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

Assessing the predictive power of measures of financial conditions for macroeconomic variables. Kostas Tsatsaronis Head of Financial Institutions Bank for International Settlements. 1. Financial sector. Real sector. Real and financial sector interactions.

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

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  1. Assessing the predictive power of measures of financial conditions for macroeconomic variables Kostas Tsatsaronis Head of Financial Institutions Bank for International Settlements 1

  2. Financial sector Real sector Real and financial sector interactions

  3. 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

  4. 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

  5. Methodological approach • Non-model driven econometrics • Data intensive but not a predominately structural approach • Establish stylised facts • Examine different economies

  6. 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

  7. 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

  8. F1 , F2 , F3 , … Summarising financial conditions Statistical procedure creating latent factors (Principal Components) Int. rates + spreads Asset prices Credit Performance of financial institutions--------------------------- ~ 40 variables Focus:top-6 latent factors ~ 50% of total variance 8

  9. 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

  10. 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

  11. Financial conditions Forecasting Specification: lag and factors selection to optimise BIC (trade-off between goodness of fit and parsimony)

  12. 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

  13. 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)

  14. Financial conditions 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

  15. 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

  16. 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

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