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Financial Conditions Indexes: A fresh look after the financial crisis

J.Hatzius, P.Hooper, F.S.Mishkin, K.L.Schoenholtz & M.W.Watson

Presented by:Rishabh Shukla

IGIDR Finance Research Group

October 8, 2015

J.Hatzius, P.Hooper, F.S.Mishkin, K.L.Schoenholtz & M.W.Watson (Presented by: Rishabh Shukla IGIDR Finance Research Group)

Financial Conditions Indexes: A fresh look after the financial crisis October 8, 2015 1 / 1

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Objective

Objective: To analyze the link between financial conditions and economic activityby constructing a Financial Conditions Index(FCI) for United States.



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Layout of the paper

What is FCI and what does it measure?

Limitations of existing FCIs

Selection of financial variablesPrediction Tests with single variable financial indicators

Principal Components Analysis methodology

Testing for FCI robustness

Possible sources of instability in the FCI



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What is FCI and what does it measure?

FCI summarizes the information about the future state of the economy contained inthe current financial variables.

Ideally FCIs should measure financial shocks, which are exogenous shifts in financialconditions.

So, true financial shocks must be distinguished from endogenous reflection of pasteconomic activity in the variablesused for constructing FCI.

Thus, FCIs aim to predict more than what past economic activity alone could tellabout financial conditions.

However, FCIs are not underpinned by a structural model and are thus vulnerable tothe Lucas crtitique.

J.Hatzius, P.Hooper, F.S.Mishkin, K.L.Schoenholtz & M.W.Watson (Presented by: Rishabh Shukla IGIDR Finance Research Group)Financial Conditions Indexes: A fresh look after the financial crisis October 8, 2015 4 / 1

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Limitations of previous FCIs

limited span of historical financial series

narrowness of the underlying series in terms of markets covered

financial variables are unpurged of endogenous movements related to business cycles


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Selection of financial variables

During the GFC,Price signals became less reliable as markets siezed up.

Non-price credit conditions tightened dramatically, such as stricter terms andreduced availability.

Credit flows slowed abruptly.

In order to capture these effects,

The authors include a sample of bank and non-bank credit variables in a variety of markets.

Survey indicators of bank lending conditions from the Fed.

Historical Coverage

This paper employs 45 series which go back to the beginning of 1970s.

However the underlying series are of different lengths leading to an unbalanced datapanel.


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Prediction tests with single variable financial indicators

Certain financial indicators can be used as leading indicators of movements in realeconomic activity.

These financial variables are determined after taking into account each variable’sauto-regressive structure.

The in-sample regression specification used to test this is :

y t +h − y t = β 0 +

p y

i =1

φi ∆y t +1−i +

p x

i =1

γ i x t +1−i + e t +i

where y t indicates the real activity indicator like logarithm of real GDP and x t denotes financial indicators like the first difference of the federal funds rate.


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Results of the prediction tests


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PCA methodology

This paper uses PCA and employs 45 financial series to build the FCI.

Let X it denote the i th financial indicator and Y t denote the vector of macroeconomicindicators then

X it = Ai (L)Y t + ν it ,

where ν it is uncorrelated with current and lagged values of Y t

ν it represents the financial variable purged of its relation with current and lagged Y.

If ν it can be decomposed as

ν it = λ

i F t + u it ,

where F t is a kX1 vector of unobserved financial factors and u it captures unique

variation in ν it which is uncorrelated with F t and Y t .Thus F t measures the co-variation or co-movement in the financial indicators.

Finding F t is the key goal of the PCA methodology.


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Testing for FCI robustness using balanced panel approach

An unbalanced panel uses a longer history of financial variables.

A balanced variant of the new FCI was created 1980 onwards.The baseline FCI outperfomed the narrower version over 1990s and 2000s.

However, during 1980s the balanced panel variant outperformed significantly.


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Testing for FCI robustness using decomposition tests

The 45 financial series are decomposed into five categories, namely:

interest rate levels and spreadsasset pricesstock and flow quantities

surveyssecond moments

No single component stands out as consistently better relative to other components.

The overall FCI does better than any of its major components and also compared tothe best single financial indicators.

Thus, pooling of a large number of financial variables leads to significantbenefits in prediciton.


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Testing for FCI robustness using purging

Purging the underlying variables of macro influences yields better results duringearly 1990s and all of 2000s.

During 2009 Q2-2009 Q4, the unpurged index is esentially neutral. This shows thatfinancial conditions were near their historical average.

However, during the same span the purged index suggested that financial conditionsremain a drag on future real activity.

Thus, the perged index is better able to predict the real economic activity.


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Possible sources of instability in the FCI

Both the evolution of financial conditions and their underlying causes need to beunderstood in order to use FCIs effectively.

Purging the index of macroeconomic influences yields substantially better results in

some periods compared to others.

The periods of success are periods of considerable financial distress like the dot-combubble burst of early 2000s and more severe financial crises in recent years.

FCIs do better in predicting real activity during periods dominated by exogenousfinancial disturbances than single variable indicator models.


References

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References

Bernanke, B. (1990), On the Predictive Power of Interest Rates and Interest Rate

Spreads, New England Economic Review, November, pages 51-68.Swiston, A. J. (June 2008). A U.S. Financial Conditions Index: Putting CreditWhere Credit is Due, IMF Working Paper.


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