The cash flow dynamics of private infrastructure project debt

Transcription

1 The cash flow dynamics of private infrastructure project debt 1/36 The cash flow dynamics of private infrastructure project debt Empirical evidence and dynamic modeling Frédéric Blanc-Brude, PhD Director, EDHEC Risk -Asia A presentation prepared for NATIXIS InfraDay 18 th November 2015

2 The cash flow dynamics of private infrastructure project debt 2/36 Agenda 1 The EDHEC/NATIXIS Research Chair 2 Paper motivation: why focus on DSCRs? 3 Data collection 4 Descriptive results 5 Dynamic modelling 6 Portfolio example 7 Conclusions

3 The cash flow dynamics of private infrastructure project debt 3/36 Section outline 1 The EDHEC/NATIXIS Research Chair 2 Paper motivation: why focus on DSCRs? 3 Data collection 4 Descriptive results 5 Dynamic modelling 6 Portfolio example 7 Conclusions

4 The cash flow dynamics of private infrastructure project debt 4/36 The EDHEC/NATIXIS Research Chair Implementing the EDHEC roadmap for the creation of investment benchmarks in infrastructure debt (Blanc-Brude, 2014): 1 Define infrastructure debt instruments 2 Create an adequate valuation framework 3 Propose a standard for data collection 4 Collect data from creditors, investors and raw sources 5 Empirical validation of the technical framework 6 Build reference portfolios for performance benchmarking Provide benchmarks for asset allocation to infrastructure debt, asset-liability management, performance monitoring, relationship with ESG characteristics, etc

5 The cash flow dynamics of private infrastructure project debt 5/36 The EDHEC/NATIXIS Research Chair Implementing the EDHEC roadmap for the creation of investment benchmarks in infrastructure debt (Blanc-Brude, 2014): Define infrastructure debt instruments Create an adequate valuation framework Propose a standard for data collection Collect data from creditors, investors and raw sources Empirical validation of the technical framework Build reference portfolios for performance benchmarking Provide benchmarks for asset allocation to infrastructure debt, asset-liability management, performance monitoring, relationship with ESG characteristics, etc

7 The cash flow dynamics of private infrastructure project debt 7/36 Motivation: the role of DSCRs In the previous publication of the EDHEC/NATIXIS Chair (Blanc-Brude et al, 2014), we show that debt service cover ratio (DSCR) dynamics can give us powerful insights about credit risk in project finance; 1 The DSCR provides an unambiguous definition of hard and soft default points and of the probability of such credit events; 2 The distribution of the DSCR (mean/variance) provides a direct measure of distance to default, which is the workhorse of the structural credit risk approach (à la Merton et al); 3 Combined with the knowledge of the current debt service, covenants and project calendar, the (conditional) volatility of DSCR t provides a direct measure of the volatility of the firm s asset value; Documenting the empirical dynamics of debt service cover ratios in infrastructure projects is central to better understanding and benchmarking such instruments

8 The cash flow dynamics of private infrastructure project debt 8/36 Motivation: the role of DSCRs In Blanc-Brude et al (2014) we built a debt valuation model taking into account the embedded options found in project finance debt which, given a DSCR t dynamic, allows computing: 1 Debt value and period returns 2 PD and LGD 3 VaR and cvar (Expected Shortfall) 4 Effective duration Until now, we had assumed a lognormal distribution of DSCRs and two typical families of projects: contracted and merchant Each family exhibited a different dynamic, resulting from different ex ante choices made by creditors about financial structure, themselves the result of the underlying risk profile of the investment; In this new paper, we collect DSCR data and validate these hypotheses

10 The cash flow dynamics of private infrastructure project debt 10/36 Data collection For this paper, we build a dataset of realised DSCRs using data manually collected and verified from the audited statements of accounts of several hundred project companies, as well as DSCR certificate data reported by private contributors (banks, managers) We hand collected 15 years of realised DSCR data 1 more than 200 projects in Europe and the United States 2 covering two broad families or revenue risk: contracted income and merchant 3 covering seven sector groups, from the early 1990s to 2015

11 The cash flow dynamics of private infrastructure project debt 11/36 Data collection: DSCR calculations Histogram of DSCR1 We consider several definitions of the DSCR: DSCR 1 = C bank + C op + C IA DS senior (1) Frequency DSCR 2 = C bank + C op + C IA + C dd DS senior (2) DSCR 3 = C bank + C op + C IA + C dd C inv DS senior, (3) Frequency DSCR Histogram of DSCR2 where C bank, C op, C IA, C dd, and C inv denote cash at bank, cash from operating activities, cash withdrawal from investment account, cash from debt drawdowns, and cash invested physical investments, respectively Frequency DSCR Histogram of DSCR DSCR

12 The cash flow dynamics of private infrastructure project debt 12/36 Data collection: a first large sample Using the 3rd definition, we build the largest sample of DSCR observations available for research today But we are just getting started DSCR dataset Number of reporting firms Contracted Merchant Investment Start Year Year

14 The cash flow dynamics of private infrastructure project debt 14/36 Lognormal fit for contracted infrastructure DSCRs Descriptive results Empirical and theoretical dens Q Q plot Density Empirical quantiles Data Theoretical quantiles Empirical and theoretical CDFs P P plot CDF Empirical probabilities Data Theoretical probabilities We find that DSCRs are (almost) lognormal In each time period, up to the 90 th quantile, realised DSCR data can be fitted to a lognormal (Gaussian) process with a high degree of fit in each family of DSCRs This can be convenient for modelling expected DSCRs

15 The cash flow dynamics of private infrastructure project debt 15/36 Descriptive results 1 We find that the two families correspond to two statistcially different processes: their mean and volatility are different at the 1% confidence level 2 Differentiating by sector is much less informative and not sufficient to explain differences in realised mean and volatility of DSCRs; 3 We find that higher leverage is strongly related to lower levels of realised DSCR volatility: as theory predicts (?), in project finance high leverage is a signal of low asset risk; Descriptive statistics and linear regression models provide some important insights, but they fail to capture DSCR dynamics in full: the DSCRs of individual projects and families are highly non-linear, auto-regressive and heteroskedastic (their variance is not constant in time)

16 The cash flow dynamics of private infrastructure project debt 16/36 Section outline 1 The EDHEC/NATIXIS Research Chair 2 Paper motivation: why focus on DSCRs? 3 Data collection 4 Descriptive results 5 Dynamic modelling 51 Project families 52 Tracking individual projects 6 Portfolio example 7 Conclusions

17 The cash flow dynamics of private infrastructure project debt 17/36 State transitions Since the DSCRs of individual projects do not necessarily follow a smooth and continuous process, we take a two steps approach to modelling their dynamics First, we argue that the DSCR process can be in one one of three states at each point in time: 1 Default: DSCR<1, in which case debt service stops; 2 Risky: DSCR >1 and lower than 90 th quantile (DSCR 6 8), in which case the DSCR is lognormally distributed; 3 Safe: DSCR > 90 th quantile (DSCRs sometimes take very high values eg bond drawdown, final year before maturity, etc) in which case PD = 0 In the default and safe states, we can ignore the DSCR dynamics (debt is either being restructured or risk-free), the estimation problem boils down to estimating: 1 The probability of the DSCR process transiting from one state to another at each point in time; 2 The mean and volatility of the DSCR in the risky state at each point in time State transitions (probability matrices) are estimated using Bayesian techniques each time new state transitions are observed

18 The cash flow dynamics of private infrastructure project debt 18/36 State transitions Here, we show the estimated transition probabilities between the safe and risk states (conditional on no default at that time) We find that contracted projects that tend to transit to the safe state are increasingly likely to stay there We compute such state transition probabilities for all three states at each point in the life of project loans, in each family Probability (%) Transition Probabilities from the Safe State for Contracted Projects S >R S >S Operation Year

19 The cash flow dynamics of private infrastructure project debt 19/36 State transitions Probabilities of the Three States for Contracted Projects Probabilities of the Three States for Merchant Projects Probability (%) R S D Probability (%) R S D Operation Year Operation Year Individual state transition probabilities are combined to obtain state probabilities at each point in time (Markov Chain): contracted projects tend to stay in the normal risk state, whereas contracted projects have a significant chance of exhibiting very high DSCRs (the safe state), they are also mosre likely to be in default

20 The cash flow dynamics of private infrastructure project debt 20/36 DSCR mean/vol trajectories Once we know the state probabilities, we need to estimate the parameters of the DSCR distribution (mean/sd) in the risky state 45 Contracted Family Merchant Family DSCR Trajectories in Mean SD Plance t18 To each group of projects corresponds a typical trajectory of DSCR mean and volatility (like a set of x,y coordinates) which we can track using standard statistical techniques (here, particule filtering) SD(%) t2 t5 t2 t18 Each time we observe new realised DSCRs, we update our estimates of the parameters of the underlying distribution (the true mean and variance of DSCR t) 20 t Mean t0

21 The cash flow dynamics of private infrastructure project debt 21/36 DSCR distribution in the risky state DSCR Density for Contracted Projects DSCR Density for Merchant Projects 08 t 1 t 2 07 t 1 t 2 t 3 t 3 t 4 t 4 t 5 t 5 t 6 t 7 06 t 6 t 7 t 8 t 8 06 t 9 t 10 t 11 t t 9 t 10 t 11 t 12 t 13 t 13 t 14 t 14 t t 15 Density 04 Density DSCR DSCR Contrated DSCRs tend to keep the same distribution (slight increase in vol over time) while merchant DSCRs tend to increase significantly in time

22 The cash flow dynamics of private infrastructure project debt 22/36 DSCR distribution in the risky state DSCR Mean for Contracted and Merchant Families DSCR SD for Contracted and Merchant Families 30 Contracted Merchant 60 Contracted Merchant Mean DSCR SD (%) Operation Year Operation Year Contrated DSCR tend to keep the same distribution (slight increase in vol) while merchant DSCRs tend to increase significantly in time

23 The cash flow dynamics of private infrastructure project debt 23/36 From DSCR to PD From the DSCR distribution, we immediately get the conditional PD at each point in the future (here viewed from t 0) Soft PD is defined as Pr(DSCR t < 105) Probability of Hard Default Probability of Soft Default 4 Contracted Merchant 4 Contracted Merchant 3 3 PD (%) 2 PD (%) Operation Year Operation Year These are familiar profiles considering earlier results reported by Moody s (2015)

24 The cash flow dynamics of private infrastructure project debt 24/36 Tracking individual projects In the absence of well-diversified investment solutions, understanding individual project dynamics can be instrumental to measuring portfolio risk With the same filtering techniques, each observed DSCR allows updating both estimates of mean and standard deviation of the true DSCR process of individual projects Filtered DSCR Mean Filtered Standard Deviation 30 Observed DSCR True Mean Bayesian Mean 95% Conf Int 015 True Bayesian 25 DSCR 20 SD Operation Year Operation Year Here, the DSCR process collapses in year 11 following an unspecified shock (eg recession) Both the true volatility of the process is also significantly lower after the shock

25 The cash flow dynamics of private infrastructure project debt 25/36 Tracking individual projects Updating both estimates of mean and volatility matters as it is their combination which provides the required risk measures Distance to lockup, soft, and hard defaults Probabilities of Lockup, Soft, and Hard Defaults 20 DtL 10 DSCR<115 DtS DSCR<110 DtD DSCR< Distance to Default 10 Probability(%) Operation Year Operation Year Here, a 35% drop in the DSCR level is accompanied by a significant drop in DSCR vol: as a result DD and EL are unchanged (but equity risk is now higher)

26 The cash flow dynamics of private infrastructure project debt 26/36 Putting it all together We can dynamically measure the likelihood to be in a given state (default, risky, safe); We can dynamically estimate the parameters of the DSCR distribution in the risky state for families of projects at each point in time; We can update individual project parameters as their DSCR trajectory is realised, especially if they are shocked away from their original family trajectory; Hence, we can build forward-looking and realised measures of cash flows distributions

28 The cash flow dynamics of private infrastructure project debt 28/36 Portofolio example We use two sets of 16 loans with long maturities originated over an 8-year period representing USD10bn of capital deployed in both contracted and merchant infrastructure: very granular data about financial structure, covenants, etc We implement the DSCR model described above (Blanc-Brude et al, 2015) We apply the valuation model described in Blanc-Brude et al (2014); Blanc-Brude and Hasan (2015) We then observed realised payments and DSCRs, recompute forward looking (expected) DSCRs and debt service, and derive expected and realised portfolio values, risk and return measures Total Energy EnvSer GovSer Trans Europe US MENA Contracted Merchant N Date Size (USD) Tenor (Year) Value (USD) Contracted 16 [2002, 2007] [96M, 19B] [16, 30] 35B Merchant 16 [2002, 2010] [68M, 41B] [16, 28] 61B Total 32 [2002, 2010] [68M, 41B] [16, 30] 96B

29 The cash flow dynamics of private infrastructure project debt 29/36 Portfolio value, expected values Portfolio Value (Contracted) Portfolio Value (Merchant) 5 95% Conf Bounds 10 95% Conf Bounds Expected Value Expected Value 4 8 Billions of Dollars 3 2 Billions of Dollars Calendar Year Calendar Year Portfolios are value-weighted, discounting using the volatility-implied term structure of discount rates (see Blanc-Brude and Hasan, 2015)

30 The cash flow dynamics of private infrastructure project debt 30/36 Portfolio value, expected and realised values Portfolio Value (Contracted) Portfolio Value (Merchant) 5 95% Conf Bounds 10 95% Conf Bounds Expected Value Expected Value Realised Value Realised Value 4 8 Billions of Dollars 3 2 Billions of Dollars Calendar Year Calendar Year Portfolios are value-weighted, discounting using the volatility-implied term structure of discount rates (see Blanc-Brude and Hasan, 2015)

31 The cash flow dynamics of private infrastructure project debt 31/36 Portfolio risk measures Loss Profile (Contracted) Loss Profile (Merchant) 20 EL 20 EL VaR VaR cvar cvar Period Loss (%) 10 Period Loss (%) Calendar Year Project time With the full distribution of cash flows and a model to compute the distribution of portfolio loses, we can derive expected and extreme loss measures Highest levels of one-year 995% portfolio VaR range from 5 to 20% Solvency-2 relevance?

32 The cash flow dynamics of private infrastructure project debt 32/36 Expected and realised portfolio returns Excess Period Returns (Contracted) Excess Period Returns (Merchant) 8 95% Conf Bounds 15 95% Conf Bounds Expected Return Expected Return Realised Return Realised Return 6 10 Excess Return (%) 4 Excess Return (%) Calendar Year Calendar Year In expectation, returns exhibit a dynamic profile because the volatility profile of the debt service also decreases in time Realised returns exhibit a similar pattern Return correlation is 30% in the contracted family and 70% in the merchant family

34 The cash flow dynamics of private infrastructure project debt 34/36 Conclusions: we are getting closer An implementable framework for computing all the measures required to define infrastructure debt at the asset allocation level now exists The same framework can be used to monitor performance and calibrate prudential frameworks Some fundamental intuitions are validated 1 Existence of distinct families of underlying cash flow processes signaled by revenue risk profile and initial financial structuring choices 2 Gaussian nature of the free cash flow process in infrastructure project finance Diversification opportunities exist across the revenue risk profiles This framework predict PD and LGDs in line with the Moody s studies and allows going further and computing VaR, cvar, duration and excess returns Preliminary empirical tests confirm that a portfolio of infrastructure debt has a high risk/reward ratio and a dynamic, decreasing risk profile Diversification opportunities exist across the project lifecycle (greenfield vs brownfield) Path-dependency is an important characteristic of infrastructure investments, as long as portfolios of unlisted infrastructure are under-diversified measure project specific risk matters

35 The cash flow dynamics of private infrastructure project debt 35/36 Conclusions: Next steps 1 Data collection continues (more geographies, more granularity in the definition of families ) 2 Defining reference portfolios will require significant input form the industry: which benchmarks are useful? for what purpose? 3 What investment solutions are available / created in relation to such benchmarks?

36 The cash flow dynamics of private infrastructure project debt 36/36 References Blanc-Brude, F (2014, June) Benchmarking Long-Term Investment in Infrastructure EDHEC-Risk Position Paper Blanc-Brude, F and M Hasan (2015) The valuation of privately-held infrastructure equity investments EDHEC-Risk Publications January Blanc-Brude, F, M Hasan, and O R H Ismail (2014) Unlisted Infrastructure Debt Valuation Performance EDHEC-Risk Publications July Blanc-Brude, F, M Hasan, and T Whittaker (2015) Cash Flow Dynamics of Private Infrastructure Project Debt, Empirical evidence and dynamic modelling EDHEC-Risk Publications November Moody s (2015) Default and recovery rates for project finance bank loans, Technical report, Moody s Investors Service