditions Good/Bad
  spread                               LV             M
UM Household Survey: Mortgage
  Credit Conditions Good/Bad
  spread                               LV             M
UM Household Survey: Durable
  Goods Credit Conditions
  Good/Bad spread                      LV             M
National Association of Credit
  Managers Index                       LV             M

Transformations
LV: Level
LVMA: Level relative to moving average
DLV: First difference
DLN: Log first difference
DLNQ: 13-week log difference

                                 Hover/Bloomberg */
Financial indicator              Call Report^ mnemonic

1-month Nonfinancial CP
  A2P2/AA credit spread          FAP1M-FCP1M
2-year Swap/Treasury yield
  spread                         T111 W2-R111G2
3-month TED spread
  (Libor-Treasury)               FLOD3-FTBS3
1-month Merrill Lynch Options
  Volatility Expectations
  (MOVE)                         SPMLV1
3-month Merrill Lynch Swaption
  Volatility Expectations
  (SMOVE)                        SPMLSV3
3-month/1-week AA Financial
  CP spread                      FFP3M-FFP7D
1-month Asset-backed/Financial
  CP credit spread               FAB1M-FFP1M
3-month Eurodollar spread
  (LIBID-Treasury)               FDB3-FTBS3
On-the-run vs. Off-the-run
  10-year Treasury liquidity
  premium                        FYCEPA-FCM10
10-year Swap/Treasury yield
  spread                         T111 WA-R111GA
3-month Financial CP/Treasury
  bill spread                    FFP3-FTBS3
Fed Funds/Overnight Treasury
  Repo rate spread               FFED-RPGT01 D*
3-month OIS/Treasury yield
  spread                         T111 W3M-R111 G3M
Agency MBS Repo Delivery
  Failures Rate                  FDDM/(FDDM+FDTM)
1-year/1-month Libor spread      FLOD1 Y FLOD1
Treasury Repo Delivery
  Failures Rate                  FDDG/(FDDG+FDTG)
Agency Repo Delivery Failures
  Rate                           FDDS/(FDDS+FDTS)
Fed Funds/Overnight Agency
  Repo rate spread               FFED-RPAG01D'
Corporate Securities Repo
  Delivery Failures Rate         FDDG/(FDDG+FDTG)
Fed Funds/Overnight MBS Repo
  rate spread                    FFED-RPMB01 D*
10-year Constant Maturity
  Treasury yield                 FCM10
Broker-dealer Debit Balances
  in Margin Accounts             SPMD
3-month/1-week Treasury Repo
  spread                         RPGT03M *-RPGT01W *
2-year/3-month Treasury yield
  spread                         FYCEP2-FTBS3
Commercial Paper Outstanding     FCPT
10-year/2-year Treasury yield
  spread                         FYCEPA-FYCEP2
3-month Eurodollar, 10-year/
  3-month swap, 2-year and
  10-year Treasury Options and
  Futures Open Interest          COPED3P+COPTN2P+COPT10P+COPIRSP
Total Repo Market Volume
  (Repurchases+Reverse
  Repurchases)                   FDFR+FDFV

Citigroup Global Markets
  ABS/5-year Treasury yield
  spread                         SYCAAB-FCM5
Bloomberg 5-year AAA CMBS
  spread to Treasuries           CMBSAAA5 *
Merrill Lynch High Yield/Moody
  5 Baa corporate bond yield
  spread                         FMLHYFBAA
CBOE S&P 500 Volatility Index
  (VIX)                          SPVIX
Credit Derivatives Research
  North America Investment
  Grade Index                    S009LIG
Credit Derivatives Research
  North America High Yield
  Index                          S009LHY
Citigroup Global Markets
  Financial/Corporate Credit
  bond spread                    SYCF-SYCT
Citigroup Global Markets
  MBS/10-year Treasury yield
  spread                         SYMT FCM10
Bond Market Association
  Municipal Swap/20-year
  Treasury yield spread          SBMAS-FCM20
20-year Treasury/State & Local
  Government 20-year General
  Obligation Bond yield spread   FSLB-FCM20
Moody's Baa corporate
  bond/10-year Treasury yield
  spread                         FBAA-FCM10
Total Money Market Mutual Fund
  Assets/Total Long-term Fund
  Assets                         ICMMA/ICIA
Nonfinancial business debt
  outstanding/GDP                XL14TCRE5/GDP
Federal, state, and local
  debt outstanding/GDP           (XL31CRE5+XL21TCR5)/GDP
Total MBS Issuance (Relative
  to 12-month MA)                N/A
S&P 500, NASDAQ, and NYSE
  Market Capitalization/GDP      (SPSP5CAP+SPNYCAPH+SPNACAP)/GDP
New US Corporate Equity
  Issuance (Relative to
  12-month MA)                   FNSIPS
Wilshire 5000 Stock Price
  Index                          SPWIE
Loan Performance Home Price
  Index                          USLPHPIS
New State & Local Government
  Debt Issues (Relative to
  12-month MA)                   FNSIS
MIT Center for Real Estate
  Transactions-Based
  Commercial Property Price
  Index                          MTBIP
Nonmortgage ABS Issuance
  (Relative to 12-month MA)      N/A
S&P 500, S&P 500 mini, NASDAQ
  100, NASDAQ mini Options and
  Futures Open Interest          COPSPMP+COPSP5P+COPNAMP+COPNASP
CMBS Issuance (Relative to
  12-month MA)                   N/A
New US Corporate Debt Issuance
  (Relative to 12-month MA)      FNSIPB
Net Notional Value of Credit
  Derivatives                    D001TOTH
S&P 500 Financials/S&P 500
  Price Index (Relative to
  2-year MA)                     S5N401/SPN5COM

Sr Loan Officer Opinion
  Survey: Tightening standards
  on Small C&I Loans             FTCIS
Sr Loan Officer Opinion          FSCIS
  Survey: Increasing spreads
  on Small C&I Loans
Sr Loan Officer Opinion          FTCRE
  Survey: Tightening standards
  on CRE Loans
Sr Loan Officer Opinion          FTCIL
  Survey: Tightening standards
  on Large C&I Loans
Sr Loan Officer Opinion          FSCIL
  Survey: Increasing spreads
  on Large C&I Loans
30-year Jumbo/Conforming         ILMJNAVG'-ILM3NAVG'
  fixed-rate mortgage spread
Credit Derivatives Research      SOOOCRI
  Counterparty Risk Index
National Federation of           NFIB20
  Independent Business Survey:
  Credit Harder to Get
30-year Conforming Mortgage/     FRM30F-FCM10
  10-year Treasury yield
  spread
American Bankers Association
  Value of Delinquent Home       USHWODA
  Equity Loans/Total Loans
American Bankers Association
 Value of Delinquent Consumer    USSUMDA
  Loans/Total Loans
American Bankers Association
  Value of Delinquent Credit
  Card Loans/Total Loans         USBKCDA
S&P US Credit Card Quality
  Index 3-month Delinquency
  Rate                           CCQID3
Noncurrent/Total Loans at
  Commercial Banks               (RCFD1407^+RCFD1403^)/RCFD2122^
American Bankers Association
  Value of Delinquent Non-card
  Revolving Credit Loans/Total
  Loans                          USREVDA
Commercial Bank C&I
  Loans/Total Assets             FABWCA/FAA
Mortgage Bankers Association
  Serious Delinquencies          USL14FA+USL149A
Total Assets of Funding
  Corporations/GDP               OA50TAO5/GDP
Mortgage Bankers Association
  Mortgage Applications
  Volume Market Index            MBAM
Total Assets of Agency and
  GSE backed mortgage
  pools/GDP                      OA41MOR5/GDP
Total Assets of ABS
  issuers/GDP                    OA67TAO5/GDP
FDIC Volatile Bank Liabilities   RCON2604A+RCFN2200A+RCFD2800^
                                 +MAX(RCFD2890A,RCFD3190^)+RCFD3548A
Commercial Bank Deposits/
  Total Assets                   FBDA/FAA
Fed funds and Reverse
  Repurchase Agreements w/
  nonbanks and Interbank
  Loans/Total Assets             (FAIFFA+FABWORA)/FAA
Total Assets of Finance
  Companies/GDP                  OA61TAQ5/GDP
Total Unused C&I Loan
  Commitments/Total Assets       RCON3423^/RCON2170A
Total REIT Assets/GDP            OA64TAO5/GDP
Total Assets of Broker-
  dealers/GDP                    OA66TAO5/GDP
Commercial Bank Real Estate
  Loans/Total Assets             FABWRA/FAA
Total Assets of Pension
  Funds/GDP                      OA57TA05/GDP
MZM Money Supply                 FMZM
Total Assets of Insurance
  Companies/GDP                  (OA51TA05+OA54TA05)/GDP
Commercial Bank 48-month New
  Car Loan/2-year Treasury
  yield spread                   FK48NC-FCM2
Consumer Credit Outstanding      FOT
Commercial Bank Securities in
  Bank Credit/Total Assets       FABYA/FAA
Commercial Bank 24-month
  Personal Loan/2-year
  Treasury yield spread          FK24P-FCM2
S&P US Credit Card Quality
  Index Receivables
  Outstanding                    CCQIO
S&P US Credit Card Quality
  Index Excess Rate Spread       CCQIX
Finance Company Receivables
  Outstanding                    FROT
Finance Company New Car Loan
  interest rate/2-year
  Treasury yield spread
Sr Loan Officer Opinion          FFINC-FCM2
  Survey: Willingness to Lend
  to Consumers                   FWILL
UM Household Survey: Auto
  Credit Conditions Good/Bad
  spread                         N/A
UM Household Survey: Mortgage
  Credit Conditions Good/Bad
  spread                         N/A
UM Household Survey: Durable
  Goods Credit Conditions
  Good/Bad spread                N/A
National Association of Credit
  Managers Index                 CMI

                                                               Adjusted
Financial indicator               Start    Category    FCI       FCI

1-month Nonfinancial CP
  A2P2/AA credit spread          1997w2       1       2.255     2.308
2-year Swap/Treasury yield
  spread                         1987w14      1       2.229     2.975
3-month TED spread
  (Libor-Treasury)               1980w23      1       1.825     3.606
1-month Merrill Lynch Options
  Volatility Expectations
  (MOVE)                         1988w15      1       1.690     1.566
3-month Merrill Lynch Swaption
  Volatility Expectations
  (SMOVE)                        1996w49      1       1.678     0.564
3-month/1-week AA Financial
  CP spread                      1997w2       1       1.582     2.037
1-month Asset-backed/Financial
  CP credit spread               2001w1       1       1.581     2.064
3-month Eurodollar spread
  (LIBID-Treasury)               1971w2       1       1.522     3.048
On-the-run vs. Off-the-run
  10-year Treasury liquidity
  premium                        1988w1       1       0.974     0.916
10-year Swap/Treasury yield
  spread                         1987w14      1       0.845     1.189
3-month Financial CP/Treasury
  bill spread                    1971w1       1       0.619     1.741
Fed Funds/Overnight Treasury
  Repo rate spread               1991w21      1       0.495     1.084
3-month OIS/Treasury yield
  spread                         2003w38      1       0.452     1.352
Agency MBS Repo Delivery
  Failures Rate                  1994w40      1       0.426     0.430
1-year/1-month Libor spread      1986w2       1       0.368     0.378
Treasury Repo Delivery
  Failures Rate                  1994w40      1       0.307     0.474
Agency Repo Delivery Failures
  Rate                           1994w40      1       0.168     0.045
Fed Funds/Overnight Agency
  Repo rate spread               1991w21      1       0.150     0.592
Corporate Securities Repo
  Delivery Failures Rate         2001w40      1       0.103     0.051
Fed Funds/Overnight MBS Repo
  rate spread                    1991w21      1       0.037     0.173
10-year Constant Maturity
  Treasury yield                 1971w2       1       -0.050    -0.208
Broker-dealer Debit Balances
  in Margin Accounts             1971w2       1       -0.122    -0.203
3-month/1-week Treasury Repo
  spread                         1991w21      1       -0.141    0.858
2-year/3-month Treasury yield
  spread                         1971w1       1       -0.237    0.167
Commercial Paper Outstanding     1998w45      1       -0.482    -0.231
10-year/2-year Treasury yield
  spread                         1971w34      1       -0.706    -0.979
3-month Eurodollar, 10-year/
  3-month swap, 2-year and
  10-year Treasury Options and
  Futures Open Interest          2002w7       1       -1.024    -0.075
Total Repo Market Volume
  (Repurchases+Reverse
  Repurchases)                   1994w40      1       -1.331    -1.078

Citigroup Global Markets
  ABS/5-year Treasury yield
  spread                         1989w52      2       2.487     2.865
Bloomberg 5-year AAA CMBS
  spread to Treasuries           1996w27      2       2.234     1.647
Merrill Lynch High Yield/Moody
  5 Baa corporate bond yield
  spread                         1997w2       2       2.116     0.659
CBOE S&P 500 Volatility Index
  (VIX)                          1990w1       2       2.074     1.815
Credit Derivatives Research
  North America Investment
  Grade Index                    2006w1       2       1.528     0.477
Credit Derivatives Research
  North America High Yield
  Index                          2006wl       2       1.516     0.495
Citigroup Global Markets
  Financial/Corporate Credit
  bond spread                    1979w52      2       1.179     1.959
Citigroup Global Markets
  MBS/10-year Treasury yield
  spread                         1979w52      2       0.848     1.568
Bond Market Association
  Municipal Swap/20-year
  Treasury yield spread          1989w27      2       0.818     1.561
20-year Treasury/State & Local
  Government 20-year General
  Obligation Bond yield spread   1971 w1      2       0.502     -0.189
Moody's Baa corporate
  bond/10-year Treasury yield
  spread                         1971 w1      2       0.348     0.936
Total Money Market Mutual Fund
  Assets/Total Long-term Fund
  Assets                         1974w52      2       0.231     0.177
Nonfinancial business debt
  outstanding/GDP                1971w13      2       0.025     0.091
Federal, state, and local
  debt outstanding/GDP           1971w13      2       0.024     0.010
Total MBS Issuance (Relative
  to 12-month MA)                2000w52      2       -0.022    -0.106
S&P 500, NASDAQ, and NYSE
  Market Capitalization/GDP      1971w13      2       -0.041    -0.079
New US Corporate Equity
  Issuance (Relative to
  12-month MA)                   1987w52      2       -0.047    0.027
Wilshire 5000 Stock Price
  Index                          1971w5       2       -0.052    -0.108
Loan Performance Home Price
  Index                          1976w9       2       -0.066    -0.146
New State & Local Government
  Debt Issues (Relative to
  12-month MA)                   2004w9       2       -0.108    -0.185
MIT Center for Real Estate
  Transactions-Based
  Commercial Property Price
  Index                          1984w26      2       -0.111    -0.128
Nonmortgage ABS Issuance
  (Relative to 12-month MA)      2000w52      2       -0.130    -0.184
S&P 500, S&P 500 mini, NASDAQ
  100, NASDAQ mini Options and
  Futures Open Interest          2000w12      2       -0.134    -0.250
CMBS Issuance (Relative to
  12-month MA)                   1990w52      2       -0.157    -0.184
New US Corporate Debt Issuance
  (Relative to 12-month MA)      1987w52      2       -0.179    -0.279
Net Notional Value of Credit
  Derivatives                    2008w45      2       -0.256    -0.522
S&P 500 Financials/S&P 500
  Price Index (Relative to
  2-year MA)                     1989w37      2       -1.860    -2.007

Sr Loan Officer Opinion
  Survey: Tightening standards
  on Small C&I Loans             1990w13      3       2.501     1.366
Sr Loan Officer Opinion          1990w13      3       2.467     1.312
  Survey: Increasing spreads
  on Small C&I Loans
Sr Loan Officer Opinion          1990w26      3       2.418     1.442
  Survey: Tightening standards
  on CRE Loans
Sr Loan Officer Opinion          1990w13      3       2.416     1.274
  Survey: Tightening standards
  on Large C&I Loans
Sr Loan Officer Opinion          1990w13      3       2.364     1.060
  Survey: Increasing spreads
  on Large C&I Loans
30-year Jumbo/Conforming         1998w23      3       2.220     2.078
  fixed-rate mortgage spread
Credit Derivatives Research      2006w1       3       1.361     0.644
  Counterparty Risk Index
National Federation of           1973w44      3       1.228     0.668
  Independent Business Survey:
  Credit Harder to Get
30-year Conforming Mortgage/     1978w35      3       1.154     1.491
  10-year Treasury yield
  spread
American Bankers Association
  Value of Delinquent Home       1999w9       3       0.284     0.169
  Equity Loans/Total Loans
American Bankers Association
 Value of Delinquent Consumer    1999w9       3       0.264     0.106
  Loans/Total Loans
American Bankers Association
  Value of Delinquent Credit
  Card Loans/Total Loans         1999w9       3       0.220     0.090
S&P US Credit Card Quality
  Index 3-month Delinquency
  Rate                           1992w9       3       0.157     0.024
Noncurrent/Total Loans at
  Commercial Banks               1984w26      3       0.139     0.146
American Bankers Association
  Value of Delinquent Non-card
  Revolving Credit Loans/Total
  Loans                          1999w9       3       0.139     0.197
Commercial Bank C&I
  Loans/Total Assets             1973w9       3       0.068     0.191
Mortgage Bankers Association
  Serious Delinquencies          1972w26      3       0.028     0.078
Total Assets of Funding
  Corporations/GDP               1971w13      3       0.022     0.022
Mortgage Bankers Association
  Mortgage Applications
  Volume Market Index            1990w2       3       0.020     -0.086
Total Assets of Agency and
  GSE backed mortgage
  pools/GDP                      1971w13      3       0.011     0.031
Total Assets of ABS
  issuers/GDP                    1983w39      3       0.005     0.025
FDIC Volatile Bank Liabilities
                                 1978w26      3       0.000     0.017
Commercial Bank Deposits/
  Total Assets                   1973w9       3       0.000     -0.026
Fed funds and Reverse
  Repurchase Agreements w/
  nonbanks and Interbank
  Loans/Total Assets             1973w9       3       -0.005    -0.060
Total Assets of Finance
  Companies/GDP                  1971w13      3       -0.009    0.012
Total Unused C&I Loan
  Commitments/Total Assets       1984w26      3       -0.011    -0.036
Total REIT Assets/GDP            1971w13      3       -0.012    0.071
Total Assets of Broker-
  dealers/GDP                    1971w13      3       -0.013    -0.035
Commercial Bank Real Estate
  Loans/Total Assets             1973w9       3       -0.019    -0.026
Total Assets of Pension
  Funds/GDP                      1971w13      3       -0.023    -0.053
MZM Money Supply                 1974w9       3       -0.028    -0.076
Total Assets of Insurance
  Companies/GDP                  1971w13      3       -0.029    -0.067
Commercial Bank 48-month New
  Car Loan/2-year Treasury
  yield spread                   1976w26      3       -0.033    -0.135
Consumer Credit Outstanding      1971w5       3       -0.039    0.057
Commercial Bank Securities in
  Bank Credit/Total Assets       1973w9       3       -0.052    -0.159
Commercial Bank 24-month
  Personal Loan/2-year
  Treasury yield spread          1976w26      3       -0.083    -0.172
S&P US Credit Card Quality
  Index Receivables
  Outstanding                    1992w9       3       -0.095    -0.013
S&P US Credit Card Quality
  Index Excess Rate Spread       1992w5       3       -0.109    -0.387
Finance Company Receivables
  Outstanding                    1985w31      3       -0.149    0.041
Finance Company New Car Loan
  interest rate/2-year
  Treasury yield spread
Sr Loan Officer Opinion          1976w26      3       -0.150    -1.130
  Survey: Willingness to Lend
  to Consumers                   1971w13      3       -0.538    -0.334
UM Household Survey: Auto
  Credit Conditions Good/Bad
  spread                         1978w5       3       -1.354    -1.321
UM Household Survey: Mortgage
  Credit Conditions Good/Bad
  spread                         1978w5       3       -1.487    -1.802
UM Household Survey: Durable
  Goods Credit Conditions
  Good/Bad spread                1978w5       3       -1.543    -1.668
National Association of Credit
  Managers Index                 2002w9       3       -2.004    -0.130

Categories

1. Money markets

2. Debt and equity markets

3. Banking system

Notes: All of the financial indicators are in basis points or
percentages. N/A means not applicable; the relevant series are
taken from Inside Mortgage Finance Publications, CRE Finance
Council, and University of Michigan data. For more information
on the indicators, please contact the authors.

REFERENCES

Adrian, T., and It. S. Shin, 2010, "Liquidity and leverage," Journal of Financial Intermediation, Vol. 19, No. 3, July, pp. 418-437.

Aruoba, S. B., F. X. Diebold, and C. Scotti, 2009, "Real-time measurement of business conditions," Journal of Business and Economic Statistics, Vol. 27, No. 4, pp. 417-427.

Brave, S., and R. A. Butters, 2010a, "Gathering insights on the forest from the trees: A new metric for financial conditions," Federal Reserve Bank of Chicago, working paper, No. WP-2010-07, August 24.

--, 2010b, "Chicago Fed National Activity Index turns ten--Analyzing its first decade of performance," Chicago FedLetter, Federal Reserve Bank of Chicago, No. 273, April.

Brave, S., and H. Genay, 2011, "Federal Reserve policies and financial market conditions during the crisis," Federal Reserve Bank of Chicago, working paper, forthcoming.

Carron, A. S., 1982, "Financial crises: Recent experience in U.S. and international markets," Brookings Papers on Economic Activity, Vol. 1982, No. 2, pp. 395-418.

Doz, C., D. Giannone, and L. Reichlin, 2006, "A quasi maximum likelihood approach for large approximate dynamic factor models," European Central Bank, working paper, No. 674, September.

Durbin, J., and S. J. Koopman, 2001, Time Series Analysis by State Space Methods, Oxford, UK, and New York: Oxford University Press.

El-Gamal, M. A., and A. M. Jaffe, 2008, "Energy, financial contagion, and the dollar," Rice University, James A. Baker III Institute for Public Policy, working paper, May.

Federal Deposit Insurance Corporation, 1997, History of the Eighties--Lessons for the Future, 2 vols., Washington, DC.

--,1984, Federal Deposit Insurance Corporation." The First Fifty Years--A History of the FDIC, 1933-1983, Washington, DC.

Hakkio, C. S., and W. R. Keeton, 2009, "Financial stress: What is it, how can it be measured, and why does it matter?," Economic Review, Federal Reserve Bank of Kansas City, Second Quarter, pp. 5-50.

Harvey, A., 1989, Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge, UK: Cambridge University Press.

Hatzius, J., P. Hooper, F. Mishkin, K. L. Schoenholtz, and M. W. Watson, 2010, "Financial conditions indexes: A fresh look after the financial crisis," University of Chicago Booth School of Business, Initiative on Global Markets, report, April 13, available at http://research.chicagobooth.edu/igm/events/ docs/2010usmpfreport.pdf.

Illing, M., and Y. Liu, 2006, "Measuring financial stress in a developed country: An application to Canada," Journal of Financial Stability, Vol. 2, No. 3, October, pp. 243-265.

Laeven, L., and F. Valencia, 2008, "Systemic banking crises: A new database," International Monetary Fund, working paper, No. WP/08/224, November.

Minsky, H. P., 1986, Stabilizing an Unstable Economy, New Haven, CT: Yale University Press.

Nelson, W. R., and R. Perli, 2007, "Selected indicators of financial stability," in Risk Measurement and Systemic Risk, Frankfurt am Main, Germany: European Central Bank, pp. 343-372.

Reinhart, C. M., and K. S. Rogoff, 2008, "This time is different: A panoramic view of eight centuries of financial crises," National Bureau of Economic Research, working paper, No. 13882, March.

Schreft, S. L., 1990, "Credit controls: 1980," Economic Review, Federal Reserve Bank of Richmond, November/ December, pp. 25-55.

Shumway, R. H., and D. S. Stoffer, 1982, "An approach to time series smoothing and forecasting using the EM algorithm," Journal of Time Series Analysis, Vol. 3, No. 4, July, pp. 253-264.

Spero, J. E., 1999, The Failure of the Franklin National Bank: Challenge to the International Banking System, Washington, DC: Beard Books.

Stock, J. H., and M. W. Watson, 2002, "Forecasting using principal components from a large number of predictors," Journal of the American Statistical Association, Vol. 97, No. 460, December, pp. 1167-1179.

--, 1999, "Forecasting inflation," Journal of Monetary Economics, Vol. 44, No. 2, October, pp. 293-335.

Theil, H., 1971, Principles of Econometrics, New York: John Wiley and Sons.

Watson, M. W., and R. F. Engle, 1983, "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Vol. 23, No. 3, December, pp. 385-400.

NOTES

(1) Hatzius et al. (2010) also construct a similar version of their index of financial conditions and relate it to changes in the federal funds rate over time. We have found very similar results to theirs; our adjusted FCI is significantly correlated with measures of monetary policy, though we have not documented this here. See Brave and Genay (2011), who relate monetary policy during the recent crisis to the adjusted FCI, for more information.

(2) Most of our 100 financial indicators have become standard fare in the financial press as a result of the recent financial crisis. Rather than describe each in further detail, we refer interested readers to the useful summaries found in Nelson and Peril (2007), Hakkio and Keeton (2009), and Hatzius et al. (2010).

(3) The literature on financial crises is quite extensive. The following works are a few of those that were instrumental in constructing our timeline of events: Federal Deposit Insurancc Corporation ( 1984, 1997), Reinhart and Rogoff (2008), Schreft (1990), Minsky (1986), Spero (1999), Laeven and Valencia (2008), Carron (1982), and El-Gamal and Jaffe (2008).

(4) Hakkio and Keeton (2009) also use the CFNAI to make similar comparisons.

(5) We use smoothed measures of the explanatory variables when appropriate to approximate the quarterly frequency of the NIPA variables being forecasted.

(6) For more details on the CFNAI, including its 85 indicators, see www.chicago fed.org/digital assets/publications/cfnai/background/ cfnai_background.pdf.

(7) To be technically correct, we varied the endpoint of the initial sample based on the forecast horizon so that the first forecast always began at 1985:Q1.

(8) Maximums of I=5 quarters and J, K = 6 months were used in its calculation.

(9) In the case of nonfarm private inventories, there is one instance in table 1 where the improvement is not apparent because of the rounding in this table.

Scott Brave is a senior business economist and R. Andrew Butters is a former associate economist in the Economic Research Department at the Federal Reserve Bank of Chicago. The authors thank Hesna Genay, Spencer Krane, Alejandro Justiniano, Gadi Barlevy, Jeff Campbell, Douglas Evanoff, and Lisa Barrow for their helpful comments.

TABLE 1
Pseudo out-of-sample relative MSFE ratios

               FCI      Adjusted     Adjusted
h    CFNAI   residual     FCI      FCI residual

A. Gross domestic product
1    0.88      0.81       0.88         0.85
2    0.98      0.82       1.06         0.96
4    1.05      0.90       1.07         1.00
6    1.06      0.88       1.07         1.01

B. Gross domestic purchases
1    1.06      0.98       1.01         1.00
2    1.14      0.90       1.14         1.06
4    1.14      0.98       1.15         1.08
6    1.17      1.05       1.19         1.11

C. Final sales
1    1.06      0.91       1.03         0.96
2    1.07      0.88       1.06         0.97
4    1.16      0.94       1.17         1.10
6    1.18      1.02       1.20         1.11

D. Nonfarm private inventories
1    0.59      0.58       0.58         0.60
2    0.37      0.37       0.37         0.37
4    0.47      0.40       0.46         0.44
6    0.64      0.56       0.63         0.61

E. Nonresidential investment
1    0.78      0.76       0.79         0.78
2    0.76      0.67       0.81         0.73
4    0.86      0.75       0.90         0.85
6    0.91      0.79       0.89         0.84

F. Residential investment
1    1.13      0.92       0.93         0.96
2    1.17      0.91       1.19         1.00
4    1.06      0.97       1.11         1.07
6    1.01      0.95       1.03         1.01

G. PCE: Durables
1    1.13      0.92       1.11         1.13
2    1.18      0.99       1.19         1.18
4    1.32      1.23       1.29         1.33
6    1.33      1.30       1.37         1.35

H. PCE: Nondurables
1    0.95      0.87       1.00         0.91
2    1.02      0.87       1.15         0.98
4    1.00      0.89       1.05         0.98
6    1.03      0.94       1.07         1.00

I. PCE: Services
1    1.12      1.03       1.10         1.07
2    1.01      0.97       1.01         1.01
4    1.01      0.94       0.98         0.99
6    1.00      0.97       1.03         1.02

Notes: The table displays mean-squared forecast error (MSFE)
ratios expressed relative to an autoregressive baseline model.
The forecasted variable is listed at the top of each panel.
Column headings for each panel denote the additional variable
added to the baseline model: The CFNAI is the three-month
moving average of the Chicago Fed National Activity Index and
is included in all four specifications. The FCI residual is
the 13-week moving average of the portion of the financial
conditions index unexplained by its historical dynamics, the
adjusted FCI is the 13-week moving average of the financial
conditions index adjusted for economic conditions, and the
adjusted FCI residual is the 13-week moving average of the
portion of the adjusted financial conditions index unexplained
by its historical dynamics-these three individually serve to
augment the model including the CFNAI. The rows in each panel
denote the forecast horizon (h) measured in quarters beyond
the end of the sample period. The sample period is recursive
beginning in 1973:Q1 and rolling forward from 1985:01 through
2010:Q2. PCE denotes personal consumption expenditures.

Source: Authors' calculations based on data from the U.S.
Bureau of Economic Analysis, National Income and Product
Accounts of the United States, from Haver Analytics.

FIGURE 2
Decomposition of variance explained by financial conditions indexes
(FCI and adjusted FCI)

A. FCI

Money markets               30
Debt and equity markets     29
Banking system              41

B. Adjusted FCI

Money markets               54
Debt and equity markets     26
Banking system              20

Note: All values are in percent.

Note: Table made from pie chart.