Quantifying qualitative information on risks (QQIR) in structured finance transactions
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How to quantify risks in the absence of historical data or time series?
This book develops a methodology to quantify expert opinion on risk factors which have significant impact on cash flow projections in life-cycle infrastructure projects. It applies fuzzy set theory, stochastic simulation, and robust statistics to formalize the "QQIR-Method", which bridges the gap between qualitative and quantitative risk assessment methods. The QQIR-Method is then applied to political risks in infrastructure projects in Asia. The book contains a number of case studies and survey interviews with tier-1 market participants. It also provides an easy to use practical guide to apply and replicate the QQIR-Method in ones own professional field. |
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Abstract
Risks can impair the success of business transactions. Structured finance transactions are exposed to numerous risks.
Some of these risk factors are well studied. They have sufficient historical and numerical data and record to allow for projections and quantifications of their possible impact on the transaction. Other risk factors may lack such information and projections for quantification become difficult. A group of experts may have opinions on such risk factors. For quantifying these perceptions on risk factors, this doctoral research proposes a new methodology for quantifying qualitative information on risks (QQIR) in structured finance transactions. It contributes to the set of risk assessment methods by closing the gap between qualitative and quantitative risk assessment methods and adds value to all transaction participants.
The proposed QQIR method is a fuzzy set approach which allows the deriving of customized probability density functions based on expert opinion as well as ranking of such aggregated opinions. It is the interface between opinions experts generate based on available information in the market and stochastic cash flow modeling and simulation and other qualitative assessments.
In this research, the QQIR method is derived from theory, validated and tested through a survey, and applied in two case studies. All data in this research is primary data.
The contribution to the sphere of knowledge is the novel development of the QQIR method. The QQIR method is a systematic, comprehensive, and mathematically thorough approach for translating expert opinions on risk factors into customized probability density functions that can be used for stochastic simulations, rankings, or other applications.
In validating the QQIR method, the QQIR method has been used to quantify the perceived impact of political risk factors on financial decision criteria in project finance. The financial criteria were the expected internal rate of return, the project leverage, the risk premium on project loans, the minimum required debt service coverage ratio, and the insurance premium. The impact was assessed through an international survey across 14 Asian countries and 14 infrastructure sectors. The results of this QQIR survey assessment were then compared with absolute values that were also collected in the same survey. The two survey results were validated by triangulation with general country and sector risk perceptions, also collected in the same survey. The validation shows that in 77.5% of all observations, the QQIR method produces mean results that are within 0.85 standard deviations from the absolute values. Also the validation shows that with increasing perceived risks, costs of equity and debt finance as well as insurances increase as well.
The QQIR method has been applies in two case studies. The application supports its validity and commercial benefit.
The QQIR method has been applied for assessing the impact of governmental actions on demand and pricing in a power project in Asia. The impact was quantified as change in investment return ratios. The case study was carried out under confidentiality agreement with a Japanese power developer. This allowed full access to confidential financial data and contracts as well as decision makers involved. The presented information is not business sensitive.
In the second case study, the QQIR method has been applied to assess the risk exposure and recovery potential of the involved parties in a guarantee contract in a water project in another county in Asia. The case study was carried out under confidentiality agreement with the Asian Development Bank (ADB). This also allowed full access to confidential financial data and contracts as well as decision makers involved. The presented information is not business sensitive.
The commercial benefit and contribution of the QQIR method to risk assessment has been thoroughly demonstrated by its validation and application in the two case studies.
In the course of the development of the QQIR method, the relevance of its development, its modules, use-friendliness, applicability, and fit in the exiting set of tool boxes of risk assessment has been thoroughly discussed in numerous individual and group presentations with investors, lenders, developers, lawyers, financial advisers, and insurers from the private and public sector in Asia and Europe.
Some of these risk factors are well studied. They have sufficient historical and numerical data and record to allow for projections and quantifications of their possible impact on the transaction. Other risk factors may lack such information and projections for quantification become difficult. A group of experts may have opinions on such risk factors. For quantifying these perceptions on risk factors, this doctoral research proposes a new methodology for quantifying qualitative information on risks (QQIR) in structured finance transactions. It contributes to the set of risk assessment methods by closing the gap between qualitative and quantitative risk assessment methods and adds value to all transaction participants.
The proposed QQIR method is a fuzzy set approach which allows the deriving of customized probability density functions based on expert opinion as well as ranking of such aggregated opinions. It is the interface between opinions experts generate based on available information in the market and stochastic cash flow modeling and simulation and other qualitative assessments.
In this research, the QQIR method is derived from theory, validated and tested through a survey, and applied in two case studies. All data in this research is primary data.
The contribution to the sphere of knowledge is the novel development of the QQIR method. The QQIR method is a systematic, comprehensive, and mathematically thorough approach for translating expert opinions on risk factors into customized probability density functions that can be used for stochastic simulations, rankings, or other applications.
In validating the QQIR method, the QQIR method has been used to quantify the perceived impact of political risk factors on financial decision criteria in project finance. The financial criteria were the expected internal rate of return, the project leverage, the risk premium on project loans, the minimum required debt service coverage ratio, and the insurance premium. The impact was assessed through an international survey across 14 Asian countries and 14 infrastructure sectors. The results of this QQIR survey assessment were then compared with absolute values that were also collected in the same survey. The two survey results were validated by triangulation with general country and sector risk perceptions, also collected in the same survey. The validation shows that in 77.5% of all observations, the QQIR method produces mean results that are within 0.85 standard deviations from the absolute values. Also the validation shows that with increasing perceived risks, costs of equity and debt finance as well as insurances increase as well.
The QQIR method has been applies in two case studies. The application supports its validity and commercial benefit.
The QQIR method has been applied for assessing the impact of governmental actions on demand and pricing in a power project in Asia. The impact was quantified as change in investment return ratios. The case study was carried out under confidentiality agreement with a Japanese power developer. This allowed full access to confidential financial data and contracts as well as decision makers involved. The presented information is not business sensitive.
In the second case study, the QQIR method has been applied to assess the risk exposure and recovery potential of the involved parties in a guarantee contract in a water project in another county in Asia. The case study was carried out under confidentiality agreement with the Asian Development Bank (ADB). This also allowed full access to confidential financial data and contracts as well as decision makers involved. The presented information is not business sensitive.
The commercial benefit and contribution of the QQIR method to risk assessment has been thoroughly demonstrated by its validation and application in the two case studies.
In the course of the development of the QQIR method, the relevance of its development, its modules, use-friendliness, applicability, and fit in the exiting set of tool boxes of risk assessment has been thoroughly discussed in numerous individual and group presentations with investors, lenders, developers, lawyers, financial advisers, and insurers from the private and public sector in Asia and Europe.