WO2003034309A1 - Systeme et procede permettant d'analyser le risque et la profitabilite de prets sans recours - Google Patents

Systeme et procede permettant d'analyser le risque et la profitabilite de prets sans recours Download PDF

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Publication number
WO2003034309A1
WO2003034309A1 PCT/US2001/042764 US0142764W WO03034309A1 WO 2003034309 A1 WO2003034309 A1 WO 2003034309A1 US 0142764 W US0142764 W US 0142764W WO 03034309 A1 WO03034309 A1 WO 03034309A1
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WIPO (PCT)
Prior art keywords
risk
loan
factor
country
values
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PCT/US2001/042764
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English (en)
Inventor
Mark W. Guthner
Iain Maclachan
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Australia And New Zeland Banking Group Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Australia And New Zeland Banking Group Limited filed Critical Australia And New Zeland Banking Group Limited
Priority to US10/344,550 priority Critical patent/US20050262013A1/en
Priority to EP01983206A priority patent/EP1436740A4/fr
Priority to AU2002214652A priority patent/AU2002214652A1/en
Priority to PCT/US2001/042764 priority patent/WO2003034309A1/fr
Publication of WO2003034309A1 publication Critical patent/WO2003034309A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance

Definitions

  • the present invention relates to banking, and more particularly, to a system and method for improved loan decision-making through risk analysis.
  • Bank loans take many forms. For example, banks loan money to consumers for their home mortgage, car financing, and other major purchases. Banks also issue loans to corporations to assist with new product development, working capital, debt payments, and other general corporate operating expenses. These types of corporate loans are considered “balance sheet” loans because the loan is disclosed on the corporate balance sheet and the lending entity would have recourse against the other assets of the business should the corporation default on the loan. Banks also issue "non-recourse" loans to corporations, which are generally tied to a particular project, held in a special purpose vehicle, and for which the lending entity does not have recourse against other assets of the parent corporation in the event of default.
  • non-recourse loans such as project or structured-corporate lending
  • project or structured-corporate lending are not homogeneous and typically involve complex contractual arrangements with unique characteristics, reflecting the involvement of many stakeholders.
  • the risk profile of one loan provides very little insight into the risk profile of another loan. This is, in fact, the intention of structured finance lending.
  • All economic enterprises have some level of financial and operating risk. Structured finance techniques carve up that risk and allocate it to the parties that are most able to accept and manage it.
  • each loan has its own unique risk profile and must be analyzed taking into consideration the distinctive elements of the underlying project and loan itself.
  • a proper framework and execution of financial analysis related to structured infrastructure lending is therefore crucial for a number of reasons.
  • NIACC cost of capital
  • credit ratings can be determined to give a lender a relative risk rating for a particular loan as measured against a standardized loan risk rating system.
  • credit models are used to compute provisioning and economic capital for financial institutions.
  • the current methodology for credit rating project finance loans is entirely qualitative. For example, a project may be rated based upon which country it is located in, which industry it is categorized in, what leverage exists, what supplier may be involved, who the off-taker is, and other factors. From these factors, an educated guess as to the credit rating to apply is often made, and calculations can determine provisioning and economic capital based on historical default rates and rating migration experience.
  • a credit rating can be assigned, such as the equivalent of A3 on a Moody's scale, for example, along with the seniority on the loan, such as "senior unsecured" for example, and an industry designation.
  • a major drawback is that it is unknown how accurate the ratings are, and it is unlikely that the historical default rates and ratings migration matrices will accurately reflect future expectations. This can and does cause suboptimal decision-making at the lender level.
  • EDF expected default frequency
  • LGD loss given default
  • VoL volatility (or uncertainty) of loss
  • CR customer credit rating
  • LI loss indicator
  • EL expected loss
  • EC economic capital
  • NIACC net income after cost of capital
  • RAROC risk adjusted return on capital
  • EL expected loss
  • EC economic capital
  • the commercial default is a function of the economic need of the project (i.e. demand for what is produced), the uncertainty of the economic variables driving the project (commodity prices, interest of inflation rates, etc.), the contractual arrangements of the project (interest rate and currency hedges, supply and offtake agreements), and the quality and experience of the participants (sponsors, operators, off-takers, suppliers, etc.).
  • the severity of loss that can be expected is a function of the cause of the default and is called the commercial loss given default (LGD).
  • LGD is dependent on the cause of the default. The LGD actually suffered will be impacted by whether the project failed because of a lack of raw materials or a prolonged spike in the price of the materials, whether a cheaper substitute entered the market, or whether interest rates spiked, etc.
  • the commercial loss given default can be determined by estimating the present value of a project's expected cash flow in a default scenario relative to the amount of debt outstanding, the location of the project (i.e.
  • ECAs Export Credit Agencies
  • MLs Multi-Lateral Institutions
  • Participation by guarantors such as ECAs and/or MLs generally reduces the overall loan risk of a project.
  • Country Risk There are two broad categories of Country Risk. They are (1) the possibility of cross border default and (2) default caused by Political Violence.
  • the risk of a cross border default is characterized by the government's ability and willingness to service foreign currency obligations.
  • the risk of default caused by political violence is characterized by war, expropriation, regulatory instability, property rights, and transparency (or lack thereof) in the legal systems, for example.
  • a proprietary Country Index (CI) can be used as a measure of this risk as can ratings provided by Moody's and S&P or other rating agencies.
  • Country LGD the severity of loss that can be expected is called the country loss given default (i.e. Country LGD).
  • Country LGD the severity of loss that can be expected is called the country loss given default (i.e. Country LGD).
  • Estimates of Country LGD are based on historical rescheduling agreements and expropriation events, and are influenced by participation by multi-lateral institutions.
  • joint EDF The likelihood that the project suffers a commercial default and country default at the same time is called a joint EDF. This is driven by the default correlation between the commercial performance of the project and country specific risks. The correlation is generally driven by the industry in question along with the existence of cross-border currency flows. If a simultaneous (commercial & country) default occurs, the level of loss that can be expected is called the joint loss given default (Joint LGD). This loss is a function of LGD estimates obtained individually for the country LGD and the commercial LGD.
  • EDF which can be represented as a binomial function
  • LGD requires statistical estimation based on sampled data, to be discussed hereinafter.
  • Expected Loss represents the level of credit losses expected over the life of the loan or specific time horizon. Actual loss for a portfolio of loans may differ from expected loss from period to period, but should on average converge to expected loss over a business cycle. EL is applied only to loans that are not yet in default, as loans already in default are considered as losses for a previous period. From a practical standpoint, Expected Loss can be calculated for a specific loan or transaction as the product of the EDF, the LGD, and the Exposure Amount (EA). Fig. 2 A shows how EL may be determined in accordance with the method of the present invention.
  • a project loan can be thought of as having a "portfolio" of risks.
  • the expected loss for country default risk and commercial default risks associated with counter party default are calculated independently.
  • Statistical mathematics can then be used to sum the effects of all the sources of risk based upon their joint probability or correlation.
  • Fig. 2B is an example distribution chart 62 showing EL, VoL, and Economic Capital.
  • CM Capital Multiplier
  • the present invention employs a model which considers each risk relevant to the loan determination, including commercial/economic and country risks. From this analysis, the present invention can determine the estimated default frequency (EDF), the loss given default (LGD), volatility of the loss (VoL), and can recommend total provision and economic capital outlays for the lender for the given project finance loan. From this information, the present invention can also be used to determine a credit rating and profitability measures for the given project loan.
  • EDF estimated default frequency
  • LGD loss given default
  • VoL volatility of the loss
  • the present invention can also be used to determine a credit rating and profitability measures for the given project loan.
  • an EDF, LGD and Volatility of Loss is needed for each of the factors above, along with the appropriate correlation between these events.
  • these factors are modeled so as to generate accurate figures to assist in loan decision making.
  • Risks introduced by macro-economic factors, (off-take price & volume, interest & foreign exchange rates, for example) can be computed outside the model of the present invention using a Monte Carlo process. The results of this analysis can be input into the model of the present invention for incorporation into the risk rating, NIACC and RAROC analysis.
  • a cash flow model can be built and thousands of scenarios sampled using a random simulation approach, such as a Monte Carlo analysis, for example. All other risks are estimated and included in the analysis using the Risk Integration Model (RIMTM) of the present invention.
  • RIMTM Risk Integration Model
  • the risk integration model (RIMTM) included as part of the present invention is an analytical tool used to perform the risk analysis to compute expected profitability estimates and a credit rating.
  • the credit rating can be computed year-by- year to determine the time and level of peak risk, as well as the average risk rating, over the life of the loan. The same can be done for the profitability analysis.
  • the RJMTM of the present invention assembles the relevant risk factors to assess the overall risk and profitability of a project loan. Making a series of choices from an input menu that describes the characteristics of the loan initiates the loan evaluation.
  • the model considers the risk of early repayment.
  • a statistical model is built-in, to assess the probability the loan will be called at any point in the life of the loan.
  • the model considers factors related to refinancing.
  • a statistical model is built in to assess the possibility that the loan will not be refinanceable at the contracted maturity date of the loan.
  • FIG. 1 is a system diagram illustrating inputs and outputs of the risk integration model of the present invention in accordance with one embodiment of the present invention.
  • FIG. 2 A is a diagram showing various default states of a loan after it has been made, and a method of determining expected loss for a loan transaction using the method of the present invention.
  • FIG. 2B is a diagram showing a sample distribution of credit loss for a hypothetical loan.
  • FIGS. 3 A and 3B are sample sets of estimated default frequency (EDF) values as may be developed in accordance with the present invention.
  • EDF estimated default frequency
  • FIG. 4 is a sample set of historical default rates as may be used in accordance with the present invention.
  • FIGS. 5A, 5B, 6A and 6C show sample input pages for use in accordance with one embodiment of the present invention.
  • FIG. 7A shows a sample table for use in inputting a specific insurer in accordance with one embodiment of the present invention.
  • FIG. 7B shows a sample partial table of booking points and associated tax rates and hurdle rates for use in connection with the present invention.
  • FIGS. 8A and 8B show a sample input page for use with analyzing macro-economic factors in accordance with one embodiment of the present invention.
  • FIGS. 8C and 8D show a sample partial input page including results from a macro- economic factor analysis which can be used in connection with a loan risk assessment in accordance with one embodiment of the present invention.
  • FIGS. 9 and 10 show sample input pages for use in determining country risk in accordance with one embodiment of the present invention.
  • FIG. 11 shows a sample worksheet for determining a country risk rating in accordance with one embodiment of the present invention.
  • FIGS. 12 and 13 show sample input pages for use in determining construction risk factors in accordance with one embodiment of the present invention.
  • FIG. 14 shows a sample rating table showing sample construction and operating phase ratings for a variety of project types in accordance with the present invention.
  • FIGS. 15 through 27 show sample scoring and rating tables for use with various risk factors of the present invention.
  • FIGS. 28A and 28B show sample tables of the probability of refinance failure in accordance with one embodiment of the present invention.
  • FIG. 29 shows a sample table of EDF vectors in connection with insurance types and refinance risk for use in accordance with the present invention.
  • FIGS. 30A and 30B show sample summary tables of EDF values for use in connection with the present invention.
  • FIGS. 31 A, 3 IB and 31C show sample output reports for use in connection with the present invention.
  • FIGS. 32 and 33 show sample rating tables for loss indicator and security indicator ratings in accordance with one embodiment of the present invention.
  • FIG. 34 shows a sample table of LGD values related to the position of a loan within capital structure for use in connection with the present invention.
  • FIG. 35 shows a sample summary table of LGD values for use in connection with the present invention.
  • FIGS. 36 through 38 show sample summary tables representing values for expected loss, volatility of LGD, and unexpected loss for use in connection with the present invention.
  • FIGS. 39 A and 39B show sample calculation tables for use in calculating NIACC in accordance with one embodiment of the present invention.
  • FIGS. 40 A, 40B and 40C shows sample tables for use in summarizing analysis for various risk factors and measures in accordance with the present invention.
  • FIG. 41 shows a sample assumptions table having measures for various LGD and standard deviations of LGD in accordance with one embodiment of the present invention.
  • FIGS. 42 A and 42B show sample calculation tables for computing profitability measures in accordance with one embodiment of the present invention.
  • FIGS. 43 through 50 show sample output graphs in accordance with one embodiment of the present invention. MODES FORCARRYING OUT THE INVENTION
  • the present invention provides a system 10 for receiving various risk factor inputs 20 related to project finance loans and providing meaningful output measures designed to assist lenders in making decisions with regard to these loans.
  • Inputs can include factors of commercial risk, shown generally at 200, factors of country risk, shown generally at 100, and macro-economic factors, shown generally at 800. Macro-economic factors can be considered a commercial risk factor and, in one embodiment of the present invention, are estimated separately from the other commercial factors.
  • a risk model 30 in accordance with the present invention is used to determine various risk measures 40, profitability measures 60, and rating and provisioning measures 80 to assist in the lender's decision-making.
  • the risk measures 40 determined can include an estimated default frequency (EDF), loss given default (LGD), and volatility of loss (VoL).
  • the decision-making measures 60, 80 which can be determined as a result of the obtained risk measures include the customer credit rating (CCR), loss given default indicator (LI), security indicator (SI), expected loss (EL), economic capital (EC), net income after cost of capital (NIACC), and risk adjusted return on capital (RAROC).
  • CCR customer credit rating
  • LI loss given default indicator
  • SI security indicator
  • EL expected loss
  • EC economic capital
  • NIACC net income after cost of capital
  • RAROC risk adjusted return on capital
  • EL 81 can be determined by considering the various scenarios of default, such as no default 82, commercial default 83, country default 84, and joint commercial and country default 85.
  • the LGDs 86, 87, 88, 89 associated therewith, and the amount of capital exposed, EL can be determined.
  • the decision- assisting measures 60, 80 will be discussed more completely hereinafter.
  • the calculations, tables, and graphs can be performed and represented on a computer spreadsheet, such as the commercially available MicrosoftTM ExcelTM spreadsheet, from Microsoft Corporation, Redmond, Washington.
  • the invention can be carried out as a dedicated software program written in C++, VISUAL BASIC, or SMALLTALK, for example, which may be accessible at an individual PC or over a network such as the Internet, for example.
  • An attribute of the present invention which contributes to the accuracy of the obtained results is that the present invention counts each risk factor associated with a prospective loan once, and only once.
  • the present invention counts each risk factor associated with a prospective loan once, and only once.
  • the country risk factors 100 the risk of political violence and the risk of cu ⁇ ency inconvertibility are considered.
  • the commercial risk factors 200 the risks associated with engineering and construction, operation, suppliers, off-takers, refinance, and macro-economic factors are considered.
  • each of the commercial risk factors are modeled within the system of the present invention except for the macro-economic factors, which can be modeled externally, such as by Monte Carlo simulation, and incorporated into the valuation of the risk elements.
  • the present invention also considers the possibility of joint commercial and country risk factors contributing to the default of a prospective loan.
  • Figs. 3A and 3B is a sample summary EDF vector table, which can be populated according to the methods described herein. As shown in the EDF value table 42 in Figs. 3A and 3B, for example, the EDF vector 430 based on the factor of supply/supplier failure is 0.001% in project year 1, 0.007% in project year 2, 0.018% in project year 3, and so on. This EDF vector is a stream of EDF values associated with default probabilities based on supplier failure over time.
  • the present invention can provide a combined EDF vector representing the EDF for each year of the project. Adding together the EDF for each year of the project results in the total or cumulative EDF for the project. This calculation can be performed as straight addition (e.g. EDF A + EDF B ) or as a compounded calculation (e.g., (1 + EDF A x (1 + EDF B ) - 1).
  • Total EDF is a useful measure by the prospective lender in the loan decision analysis. The more accurate the individual EDF vectors for the risk factors and their elements, the better the lender can predict risk and profitability measures for the prospective loans.
  • the EDF vectors for each risk factor can be obtained based upon a score or rating for each risk factor and a pre-determined table representing sets of EDF values extending over a given range of time for a given range of risk ratings.
  • the time period is 30 years and the range of risk ratings extends from 1 to 20.
  • the risk ratings may alternatively be based on well-known risk ratings, such as Moody's or S&P's, for example.
  • Fig. 4 shows a portion of a sample EDF table 44, projecting a set of EDF values over a given period of time for a given range of risk ratings (see column identified at 165).
  • a risk factor having a rating of A3 for a given project would have an associated EDF vector with a default frequency of 0.039% in year 1, 0.111% in year 2, and so forth, if based on the EDF table shown in Fig. 4.
  • the risk factor score or rating is determined depending upon the input for that risk factor and the scoring system or rating scale used in connection with the present invention. The following discussion will describe how EDF vectors are determined for each risk factor.
  • Figs. 5 A, 5B, 6 A and 6C show sample input pages 21 and 22 depicting one embodiment of the various risk factors and elements which can be input into the risk model of the present invention. It will be appreciated that for some determinations, additional elements can be added while for others fewer elements are necessary.
  • the input page can include a project identification area for inputting the project name, customer name, facility identifier, project participants and other project description information. This information can include the identification of parties providing credit or economic support to the project. Other information which can be entered can be described as follows.
  • the country risks 100 can be identified and represented on the input page. As shown in Fig. 9, for each project, there can be identified the country 102 in which the project construction and engineering is taking place, and a country 104 in which the project will be operated. In many cases, the project is developed and operated in the same country. However, in some cases these locations are different, such as in the construction of a super tanker or power barge where construction takes place in a location different from where it will operate.
  • a sovereign rating can be provided as at 106 as is known in the art.
  • each country can have a country ceiling rating as provided by Moody'sTM or Standard & Poor'sTM (S&PTM).
  • S&PTM Standard & Poor'sTM
  • Other embodiments can incorporate ratings provided by other rating services.
  • This rating can represent a country's willingness and ability to meet foreign currency obligations.
  • a default table such as shown in Fig. 4, the sovereign rating can be used to estimate the country EDF.
  • Revenue and funding mismatch can occur when the currency in which the project receives payment (revenue) is different from the currency in which the project is funded.
  • the system of the present invention can consider elements such as what the hard currency export revenue is as a percentage of total revenue, as at 108, and what the hard currency borrowings are as a percentage of total debt, as at 110, as shown in Fig. 9. These factors become more important in the consideration of LGD, described hereinafter.
  • Fig. 10 is an example of a partial table 120 showing Country Risk Indices (CRT) for several countries as provided by EuroMoney Bank.
  • the Country Risk Indices are provided as EuroMoney country risk indices.
  • the Country Risk Indices can be provided by World Markets Research Center PLC, or a commercially developed proprietary index.
  • This index can be used in a number of ways.
  • the rating can be used to split the total country default rate into its component parts, cross border (currency inconvertibility) risk and political violence (war, expropriation, regulatory instability, etc.) risk.
  • a low rating means the fraction of country EDF attributable to political violence is high.
  • a high rating means the fraction of total country EDF attributable to political violence is small.
  • Fig. 11 provides a table 133 showing an example calculation of a country loss factor in accordance with the present invention.
  • the EuroMoney Country Risk Index or CRI taken from a table such as the one shown in Fig. 10 might be 59.66%, for example.
  • the CI is the index value 130 for Thailand
  • the resulting raw country loss factor value 132 for Thailand is (100 - CI) or 40.34%, in this example.
  • the final country loss factor is determined by taking the raw country loss factor and adjusting the value obtained based on commercial loss mitigants.
  • the national importance of the project in this example has been taken from the national importance table 140 representing that the project involves a critical domestic.
  • the Country Adjustment Factor 145 can then be represented on the input page, as shown at 145 in Fig. 5 A, to be used by the system of the present invention in the calculation of risk and profitability measures to be described.
  • the input element of the present invention may optionally include a country risk element associated with political and/or regulatory stability, war, and expropriation.
  • Standard risk evaluation can be employed so that consideration is given to the country's history of expropriation, the country's history of creeping expropriation through regulatory restriction or change in tax law, and the political stability of the country in general.
  • the user can quantitatively measure the political violence risk of the project using the sovereign credit rating and a relative ranking, such as Not Meaningful, Low, Moderate, and High, for example.
  • a score can be given to the political risk element and factored into the country adjustment factor for further refinement and accuracy of results.
  • an EDF can be computed as part of the overall calculation of EDF for the project.
  • the total country EDF is computed based on the sovereign credit rating of the country in question.
  • the political violence EDF is computed by taking the product of the EDF associated with the sovereign rating and the unadjusted or raw country loss factor 132.
  • the cross border EDF is what remains after the political violence EDF is subtracted from the total.
  • a country having an equivalent sovereign rating of Bal or a rating index of 12, taken from Fig. 4
  • the EDF for country risks can be determined as follows.
  • the country sovereign rating of the country where the project will be operated is input into the system of the present invention.
  • the country sovereign rating can be used to project a set of EDF values for each year of the project operation. These values can be taken from Moody's ratings, S&P's ratings, another rating system, or a combination of rating systems.
  • This set of EDF values, or this EDF vector represents the total country EDF and may be taken from the table shown in Fig. 4, for example. In the table shown in Fig. 3A, for example, a combination of rating systems are employed, and an average 152 of the obtained EDF values is used as the EDF vector representing total country risk.
  • the total country EDF can then be divided into a political violence EDF and a currency inconvertibility EDF.
  • the political violence EDF can be determined by multiplying the country sovereign rating by the raw country loss factor. If the country sovereign rating is 12, for example, and the raw country loss factor is 40.34%, for example, the political violence rating will be approximately 4.8. This number can be rounded to 5, as identified on the EDF vector table shown in Fig. 3 A at 154. Applying the political violence rating to the previously established table in Fig. 4 having a set of EDF values for given ratings over a given period of time, a political violence EDF vector 156 can be determined. In one embodiment of the invention, the political violence rating can be used to obtain multiple EDF vectors, using different historical default rating tables, and an average of the obtained EDF values can be determined.
  • the cu ⁇ ency inconvertibility EDF vector 160 can then be determined by subtracting the EDF value associated with the political violence EDF from the EDF value associated with the total country EDF for each year having an EDF value.
  • the total country EDF may be 1.215%, for example, and the political violence EDF may be 0.003%.
  • the cu ⁇ ency inconvertibility EDF for year one would be 1.212%.
  • construction risk addresses the risk the project will not be completed within budget or that it will not perform to specifications such that the project will not be able to repay all of its debts.
  • the user can choose from construction risk labels such as Not Meaningful (such as for gas fired power plants, for example), Low (such as for modern tankers, or coal fired power plants, for example), Moderate (such as for petroleum refining, petrochemical plants, for example), and High (such as for large complex projects, including nuclear projects and projects dependent on untested technology, for example).
  • historical statistics can be used where available to estimate the probability of default during the construction phase of a project given the kind of project under construction.
  • Sponsor funding risk is part of the construction phase risk.
  • a project can default during the construction phase because the sponsor goes bankrupt during construction and cannot deliver the funds necessary to complete construction.
  • the contribution of sponsor default to the overall construction EDF is a function of the sponsor's credit rating 314 (as provided by Moody's, S&P's, or other rating system, for example), the fraction of total construction costs funded by the sponsor 318 and the timing of the sponsor construction payments 316 and the position of the tranche in the bo ⁇ ower's capital structure 319.
  • the sponsor can fund the project construction 1) "up front” in which case there is no funding risk, 2) "pro-rata” over the construction period where the risk is spread over the construction period or, 3) "at completion” where funding risk is concentrated in the year of project completion.
  • the sponsor's equity contribution can range from 0% to 100%.
  • the project type 311 and technology employed 312 further factor into the determination of the probability of default within the engineering and construction phase.
  • the system can consider contractor information 321, third party completion guarantee information 330, and construction progress 338 as part of the construction risk elements in table 320.
  • Contractor information 321 can include the engineering and construction contractor's credit rating 322 (as can be represented by a Moody's or S&P rating), the contractor's experience 324, and the maximum contractor liquidated damages as a percentage of the project cost 326.
  • the contractor's experience can be rated as experienced, not experienced, or not applicable.
  • the contractor liquidated damages figure can be represented anywhere from 0% to 100%.
  • Third party completion guarantees 330 reduce the probability of default caused by a construction failure. If the project benefits from a third party completion guarantee, this is acknowledged as at 332.
  • the completion guarantee input element can include input for the existence of sponsor contingent equity, such as where the project sponsor provides an equity investment in the project in the event of a cost overrun and thereby reduces the probability of default.
  • the third party completion guarantee or sponsor contingent equity can be represented as either existent or not applicable.
  • the system can accept as input the completion guarantor credit rating (CCR) 334 (as represented by Moody'sTM or S&PTM, for example) of the guaranteeing party as well as the percentage of debt covered by the completion guarantee 336.
  • the input range for the percentage of debt covered 336 can range from 0% to 100%.
  • the construction progress 338 can also be considered in the risk analysis of the present invention and, in one embodiment, can be input as on budget, ahead of budget, or behind budget with an appropriate percentage ahead or behind.
  • the progress of construction can play a significant role in the determination of loan risk and profitability measures.
  • pre-determined input options as shown and described herein are presented as options, and additional or fewer relevant input options may be presented to the user as desired and as determined to be proper for promoting the optimal accuracy of calculations and determinations by the present invention.
  • a construction risk base rating can first be determined based upon the project type 311. For example, if the project is related to natural gas power generation, it may be determined to have a base construction phase score of 6.
  • a scoring table 340 such as shown in Fig. 14 can be used in determimng a set of base construction phase scores for various project types. The score is an indication of where in the table of EDF vectors (Fig. 4, for example) a particular risk factor will fall.
  • the construction is performed for a natural gas power plant, which is shown to have a construction phase risk rating equivalent to a Moody's Aal rating, as indicated at 342 in Fig. 14.
  • This Aal rating can be based on historical information or on a proprietary rating system developed in connection with the invention.
  • the equivalent rating score can be obtained by referring to the EDF table shown in Fig. 4, as at 342. This rating is 3, and can then be transferred to a calculation table 344, as shown in Fig. 15, to be used in determining the appropriate EDF vector to use for the construction risk factor.
  • additional points can be added to the base construction phase score based on the inputs previously described, as shown in Fig. 15. For example, if the country in which the project construction is taking place is a developing country, an additional point can be added as at 345 in Fig. 15 to yield a temporary score of 4 for the construction phase. If the country of project construction is developed, no additional point would be added in this embodiment of the present invention. Whether a country is a developing country can be determined by consulting the country risk indicator table in the column designated 345 as shown in Fig. 10. This element can also be represented on the input page of Fig. 9 as at 345.
  • Fig. 15 Depending upon the progress of the construction, additional points can be added as shown in Fig. 15 at 348.
  • the construction progress is determined to be on budget, and thus an additional point is added to the construction risk factor score, giving a total score of four for this factor.
  • a sample table 380 showing the construction progress elements and corresponding scores is shown in Fig. 18.
  • the total score 349 then co ⁇ esponds to a respective set of EDF values for a given risk rating.
  • the Moody's equivalent risk rating is Aa2, as shown at 350 in Fig. 12.
  • the risk score and the Moody's equivalent rating co ⁇ espond to an EDF vector from the table of vectors provided in Fig. 4. This EDF vector is considered the initial Net Construction Risk and can be shown in the EDF vector table of Fig..3 A at 390.
  • This vector can be adjusted based upon the inputs described relating to the sponsor's credit rating 314, the sponsor's equity contribution method 316 and percentage 318, the position of the tranche within the borrower's capital structure 319, the contractor's credit rating 322, the presence or absence of a completion guarantor 332 and their credit rating if present 334, the maximum contractor liquidated damages as a percentage of project cost 326, and the percentage of debt covered by the completion guarantor 336. Scores and ratings for each of the above elements can be obtained by consulting an appropriate scoring table and co ⁇ esponding EDF vector.
  • a sample completion guarantee scoring table 392 is shown in Fig. 19
  • a sample sponsor equity contribution scoring table 393 is shown in Fig.
  • a sample liquidated damages scoring table 394 is shown in Fig. 21.
  • the funding risk given the sponsor funding method would use the score for the type of funding provided by the sponsor as shown in Fig. 20 (in this case, 1.0) and multiply that score by the average EDF vector rating for the sponsor.
  • the funding method score can be 0, 0.5, or 1.0 depending upon whether the sponsor's equity contribution is up-front, pro-rata, or at completion.
  • the final net construction risk is determined by summing the following values: (a) the initial net construction risk multiplied by (1 - the liquidated damages percentage) multiplied by (1 - the percentage of debt covered by the completion guarantor); (b) the contractor default rate (which may be an average of available rates based on the contractor's credit rating) multiplied by the percentage of contractor liquidated damages, multiplied by ( 1 - the percentage of debt covered by the completion guarantor); (c) the completion guarantor default rate (which may be an average of available rates based on the completion guarantor's credit rating) multiplied by the percentage of debt covered by the completion guarantor; and (d) the funding risk percentage given the funding method multiplied by the percentage of construction risk before guarantees.
  • the final net construction risk thus represents a set of EDF values over a given period of time, or the final net construction risk EDF vector, shown at 396 in Fig. 3B.
  • the final net construction risk EDF vector may or may not be used in determining the total commercial risk EDF vector, depending upon whether the project has already begun operating. For example, if the project is already begun production, the construction risk is zero, because the construction is already complete and there is no risk that construction will not be completed.
  • the operating risk element addresses, for example, the risk that the plant operates as designed, project operators mismanage plant operations or forgo required maintenance and further addresses the level of technical difficulty in operating the plant. It can also address the risk present due to the variations in grade of the resulting product (e.g., an oil refinery in California may produce a tar-like oil product while one in Saudi Arabia might produce a smooth oil product - thus, the California refinery would be less efficient as it may require re-processing of the oil product.)
  • the operating risk element further includes the potential for operating cost overruns that impair the plant's ability to service its debt.
  • Elements considered within operating risk factors include the type of project for which financing is sought, as indicated at 311 in Fig. 12.
  • the project may be an industrial transportation project (such as an LNG tanker), an oil and gas extraction project, or a power generation project using natural gas as an energy source.
  • the technology used and whether it is proven or untested can also be input as at 312 into the decision analysis.
  • the system of the present invention first determines a score by finding power generation-natural gas on the supplied commodity table 410, as shown in Fig. 23.
  • This score co ⁇ esponds to a rating on the EDF vector table in Fig. 4, which can be recorded as at 422 in the operating risk scoring table 420 of Fig. 22.
  • the score or rating 422 provided for technical operating risk can be adjusted based upon the location of the project, as further shown in the scoring table 420 of Fig. 22. For example, if the country is a developing country, as discussed previously, a point may be added to the operating risk factor score.
  • the total score 424 can then be co ⁇ elated to a risk rating for which there is a co ⁇ esponding EDF vector, as taken from the table shown in Fig. 4.
  • This vector 430 is the net operating risk vector, and can be represented on an EDF vector table as shown in Fig. 3B.
  • the system of the present invention can consider the primary commodity to be supplied as part of a separate commercial risk element factor related to supplier risk.
  • This commodity can be, for example, water, oil, natural gas, coal, or a petrochemical.
  • the system of the present invention can also include for consideration the transportation requirements for the supplied commodity, which may be sourced on location, sourced intra-state, sourced across state lines, and or sourced internationally.
  • the transportation requirements scoring table 510 can appear as shown in Fig. 24. In general, the easier the transport and closer the supply of the commodity, the lower the associated risk.
  • the system of the present invention first determines a score by finding natural gas on the commodity to be supplied, as shown in Fig. 23.
  • the score or rating provided for supply/supplier risk 515 can be adjusted based upon the transportation adjustment factor score 525 discussed above, as shown in Fig. 25. commodity to be supplied.
  • the net supply risk score 535 can then be co ⁇ elated to a risk rating for which there is a co ⁇ esponding EDF vector, as taken from the table shown in Fig. 4.
  • This vector 545 is the net supplier risk vector, and can be represented on an EDF vector table as shown in Fig. 3B.
  • An additional risk element considered by the present invention can include the off-taker's credit rating 602, which can be used in scoring tables 604, 606 as shown in Figs. 26 and 27.
  • the input page (Fig. 5) allows the user to indicate whether the off-taker is the central host government, or is owned by the central host government as at 610.
  • An off-taker that is a private company will have a different estimated LGD than that of a government off-taker.
  • regional governments can be treated as private enterprises.
  • the lack or presence of an easy substitute off-taker can also be considered as at 612 by the present invention in the overall risk assessment.
  • the present invention can use one or more EDF vectors taken based on the Off Taker's credit rating.
  • the Off Taker's local cu ⁇ ency credit rating may be adjusted based on the presence of an easy substitute off-taker and can be scored as shown in the tables 604, 606 of Figs. 26 and 27, respectively.
  • the Off Taker score may also be adjusted based on whether the government is the off taker and whether there is an easy substitute of an off-taker. For example, if the government is not the principal off-taker, a point can be subtracted from the score established initially by the off-taker credit rating to reflect the fact that operating default rates are less than financial default rates.
  • the off-taker risk factor EDF vector can be taken from the table of EDF vectors as shown by way of example in Fig. 4, and can be added to the EDF vector table shown by way of example in Fig. 3B at 620.
  • Refinance Risk The structure of some loans is a ⁇ anged such that the loan matures within five to seven years, and thus the vast majority of the principal is due at the time of maturity. Knowing this large balloon payment is coming due, most companies will begin the process of refinancing far in advance of the actual loan maturity date.
  • a project can be refinanced - (1) at the project level, where the project is refinanced on a non-recourse basis and remains an independent project or (2) at the parent company level, where the project is refinanced at the corporate level and the asset folded into the corporation.
  • a project can generally get financing if it is rated at a certain level or higher.
  • this hurdle level can be a BB rating under the Moody's rating system.
  • a generic corporate bo ⁇ ower can generally get financing if it is rated B or higher, under a Moody's rating system for example.
  • the present invention considers this refinancing risk by estimating two factors on the day of the balloon payment.
  • the present invention estimates the probability the parent corporation will be rated CCC or lower and neither the project nor the sponsor has defaulted.
  • rating migration matrices are employed to provide this estimation.
  • sample rating migration matrices 710, 720 provides historic probability estimates that a corporation having a given rating will be rated at CCC or lower over time.
  • the present invention estimates the probability the project is rated B or lower and has not defaulted.
  • the RIM model of the present invention estimates the rating of a project each year in the future, based on its expected default rate. Through statistical analysis, the likelihood the default rate is higher than what is estimated can be estimated. With this information, the system of the present invention can estimate the likelihood the project will have a lower rating in the future than what is originally expected, without having reached the point of default.
  • a sample table 710 representing these EDF values is shown in Fig. 28A. If the project has an off taker, the credit rating of the off taker drives the credit rating of the project. In this case, the method of estimating the off taker's future rating being B or below (but not defaulted) is derived from the rating migration method.
  • the lowest EDF vector can be used in the determination of overall commercial factor EDF.
  • the lowest EDF vector is selected because, since the corporate sponsor has multiple options for refinancing, the probability of refinancing failure is the lowest of the probabilities determined.
  • the EDF vector 724 for sponsor refinance risk can be taken from the co ⁇ esponding rating from the historical rating migration matrix 720 shown in Fig. 28B.
  • the EDF vector for project 726 and off- taker 728 refinance risk can be determined in a similar way by referring to the sample tables shown in Figs. 28 A and 28B. Since the corporate sponsor has multiple options for refinancing, the total refinancing risk is the lowest of the EDF vectors obtained, as shown in Fig. 29 at 730, for example. If there is no off-taker, the total refinancing risk is the lower of the EDF vectors between the project refinancing risk and the sponsor refinancing risk.
  • the present invention can receive inputs related to the base lending rate 750 and the booking point 752.
  • the base interest rate 750 is the benchmark rate to which the loan margin is added. In most cases, this is LU3OR (London Interbank Offered Rate) or the bank bill rate.
  • the booking point 382 defines the after tax hurdle rate 754 and the effective tax rate 756, which are key components in the NIACC computation. As shown in Fig. 7B, this information can be provided in a table 760.
  • the present invention also considers the additional commercial risk factor related to macro-economic elements which can affect the financial performance of the project. This can include a fluctuating market price of commodities, interest rates, foreign exchange rates, a fall in the demand of project off-take, or a rise in the price of project inputs, for example.
  • the most potent risk mitigant in a project loan is a strong economic under-pinning. Contracts cannot remove risk, only shift it to participants who are better able to manage or tolerate it. There will be an economic incentive for contracts to be broken, if the terms of the contract become uneconomic by the disadvantaged party.
  • the risks and loss estimates due to the movements of macro-economic factors are computed in a Monte Carlo analysis of the project's cash flows separate from the Risk Integration Model of the present invention.
  • a Monte Carlo analysis is a specific type of modeling simulation which samples from thousands of scenarios randomly chosen which are consistent withjhe historical behavior of the economic variable in question to help predict how a system will behave over time.
  • the Monte Carlo analysis is intended to quantify how robust the project economics are.
  • One of the powerful advantages of the technique is that it can uncover potential problems or risk concentrations. As a result, it can provide some insight on project structures that minimize that risk.
  • the cash flow model is stressed with the random scenarios generated to determine the likelihood that the debt of the project cannot be serviced. If a default scenario is found, a default is tallied in the year of default.
  • the LGD is estimated by examining the free cash flow of the project cash flows given the stressed scenario from the point of default onward. The present value of those cash flows are computed using a distressed debt discount rate. This value is reduced further by the country LGD derived from the country risk index described earlier (as an estimate of the cost of recovery, the cost of interference by local government and/or the degree to which an independent judicial system and bankruptcy law exists).
  • the value of the net cash flows is compared to the debt outstanding. If the value of the cash flows is less that the value of debt, the LGD is recorded as a percentage of debt outstanding.
  • Parametric Factors for Model Input Modeling a Macro Economic Factor requires the definition and calibration of a number of parameters. Those factors first include the probability distribution, which in one embodiment of the invention can be defined as either normal or log-normal. In another embodiment of the invention, such as for off-take volumes, for example, a triangle distribution can be employed. The second factor is the current market price of the variable. The third factor is the long term equlibrium value, or the price the commodity is likely to gravitate to in the long run. This factor can be an estimate from an established authority or from historical data. Also considered is the volatility estimate, or the uncertainty of future prices. This factor can also be determined by an established authority or from historical data.
  • Figs. 8 A and 8B show one embodiment of an input page 830 which can be used in accordance with the present invention to determine a default probability associated with macro-economic factor risk.
  • One embodiment of the present invention incorporates an analysis based on equal time intervals, generally annual time periods.
  • an analysis is conducted over unequal time intervals.
  • the data collected from the Monte Carlo analysis is unique to the project, and includes an EDF, LGD and a Standard Deviation of LGD which will differ for each given time period, year by year.
  • EDF is the Annual Expected Default Frequency as indicated at 802
  • LGD is the Loss Given Default as indicated at 804
  • Standard Deviation of LGD is the variability of LGD, as indicated at 806.
  • the draw down schedule and the amortizing schedule for the loan tranche being analyzed can be entered as at 808.
  • drawdowns are entered as negative numbers and principal repayments are entered as positive figures.
  • the drawn amount can be based on the final hold estimate.
  • origination, commitment and agency fees have a substantial impact on net income after cost of capital (NIACC) and risk-adjusted return on capital (RAROC).
  • NIACC net income after cost of capital
  • RAROC risk-adjusted return on capital
  • the pro-rata amount (based on exposure) of origination fees can be entered. In this way, fees may be properly included as the loan ages.
  • the value of the escrow account can be considered, for example, if the terms of the contract call for an offshore escrow account and funding for this account comes from outside the country.
  • An offshore account is a loss mitigant if cu ⁇ ency default occurs. For it to be effective, the source of funds must be out of reach of the local government and held overseas so that they cannot be subject to local government interference.
  • the base interest rate and the loan margin are also included. Since the loan margin generally changes over the life of the loan, it must be input on an annual basis. In one embodiment of the invention, for a project having debt tranches with different maturity dates, insurance coverage, loan margins, and the like, a separate RJM analysis can be performed for each tranche.
  • Crystal Ball a user can define what variables are to be stochastically generated. The user can control the distribution, the mean, the variance and the co ⁇ elation. In one embodiment, an add-on application such as Crystal BallTM is used to conduct the Monte Carlo analysis.
  • the EDF of the project can be determined in any year during the life of the project, given its financial structures (i.e. contractual arrangements, hedges, reserve accounts, etc.). This is possible because of the known probability distribution and the parameters (current price, long-term equilibrium price, variance, and rate of mean reversion) that describe the behavior of the exogenous macro economic variables.
  • the probability distribution is applied to all the exogenous economic variables and run through the cash flow model.
  • PRI political risk insurance
  • CRT commercial risk insurance
  • PRI & CRT comprehensive risk insurance
  • a project with no risk insurance can be called "clean" or uncovered.
  • ECAs export credit agencies
  • GOEs government owned enterprises
  • guarantees provided by private entities are becoming increasingly common.
  • PRI can help limit the exposure to cross border defaults, including the inability or unwillingness of the host government to provide hard cu ⁇ ency (through its central bank). PRI can also help limit the exposure to expropriation, or the possibility the host government will nationalize the business, either directly or through regulation - (i.e. political violence). Further, PRI can help limit the exposure due to war, wherein the project is unable to service its debt due to war or other political disruptions (also refe ⁇ ed to as Political Violence). In general, for lenders to benefit from PRI due to the lack of available foreign exchange reserves by the local central bank, the project must be able to generate local currency (that is it must not be in commercial default). In the prefe ⁇ ed method, the PRI insurer accepts the local currency and pays the lenders in the appropriate cu ⁇ ency.
  • PRI can be structured to cover off-taker performance, if the off- taker is a government entity. Properly structured, the PRI can be used to ensure the GOE adheres to the terms and conditions of the off-take contract. In such a circumstance, non-performance can be claimed against the expropriation clause of the PRI contract.
  • This type of guarantee is refe ⁇ ed to as Extended PRI or a Partial Risk Guarantee, and is a choice in the model of the present invention.
  • CRI can help limit exposure to defaults caused by operating failure, supply or supplier disruptions, and off-taker default, as well as defaults caused by macro- economic factors such as a loss of sales volume, drop in price, spike in interest rates, etc.
  • the provider of CRI will present local cu ⁇ ency to the project or central bank of the host country for conversion to the appropriate cu ⁇ ency. If the host government cannot or will not provide hard cu ⁇ ency, lenders are stuck with payment in the cu ⁇ ency the project has to offer.
  • Guarantees from the contractor or guarantees purchased from a private third party generally cover construction risk.
  • the present invention accommodates consideration of the type of guarantor and the type of insurance.
  • the type of insurance can be entered as at 902.
  • the selection can be Comprehensive, PRI, CRI, or Clean.
  • "Clean” means that no insurance or guarantees have been provided.
  • the system can also accept as input whether the loan is part of a "B" loan program, as at 904.
  • the International Finance Corporation IFC - the World Bank's private lending arm
  • IDB Inter-American Development Bank
  • ADB Asian Development Bank
  • the lender of record for "B" loans is the sponsoring Multilateral Agency giving the loans prefe ⁇ ed lender status.
  • Projects may further be characterized as having only a percentage of the project loan insured. For example, a given project loan may be insured 80% through comprehensive coverage, 10% through PRI, and 10% can be clean. This information can be entered as at 908 in Fig. 5B.
  • the CRI details can be left blank. If the tranche is covered with CRI only, the PRI details can be left blank. If the tranche has comprehensive coverage, the details for both PRI and CRI can be input into the system.
  • the provider of the insurance can also be specified as at 910, as well as the start and end date of the insurance coverage, as at 912. With regard to the term, the year in which the project begins operation is important because it affects when construction risks end and operating risks begin. If early repayment is expected, the expected call date can also be entered and the RIM of the present invention will perform an analysis to both the call and maturity dates.
  • Provider identification 910 can be taken from a separate table shown in Fig. 7 A which can have the provider 910 identified along with a co ⁇ esponding risk rating 920.
  • the type of insurance can have an associated EDF vector, as shown generally at 925 in Fig. 29 and can be considered in the determination of the overall project EDF as shown in Fig. 30B at 930.
  • the credit rating of the gaurantor can be discounted to take into account difficulties in the structure of the guarentee contacts (e.g. the inability to appeal a denied claim).
  • the cumulative EDF for each insurance piece can be determined. This involves determining joint commercial and country EDF values. Joint commercial and country risk values represent the possibility that a project defaults for both country reasons and commercial reasons. By the present invention, consideration is given to the joint risk of supplier default and currency inconvertibility default, off-taker default and cu ⁇ ency inconvertibility default, and macro-economic causation of default and cu ⁇ ency inconvertibility default. Other joint risks may be considered as deemed appropriate. Computations can be performed to determine these joint probabilities, driven by co ⁇ elation between risk factors, and the resulting EDF vectors can be combined with the individual risk factor EDF vectors in a table for each type of insurance coverage, an example of which is shown in Figs. 30A and 30B.
  • the commercial risk factor EDFs can be grouped as at 250, the country risk factor EDFs can be grouped, as at 170, and the joint risk factor EDFs can be grouped as at 180. Calculations can be performed to obtain total EDF for each period, as at 185, and a cumulative value can be obtained, as at 190. Additional insurance factor EDF's 930 can also be included. These values are obtained for each of the insurance types involved, and a final calculated value for EDF for the project loan can be determined and output on a summary page, as shown by way of example at 195 in Fig. 3 IB.
  • the present invention thus considers all risk factors, singly and in combination, that can cause a project to default and thereby result in the inability to pay back a loan. From the EDF values obtained, other measures can be determined which further assist in the loan decision-maker's analysis.
  • LGD Loss Given Default
  • the ways a project can default include contractor engineering and construction default, operating default, supplier default, off-taker default, default for macro-economic reasons, default through inability to obtain refinancing, political violence, cu ⁇ ency inconvertibility, and combinations of several of the above.
  • LGD can be estimated according to various assumptions.
  • the default Operating, Supplier and Offtaker LGDs are derived in part, from historical statistics. Three factors considered to drive LGD are (1) where the loan stands in the capital structure (see table 855 in Fig. 34); (2) the industry involved with the project (for example, senior unsecured utilities have a historical loss rate far below the LGD for senior unsecured financial institutions); (3) financial leverage; and (4) the physical location of the project. For example, the LGD for a project with a book debt/equity ratio of 80% will be higher than the LGD for the same project with a debt/equity ratio of 50%. In accordance with one embodiment of the present invention, to compute LGD through time, the LGD from the table 855 in Fig. 34 is taken and multiplied by the fraction of debt outstanding/original debt amount.
  • LGD values can be adjusted based on factors such as, for example, the LGD for a refinance failure being generally thought of as less than the LGD for operating or supplier default.
  • the LGD for a refinance failure can be considered as 50% of the LGD for operating or supplier default in one embodiment of the present invention. This is because the nature of the loss is different.
  • the lenders may get 100% of their principal back, but may have to wait years to get that recovery and may earn an interest rate below market levels.
  • LGD can be determined by examining the cash flow generating ability of the project once default occurs. Default will generally occur given a difficult economic environment. To determine LGD, the cash flow generating power of the project is considered given this tough operating environment.
  • the LGD can be determined by first estimating the free cash flow of the project before debt service, discounting those cash flows at the appropriate rate reflecting the distressed nature of the project to determine the residual value of the cash flows.
  • the interest rate used can be the base rate plus the highest margin of all the tranches in the deal + 800 basis points. In a specific embodiment, such as for a mining project, for example, approximately 900 to 1,000 basis points can be added since recovery can be more difficult in mining projects. Next the cost of restructuring
  • the LGD can be computed through the following steps: (1) Compute the present value of the projects free cash flow after tax; (2) Compute the ratio of the PV - Free Cash Flow/Value of Debt; and (3) Take (2) above and subtract a cost of recovery (this percentage is the same as a cross border default LGD). Note that this is the LGD of just one scenario.
  • the LGD estimate for any one-year is the average of all LGDs observed for that year.
  • Country LGD Country risks are important to identify because, when default occurs, the cost of recovery is impacted by the physical location of the project. All things being equal, the amount lenders can recover in a developing country is far lower than the amount that lenders can expect to recover if the project were in a developed country. Such factors as government interference, corruption in the legal system or simply the lack of transparency or a bankruptcy law, for example, all contribute to the cost of loan recovery. How a project fits into the economic development plans of the host government, the financial participation of influential third parties, and the project's ability to generate foreign cu ⁇ ency reserves or reduce the country's need to spend foreign currency reserves on imports, can have an effect on the recoveries during the work-out of a commercial default.
  • the country's particular CRI rating can also be used, along with other items, to determine both a political violence loss factor and cross border loss factor for the given country.
  • a low CRI co ⁇ esponds to a low recovery rate (i.e. high LGD), while a high CRI corresponds to a high recovery rate (i.e. low LGD).
  • the political violence LGD is unadjusted for mitigants.
  • a score can be determined based upon whether the guarantee is from a multilateral (such as ADB, IDB, IFC, World Bank, etc.), an ECA (Export Credit Agency) or a local bank, whether the guarantee includes participation by a local bank, whether the guarantee is from the sponsor, or whether the guarantee is non-existent.
  • a multilateral such as ADB, IDB, IFC, World Bank, etc.
  • ECA Export Credit Agency
  • the national importance factor 140 also can present a score depending upon whether the project is a critical export (such as oil and gas, or mining, for example), an import substitution (such as fertilizer, for example), a critical domestic (such as water, power, telecommunications, for example), a moderate (such as automobile or steel manufacturing, for example), or a marginal project (such as a tooth paste factory, for example). Once classified, the appropriate category can be added to the input page of the present invention.
  • a critical export such as oil and gas, or mining, for example
  • an import substitution such as fertilizer, for example
  • a critical domestic such as water, power, telecommunications, for example
  • a moderate such as automobile or steel manufacturing, for example
  • a marginal project such as a tooth paste factory, for example
  • the cross border LGD can be estimated based on a statistical analysis of historical country defaults and reschedulings confroling for such factors as (but not exclusively) reserves relative to imports, GDP per capita and total debt stock relative to exports. The relationship can give an expected cross border
  • this can be derived from the Euromoney Country Risk Index, an example of which is shown in the table 120 in Fig. 10.
  • LGD Political Violence (100-EM CRI).
  • Cross Border LGD LGD PV ""(Guarantee Factor)*(National Importance factor).
  • the guarantee factor 136 and the national importance factor 140 can be combined with the Political Violence LGD to determine Cross Border LGD.
  • Fig. 11 shows an example table which can be used in determimng Cross Border LGD.
  • LGD For cross-border LGD, historical loss rates can be used. Bank debt is renegotiated at the London Club. Loss in rescheduling can occur by a direct write down of the debt or a reduction in the interest charge. It can also take the form of an elimination of interest for a specified period of time. Based on an analysis of these rescheduling, the LGD can be explained by
  • LGD Joint 1- (1-LGD[1])(1-LGD[2]).
  • a table can be developed in connection with the present invention which represents LGD for each insurance piece.
  • Fig. 35 shows an example of such a table 850.
  • the total LGD factor can be determined and reported on a summary page as shown at 196 in Fig. 31 A.
  • LGD is shown as a weighted average.
  • Standard deviation is estimated by defining a 100% loss as a 4 standard deviation event.
  • Standard deviation (100%-LGD)/4. This method is used for all risk factors except for the Macro Economic risks.
  • an LGD is computed for each year. This represents the average LGD observed for all the scenarios run.
  • the standard deviation of LGD is computed by applying the traditional standard deviation equation to the LGDs observed in the Monte Carlo exercise.
  • other measures which are determined by the system and method of the present invention include the unexpected loss, the expected loss, the volatility of the loss given default. These measures can be determined in connection with cash flow, exposure amount, and other elements as described herein, and can be represented in tables 50, 51, and 52 as shown, for example, in Fig. 36 (expected loss), Fig. 37 (volatility of LGD), and Fig. 38 (unexpected loss), respectively.
  • the system of the present invention can apply the standard deviation function to the percent LGD for each defaulted scenario.
  • a computer system having a user-defined function can be employed to perform this calculation.
  • the LGD and standard deviations of LGD can be tabulated in an assumptions rating table 54 having associated vectors as shown in Fig. 41, for example.
  • Figs. 31A, 31B and 31C show sample results pages (shown at 970, 972, and 974, respectively) in accordance with the present invention.
  • the system, of the present invention can produce a series of graphs which describe the performance of the loan over its contracted life.
  • Figs. 43 through 50 are examples of these graphs.
  • the EDF chart 980 gives a pictorial view of the default risk profile of a sample project over time. The charts demonstrating the risk and profitability profile of the loan can include the contribution of the insurance coverage.
  • Fig. 44 shows an LGD chart 982 giving a pictorial view of the Loss Given Default profile of a sample project over time.
  • the exposure profile 984 shows the contracted drawn loan amount over the life of a sample project.
  • the Expected Loss chart 986 gives a pictorial view of the changing Loss Profile over the life of a sample project.
  • the Economic Capital (EC) chart 988 gives a pictorial view of the amount of capital that should be put aside to support the default risk of a sample loan. In most cases, EC will be the highest at or near the point of maximum EL. As shown in Fig.
  • Risk Adjusted Yield 990 is defined to be the Nominal Yield less EL (dotted line) and represents the income the lender can expect to earn on the sample loan on a risk adjusted basis. Should the credit risk of the loan improve over time, the risk of early repayment increases, reducing the probability the lender will earn a high margin.
  • Risk and Call Adjusted Yield is defined to be the Risk Adjusted Yield less the Risk of Prepayment (solid line) and this represents the return which can be expected on a risk and call adjusted basis.
  • Risk and Call Adjusted Margin (Fig. 49) repeats this analysis examining the loan margin only, as shown by way of example at 992.
  • the present invention can be used to analyze provisioning and profitability measures. For example, once EDF, LGD, and VoL have been determined in connection with a prospective loan, provisioning and economic capital measures can be determined, as well as net income after cost of capital (NIACC), risk-adjusted return on capital (RAROC), and return on assets (ROA).
  • NIACC net income after cost of capital
  • RAROC risk-adjusted return on capital
  • ROA return on assets
  • Figs. 40A, 40B and 40C show respective portions 967 A, 967B, and 967C of a sample summary results page showing sample values for various measures.
  • NIACC as a percentage of principal balance provides an analysis of the profitability profile of the loan over its life. Since EL and Economic Capital rise over the life of this loan, NIACC falls over time. This is shown in sample output graph 994 in Fig. 50.
  • Additional measures such as an overall customer credit rating and a shadow customer credit rating can be taken from columns 87 and 89, respectively in Fig. 4, and reported as shown in Fig. 3 IB.
  • a loss indicator rating, and a security indicator rating can be reported.
  • the security indicator rating can be based upon what assets the lender has security over which can be taken in a bankruptcy, including the percentage of assets covered, for example.
  • the loss indicator rating and the security indicator rating can also be based upon the LGD value obtained earlier, and can be represented as shown in tables 91 and 93 in Figs. 32 and 33, respectively.
  • Net income after cost of capital can be determined as follows. For any one year, NIACC is computed by taking the net margin and subtracting expected loss and economic capital charge. Net margin equals the gross rate less a base rate, such as LIBOR. Economic capital charge equals the product of economic capital and the required return on equity. This determination can be represented as:
  • NIACC NIACC computation page 965 is shown in Figs. 39A and 39B.
  • loans are examined on an annual basis and on an equal time period basis.
  • computations can be conducted on a semi-annual, quarterly, monthly, or other time period basis.
  • the present invention can also be adapted to handle uneven time periods.
  • the Return on Equity is the internal rate of return on cash flows on lender equity.
  • the economic capital represents the equity the bank must put up at the beginning of the period under review. This is cash out flow.
  • Cash inflow is equal to the return on capital plus a return of the capital put up.
  • Fig. 42A shows a sample NIACC computation in table 67. Computing Return on Assets
  • Return on assets is computed by taking the ERR of Gross Cash Flows and subtracting the Base Lending rate (such as LIBOR). In essense, it is the average margin, plus fee income spread over the life of the loan (even if it is paid all in 1 period). If Fees are zero, Return on Assets is simply the average loan margin. Fig.
  • the nominal cash flow of the fees is shown as different in the two computations.
  • To compute Return on Assets the actual fees and payment date of those fees in the computation can be used.
  • Computing return on equity the cash flows over the life of the loan can be spread out assuming the fees are deposited in a bank account, earning the base rate (LIBOR) and paid out over time weighted by the loan amount outstanding.
  • the bank does this to spread the fee income out over the life of the loan. From a practical standpoint, it is possible to have fees in excess of the economic capital requirement. When this occurs, the loan is essentially self- capitalizing, resulting in an infinite ROE. The smoothing eliminates this possibility in all but the most extreme cases.

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Abstract

L'invention concerne un système et un procédé permettant d'aider les prêteurs à prendre des décisions concernant des prêts sans recours. L'invention concerne également un modèle qui considère chaque risque relevant de la détermination d'un prêt, notamment les facteurs de risque commerciaux (200) ou de risque-pays (100). A partir de cette analyse, cette invention permet de déterminer la fréquence estimée de défaut de paiement (EDF), la perte en cas de défaut de paiement donné (LGD), la volatilité de la perte, et de faire des recommandations de dépenses en provision totale et en capital économique au prêteur du prêt sans recours donné. A partir de ces informations, on peut également utiliser ladite invention pour déterminer un taux de crédit et des mesures de profitabilité (60) pour le prêt donné.
PCT/US2001/042764 2001-10-16 2001-10-16 Systeme et procede permettant d'analyser le risque et la profitabilite de prets sans recours WO2003034309A1 (fr)

Priority Applications (4)

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US10/344,550 US20050262013A1 (en) 2001-10-16 2001-10-16 System and method for analyzing risk and profitability of non-recourse loans
EP01983206A EP1436740A4 (fr) 2001-10-16 2001-10-16 Systeme et procede permettant d'analyser le risque et la profitabilite de prets sans recours
AU2002214652A AU2002214652A1 (en) 2001-10-16 2001-10-16 System and method for analyzing risk and profitability of non-recourse loans
PCT/US2001/042764 WO2003034309A1 (fr) 2001-10-16 2001-10-16 Systeme et procede permettant d'analyser le risque et la profitabilite de prets sans recours

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Cited By (3)

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US6772886B2 (en) 1999-12-02 2004-08-10 Giesecke & Devrient Gmbh Device for sorting bills
US6856973B1 (en) * 1999-12-29 2005-02-15 General Electric Capital Corporation Methods and systems for assessing creditworthiness of a country
US20140324673A1 (en) * 2013-04-30 2014-10-30 Bank Of America Corporation Cross Border Competencies Tool

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US5774833A (en) * 1995-12-08 1998-06-30 Motorola, Inc. Method for syntactic and semantic analysis of patent text and drawings
US6202053B1 (en) * 1998-01-23 2001-03-13 First Usa Bank, Na Method and apparatus for generating segmentation scorecards for evaluating credit risk of bank card applicants
US6311169B2 (en) * 1998-06-11 2001-10-30 Consumer Credit Associates, Inc. On-line consumer credit data reporting system

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US5774833A (en) * 1995-12-08 1998-06-30 Motorola, Inc. Method for syntactic and semantic analysis of patent text and drawings
US6202053B1 (en) * 1998-01-23 2001-03-13 First Usa Bank, Na Method and apparatus for generating segmentation scorecards for evaluating credit risk of bank card applicants
US6311169B2 (en) * 1998-06-11 2001-10-30 Consumer Credit Associates, Inc. On-line consumer credit data reporting system

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6772886B2 (en) 1999-12-02 2004-08-10 Giesecke & Devrient Gmbh Device for sorting bills
US6856973B1 (en) * 1999-12-29 2005-02-15 General Electric Capital Corporation Methods and systems for assessing creditworthiness of a country
US20140324673A1 (en) * 2013-04-30 2014-10-30 Bank Of America Corporation Cross Border Competencies Tool

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