Credit Risk for Merchant Power Business in India
Historically, credit risk analysis has always involved standards to classify loans or bonds (for underwriting purpose) into different risk categories, represented usually by alphabets and symbols such as AA+. A letter ranking or credit scoring is commonly done through assessment of probability of default (loan not being repaid) . Such probability of default assessment is done through estimating different financial ratios and further estimating the probability of ratio at which the estimated value falls below the benchmark level. Needless to mention, a high probability of default indicates a high credit risk for lender’s. Nonetheless, it is not necessary a high risk venture would discourage lenders since according to the risk level, they can charge the risk premium or credit spread. It needs to be mentioned that lending to MPPs has a similar payoff structure as that of a seller put option. Similar to the risk of market price falling below the option price in case of a put option, causing a downside risk for the option writer; there is a downside risk for the lender to lose up to the loan amount while lending to MPPs (driven by volatile cash flows). On the other end, there is an upside payoff that is limited to the pre-determined credit or underwriting spread. Accordingly, option analysis can be used to directly measure credit spreads (the cost of capital on risky debt) through measurement of probability of default and loss given default quantified as per the formula
Credit or Underwriting Spread (in percent) = (Probability of Default * Loss Given Default) *100/ Total Project Debt
In order to quantify probability of default or loss given default, volatility of major project inputs, driving cash flows needs to be estimated which can be used further in the project finance model. Greater the volatility of such inputs, higher would be the probability of loss and higher would be the loss given default.
Credit Spread for Merchant Power Business in India
Estimation of credit spread is an imperative exercise while pricing loans or underwriting bonds given significant presence of credit risks. The credit spread is vital since it equates the benefits (higher earnings to the lenders of project finance debt in non-default scenarios) with the costs (the loss to the lenders in default scenarios). Since benefits are the earned yield above the risk free rate (scenarios where there is zero probability of default), credit spread is equal to the risk premium. Hence to determine pricing of loans, the following formula is usually established
Risk adjusted discount rate or Opportunity cost of lenders = Risk free rate + Risk Premium or Credit Spread
The opportunity cost of lenders is usually adopted as a benchmark for charging fixed interest rate by lending purpose. We have considered two case variants of merchant power plants of which one is 100 percent imported coal based while other is 100 percent imported gas (LNG) based. In case of 100 percent imported coal basis pure MPP, probable interest range or benchmark of lenders should be around 10.34 – 12.04 percent . Similarly, in case of hypothetical case of 100 percent imported gas based MPP, interest should vary between 13.34 – 15.03 percent.
However, in actual the interest rate range would be higher since in our simulation exercise of risk analysis, only two most important risks are considered which are as electricity price and fuel price volatility. Apart from these risks, various other risks are present such as operational risks, political and regulatory risks, off taker risk, force majeure risk etc. Depending on the perception of lenders, it can charge an extra risk premium of 2-2.5 percent. Hence overall lending benchmark would most probably vary between 12.34-14.54 percent for 100 percent imported coal based MPP. Correspondingly, lending benchmark for 100 percent imported gas based pure MPP would most probably vary between 15.34–17.53 percent.
Since the project cost of an imported coal based MPP is high enough for a developer to provide 40 percent equity, the most appropriate lending rate would lie between 14.04-14.54 percent. Contrary to the case of imported coal based MPP, the debt to capital ratio of 60 percent is ideal since interest rate of over 17 percent would almost certainly discourage the developers from availing loan. Further the equity requirements for 60 percent financial leverage in case of imported gas would approximately be similar to the 70 percent financial leverage case of imported coal. Hence the most relevant lending rate for 100 percent imported gas based pure MPP would be around 15.34-15.84 percent.
Credit Risks for Financers of Merchant Power Plants
Risk analysis becomes imperative not only from the point of view of pricing loans as earlier discussed, but also with perspective of setting up of benchmarks and introduction of risk covenants. Accordingly, Monte Carlo analysis has been carried out to determine the probability of default for MPPs with minimum DSCR as random function variable. For the exercise same scenario analysis has been done on the merchant power price and fuel price volatility as mentioned in the above sections. The underlying logic for determining the default cases for MPPs can be derived from the minimum DSCR calculations. MPPs are likely to default if the minimum DSCR is below one during any year of the loan repayment period. Monte Carlo simulation was iterated for 1,000 random variables to elicit the probable default cases wherein the minimum DSCR is below one.
Case of Imported Coal based MPP
Exhibit 02 and Exhibit 03 represents the normal probability distribution and cumulative probability distribution curve respectively as derived from Monte Carlo simulation on DSCR for our hypothetical case of 100 percent imported coal based pure MPP with D/E ratio of 70:30.
It is remarkable to note that the financer of the MPP projects bears the default risk of 36.56 percent which is on the higher side. For a MPP project with the D/E ratio of 60:40, the default risk was estimated to be lower at 29.08 percent. This illustrates the importance of financial and contractual enhancements for MPPs to ease the financing for MPP through reduction in the default risk especially for higher debt projects.
Case of Imported Gas based MPP
Similar to imported coal based MPP, Monte Carlo simulation on DSCR was exercised for LNG based MPPs. The cumulative distribution curves for LNG based MPPs with D/E ratio of 70:30 and 60:40 are illustrated in Exhibit 04 and Exhibit 05 respectively.
The comparative analysis of Monte Carlo simulation for imported coal based MPP and LNG based MPP to highlight the risks beared by financers has been illustrated in Exhibit 06. It is observed that risk of default by MPPs for financers range from high to moderate. Hence, in order to ease financing for MPPs, it would be pivotal for MPPs to implement financial and contractual enhancements. Enincon Perspectives believes that it would be essential for MPPs to facilitate the lending through strengthening the debt servicing. Financial enhancements which include the effective use of covenants and contractual enhancements through long-term bilateral contracts and innovative fuel supply arrangements could play a crucial role in augmenting debt servicing.
Long Run Marginal Cost Framework for MPPs in India
In the context of the credit risk assessment exercise for MPPs, it is pertinent to mention that the biggest factor that drives and would continue to determine the accuracy is validity of forward price. Therefore, it is extremely vital to forecast the prices with high degree of accuracy so as to avoid any skewed results.
Forecast of the electricity prices with a high degree of accuracy would invariably depend on the assessment of long run marginal cost. The underlying logic for long run marginal costs calculations is the fact that the merchant prices are expected to converge to certain levels and the assumption of wide fluctuations in merchant prices above and below the long-run marginal cost is unreasonable. There have been umpteen examples where lenders have fallen into a trap by disregarding equilibrium principles and considering inefficient or irrational economic behaviour to be long lasting. As an example, price forecasts in Argentina were assumed with the contention that inefficient capacity mix would remain in place, thereby neglecting the downward trend of long-run prices defined by more efficient capacity based on superior technology. Hence, projects based upon unreasonable price forecasts with wide gaps in comparison to the long-run margin costs needs to be re-evaluated. Moreover, need of such comprehensive assessment of long run marginal costs cannot be appreciated more given the power market dynamics prevailing in the country. It is so since future price predictions are built upon complex dynamics of energy and peak deficits prevailing in the country along with the likely trend of capacity additions in the future. Hence, a synergy of the forecasting model with the long-run marginal costs is important to validate the results of price forecasting.
Further determination of such long-run marginal costs can be most perfectly done through evaluation of the capacity charges and energy charges. It would necessitate analysis of the existing price trends in the short-term markets and determination of the equilibrium prices through load profiles along with the cost characteristics of the project. In case the prevailing market prices are higher than the equilibrium prices, inherently determined by cost features, it is expected that the merchant prices would follow a downward trend and vice versa. Hence this type of analysis is essential for the lenders for following a realistic approach while lending for a MPP project.