Combined Credit And Political Risk Paper

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Combined Credit And Political Risk Paper

  1. 1. An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance Athula Alwis, Simon Ying and Vladimir Kremerman Global Credit, Surety and Political Risk Practice and Willis Analytics Willis Re Abstract: Emerging market combined credit and political risk insurance covers risks associated with non-payment, bankruptcy and credit default of obligors in emerging market countries, whether it is triggered due to either commercial credit risk or political risk. Political risk is concerned with the risk associated with government intervention and restriction of trade and investment into emerging markets. It may encompass long-term perils (investment related), such as the confiscation, expropriation or nationalization of an infrastructure project in an emerging market, or short-term perils (export trade related) such as contract frustration, embargo or currency inconvertibility. Thus, the real risk is a combination of commercial credit risk and emerging market political risk. Cross border transactions to, from and between developing / emerging markets have increased exponentially over the last ten years. Exporters and investors from the developed countries assume billions of dollars worth of credit exposure every year. This paper will examine emerging market combined credit and political risk from a financial modeling perspective and propose a new methodology based on “jump diffusion” to quantify the risk reward profile. It will recommend a stochastic modeling process to develop economic capital requirements to support this potentially catastrophic risk. Key Words: Correlation, Country Risk Ratings, Credit Risk, Default Rates, Emerging Markets, Export Credit, Gaussian Copula, Jump Diffusion, Political Risk, Pure Diffusion, Recovery Rates, Merton Model, Reinsurance, Sovereign Ceiling, Student Copula, Trade Credit An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance -1-
  2. 2. Robert Merton “At times we can lose sight of the ultimate purpose of the models when their mathematics become too interesting. The mathematics of financial models can be applied precisely, but the models are not all precise in their application to the complex real world. Their accuracy as a useful approximation to that world varies significantly across time and place. The models should be applied in practice only tentatively, with careful assessment of their limitations in each application.” An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance -2-
  3. 3. Section 1: Introduction The practitioner’s ability to reasonably quantify and manage credit risk in an analytical framework has existed for close to 20 years. The 2001 paper by David Li is one of the most important contributions to credit analytics since Robert Merton’s seminal work in early 1970s. Fischer Black, Myron Scholes and Robert Merton developed and enhanced the “Black-Scholes” option pricing model based on the premise that a company’s equity investors have a call option on the firm’s value. Vasicek-Kealhofer’s structured model in 1989, Jarrow-Turnbull’s reduced form model in 1995 and Duffie-Singleton’s 1999 model have been major milestones in the history of credit modeling. The advancement of powerful computers and computing techniques has tremendously helped analysts apply mathematical concepts developed decades ago with accuracy and efficiency. David Li’s work showing how to incorporate a Gaussian copula was an inspiration in credit modeling and paved the way for broader and more sophisticated credit modeling techniques. In contrast, the analysis of political risk reinsurance received very little attention until the Argentina crisis in 2001 and the subsequent exodus of capacity from the insurance and reinsurance market. In the early part of this decade, several reinsurers began the arduous task of quantifying political risk associated with an investment, loan or trade portfolio. Willis Analytics completed Phase I of its industry political risk study in 2006 to develop the first ever comprehensive industry analysis of default rates, recovery rates and a correlation matrix for this class of business. Please refer to Appendix A for a definition of political risk. Until now, there have been no research papers on how to analyze combined political risk and credit risk. At the same time, the cross border transactions in global trade have become the fastest growing insurance sector within both credit and political risk in the world today. In a confluence of credit modeling, political risk theory and option pricing theory, this paper presents a realistic stochastic methodology to model combined credit and political risk reinsurance for an emerging market portfolio. Section 2: Combined Political and Credit Risk Historically, the trade and commodity finance markets supported the imports and exports of emerging market governments and state-owned entities. The privatization in emerging markets, mainly BRIC countries (Brazil, Russia, India and China) over the last ten years, has introduced a need for additional insurance protection. Trade and commodity finance, involving cross border transactions, now requires not only traditional credit protection covering insolvency, credit default and non-payment, but also political risk insurance covering contract frustration, currency inconvertibility / exchange transfer, expropriation and war / political violence. In trade finance, practitioners use insurance to mitigate two main types of risk. The risk of insolvency and non- payment represented by a local buyer in an import transaction can be mitigated by a local bank that will issue a payment guarantee to the exporter to cover the imported goods or services. Then the risk of non-honoring the guarantee by the local bank can be transferred to an insurance company through a combined political risk and credit risk policy. A multi-national bank’s risk in a pre-export finance transaction into an emerging market country can be mitigated through a combined political risk and credit cover issued by an insurance company. There are trade-driven commodity finance transactions that require political and credit risk protection provided by insurance entities. A large portfolio of these insurance transactions can be pooled together and structured to obtain risk transfer and capital relief. An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance -3-
  4. 4. Section 3: The Analytical Process Frequency Severity Pair-wise Correlation Combined Political Risk & Credit Model Loss and Counts Distribution Portfolio Analysis Reinsurance and Capital Markets Value Based Capital Management Structured Solutions There are three main drivers of the Combined Political Risk and Credit Model (“the model”). 1. Frequency: Probability of Default 2. Severity: Loss Given Default (1 – Recovery Rate) 3. Correlation: Contagion Risk (Propensity of defaults to group together) The mechanics of this modeling structure are based on modern collateralized debt obligations (CDO) technology. One key difference is that the underlying risks here are transparent, thus avoiding the concerns of traceability evident in the CDO market. Rating agencies have historically developed credit default rates that have been predictive for the most part. Several insurers, reinsurers and intermediaries have attempted to develop political risk default rates based on historical data. However, default rates combining both political risk and credit risk have not yet been developed. The main technical goal of this paper is to present two methodologies to produce a framework for combining individual default rates for credit risk and political risk into a joint default rate. Severity distributions and correlation assumptions for pure credit risk have been analyzed and published by numerous practitioners and academics over the last decade. For political risk, Willis Analytics conducted an industry study based on 40 years of data to develop reasonable default rates, loss given default (severity) indications and a correlation matrix based on regional contagion. The remainder of the paper will focus on two approaches for developing a joint credit / political risk default rate based on these separate default parameters. An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance -4-
  5. 5. Section 4: Diffusion Process – A Conservative Approach The simplest method to represent combined credit and political risk frequency is to sum the default rates and adjust for double counting by subtracting the probability of both events occurring at the same time. Pr (A U B) = Pr (A) + Pr (B) – Pr (A ∩ B), where Pr (A) is the default rate for credit risk excluding political risk, while Pr (B) is the default rate for political risk. The part that represents the intersection (i.e., both events occurring at the same time: Pr (A ∩ B)) can be calculated using the two separate default rates and a copula function that describes their dependency. Please refer to Appendix B for a technical explanation of how to combine these default rates using a Normal copula. This is a technically sound starting point to model a structure. However, this model does not accurately reflect the coverage offered under emerging market combined credit and political risk insurance. While any non- political related credit default (A) is a covered loss, not every political risk event (B) leads to a covered loss. For example, nationalization of foreign oil companies in an emerging market country may not affect the default probabilities for companies in other sectors. If the portfolio under analysis has exposure only in sectors and industries unaffected by the political risk event, then the probability of a default triggered by the political risk event would be extremely low. Hence, the methodology shown above is clearly conservative. While this diffusion methodology is simple and practical, its conservatism implies that the calculated mean loss and other risk parameters, including tail risks, are likely to be overstated. Hence capital allocation based on Value at Risk (VAR), Tail Value at Risk (TVAR) or similar parameters derived from the diffusion model would produce overstated capital requirements. Section 5: Jump Diffusion – A Realistic Approach In emerging markets, political risk events are more likely to suddenly and markedly increase the likelihood of credit events, rather than directly causing credit events. Therefore, the financial modeling should reflect credit defaults due to financial and economic reasons that are represented by traditional credit default rates as well as the stressed environment during and after a political risk event that could lead to sudden correlated credit defaults. In other words, the model must be able to represent both traditional credit defaults and the impact of “jumps” in default scenarios due to political risk events. However, every political risk event does not necessarily lead to a corresponding credit event (as is the case in the conservative approach of Section 4). Robert Merton’s 1976 paper “Option Pricing – When Underlying Stock Returns Are Discontinuous” was the first known application of a jump diffusion model. Robert Merton adjusted the Black-Scholes option pricing formula to reflect “spikes” in stock prices due to sudden additional information. Consider stock prices S and the geometric Brownian motion dS = µSdt + σSdw where µ (drift term) and σ (volatility) are functions of S and t Then, the jump diffusion model can be shown as dS = µSdt + σSdw + J΄SdN, where J΄= J -1 An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance -5-
  6. 6. and the jump size J (an impulse factor representing the effect precipitated by the arrival of new information in the stock market) is an independent identically distributed random variable and N is the Poisson process, while w represents a standard Wiener process. Through this adjustment, Merton was able to address the issue of option pricing even when the stock price dynamics cannot be represented by a continuous sample path. In this application, the size of the political risk jumps would be derived from historical political risk data that shows the percentage of limits that are in default within a sovereign nation due to a specific political risk event. The size of the jump would vary by size of the country and region. The graph in Exhibit 1 presents the change in combined default rate based on the size of the jump (i.e., effect of the political risk event). At the beginning, when the size of the jump is zero, there is no effect from the political risk event; thus, the credit default rate is the combined default rate. For example, political violence in one remote rural corner of an emerging market country may not affect transactions located in other, more urban areas of the same country. On the right hand side of the curve, when the effect of the jump is 100%, this framework results in the conservative approach shown in Section 4. Exhibit 1 0.16 0.14 Probability of credit and political risk event 0.12 0.1 0.08 0.06 0.04 0 0.2 0.4 0.6 0.8 1 Size of jump (%) Political Risk default rate = 10.0% Credit Risk default rate = 5.0% Section 6: Output: Economic Capital The immediate output of the model is a correlated multi-variate distribution of losses and counts that reflect the effects of both credit and political risk exposures in emerging market countries. The distribution can be summarized to generate benchmarks such as mean, median and standard deviation. But more importantly, it will present the entire spectrum of the loss distribution including the tail. Exhibit 2 contains an output derived from a sample distribution. An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance -6-
  7. 7. Exhibit 2 Loss and Counts Distribution Expected 1,825,527 0.61 Standard Deviation 5,682,749 1.33 CoV 311% 220% Median – – Minimum – – Maximum 54,813,902 18 Return Period Percentile Losses Counts 1 in 2 50.0% – 1 1 in 4 75.0% 379,476 1 1 in 10 90.0% 2,667,853 2 1 in 20 95.0% 11,144,518 3 1 in 50 98.0% 18,632,452 5 1 in 100 99.0% 30,006,859 6 1 in 200 99.5% 38,756,339 11 The standard reinsurance underwriting measures used in decision making can be easily derived from the loss distribution. Exhibit 3 shows a calculation of “upside” and “downside” risk calibrated by the traditional reinsurance carriers to reflect the potential risk involved in a reinsurance portfolio. The “upside” represents the possibility of making an underwriting profit after paying claims and expenses related to the business. Exhibit 3 Gross Premium 11,250,000 Commission + Expense 20.0% Gross Capital 29,756,339 Probability of Gross UW Upside 93.7% Average Gross UW Upside 8,785,246 Probability of Gross UW Downside 6.3% Average Gross UW Downside (8,147,273) Average Gross UW Profit / (Loss) 7,174,473 Capital Charge @ 0 % – Gross Profit Net of Capital Charge 7,174,473 Gross Profit Margin Net of Capital Charge 63.8% Gross Combined Ratio 36.2% The reinsurance practitioners use various barriers to determine the risk inherent in an insurance portfolio. The chance that the probability of downside risk being greater than a pre determined barrier is artificially high when the conservative approach described in section 4 is applied to a combined credit and political risk portfolio, perhaps, influencing the risk manager to reject a profitable transaction. An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance -7-
  8. 8. Exhibit 4 contains another view of the loss and counts distribution that is ready to be structured and layered for risk transfer solutions. Exhibit 4 Gross Loss Summary Expected Values in Year of Analysis Expected Premium 11,250,000 Expected Expenses 2,250,000 Simulation Calculation Summary Annual Annual # of Size of Loss Underwriting Total Large Large Large Ratio Result Losses Losses Losses Losses mean 16.23% 7,174,473 1,825,527 1,825,527 0.6058 3,013,415 st dev 50.51% 5,682,749 5,682,749 5,682,749 1.3329 5,063,648 cov* 3.1129% 0.7921 3.1129 3.1129 2.2002 1.6804 0.1%ile 487.23% (45,813,902) 0 0 0 3,640 0.4%ile 372.37% (32,891,624) 0 0 0 7,878 0.5%ile 344.50% (29,756,339) 0 0 0 9,231 1%ile 266.73% (21,006,859) 0 0 0 13,064 5%ile 99.06% (2,144,518) 0 0 0 42,738 25%ile 3.37% 8,620,524 0 0 0 416,773 50%ile 0.00% 9,000,000 0 0 0 1,253,358 75%ile 0.00% 9,000,000 369,476 369,476 1 3,402,075 95%ile 0.00% 9,000,000 11,144,518 11,144,518 3 11,429,821 99%ile 0.00% 9,000,000 30,006,859 30,006,859 6 26,322,196 99.5%ile 0.00% 9,000,000 38,756,339 38,756,339 8 33,282,391 99.6%ile 0.00% 9,000,000 41,891,624 41,891,624 9 35,683,494 99.9%ile 0.00% 9,000,000 54,813,902 54,813,902 11 47,322,750 Exhibit 4 provides valuable insights to a risk manager to make a decision on various risk transfer mechanisms such as quota share (QS), excess of loss (XOL), aggregate stop loss or any other combination to get the appropriate protection for a portfolio of risks. If a valid correlation matrix is used in the analysis, the 99th percentile (1 in 100 year loss) is a reasonable indicator of capital need on a stand alone basis. Exhibit 5 on the next page presents various risk transfer options considered for a combined credit and political risk portfolio by comparing the entire range of the loss distribution. An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance -8-
  9. 9. Exhibit 5 This exhibit was produced by proprietary modeling software, Willis iFM Exhibit 6 focuses on the amount of capital relief achieved through various risk transfer options. In addition, it allows the practitioner to estimate the return on capital for the entire gross portfolio and the portfolio net of reinsurance transactions (i.e., risk transfer mechanisms). Exhibit 6 Impact on Profitability – Return on Capital Expected Profit and Loss Account 20% QS 60% QS Pure XL Pure XL Gross and XL and XL 21xs4 22.5xl2.5 Gross Premium 11,250,000 11,250,000 11,250,000 11,250,000 11,250,000 Reinsurance Premium 0 4,671,884 7,567,151 2,686,614 3,282,145 Net Premium 11,250,000 6,578,116 3,682,849 8,563,386 7,967,855 Net Retained Loss 1,825,527 802,914 545,675 1,130,014 895,965 Expenses 2,250,000 2,250,000 2,250,000 2,250,000 2,250,000 Ceding Commission 0 562,500 1,687,500 0 0 Profit Commission 0 33,249 99,746 0 0 Underwriting Result (A) 7,174,473 4,120,951 2,674,419 5,183,372 4,821,890 Capital at Risk Value at Risk (1 in 200 years) 29,756,339 10,902,817 6,896,050 14,988,637 12,660,635 Cost of Capital (B) 4,761,014 1,744,451 1,103,368 2,398,182 2,025,702 Economic Result (A-B) 2,413,459 2,376,500 1,571,051 2,785,190 2,796,188 Economic Return on Capital 24.111% 37.797% 38.782% 34.582% 38.086% An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance -9-
  10. 10. Section 7: Conclusion It is the authors’ belief that the approach based on “jump diffusion” is a more realistic methodology to model combined political and credit risk. The goal of the authors is to begin a dialogue on how best to analyze the risk reward profile of a combined credit and political risk portfolio generated from emerging markets. Cross border transactions involving both trade and commodity finance will continue to grow as the emerging markets march towards economic prosperity. In that regard, this paper is a small step in the right direction to provide a mathematical framework for understanding this important risk. An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance - 10 -
  11. 11. Appendix A Definition of Political Risk Political Risk can be defined as the company’s exposure to the risk of a political event that would diminish the value of an investment or a loan. The major political risk covers (classes) are: 1. Currency Inconvertibility (CI) and Exchange Transfer (FX) 2. Confiscation, Expropriation and Nationalization (CEN) 3. Political Violence (PV) or War (including revolution, insurrection, politically motivated civil strife, terrorism) 4. Breach of Contract, Contract Frustration (CF), Contract Repudiation (CR) 5. Wrongful Calling of Guarantee (WCG) 1. Currency Inconvertibility (CI) and Exchange Transfer (FX): Inability of an investor / lender to convert profits, investment returns and debt service from local currency to hard currency ($ € £) Inability of an investor / lender to transfer hard currency out of the country of risk 2. Confiscation, Expropriation and Nationalization (CEN): Loss of funds or assets due to confiscation, expropriation or nationalization by the host government of the country of risk Any unlawful action by the host government depriving the investor of fundamental rights in a project (creeping expropriation) 3. Political Violence (PV) or War (including revolution, insurrection, politically motivated civil strife, terrorism) Loss of funds or assets due to political violence or war 4. Breach of Contract, Contract Frustration (CF), Contract Repudiation (CR) Loss of funds or assets due to arbitrary non-honoring of a contract by a foreign government (or a semi- government entity) or breach of contract by a private business entity due to an arbitrary act of a foreign government Loss of funds due to non-payment of a loan or guarantee 5. Wrongful Calling of Guarantee (WCG) Loss of funds or assets due to the host government arbitrarily calling its bonds or a business entity being forced to call guarantees due to political events (the bonds are generally backed by irrevocable letters of credit which are callable on demand) An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance - 11 -
  12. 12. Appendix B – Pure Diffusion The basis of this approach in the context of quot;pure diffusionquot; for political (P) and credit (C) risks is presented below. The coupled stochastic differential equations are as follows: dC = C ( μ dt + σ dW (t )), c cc dP = P( μ dt + σ dW (t )). p p p The notation is based on the literature from geometric Brownian motion. The correlation expression for Brownian motion is given by the standard expression, Cov(Wc (t ),W p (t )) = ρ t. As a result, the probability of a combined credit and political risk event, taking place when either C or P is below its corresponding barrier at time t, equals u = u c + u p − u c, p The probability of credit and political risk events happening at the same time (at time t) is given by the following equation, u c , p = Φ 2 (Φ −1 (u P ), Φ −1 (u C ), ρ ). Φ2 Here is a normal copula. The probability of a political event equals ln(K p (t) / P(0)) − (μ p −σ p / 2)t 2 uP = Φ( ), σp t K p (t ) is a barrier value for political risk at time t. The probability for a credit event has a similar expression. An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance - 12 -
  13. 13. Acknowledgements The authors would like to express their sincere appreciation to Julian Edwards, Steve Capon, Meirion Board, Esin Celasun, Peter Sprent and Ewa Rose of Ace Global Markets, London for their support, insights and assistance for this project. Christophe Meurier, Marina Cottaris and Florence De Rivaz of BNP Paribas deserve sincere appreciation from the authors for providing valuable insights at the testing stage of the project. In addition, our colleagues Mark Jenkins, James Cattanach, Andrew Pace, Rick Bowering, Brigitte Jaeger, Stefania Ilina, Andy Law, Catherine Prevost and Hilary Price deserve sincere appreciation for their encouragement and support. The authors are grateful for the time and efforts of Maria Morrill, Harjeet Dhillon, Alice Underwood, Yves Provencher, Ian Cook, and Rowan Douglas of Willis Analytics in reviewing various drafts of this paper and providing valuable feedback. Finally, we want to express our appreciation for the commitment to this project and to long term research and development demonstrated by Peter Hearn, CEO, Willis Re; James Vickers, Chairman, Willis Re International; Jason Howard, CEO, Willis Re International and other members of the Willis Re executive team. The ideas and opinions presented in this paper belong to the authors, as are the errors that have not been corrected by the time this paper was released for publication. An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance - 13 -
  14. 14. Bibliography 1. Ace Global Markets, (2002): White Paper on Structured Credit Insurance 2. Alwis, A., Kremerman, V., Lantsman, Y., Harger, J., Shi, J., (2006): Political Risk Reinsurance Pricing – A Capital Market Approach, Forum, International Congress of Actuaries (ICA) 3. Alwis, A., Kremerman, V., Shi, J., (2005): D&O Reinsurance Pricing – A Financial Market Approach, Forum, Casualty Actuarial Society 4. Bohn, J. R. (1999): Characterizing Credit Spreads, Haas School of Business, University of California 5. Carr, P., Wu, L., (2001): A Simple Robust Test for Presence of Jumps in Asset Prices, Bank of America Securities and Graduate School of Business Fordham University 6. Das, S., Freed L., Geng, G., Kapadia, N., (2005): Correlated Default Risk, Santa Clara Univ. 7. Delianedis, G. and Geske, R. (2001) : The component of corporate credit spreads: Default, Recovery, Tax, Jumps, Liquidity, and Market Factors”, eScholarship Repository, University of California 8. Dhrymes, P. J., (1986): Handbook of Econometrics, Volume III, Elsevier Science Publishers BV 9. Duffie, D. and Singleton, K. J. (1999): Modeling Term Structures of Defaultable Bonds, The Review of Financial Studies, Oxford University Press 10. Duffie, D. and Kenneth, J.S. (2003). Credit Risk: Pricing, Measurement, and Management, chapter 3, Princeton University Press 11. Glasserman, P. (2003): Tail Approximations for Portfolio Credit Risk, Columbia Business School 12. Li, Davis X. (1999): On Default Correlation: A Copula Function Approach. The RiskMetrics Group, Working Paper Number 99-07 13. Lindskog, F., (2000):Modeling Dependence with Copulas and Applications to Risk Management, Swiss Federal Institute of Technology Zurich 14. Lubochinsky, C. (2002): “How much credit should be given to credit spreads,” Financial Stability Review 15. Merton, R. C. (1974): On Pricing of Corporate Debt: The Risk Structure of Interest Rates, Journal of Finance, 29, pp. 449-470 16. Merton, R. C. (1975): Option Pricing – When Underlying Stock Returns Are Discontinuous: Massachusetts Institute of Technology 17. Nelsen, R. B.(1999): “Introduction to Copulas”, Springer-Verlag Telos 18. Pugachevsky, D. (2002): Correlations in Multi-Credit Models. 5th Columbia-JAFEE Conference on Mathematics in Finance, 5-6 April 2002 19. Rebonato, R., Jackel, P. (1999): The Most General Methodology to Create a Valid Correlation Matrix for Risk Management and Option Pricing Purposes. Quantitative Research Centre of the NatWest Group. 20. Rogge, E., Schonbucher, P. J. (2003): Modeling Dynamic Portfolio Credit Risk, Department of Mathematics, Imperial College and ABN AMRO Bank, London and Department of Mathematics, ETH Zurich, Zurich 21. Schonbucher, P. J. (2001): Factor Models – Portfolio credit risks when defaults are correlated, Journal of Risk Finance 22. Zhou, C., (1997): A Jump Diffusion Approach to Modeling Credit Risk and Valuing Defaultable Securities, Federal Reserve Board An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance - 14 -
  15. 15. Glossary Copula – A function that joins univariate distribution functions to form multivariate distribution functions. A copula of a multivariate distribution can be thought of as the instrument that describes the dependence structure. Credit Risk – The risk due to uncertainty in a counterparty's (also called an obligor or credit's) ability to meet its obligations. Because there are many types of counterparties, from individuals to sovereign governments, and many different types of obligations, from auto loans to derivatives transactions, credit risk takes many forms. Credit Spread – For a bond, this equals the difference between yield on a risky bond and yield on a default- free government bond with a similar maturity. Recovery Rate – In the event of a default, the recovery rate is the fraction of the exposure that may be recovered through bankruptcy proceedings or some other form of settlement. Biographies of Authors Athula Alwis is Senior Vice President, Global Credit, Surety and Political Risk Practice at Willis Re Inc. in New York City, New York. Athula provides capital and risk management services to Willis Re clients worldwide for credit, D&O, political risk, surety and other financial products lines of business. He is the global coordinator for credit, surety and political risk practice at Willis Re Inc. Athula has a BS (First Class Honors) in Mathematics from University of Colombo, Sri Lanka and a MS in Mathematics from Syracuse University, New York. He is an Associate of the Casualty Actuarial Society (CAS) and a member of the American Academy of Actuaries (AAA). Athula is a member of the CAS Regional Committees for both Asia and Europe, and is a frequent presenter at industry conferences. Athula co-authored actuarial papers titled “Credit & Surety Pricing and the Effects of Financial Market Convergence” in 2002, “D&O Reinsurance Pricing – A Financial Market Approach” in 2005 and “Political Risk Reinsurance – A Capital Market Approach” in 2006. Vladimir Kremerman is Assistant Vice President, Analytical Services at Willis Re Inc. in New York City, New York. He is responsible for property / casualty and specialty lines reinsurance analysis. Vladimir has a Ph.D. in Physics from Vilnius State University. He worked as a physicist at Semiconductor Physics Institute of Lithuanian Academy of Sciences and at the Center for Ultrafast Photonics in City University, New York. He authored / coauthored numerous papers on statistical mechanics and actuarial papers on Directors and Officers Reinsurance Pricing and Political Risk Reinsurance Pricing. Simon Ying is an actuarial analyst working from the Willis Re New York office. His responsibilities include reinsurance pricing analyses and research and development projects for the global credit, surety and political risk practice. He joined Willis Re in July 2007 as an actuarial analyst. Simon is a graduate of the NYU Stern School of Business in New York City with a major in actuarial science and a minor in philosophy, and is currently pursuing a fellowship designation in the Casualty Actuarial Society (CAS). An Inquiry into Emerging Market Combined Credit & Political Risk Reinsurance - 15 -

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