Infarstructure debt for institutional investors

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  • 1. Infrastructure debt for institutional investorsWho is afraid of construction risk? Frdric Blanc-Brude, Research DirectorEDHEC Risk Institute-AsiaNATIXIS/EDHEC Research Chair on Infrastructure Debt

2. Agenda The quandary: financing infrastructure constructionrisk The nature of infrastructure debt Determinants of credit spreads Systematic drivers of credit risk Correlations and portfolio construction Conclusions2 3. The quandary: Who is afraid of construction risk? Growing interest of institutional investors forlong-term infrastructure investment LDI & avoidance of market volatility Growing political pressure to involve institutionalmoney into the financing of new infrastructureinvestments The difference boils down (in part) to the questionof "construction risk" i.e. who should bear the riskof building new infrastructure?3 4. 1The nature of infrastructure debt 5. The nature of infrastructure debt The infrastructure debt universe Project finance debt represents the majority of this universe Relevant subset from an institutional investment point ofview: unlisted, very large, 30-year track record, futureorigination Project finance captures the characteristics of underlyinginfrastructure investments Project finance benefits from a clear and internationallyrecognised definition since Basel-25 6. Infrastructure project financingvolumes6 7. Basel-2 definition"Project Finance (PF) is a method of funding in which investorslooks primarily to the revenues generated by a singleproject, both as the source of repayment and as security forthe exposure. In such transactions, investors are usually paidsolely or almost exclusively out of the money generated by thecontracts for the facilitys output, such as the electricity sold bya power plant. The borrower is usually an SPE that is notpermitted to perform any function other than developing,owning, and operating the installation. The consequence isthat repayment depends primarily on the projects cash Flowand on the collateral value of the projects assets." (BIS, 2005) 7 8. Project finance SPE structure Source: Moodys (2013) 8 9. The economics of project financing Separate incorporation: self-selection of theproject sponsors Role of initial investment (construction phase) and project lifecycle Leverage: project selection by the lenders Non-recourse financing: an optimisation exercise Role of lenders in SPE corporate governance High leverage = low asset risk Financial economics of the single-investment firmwith high (initial) leverage and a long-term horizon Impact of time vs. impact of de-leveraging Project finance is different from standard corporatedebt 9 10. Continuous de-leveragingand the single-project firm 11. 2The determinants of infrastructure debt credit spreads 12. Credit spread determinants The immense majority of project finance debtis priced against a floating benchmark e.g.LIBOR Three types of spread term structures: flat,down-trending and up-trending Individual loans have different spreads at different points in time Average loan spreads are a function of 3types of factors Loan characteristics Macro-level factors Project level factors Systematic drivers of credit spreads exist inboth cross-sectional (average) andlongitudinal dimensions 13. Average loan spread determinants Loan characteristics Maturity Size Syndicate size Macro-level factors Country risks Credit cycle Business cycle Project-level factors Revenue risk models (determine business cycle impact) Construction risk Operating risks Leverage 14. Average loan spread determinants Existing studies pre-exist the 2007-9 financialcrisis New datasets: 1995 to 2012 NATIXIS: 444 project loans Thomson-Reuters: 1,962 project loans Results of linear regressions confirm existingliterature insights despite the impact of the crisisof average spreads Project finance loans have lower spreads if they have longer maturities and a larger size Revenue risk models are a significant driver of credit spreads Construction risk is not (proxies suggest) After 2008, the collapse of benchmark rates had a very significant positive impact on spreads 15. Panel regression results (coef. estimates) 16. Average credit spreads 17. Longitudinal spread determinants Two sub-samples: down-trending and up-trending (according to the average difference of annual change in spread) Spreads change in time to reflect changein risk profile (down) or to trigger arefinancing operation (a re-setting of riskpricing to match the change in risk profile) Statistical results (panel regression withfixed effects) are very significant We observe differential risk pricing duringthe lifecycle 18. Longitudinal spread determinants (panel regression fixed effects) 19. Generic spread profiles of infrastructuredebt 20. 3Systematic drivers of credit risk in infrastructure debt 21. Return and risk measures Once the determinants of credit spreads(yield to maturity) is known, the exceptedreturn is a function of default and recoveryrates and can be written: EARi = YTMi ELi (Altman 1996) With the expected loss ELi = LGDi x PDi Likewise, the unexpected loss is written ULi = LGDi x (PDi x (1-PDi)) 22. Credit risk studies for project debt Majors data collection efforts by ratingagencies have been on-going for morethan ten years 10-year cumulative probabilities of defaultare observed to be around 10% Loss-given default (1-recovery) fluctuatesbetween 25% and 0%. In more than twothirds of cases in the largest sample,recovery rate =100% Credit risk dynamics make the marginalPDs more informative 23. Predictable credit risk migrations Source: Moodys (2013) 24. Default intensity as a functionof year-from-origination 0.0250.025Observed PDObserved PDFitted PD Fitted PD 0.02 0.02 Prop. of DefaultsProp. of Defaults0.0150.015 0.01Year 00.01 Year 10.0050.0050 00 510 1520 0 5 1015 20YearYear 0.030.025Observed PD Observed PDFitted PD Fitted PD0.0250.02 0.02Prop. of Defaults Prop. of Defaults 0.0150.015Year 20.01 Year 3 0.01 0.0050.0050 02 4 6 8 1012 141618 20 2 4 6 8 1012 14161820 Year Year 25. Default intensity as a functionof year-from-origination 26. Risk adjusted measure of infrastructure debt as a function of year-from-origination The excepted return can now be written as afunction of time from origination: EARit = YTMit ELit With the expected loss ELit = LGDit x PDit Likewise, the unexpected loss is written ULit = LGDit x (PDit x (1-PDit)) Like credit spreads, both expected return and riskare a function of risk factors for the averageinstrument i over a lifecycle lifecycle defined by t=1,2,T This plays an instrumental role at the portfolioconstruction stage: the lifecycle becomes animportant dimension of efficient infrastructure debtportfolios 27. 4Correlations & Portfolio Construction 28. Portfolio return & risk measures Using the expected and unexpected lossesalready defined, we can write The debt portfolios return measure:Rp = i=1N wi.EARit The debt portfolios risk measure:ULp = i=1N j=1N wi.wj.ULit.ULjt.ijtFor debt instruments i and j at time fromorigination t 29. Default correlations Existing research on default correlation incorporate debt boils down to two stylised facts Default correlations are low in normal times Default correlations are a function of the business cycle Casual observation of project finance defaultrates suggests that the business cycle playsan important role But we know that year-from-origination andproject-specific factors should also explaindefaults at any given point in the businesscycle We use panel regression to separate the effect of the business cycle from that of the project cycle on the covariance of default probabilities 30. Project finance PDs by calendar year(global sample) Source: Moodys (2013) 31. Marginal PDs by calendar yearvs. year of origination 32. Panel regression(calendar years fixed effect) 33. Default correlations of PDsbetween years of origination (significant 1%) 34. Portfolio construction With these (partial) estimates of defaultcorrelations we can compute portfolio returnsfor a single period using the variable year-from-origination to capture the effect of thelifecyle on expected returns and risk The objective is to illustrate the diversification potential of investing across the infrastructure project lifecycle We built to portfolios: One invested across ten years of project lifecycle (including construction) Another one invested only in post-construction/mature years (after year 5) 35. Efficient frontier with and withoutconstruction risk (illustration)200190Expected returns (basis points)180Including construction risk170160Post construction debtportfolio frontier1501400.5 11.522.5 3Risk (basis points)141.15Expected returns (basis points) 141.10141.05141.00140.95 excluding construction risk140.90 0.8 0.9 11.11.2 1.3 1.4 1.5 Risk (basis points) 36. 5Conclusions 37. Infrastructure debt portfolio construction: remunerated & systematic risk factors Theory and evidence suggest that within alarge sample of project finance loans,several subsets can be identified thatcapture remunerated exposure todifferent systematic risk factors Two subsets standout as prime candidatesto improve portfolio diversification Revenue risk models creating three subsets:full, partial and no commercial risk The project lifecycle, which captures theevolution of the single-investment firm fromthe investment, including construction, to theoperating stage. 38. Infrastructure debt:the benefits lifecycle diversification We have show that substantial diversificationbenefits can be created by investing ininfrastructure project debt at different points in theinfrastructure project lifecycle. This conclusion is a direct consequence of: The systematic change of risk profile of infrastructure project debt during its life The matching change in spreads observed in project loans as they age The differences in default correlations between different years from originati