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Author Dong, Feng
Title Essays in Advanced Risk Management and Quantitative Strategies in Infrastructure Finance
book jacket
Descript 215 p
Note Source: Dissertation Abstracts International, Volume: 72-05, Section: B, page:
Thesis (Ph.D.)--Columbia University, 2010
The World Bank estimates that public authorities worldwide annually spend about $800 billion on infrastructure investment and maintenance. This amount is expected to grow substantially over the next years. Consequently, governments face the daunting challenge of satisfying growing public infrastructure demands with a constrained budget. As a way of augmenting public infrastructural capital budgets, governments have welcomed private participation in infrastructure (PPI). However, PPI is a viable and economically efficient alternative for relieving the economic burdens of governments only if the project risks are allocated appropriately among project stakeholders
This dissertation originates five different but related essays in advanced risk management and quantitative strategies for PPI
Essay 1: Valuing Callable and Putable Revenue-Performance-Linked Project Backed Securities
Public owners face a constant demand for developing new projects and for funding the renewal, maintenance and operation of existing infrastructure projects. One way to raise capital to provide new financial resources to constrained budgets is to securitize a stream of revenue cash flows from a portfolio of mature infrastructure projects. This essay presents new types of project backed securities---namely, callable and putable revenue performance-linked project backed securities. In this new project backed securities setting, revenue and interest rate risks for issuers and buyers can be limited within a cutoff area. This risk hedging feature is expected to facilitate the trading of such products to the advantage of the public issuers
Essay 2: Commodity Risk Management: Pricing Swing Options via Bi-Boundary Monte Carlo Method
Build-Operate-Transfer (BOT) arrangements may involve commodity exchanges among project stakeholders (i.e., sponsors, off-takers, general contractor, and suppliers) through forward commodity contracts. Usually, swing options are employed to add risk hedging flexibility to forward commodity contracts. Current swing option valuation methods are not computationally efficient when the underlying stochastic process is high dimensional. This essay presents a Monte Carlo method, namely Bi-Boundary method, which yield a positively biased estimate, an upper bound, of the true swing option value. Thus, a reliable point estimate can be calculated by coupling the Bi-Boundary upper bound value with a lower bound value, estimated through the standard regression-based Monte Carlo method. The numerical examples show that this procedure compares favorably with lattice swing option valuation methods and successfully applies to high multi-factor models
Essay 3: Improve Economic Efficiency of Public-Private Partnerships for Infrastructure Development by Contractual Flexibility Analysis in a Highly Uncertain Context
PPPs, as long-term contractual relationships between the public and private sectors, usually have a rigid contractual structure. This rigid contractual setting can reduce transaction costs but sacrifice the opportunities to make PPPs more economically efficient by not addressing the future downside risks during the long-term concession appropriately and flexibly. This essay aims at presenting a novel type of proactive uncertainty management, contractual flexibility analysis, which can improve the economic efficiency of PPPs by incorporating flexibilities into the current way of contract structuring
Essay 4: Stochastic Optimization of Capital Structure in Privately Funded Infrastructure Projects
Traditional capital structure optimization methods assume that there are only two types of financial sources (i.e., equity and debt capital) available for funding infrastructure projects. This assumption is not valid anymore in modern Build-Operate-Transfer (BOT) and infrastructure project financing settings, where increasing inflows of capital are invested through sophisticated private-equity funds. In fact, besides investing in common shares, modern equity investors also want to invest in mezzanine financial instruments to take advantage of their potential gain opportunities. However, the contingent claim embedded in mezzanine convertible securities makes the traditional deterministic capital structuring optimization methods unsuitable. This essay presents a new optimization model for project capital structure with mezzanine convertible securities. This optimization method has the ability to compute the stopping time of the stochastic convertible contingent claim by combining a regression-based stochastic dynamic programming technique with the traditional project capital structuring approach
Essay 5: Copula-Based Portfolio Credit Risk Assessment in Infrastructure Project Financing
Current credit risk assessment models developed for infrastructure financing assume that project debt lenders invest in one single project at a time, or equivalently, that lenders own a portfolio of project loans, whose defaults are statistically independent. However, in order to reduce idiosyncratic and concentration risk, lenders usually underwrite loans, whose defaults are indeed correlated: possibly negatively correlated or, alternatively, with a weakly positive dependency. This essay presents a copula-based model to measure the credit risk of a portfolio of correlated project loans from a lender's perspective. The copula-based model ingeniously combines the Variance Model with a double stochastic intensity model. By measuring their portfolio credit risk more correctly, lenders can properly price the interest rate charged on project sponsor loans. (Abstract shortened by UMI.)
School code: 0054
Host Item Dissertation Abstracts International 72-05B
Subject Economics, Finance
Engineering, Civil
Operations Research
Alt Author Columbia University
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