Bank of Greece, 4 February 2010

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Assessing the predictive power of measures of financial conditions for macroeconomic variables

Kostas TsatsaronisHead of Financial Institutions Bank for International Settlements

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Real and financial sector interactions

Real sector

Financial sector

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Real and financial sector interactions

Take the “real” sector point of view

– How does the financial sector influence the macroeconomic picture?

Forecasting: better understand business cycle Modelling: stylised facts about interaction between

business and financial cycle Policy:

– Information content of financial variables

– The reaction function of monetary policy

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Objective

Question: Can we summarise the links between financial conditions and the macroeconomy in a single simple measure?

Yardstick: How do measures of financial conditions fare as forecasters of macroeconomic variables in the one-to-two year horizon.

Variables: GDP Gap, Investment, inflation

Countries: United States, Germany, United Kingdom

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Methodological approach

Non-model driven econometrics

Data intensive but not a predominately structural approach

– Establish stylised facts

Examine different economies

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Results

Financial conditions factors have important information content

Financial conditions factors have independent information content:

• Information is complementary to asset prices

Financial conditions factors have more information content for real variables than for inflation

Financial conditions factors perform better at longer horizons

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Summarising financial conditions

Distil common information from a large number of variables into small number of factors

– Stock and Watson (2002)

Focus exclusively on financial variables

Use as many as possible

Representing as broad an array of financial sector activity as possible

Keep the balance between prices and quantities

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Summarising financial conditions

Statistical procedure creating latent factors (Principal Components)

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Int. rates + spreads

Asset prices

Credit

Performance of financial institutions--------------------------- ~ 40 variables

F1 , F2 , F3 , …

Focus: top-6 latent factors ~ 50% of total variance

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Data

Bank assets and liabilities & income statements Interest rates Exchange rates Equity market indicators Real estate indicators Flow of funds variables Balance of payments variables Other

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Data handling

Deal with stationarity

Perform normalisation

Quarterly interpolation of annual series

– Project annual series onto annualised factors

– Use mapping to interpolate into quarterly• Flow and stock variables• Level ad first differenced series

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Forecasting

kt

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n

jjtlljkt FInflationlagsOwny

6,,1 0,

Specification: lag and factors selection to optimise BIC (trade-off between goodness of fit and parsimony)

Financial conditions

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Results

Financial conditions factors have information content• Significant coefficients

• Output and investment: goodInflation: not so good

Overall forecasting performance quite good:• R2 range 40-85%

• Not so sharp decline in longer horizon

Small number of factors • Explain 20% of variance

• Stable set across horizons

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Horse race against asset prices

Is the informational content of the financial factors essentially the same as that of the yield curve and equity prices?

Horse race regression (encompassing)

ktl j

jtlljkt FpricesAssety ,

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Table 3

“Horse race” against selected asset prices: predicting the output gap

US Germany UK

k=4 k=8 k=4 k=8 k=4 k=8

R-sq adj 61% 42% 50% 44% 91% 75%

Excl. PCs 0.121 -- 0.003 0.001 0.0003 0.0001

Excl. Other

0.035 0.419 0.011 0.971 0.0000 0.0000

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A Financial Conditions Index?

The linear combination of the principal components represents a relationship among financial variables that is correlated forward with real variables:

• Positive values are good for the economy• Negative values are harmful

kt

l

n

jjtlljkt FInflationlagsOwny

6,,1 0,

Financial conditions

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A Financial Conditions Index?

The weights of the original data are fairly constant across different lags

• One could construct an FCI using only contemporaneous values of the original series and then take lags of this composite series

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Future work

Expand the set of countries in the analysis

Examine for threshold and asymmetric effects in the relationship between financial and real variables

How stable is the composition of the FCI?

– Out of sample performance