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Financial stress and economic activity in Germany

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Abstract

The financial crisis and the European sovereign debt crisis have shown that financial stress may be an important driver for economic activity. In this paper, I derive a financial stress index for Germany, using a dynamic approximate factor model that summarizes a stress component of various financial variables. Subsequently, I analyze the effects of financial stress on economic activity in a threshold vector autoregressive model. I find that if the index exceeds a certain threshold, an increase in financial stress causes economic activity to decelerate significantly, whereas if it is below this threshold, economic activity remains nearly unaffected.

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Notes

  1. In fact, Bloom (2009) does not use a FSI, but instead uses the S&P stock market volatility, which he interprets as a measure of market uncertainty.

  2. Therefore, this methodology is quite prominent in the forecasting literature (see Stock and Watson 2002; Giannone et al. 2008; Doz et al. 2011).

  3. I assume that a single factor reflects "financial stress". Including more factors does not significantly change the outcome. Results are available upon request.

  4. The only quarterly variable in the data sample is expected bank lending from the Bank Lending Survey. For this variable, I construct a partially observed monthly counterpart as in Mariano and Murasawa (2003) and Bańbura and Modugno (2012).

  5. Results for a Markov-switching model using the indicator are available upon request.

  6. An alternative ordering, where industrial production is independent and the FSI is contemporaneously dependent of all other shocks, yields qualitatively similar results, which are available upon request.

  7. See Holló et al. (2012).

  8. The p values for the Hansen test is computed by bootstrapping techniques with 1,000 replications. Detailed results of the Hansen test statistic are available upon request.

  9. See Cardarelli et al. (2011).

References

  • Abel AB (1983) Optimal investment under uncertainty. Am Econ Rev 73(1):228–233

    Google Scholar 

  • Afonso A, Baxa J, Slavík M (2011) Fiscal developments and financial stress: a threshold VAR analysis. Working Paper Series 1319, European Central Bank

  • Balakrishnan R, Danninger S, Elekdag S, Tytell I (2009) The transmission of financial stress from advanced to emerging economies. IMF Working Papers 09/133, International Monetary Fund

  • Balke N (2000) Credit and economic activity: credit regimes and nonlinear propagation of shocks. Rev Econ Stat 82(2):344–349

    Article  Google Scholar 

  • Bańbura M, Modugno M (2012) Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. J Appl Econom 1–27. doi:10.1002/jae.2306

  • Basu S, Bundick B (2012) Uncertainty shocks in a model of effective demand. NBER Working Paper 18420

  • Behr P, Steffen S (2006) Risikoexposure deutscher Universal- und Hypothekenbanken gegenüber makroökonomischen Schocks. Kredit und Kapital 39:513–536

    Google Scholar 

  • Bernanke BS (1983) Irreversibility, uncertainty, and cyclical investment. Q J Econ 98(1):85–106

    Article  Google Scholar 

  • Bloom N (2009) The impact of uncertainty shocks. Econometrica 77(3):623–685

    Article  Google Scholar 

  • Borio C (2011a) Central banking post-crisis: what compass for uncharted waters? BIS Working Papers 353, Bank for International Settlements

  • Borio C (2011b) Rediscovering the macroeconomic roots of financial stability policy: journey, challenges and a way forward. BIS Working Papers 354, Bank for International Settlements

  • Borio C, Drehmann M (2009) Assessing the risk of banking crises: revisited. BIS Q Rev 1(2):29–46

    Google Scholar 

  • Borio C, Lowe P (2002) Asset prices, financial and monetary stability: exploring the nexus. BIS Working Papers 114, Bank for International Settlements

  • Brave S, Butters RA (2011) Monitoring financial stability: a financial conditions index approach. Econ Perspect 1(Q I):22–43

    Google Scholar 

  • Cardarelli R, Elekdag S, Lall S (2011) Financial stress and economic contractions. J Financial Stab 7(2):78–97

    Article  Google Scholar 

  • Davig T, Hakkio C (2010) What is the effect of financial stress on economic activity?. Federal Reserve Bank of Kansas City. Econ Rev (Q II):35–62

  • Dempster A, Laird N, Rubin D (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39:1–38

    Google Scholar 

  • Doz C, Giannone D, Reichlin L (2011) A two-step estimator for large approximate dynamic factor models based on Kalman filtering. J Econom 164(1):188–205

    Article  Google Scholar 

  • European Central Bank (2011) Financial Stability Review, June 2011

  • Giannone D, Reichlin L, Small D (2008) Nowcasting: the real-time informational content of macroeconomic data. J Monet Econ 55(4):665–676

    Article  Google Scholar 

  • Goodhart CEA (2011) The changing role of central banks. Financial Hist Rev 18(2):135–154

    Article  Google Scholar 

  • Grimaldi MB (2010) Detecting and interpreting financial stress in the euro area. Working Paper Series 1214, European Central Bank

  • Hamilton JD (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57(2):357–84

    Article  Google Scholar 

  • Hansen BE (1999) Testing for linearity. J Econ Surv 13(5):551–76

    Article  Google Scholar 

  • Hartman R (1972) The effects of price and cost uncertainty on investment. J Econ Theory 5(2):258–266

    Article  Google Scholar 

  • Hakkio CS, Keeton WR (2009) Financial stress: what is it, how can it be measured, and why does it matter?. Federal Reserve Bank Kansas City. Econ Rev (Q II):5–50

  • Hatzius J, Hooper P, Mishkin FS, Schoenholtz KL, Watson MW (2010) Financial conditions indexes: a fresh look after the financial crisis. NBER Working Paper 16150

  • Holló D, Kremer M, Lo Duca M (2012) CISS: a composite indicator of systemic stress in the financial system. Working Paper Series 1426, European Central Bank

  • Illing M, Liu Y (2006) Measuring financial stress in a developed country: an application to canada. J Financial Stab 2(3):243–265

    Article  Google Scholar 

  • International Monetary Fund (2011) World Economic Outlook, April 2011

  • Kliesen KL, Smith DC (2010) Measuring financial market stress. Economic Synopses, Federal Reserve Bank of St. Louis (2)

  • Koop G, Pesaran M, Potter SM (1996) Impulse response analysis in nonlinear multivariate models. J Econom 74(1):119–147

    Article  Google Scholar 

  • Mallick SK, Sousa RM (2011) The real effects of financial stress in the euro zone. NIPE Working Papers 12/2011, NIPE—Universidade do Minho

  • Mariano RS, Murasawa Y (2003) A new coincident index of business cycles based on monthly and quarterly series. J Appl Econom 18(4):427–443

    Article  Google Scholar 

  • Matheson T (2011) Financial conditions indexes for the united states and euro area. IMF Working Papers 11/93, International Monetary Fund

  • Ng T (2011) The predictive content of financial cycle measures for output fluctuations. BIS Q Rev 1:53–65

    Google Scholar 

  • Stock JH, Watson MW (2002) Macroeconomic forecasting using diffusion indexes. J Bus Econ Stat 20(2):147–62

    Article  Google Scholar 

  • Tong H (1978) On a threshold model. In: Chen CH (ed) Pattern recognition and signal processing. NATO ASI Series E: Applied Sec. No. 29, Sijthoff & Noordhoff, pp 575–586

  • Tsay RS (1998) Testing and modeling multivariate threshold models. J Am Stat Assoc 93:1188–1202

    Article  Google Scholar 

  • Unsal DF, Osorio C, Pongsaparn R (2011) A quantitative assessment of financial conditions in Asia. IMF Working Papers 11/170, International Monetary Fund

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Authors and Affiliations

  1. The Kiel Institute for the World Economy, Hindenburgufer 66, 24105, Kiel, Germany

    Björn van Roye

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Appendix

Appendix

1.1 Data

All data series included in the index, including the native frequency, the first observation, the data category and the original source are presented in Table 2.

Table 2 Data description
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1.1.1 Variables related to the banking sector

TED spread

The TED spread is calculated as the difference between the 1- and 12-month money market rate (Fibor/Euribor). The TED spread is an important money market indicator, indicating liquidity and confidence in the banking sector. A shortage of liquidity causes a decrease in liquidity supply in the money market, which causes an increase in the TED spread and contributes positively to the FSI.

Money market spread

The money market spread is the difference between the 3-month Euro Interbank Offered Rate (Euribor, which is the average interest rate at which European banks lend unsecured funds to other market participants) and the Eurepo (the benchmark for secured money market operations). An increase in the spread reflects an increase in uncertainty in the money market and can be interpreted as a risk premium.

β of the banking sector

The beta of the banking sector is determined as the covariance of stock market and banking returns divided by the standard deviation of stock market returns. It follows from the standard capital asset pricing model (CAPM). A beta larger than one indicates that banking stocks shift more than proportionally than the overall stock market and that the banking sector is thus riskier (see also Balakrishnan et al. 2009).

Banking equity risk index

The banking equity index is a capital weighted total return index calculated by Thomson Financial Datastream. It consists of eight German Banks that have been included in the index continuously since 1973 and further 10 banks that were gradually included over the course of the sample period. I calculate the risk premium as in Behr and Steffen (2006), where it is constructed as a fraction bank stock returns over a risk-free interest rate. I determine the yield of the banking equity index by using daily log-differences of the time series and then subtract it from a risk-free interest rate. In this case, I use the 1-month secured money market rate (1m Eurepo).

Bank securities spread

This indicator is measured by the difference between bank securities with the maturity of 2 years and AAA-rated (German) government bonds with the same maturity. An increase in the spread reflects that investors perceive the risk in the banking sector to be on the rise. The time series for bank securities is taken from the banking statistics from the Bundesbank.

Expected bank lending (BLS)

This indicator comes from the ECB’s Bank Lending Survey. In this survey, banks are asked to report their assessment of how credit lending standards will evolve within the next 3 months. The Bundesbank reports the national results for the survey. The survey is conducted on a quarterly basis. Increasing values indicate an expected tightening in lending standards which contributes positively to the FSI.

ifo credit constraint indicator

This indicator comes from a survey conducted by the ifo Institute. In this survey, firms are asked to report their assessment of how credit lending standards are currently evolving. Increasing values of the indicator reflect a tightening of credit conditions, which contributes positively to the FSI. The ifo credit conditions indicator is reported on a monthly basis.

CDS on financial corporations

This index is an average of 5-year credit default swaps on the most important (largest ten) financial corporations, i.e. commercial banks. Increasing values of the index reflects that investors perceive the risk in the financial sector to be on the rise.

Excess liquidity

Value of bank deposits at the ECB that exceed the minimum reserve requirements. High use of the ECB deposit facility reflects uncertainty in the interbank market. Banks prefer to hold their excess reserves with the ECB rather than to lend it to the non-financial sector or to other banks via the interbank market (Fig. 9).

Fig. 9
figure 9

Variables related to the banking sector. Sources: Thomson Financial Datastream, European Central Bank, ifo institute, Deutsche Bundesbank, own calculations

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1.1.2 Variables related to the capital market

Corporate bond spread

The corporate bond spread is the difference between the yield on BBB-rated corporate bonds with a maturity of 5 years and the yield on AAA-rated (German) government bonds with the same maturity. The spread increases with higher perceived risk in the corporate bond market. This spreads contains credit, liquidity, and market risk premia.

Corporate loan spread

The corporate credit spread measures the difference between the yield on 1–2 years loans to non-financial corporations and the rate for secured money market transactions (Eurepo).

Housing loan spread

The housing spread measures the difference between the interest rate on all housing loans to private households and the interest rate for secured money market transactions (3m Eurepo).

CDS on corporate sector

This index is an average of 5-year credit default swaps on the DAX 30 non-financial corporations’ outstanding debt. For the euro area, it is a simple average of non-financial firms, using data for different sectors from Thomson Financial Datastream. Increasing values of this index indicate that investors perceive the risk that non-financial corporate will default on their debt to be on the rise.

VDAX/HVDAX

The VDAX measures implied stock volatility. Usually, an increase in stock market volatility reflects a higher degree of uncertainty and risk perception. This time series is available from 1996M1. Before 1996 I use the historical volatility of the DAX (HVDAX), estimated with a GARCH(1,1) model of the realized stock return volatility of the DAX30. The correlation of this time series between 1996 and 2011 is over 90 %.

Stock market returns

This variable measures the inverted monthly year-on-year yield of the DAX. Increasing values contribute positively to the FSI.

Term spread

The term spread reflects bank profitability. I determine this indicator by taking the difference between the short- and long-term yields on government bonds. It can be seen as a measurement for the possible degree of maturity transformation. Usually, banks generate profits by intermediating from short-term liabilities (deposits) to long-term assets (loans). A negative slope of the yield curve, i.e. a negative term spread, therefore indicates a decrease in bank profitability. Footnote 9

Corr ( REX , DAX )

The REX is a fixed-income performance index. Increasing interest rates imply a decreasing REX index. Hence, a negative correlation between REX and DAX indicates a positive correlation between DAX and the general level of interest rates (Fig. 10).

Fig. 10
figure 10

Variables related to the capital market. Source: Thomson Financial Datastream, Deutsche Bundesbank, European Central Bank, own calculations

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1.2 Variable related to the foreign exchange market

REERV [GARCH(1,1)]

This index measures the volatility of the real effective exchange rate (REER). The REER is deflated by the consumer price index with respect to 20 trading partners. An ARCH-test rejected the null hypothesis of the lack of GARCH effects at a significance level of 95 %. Hence, in order to determine real exchange rate volatility, I use a GARCH(1,1) model. The results are displayed below (Table 3; Fig. 11).

Table 3 Estimation results of the GARCH(1,1) model
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Fig. 11
figure 11

Real effective exchange rate. Sources: Thomson Financial Datastream, own calculations

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1.3 Linear impulse response functions

1.3.1 Linear VAR

See Fig. 12.

Fig. 12
figure 12

Impulse response function of the linear VAR to a shock in financial stress. Notes: error bands are based on 1.000 Monte Carlo draws with a significance level of 95 %

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1.3.2 Regime dependent linear impulse response functions

See Figs. 13 and 14.

Fig. 13
figure 13

Impulse response function high stress regime. Notes: error bands are based on 1.000 Monte Carlo draws with a significance level of 95 %

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Fig. 14
figure 14

Impulse response function low stress regime. Notes: error bands are based on 1.000 Monte Carlo draws with a significance level of 95 %

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1.4 Nonlinear impulse response functions

See Fig. 15.

Fig. 15
figure 15

Nonlinear impulse response functions. Notes: +2SD reflects the response of a positive two standard deviation shocks of the FSI; +1SD reflects the response of a positive one standard deviation shocks of the FSI; −2SD reflects the response of a negative two standard deviation shocks of the FSI; −1SD reflects the response of a negative one standard deviation shocks of the FSI; vectors of shocks are drawn 500 times

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van Roye, B. Financial stress and economic activity in Germany. Empirica 41, 101–126 (2014). https://doi.org/10.1007/s10663-013-9224-0

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