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Measuring Inter-Industry Financial Transmission of Shocks

Federal Deposit Insurance Corporation – CRF 2006 Fall Workshop. Measuring Inter-Industry Financial Transmission of Shocks. Work in Progress Report. October 25 th 2006 Daniel Paravisini Columbia University GSB. Motivation. Financial intermediaries may transmit real shocks across industries

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Measuring Inter-Industry Financial Transmission of Shocks

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  1. Federal Deposit Insurance Corporation – CRF 2006 Fall Workshop Measuring Inter-Industry Financial Transmission of Shocks Work in Progress Report October 25th 2006 Daniel Paravisini Columbia University GSB

  2. Motivation • Financial intermediaries may transmit real shocks across industries • Loan/equity losses weaken bank balance sheets and induce decline in supply of credit (Holmstrom and Tirole (1997)) • Natural experiment evidence: Peek and Rosengren (1997) • Chava and Purnanandam (2006), Gan (2006) • Open questions: • Cross section: Through which banks? • Within banks: Change with bank characteristics (derivatives, securitization)? • Time series: Change with the business cycle, monetary policy?

  3. This Presentation • Methodology to measure financial transmission more generally • Reduced form approach • Compare firms that differ according to the exposure of their lenders to shocks • Illustrate with application • Measure the financial transmission of the Telecoms defaults in 2002 (WorldCom, Adelphia)

  4. Example: Financial Transmission of Telecoms Defaults in 2002 Texas based, energy sector, similar size Mission Resources Corp Swift Energy Company JP Morgan Chase 3.1% of loan portfolio to WorlCom, Adelphia Bank One 0.2% of loan portfolio to WorlCom, Adelphia Main Lender, Q1 2002 Q2 2002 WorldCom, Adelphia Default • Differential responses to the shock across otherwise similar firms can be attributed to financial transmission

  5. Main Potential Concerns • Data requirements • Bank loan portfolio composition • Link firms to their lenders Dealscan • Sample: large banks, public firms • Requires differences in bank exposures • Hedging by banks and firms • Fraction of lending may overestimate exposure (credit derivatives, loans sales) • Multiple sources of capital Potentially find no effect

  6. Results from Telecoms Application • Banks exposed to defaults reduce supply of credit • Firms experience a 3 percentage point decline in leverage if their lenders had high exposure to WorldCom/Adelphia before defaults • Heterogeneity across banks • Smaller/none for banks with larger use of credit derivatives

  7. Roadmap • Data and variable definition • Dealscan, Call Reports, Compustat • Proxy for industry composition of loan portfolios • Descriptive statistics • Application: transmission of Telecoms defaults • Classify banks by exposure to Adelphia/Worldcom • Classify firms by exposure of their lenders • Firm level specification • Results • Conclusions and next steps

  8. Data: Portfolio Proxy Construction • Dealscan initial sample (1990-2005): • 45,459 loans to U.S. firms (96% syndicated) • 2,706 different lenders • Missing repayment, renegotiated lines of credit • Term loans: repaid linearly between origination and maturity • Credit lines: outstanding until min{maturity, 3 years} • Lender shares missing/incomplete (72% of facilities) • Logit on observable characteristics to impute lender shares (lender, year of origination, borrower industry, loan type, lead, deal amount, facility amount, maturity, secured, number of participants) • 75% of facilities with imputed shares

  9. Descriptive Statistics: Portfolio Proxy • Calculate amount outstanding for every firm/bank/quarter • Implied by imputed lender shares and repayment schedule by facility • Total outstanding by bank/quarter: • Average 52.3% of C&I loans from Call Reports using the 1995 to 2004 sample • Substantial time series and cross sectional variation in industry composition of portfolios

  10. Time Series of Total Bank Portfolio Allocation, top 6 industries (2-digit SIC)

  11. Portfolio Composition of two Banks in 2002, top industries (2-digit SIC) Bank of America Citibank Non-depository institutions

  12. Portfolio Composition of two Banks in 2002, top industries (2-digit SIC) Bank of America Citibank Electric, Gas and Sanitary Services

  13. Roadmap • Data and variable definition • Dealscan, Call Reports, Compustat • Proxy for industry composition of loan portfolios • Descriptive statistics • Application: transmission of Telecoms defaults • Classify banks by exposure to Adelphia/Worldcom • Classify firms according to exposure of their lenders • Firm level specification • Results • Conclusions and next steps

  14. Banks Classified by Fraction of Lending to Adelphia/WorldCom in 2002-Q1 • Debt with 36 banks (avg fraction of loans = 1.7%, median = 0.05%) • Define a bank as ‘exposed’ if fraction of lending in top 10th-percentile in Q1 Table II: Bank Descriptive Statistics, by exposure (2002)

  15. Roadmap • Data and variable definition • Dealscan, Call Reports, Compustat • Proxy for industry composition of loan portfolios • Descriptive statistics • Application: transmission of Telecoms defaults • Classify banks by exposure to Adelphia/Worldcom • Classify firms according to exposure of their lenders • Firm level specification • Results • Conclusions and next steps

  16. Firms Classified by Exposure of Lender Table III: Firm Descriptive Statistics, by exposure (2002) • Match Dealscan Borrowers with Compustat • Classify firms by exposure of lenders (weighted by debt amount)

  17. Roadmap • Data and variable definition • Dealscan, Call Reports, Compustat • Proxy for industry composition of loan portfolios • Descriptive statistics • Application: transmission of Telecoms defaults • Classify banks by exposure to Adelphia/Worldcom • Classify firms by exposure of their lenders • Firm level specification • Results • Conclusions and next steps

  18. Baseline Specification • Goal: compare variation of outcomes across firms classified by exposure of lenders Yit = αi + αIndustry×t + αState×t + β(DumExposedi).Postt+ εit • Yit: outcome of firm i at quarter t (e.g. leverage) • DumExposedi: 1 if lenders are exposed • Postt: 1 if in Q2 (sample Q1 and Q2 of 2002) • αi: Deviations from firm mean (FE) • αIndustry×t , αState×t: Relative to firms in same industry/state

  19. Effect on Leverage Table IV: Financial Transmission of Telecom Defaults

  20. Specification w/ Bank Heterogeneity • Goal 2: account for differential effect across banks Yit = αi + αIndustry×t + αState×t + β(DumExposedi).Postt + + βH(DumExposedi)(DumHedgei).Postt+ εit • DumHedgei: 1 if lender has high derivative exposure/assets • DumLargei: 1 if lender is large (assets) • DumLiquidi: 1 if lender is has high liquid assets/assets • Specification includes all direct effects/interactions

  21. Effect on Leverage (bank heterogeneity) Table VI: Financial Transmission of Telecom Defaults

  22. Effect on Investment Table V: Financial Transmission of Telecom Defaults

  23. Summary of Results: Telecoms Application • Effect on supply of credit by exposed bank • Borrowers of exposed banks experience a 3 percentage point decline in leverage • Effect on total supply of capital? • No overall effect on investment, stock returns • Look deeper into firm heterogeneity • Evidence of bank heterogeneity • Smaller effect for banks with larger use of credit derivatives • No evidence across size or liquid assets

  24. Conclusion and Next Steps • Methodology useful to identify financial transmission • Generalization: aggregate industry defaults/rating migrations (S&P) • Potential questions • Which banks are more likely to be a conduit for financial transmission? Does the magnitude change, within banks, when bank characteristics change? • Which firms can substitute sources of finance? • Does the magnitude of financial transmission change with the business cycle, monetary policy? • Financial transmission versus ‘real’ transmission

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