Long-Term Investment in Infrastructure & Solvency-2

Transcription

1 Long-Term Investment in Infrastructure & Solvency-2 1/38 Long-Term Investment in Infrastructure & Solvency-2 Implications for the design of the Standard Formula Frédéric Blanc-Brude & Omneia RH Ismail EDHEC-Risk A presentation prepared for the Chairman of the European Insurance and Occupational Pensions Authority Frankfurt, Friday, October 11 th 2013

2 Long-Term Investment in Infrastructure & Solvency-2 2/38 Agenda 1 De ning long-term infrastructure investment for Solvency-2 2 Proposed approach to measure risk in project nance 3 First results and future steps 4 The roadmap: making infrastructure relevant for institutional investors 5 How you can help

3 Long-Term Investment in Infrastructure & Solvency-2 3/38 Long-term investment, infrastructure & Solvency-2 Does the Standard Formula need to be changed to accommodate long-term investments? What is the potential contribution to systemic risk of long-term investments? 1 What is the likelihood of large losses? 2 How correlated are these losses with bad states of the world? 3 Is this pro le different from existing risk factor modules in the SF? Infrastructure projects are one of the most signi cant type of long-term investment 1 They are genuinely long-term: large sunk costs imply a delayed payoff for investors 2 They play a particularly important role in the economy (endogenous growth) But de ning infrastructure from an investment and regulatory point of view is proving very dif cult: 1 Almost any type of investment product can be labelled infrastructure 2 The known performance of listed and unlisted equity investments in infrastructure makes them hard to distinguish from risk factors that are already identi ed in the SF (see Blanc-Brude, 2013, for a review)

4 Long-Term Investment in Infrastructure & Solvency-2 4/38 De ning infrastructure investments for Solvency-2 Focusing on tangible infrastructure is the wrong approach: we are interested in nancial instruments and the risk factor exposures they create for investors Our proposed solution to address the public policy agenda (channelling institutional money towards infrastructure) within the prudential regulatory framework is to ignore the notion of infrastructure and instead to focus on project nance This approach greatly clari es the debate: 1 Project nance is well-de ned as a set of nancial instruments (cf Basel-2) 2 Project nance is a unique form of corporate governance and can be expected to create new and unique risk factor exposures compared to other types of underlyings 3 Project nance encompasses the immense majority of investable and stand-alone infrastructure projects in the world today and in all likelihood will be used to deliver most future projects (eg the various public-private partnership programs in Europe mostly imply project nancing according to the Basel-2 de nition)

5 Long-Term Investment in Infrastructure & Solvency-2 5/38 Project nance: creating long-term commitment Project nance is the evolutionary response to the perennial long-term investment problem under asymmetrical information A unique type of corporate structure: a single-project rm with no other asset than receivables and a nite life A double ltering mechanism: separate incorporation + leverage A structured approach: splitting the free cash ow of the rm by level of predictability: high leverage signals low asset risk A new perspective: project nance is not the median infrastructure project (that is a good thing!)

6 Long-Term Investment in Infrastructure & Solvency-2 6/38 Project nance and Solvency-2 New question: what is the potential contribution to systemic risk of project nance investments? 1 What is the likelihood of facing large losses? 2 How correlated are such losses with bad states of the world? 3 Are we looking at new and unique risk factor exposures not covered by the SF? To answer these questions we must rst 1 Determine the risk pro le of project nance debt and equity as underlyings 2 Benchmark investments in well-diversi ed baskets of project nance debt and equity 3 Determine how accessible to the average investor such exposures might be We propose to develop a new methodology designed to measure risk and value for project nance instruments ie unlisted, very illiquid assets 1 There is no traded underlying (by de nition) 2 In incomplete markets we cannot apply the traditional asset valuation framework relying on hedging/replication arguments 3 In incomplete markets, value is not driven by the law of one price, it is subjectively determined by investors preferences and risk aversion

7 Long-Term Investment in Infrastructure & Solvency-2 7/38 Approach Our objective is to build a simple methodology using the minimum amount of standardised data 1 We focus on data that we know already exists 2 Standardisation also improves data collection and transparency Project nance allows the observation of an investment base case for each instrument in the nancial structure The existence of this base case allows us to circumvent the valuation puzzle in incomplete markets 1 At nancial close, the debt (principal and interest) and equity (dividend) base cases embody the subjective valuation of investors, including their required rate of return 2 Any expected or observed deviation from the base case is a measure of the risk taken by investors 3 Hence, by observing these deviations, we can measure risk without valuing assets 4 We show that all necessary information to assess the risk pro le of both debt and equity tranches in project nance is contained in the rm s debt service cover ratio or DSCR

8 Long-Term Investment in Infrastructure & Solvency-2 8/38 In project nance, the DSCR at time t is de ned as: Measuring credit risk in infrastructure project nance DSCR t = Cash Flow Available for Debt Service (CFADS) t Debt Service (Principal+Interest) t Understanding what drives the volatility of DSCR t is both necessary and suf cient to document the credit risk of infrastructure debt The default point at time t is unambiguously DSCR t = 1 The conditional probability of default p t at time t is written: p t = Pr(DSCR t < 1 min j<tdscr j 1) And the conditional probability of emerging from default is: q t = Pr(DSCR t 1 DSCR t 1 < 1)

9 Long-Term Investment in Infrastructure & Solvency-2 9/38 Measuring credit risk in infrastructure project nance In Blanc-Brude and Ismail (2013), we show that distance to default can be written: DD t = 1 σ DSCRt Debt Service t 1 Debt Service t (1 1 DSCR t ) where σ DSCRt is the standard deviation of the annual percentage change in the DSCR value Likewise, loss at time t (loss given default) is written as a function of DSCR t L t = B t T i=t+1 D i (1 + r t ) (i t) (1 p i w i (1 E(DSCR i ))) 1 with: w t = 1+p t 1 q t /(1 p t 1, and Bt the debt base case Discounting is done using the ) base case debt yield-to-maturity at time t Hence knowledge of the base case debt service cash ow and the rst two moments of the distribution of DSCR t suf ce to document credit risk, including computing a loss function

10 Long-Term Investment in Infrastructure & Solvency-2 10/38 Measuring equity risk in infrastructure project nance If the SPE defaults, no payment is made to equity in that period The dif culty is estimating the variability of the equity payoff, when the SPE does not default Blanc-Brude and Ismail (2013) de ne ESCR t the equity service cover ratio as: ESCR t = dividend t dividend base case,t If E(L t ) = max(npv basecase,t E(NPV t ), 0), NPV basecase,t is simply the discounted value of the base case PV i t = T i=t X i i, (1+r i ) i and NPV i t can be written as a function of ESCR t and the base case cash ows E( NPV i t ) = T i=t E( ESCR t ) X0 i X 0 0 (1+r i ) i (1 k t ) 1 where k t is the probability of no dividend payment at time t (either due to lock-up or project default) Equity risk (expected loss and VaR) can be expressed using the distribution of ESCR t and base case cash ows

11 Long-Term Investment in Infrastructure & Solvency-2 11/38 Measuring equity risk in infrastructure project nance In fact, the likelihood of making a loss on the equity side can also be expressed as a measure of the debt service cover ratio (DSCR) P(ESCR t < 1) = P ( { }) dividendbase DSCR t < max case,t debt service base case,t + 1, 1x t where 1x t, for x 0, the lock-up threshold at time t Hence, equity risk can be measured without observing realised equity cash ows, using the equity and debt base cases and the distribution of the DSCR t

12 Long-Term Investment in Infrastructure & Solvency-2 12/38 A Bayesian approach to risk benchmarking in project nance Key credit and equity risk measures can be written as function of the base case and the distribution of DSCR t, including a loss density function Prior formulation: we formulate generic base case cash ows for infrastructure projects and propose to make an assumption about the probability distribution of the rm s free cash ow (cash ow available for debt service) over time Simulated risk measures are produced using Monte Carlo simulation Posterior formulation requires the collection of standardised data, as de ned by the methodology used to formulate the prior

13 Long-Term Investment in Infrastructure & Solvency-2 13/38 Prior formulation A generic economic infrastructure project with traf c risk with a growing DSCR t from time 0 because of the inherent uncertainty of long-term commercial prospects Variable Generic project Assumption CAPEX 100m Financial structure single equity and debt tranches SPE t 0 leverage 75% Debt ammortisation pro le constant at 6% int Debt Maturity 20 years Project Life 22 years DSCR at time 0 and T 13 and 16 Equity lock-up threshold DSCR=11 Prior assumptions Project free cash ow distribution Lognormal σ 2 DSCR σ 2 DSCR t+1 +01%

14 Long-Term Investment in Infrastructure & Solvency-2 14/38 Simulation results: debt Distance to default Expected loss or loss given default 500% 450% 400% 350% 300% 250% 200% 150% 100% 050% 000% Distance-to-default and default frequency mapping Probability of default 600% 500% 400% 300% 200% y = 32297e -6193x R² = % 000% Distance to default 995% one-year value at risk 900% 800% 700% 600% 500% 400% 300% 200% 100% 00%

15 Long-Term Investment in Infrastructure & Solvency-2 15/38 Simulation results: equity Base case equity cash ows Expected loss and gain 14% 13% 12% 11% 10% % one-year value at risk 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% % 8% 7% 6% Average downside Average upside

16 Long-Term Investment in Infrastructure & Solvency-2 16/38 Conclusion: the roadmap We have established a methodology to measure risk in both senior and junior tranches of project nance investments: the main vehicle used to deliver infrastructure nancing This methodology relies on the parsimonious collection of standardised data about individual projects base case cash ows and the debt service cover ratio A number of steps remain to make long-term infrastructure investment relevant to institutional investors and identify the opportunity to modify the Standard Formula We call these steps the roadmap to develop infrastructure investment for long-term investors 1 Observed data will allow the formulation of a posterior probability distribution of DSCR t 2 The documented risk pro le of individual instruments (debt & equity) will allow the design of speci c, well-diversi ed investment benchmarks according to clearly de ned strategies and horizons 3 These benchmarks will reveal : whether the risk factor exposures created by infrastructure project nance are different from existing risk modules in the standard formula the correlation of project nance high losses with other states of the world the accessibility of investment solutions offering exposure to the risk factors identi ed

17 Long-Term Investment in Infrastructure & Solvency-2 17/38 The road map: making infrastructure relevant for investors

18 Long-Term Investment in Infrastructure & Solvency-2 18/38 The road map: progress to date

19 Long-Term Investment in Infrastructure & Solvency-2 19/38 Contribute! You can help develop this research and Recognise the validity of our proposed shift from the notion of infrastructure to that of project nance as a relevant form of long-term investment from an investment and regulatory perspective Support our minimum data requirement and cash ow reporting standard for project nance investors Help us collect data from public and private banks

20 Long-Term Investment in Infrastructure & Solvency-2 20/38 SUPPLEMENTARY MATERIAL 1 Measuring the credit risk of unlisted infrastructure debt 2 Measuring risk in unlisted infrastructure equity investments 3 Data collection requirements

21 Long-Term Investment in Infrastructure & Solvency-2 21/38 Measuring credit risk in project nance: a structural approach The dif culty with reduced form credit risk models: they rely heavily on historical data, while structural models require precise knowledge of underlying quantities (volatility) and a clear de nition of the default point + the many assumptions of the Merton model In the case of project nance debt, the structural approach is highly relevant: we can measure the volatility of the underlying and de ne the default point relatively easily using the debt service cover ratio of the borrower In project nance, the DSCR at time t is de ned as: DSCRt = Cash Flow Available for Debt Service (CFADS)t Debt Service (Principal+Interest)t Understanding what drives the volatility of DSCRt is both necessary and suf cient to document the credit risk of infrastructure debt

22 Long-Term Investment in Infrastructure & Solvency-2 22/38 De ning and predicting default Default is de ned as: Default t CFADS t < Debt Service (Principal+Interest) t Default t DSCR t CFADS t Debt Service (Principal+Interest) t < 1 In other words, the default point at time t is unambiguously DSCR t = 1 and the the probability of default p t at time t is written: p t = Pr(DSCR t < 1 min j<t DSCR j 1)

23 Long-Term Investment in Infrastructure & Solvency-2 23/38 DSCR and distance to default Without loss of generality, we can write (Crosbie and Bohn, 2003): Distance to Default = [Market value of assets] [Default point] [Market value of assets][asset volatility] In Blanc-Brude and Ismail (2013), we show that distance to default can be written: DD t = 1 σ DSCRt Debt Service t 1 Debt Service t (1 1 DSCR t ) where σ DSCRt is the standard deviation of the annual percentage change in the DSCR value Hence, the distribution of DSCR t together with the debt repayment pro le (growth rate of the debt service) ie the debt base case, are suf cient inputs to estimate the Distance to Default of project nance loans

24 Long-Term Investment in Infrastructure & Solvency-2 24/38 DSCR and emergence from default Project nance debt is expected to have high recovery rates because the credit risk of individual borrowers is actively managed during the life of each loan In effect, lenders can use their extensive control rights to restructure or extend individual facilities, effectively lending against the balance of the projects cash ows (either during or after the loan s life) Hence, loans that default are also likely to emerge from default in the next period The distribution of DSCR t also captures this phenomenon: q t = Pr(DSCR t 1 DSCR t 1 < 1)

25 Long-Term Investment in Infrastructure & Solvency-2 25/38 Loss given default In Blanc-Brude and Ismail (2013), we show that the loss at time t is written as the difference between the discounted base case debt cash ows and a factor which is the a function of the distribution of DSCR t with: w t = L t = B t 1 1+p t 1 q t /(1 p t 1 ) T i=t+1 D i (1 + r t ) (i t) (1 p i w i (1 E(DSCR i ))) Discounting is done using the debt yield-to-maturity at time t

26 Long-Term Investment in Infrastructure & Solvency-2 26/38 Measuring equity risk in project nance: a parsimonious approach We know that if the SPE defaults on its debt obligation, no payment will be made to equity in that period The dif culty is estimating conditional equity risk ie the variability of the equity payoff, when the SPE does not default Valuation is tricky because there is no traded asset: in incomplete markets we cannot apply the usual approach of valuing assets in complete markets with hedging/replicating arguments Instead, we use the investment base case to measure equity risk without explicitly valuing the investment Risk is simply the expected or observed deviation from a base case, which may be valued differently by different investors

27 Long-Term Investment in Infrastructure & Solvency-2 27/38 A Bayesian base case { Ex ante cash ows: notional values The prior view Expected cash ows: likelihood to meet the base case The prior probability distribution of cash ows embodies an investor s subjective valuation and required rate of return at t 0 Ex post cash ows: empirical observations allowing the revision of investors prior knowledge (the posterior probability distribution of cash ows)

28 Long-Term Investment in Infrastructure & Solvency-2 28/38 A simple measure of equity risk The Equity Service Cover Ratio (ESCR) is de ned as: ESCR t = dividend t dividend base case,t where, dividend t is either the expected or the ex post cash ow to equity at time t and, dividend basecase,t is the ex ante cash ow to equity at time t de ned in the base case in each period t=1,2,t for a project nancing of maturity T Accounting for construction risk : we can normalise the ESCR by the ex post initial investment so that we measure risk per dollar actually invested: ESCR t

29 Long-Term Investment in Infrastructure & Solvency-2 29/38 Accounting for credit risk In some states of the world, the equity payoff is zero by construction: senior default or equity lock-up { 1 if equity lock-up or senior debt default l i t = 1 ESCR i t if no lock-up nor default that is, the percentage loss per dollar invested in period t is either 1 is the project is in default at that time or the equity is locked up, or it is 1 minus any negative divergence from the base case The prior probability distribution of cash ows is itself a conditional probability distribution

30 Long-Term Investment in Infrastructure & Solvency-2 30/38 Measuring infrastructure equity downside risk The investor s prior at t 0 is written E(L t ) = max(npv basecase,t E(NPV t ), 0) NPV basecase,t is simply the discounted value of the base case PV i t = T i=t X i i (1+r i ) i NPV i t can be written as a function of ESCR t and the base case cash ows PV i t = X i i T X i ESCR T i t X0 i 0 X 0 0 (1 + r t=i i ) i = (1 + r t=i i ) i E( NPV T i t ) = i=t E( ESCR t ) X0 i X 0 0 (1 + r i ) i (1 k t ) 1 where k t is the probability of no dividend payment at time t (either due to lock-up or project default) Discounting: the equity yield (to maturity) implied by the base case approximates the interest rate term structure

31 Long-Term Investment in Infrastructure & Solvency-2 31/38 Infrastructure equity loss function If we know the distribution of ESCR t, we can compute a loss density function according to: L t = max(npv 0 t NPV t, 0) Expected loss is the mean of this distribution 995% value-at-risk is its 05% quantile To compute these risk measures, we only need: The dividend payout base case X 0 Expected cash ows to equity X i in each state of the world i, OR Observed cash ows in realised states of the world The probability of equity payout (1 k t ) in each period

32 Long-Term Investment in Infrastructure & Solvency-2 32/38 Alternative route: From a credit to an equity risk measure Intuition: for a simple SPE with a senior and a junior tranche, equity risk is bounded by the SPE s credit risk on one side, and by the project s total investment risk on the other We can express the probability of loss on the equity side as a function of a simple and widely used credit risk metric: the debt service cover ratio P(ESCR t < 1) = P ( { }) dividendbase DSCR t < max case,t debt service base case,t + 1, 1x t where 1x t, for x 0, is the lock-up threshold at time t and P(DSCR t > 1x) = 1 p t 1 1+p t 1 q t /(1 p t 1 ) P(1 < DSCR t < 1x) with the probability of default p t and the probability of emergence from default q t de ned above

33 Long-Term Investment in Infrastructure & Solvency-2 33/38 3 Data collection implications density 2 1 From an investor s perspective buying a basket of project loans, full knowledge of the distribution of DSCR t is suf cient to characterise the credit risk of infrastructure debt In combination with the debt base case (principal + interest) DSCR t captures lines relevant dimensions of asset value, asset volatility and the probability of reaching availpay the default point and to emerge from default merchant However, there are in all likelihood, several distributions of DSCRpartcont t, determined by each systematic risk factor driving the CFADS, in particular, Revenue risk factors 3 Counter party risks Other factors? econometric testing will be required in due course The main benchmarking objective is to document the distribution of DSCR t and its statistical determinants 0 density 2 lines availpay merchant partcont

34 Long-Term Investment in Infrastructure & Solvency-2 34/38 Relevant publications: NATIXIS & EDHEC-Risk Research Chair on Infrastructure Debt Investment Who is afraid of construction risk? Infrastructure debt portfolio construction EDHEC-Risk Publications Frédéric Blanc-Brude & Omenia RH Ismail July 2013 Available at wwwedhec-riskcom/multistyle_multiclass/natixis_research_chair and cibnatixiscom/infrastructure

35 Long-Term Investment in Infrastructure & Solvency-2 35/38 Relevant publications: Meridiam Infrastrutcure, Campbell Lutyens & EDHEC-Risk Research Chair on Infrastructure Equity Investment An EDHEC-Risk Publication Towards Efficient Benchmarks for Infrastructure Equity Investments A review of the literature on infrastructure equity investment and directions for future research January 2013 with the support of Towards Ef cient Benchmarks for Infrastructure Equity Investments EDHEC-Risk Publications Frédéric Blanc-Brude January 2013 Available at wwwedhec-riskcom/multistyle_multiclass/meridiam_infrastructure_ and_campbell_lutyens_research_chair

36 Long-Term Investment in Infrastructure & Solvency-2 36/38 Relevant publications: working papers Construction risk in project nance EDHEC Business School Working Paper Frédéric Blanc-Brude & Dejan Makovsek January 2013 Measuring the credit risk of unlisted infrastructure debt EDHEC Business School Working Paper Frédéric Blanc-Brude & Omenia RH Ismail August 2013

37 Long-Term Investment in Infrastructure & Solvency-2 37/38 Relevant publications: working papers Measuring Risk in Unlisted Infrastructure Equity Investments EDHEC Business School Working Paper Frédéric Blanc-Brude & Omenia RH Ismail September 2013

38 Long-Term Investment in Infrastructure & Solvency-2 38/38 References Blanc-Brude, F (2013) Towards ef cient benchmarks for infrastructure equity investments Meridam & Campbell Lutyens Research Chair on infrastructure equity investment EDHEC-Risk : EDHEC-Risk Blanc-Brude, F and O R H Ismail (2013) Measuring unlisted infrastructure debt credit risk EDHEC-Risk Crosbie, P and J Bohn (2003) Modeling default risk Technical report