目標規劃與蒙地卡羅模擬在BOT/BOO財務評估之應用

Abstract

由於BOT模式多應用於大型公共建設之開發，所需募集之資金相當龐大，在整體計畫的財務評估上亦顯繁雜，而此尤以財務風險評估為最。再者，以現行BOT財務規劃方法而言，多僅進行單純的財務運算，以此方式所能得到的相關資訊不僅量少且費時。若上述工作能透過電腦的輔助，針對BOT整體財務評估建構一套完整且易於操作的分析系統，將有助於政府及民間機構對於BOT專案財務評估工作之進行。另，民間投資者多以計劃的投資報酬率或還本期來做為BOT計劃可行性的判斷工具，與政府機構採用的自償率判定有些許的不同。為滿足不同使用者在BOT財務規劃上的需求，在系統之建構上需加以考量，將各類財務評估指標予以分類輸出，並利用目標規劃（Goal Programming）所建構出之目標搜尋模式（Goal Seeking Module）進行資訊回饋計算，提供使用者多方面分析運用的資訊。至於財務風險評估方面，目前多採敏感度分析進行分析，鮮少利用蒙地卡羅模擬（Monte Carlo Simulation）進行評估，然而蒙地卡羅模擬能以較接近現實的狀態來評估計劃的總投資風險，因其在執行上較為複雜，若能利用電腦軟體工具的串連與統整，將蒙地卡羅模擬納入BOT計劃財務分析模式之中，如此不僅可增進系統之完整性，更可讓使用者能更精確地進行BOT專案的財務評估工作，以確保BOT專案的順利施行。

The feasibility of a BOT financial plan involves simultaneous decision-making by three major project players, namely the concession company, the bank syndicate, and the host government. The concession company usually focuses on financial efficiency (e.g., minimizing financing costs), while the bank cares about the “bankability” of the project (e.g., satisfying certain financial covenants), and the government cares about the “affordability” of the services to be provided. How to develop a common platform for those players to work together in BOT financial modeling is an open issue. The feasibility of a BOT financial plan also involves in-depth risk analysis. In practice, sensitivity/scenario analyses have been used in most BOT projects, but they deal only with a very limited set of scenarios developed by the users, and they lack a systematical approach for the users to discover, understand and model the interdependence of key financial variables. This research is a preliminary attempt to incorporate goal programming and Monte Carlo simulation into BOT financial planning and evaluation. Goal programming allows BOT project players to choose their own financial goals and to find out what would be needed in their respective decision making to achieve the goals. Monte Carlo simulation provides a systematical approach to identify key financial variables their and inter-dependence. It also provides a realistic way to develop and analyze an almost unlimited amount of scenarios. Computer software such as Microsoft Excel and @Risk are chosen for the development and implementation of an integrated BOT financial model. The success of the model, we believe, would greatly improve the efficiency and effectiveness of BOT financial planning and evaluation.