1. Infrastructure debt for institutional investors
Who is afraid of construction risk?
Frédéric Blanc-Brude, Research Director
EDHEC Risk Institute-Asia
NATIXIS/EDHEC Research Chair on Infrastructure Debt

2. Agenda
• The quandary: financing infrastructure construction
risk
• The nature of infrastructure debt
• Determinants of credit spreads
• Systematic drivers of credit risk
• Correlations and portfolio construction
• Conclusions
2

3. The quandary:
Who is afraid of construction risk?
• Growing interest of institutional investors for
long-term infrastructure investment
– LDI & avoidance of market volatility
• Growing political pressure to involve institutional
money into the financing of new infrastructure
investments
• The difference boils down (in part) to the question
of "construction risk" i.e. who should bear the risk
of building new infrastructure?
3

4. 1
The 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 of
view: unlisted, very large, 30-year track record, future
origination
• Project finance captures the characteristics of underlying
infrastructure investments
• Project finance benefits from a clear and internationally
recognised definition since Basel-2
5

6. Infrastructure project financing
volumes
6

7. Basel-2 definition
"Project Finance (PF) is a method of funding in which investors
looks primarily to the revenues generated by a single
project, both as the source of repayment and as security for
the exposure. In such transactions, investors are usually paid
solely or almost exclusively out of the money generated by the
contracts for the facility's output, such as the electricity sold by
a power plant. The borrower is usually an SPE that is not
permitted to perform any function other than developing,
owning, and operating the installation. The consequence is
that repayment depends primarily on the project's cash Flow
and on the collateral value of the project’s assets." (BIS, 2005)
7

8. Project finance SPE structure
Source: Moody’s (2013)
8

9. The economics of project financing
• Separate incorporation: self-selection of the
project 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 firm
with high (initial) leverage and a long-term horizon
– Impact of time vs. impact of de-leveraging
• Project finance is different from standard corporate
debt 9

10. Continuous de-leveraging
and the single-project firm

11. 2
The determinants of infrastructure debt credit spreads

12. Credit spread determinants
• The immense majority of project finance debt
is 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 3
types of factors
– Loan characteristics
– Macro-level factors
– Project level factors
• Systematic drivers of credit spreads exist in
both cross-sectional (average) and
longitudinal 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 financial
crisis
• New datasets: 1995 to 2012
– NATIXIS: 444 project loans
– Thomson-Reuters: 1,962 project loans
• Results of linear regressions confirm existing
literature insights despite the impact of the crisis
of 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 change
in risk profile (down) or to trigger a
refinancing operation (a re-setting of risk
pricing to match the change in risk profile)
• Statistical results (panel regression with
fixed effects) are very significant
• We observe differential risk pricing during
the lifecycle

18. Longitudinal spread determinants
(panel regression fixed effects)

19. Generic spread profiles of infrastructure
debt

20. 3
Systematic drivers of credit risk in infrastructure debt

21. Return and risk measures
• Once the determinants of credit spreads
(yield to maturity) is known, the excepted
return is a function of default and recovery
rates 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 rating
agencies have been on-going for more
than ten years
• 10-year cumulative probabilities of default
are observed to be around 10%
• Loss-given default (1-recovery) fluctuates
between 25% and 0%. In more than two
thirds of cases in the largest sample,
recovery rate =100%
• Credit risk dynamics make the marginal
PDs more informative

23. Predictable credit risk migrations
Source: Moody’s (2013)

24. Default intensity as a function
of year-from-origination
0.025
0.025
Observed PD
Observed PD
Fitted PD Fitted PD
0.02 0.02
Prop. of Defaults
Prop. of Defaults
0.015 0.015
0.01
Year 0
0.01
Year 1
0.005 0.005
0 0
0 5 10 15 20 0 5 10 15 20
Year Year
0.03 0.025
Observed PD Observed PD
Fitted PD Fitted PD
0.025
0.02
0.02
Prop. of Defaults
Prop. of Defaults
0.015
0.015
Year 2 0.01 Year 3
0.01
0.005
0.005
0 0
2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20
Year Year

25. Default intensity as a function
of 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 a
function 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 risk
are a function of risk factors for the average
instrument i over a lifecycle lifecycle defined by t
=1,2,…T
• This plays an instrumental role at the portfolio
construction stage: the lifecycle becomes an
important dimension of efficient infrastructure debt
portfolios

27. 4
Correlations & Portfolio Construction

28. Portfolio return & risk measures
• Using the expected and unexpected losses
already defined, we can write
• The debt portfolio’s return measure:
Rp = Σi=1N wi.EARit
• The debt portfolio’s risk measure:
ULp = Σi=1N Σj=1N wi.wj.ULit.ULjt.ρijt
For debt instruments i and j at time from
origination t

29. Default correlations
• Existing research on default correlation in
corporate 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 default
rates suggests that the business cycle plays
an important role
• But we know that year-from-origination and
project-specific factors should also explain
defaults at any given point in the business
cycle…
– 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: Moody’s (2013)

31. Marginal PDs by calendar year
vs. year of origination

32. Panel regression
(calendar years fixed effect)

33. Default correlations of PDs
between years of origination (significant
1%)

34. Portfolio construction
• With these (partial) estimates of default
correlations we can compute portfolio returns
for a single period using the variable ‘year-
from-origination’ to capture the effect of the
lifecyle 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 without
construction risk (illustration)
200
190
Expected returns (basis points)
180
Including ‘construction risk’
170
160
Post construction debt
portfolio frontier
150
140
0.5 1 1.5 2 2.5 3
Risk (basis points)
141.15
Expected returns (basis points) 141.10
141.05
141.00
140.95
excluding ‘construction risk’
140.90
0.8 0.9 1 1.1 1.2 1.3 1.4 1.5
Risk (basis points)

36. 5
Conclusions

37. Infrastructure debt portfolio
construction:
remunerated & systematic risk factors
• Theory and evidence suggest that within a
large sample of project finance loans,
several subsets can be identified that
capture remunerated exposure to
different systematic risk factors
• Two subsets standout as prime candidates
to improve portfolio diversification
– Revenue risk models creating three subsets:
full, partial and no commercial risk
– The project lifecycle, which captures the
evolution of the ‘single-investment firm’ from
the investment, including construction, to the
operating stage.

38. Infrastructure debt:
the benefits ‘lifecycle diversification’
• We have show that substantial diversification
benefits can be created by investing in
infrastructure project debt at different points in the
infrastructure 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 origination
• If investing across the entire lifecycle of
infrastructure projects improves diversification
then investors should welcome ‘construction risk’
in their infrastructure debt portfolios

39. What construction risk anyway?
• Recent research on construction risk confirms what theory
suggests: on average, in project finance, construction risk is
idiosyncratic (zero-mean = fully diversifiable) and is not as
high as in public infrastructure projects.
Construction cost overruns
in public and private infrastructure projects
70
60
50
40
30
20
10
0
-80 -60 -40 -20 0 20 40 60 80 100 120 140 160 180 200 220 240 260 280
Public construction risk - decision to build (Flyvbjerg dataset, n=110, 1950-2000)
Project finance construction risk - financial close (NATIXIS dataset, n=75, 1993-2010)
Blanc-Brude & Makovsek 2013

40. So who is afraid of construction
risk?
• Most existing infrastructure project finance debt prices
the changes in project risk profiles
– The average change in systematic credit risk is
predictable, and systematic risk is remunerated
– It is not only a feature of ‘legacy’ project debt. What
matters is that with project finance, by design, credit risk
can be priced over the lifecycle.
• As a consequence, institutional investors need
‘construction risk’ to build efficient portfolios of
infrastructure debt
– As long as risk is priced across the lifecycle this
conclusion holds
• Conversely, for this conclusion to hold, risk should be
priced across the lifecycle: this unique feature of
project financing allows solving the initial quandary
– Adequate pricing of systematic risk across the
infrastructure project lifecycle can lead to both more
efficient infrastructure debt portfolios and the financing of
new infrastructure to support growth in Europe and

41. Selected references
• Altman, E. (1996, October). Corporate Bond and Commercial Loan
Portfolio Analysis. Centre for Financial Institutions Working Papers
96-41, Wharton School Centre for Financial Institutions, University
of Pennsylvania.
• Blanc-Brude, F. and D. Makovsek (2013, January). Construction
risk in infrastructure project Finance, EDHEC Business School
Working Papers
• Blanc-Brude, F. and R. Strange (2007). How Banks Price Loans to
Public-Private Partnerships: Evidence from the European Markets.
Journal of Applied Corporate Finance 19(4), 94--106.
• Moody's (2013, February). Default and recovery rates for project
Finance bank loans1983-2011. Technical report, Moody's Investor
Service, London, UK.

42. Who is afraid of
construction risk?
by
Frédéric Blanc-Brude*
Omneia Ismail
Available online at:
www.edhec-
risk.com/multistyle_multiclass/N
atixis_Research_Chair
And in hard copy at this event
*frederic.blanc-
brude@edhec.edu