本文考量於Excel介面下設計一「壽險公司現金流量模型」，透過保險財管、精算理論的採用與大量隨機模擬亂數的應用，欲建構一結合理論基礎與實務運用的動態財務分析系統雛形。 模型中，資產面的模擬項目共有七項：1.債券與放款：採用CIR或Vesicek兩利率模型供選擇進行利率期間結構生成，以模擬出各到期期限的債券及放款價格。2.股票：以資本資產訂價模型(CAPM)來模擬各類股股票價格的變動與股票投資報酬。3.不動產：使用幾何布朗運動模擬不動產價值與租金收入。4.國外投資：利用幾何布朗運動模擬匯率的變動。5.現金及銀行存款。6.應收款項，考量壞帳情況下，逐年比率攤回殘餘金額。7.其他資產。 負債面採用定期險、終身生死合險與遞延年金險模擬壽險公司業務經營的現金流量情況。藉由資產與負債的整合，可模擬出公司未來十年內各年度的損益情況，讓使用者了解於承受總體經濟各項不確定風險下，壽險公司資產面、負債面與業主權益的現金流量情況。 文末引用個案範例，進行實務操作的說明，示範如何應用本模型來進行最適資產配置決策與敏感度分析，以證明本系統的合理可行性。最後，並對此系統提出檢討與展望，期待後續研究可加入程式語言的應用而建構出一完備的動態財務分析系統。 / The main purpose of this study is to construct a dynamic cash flow testing for the life insurance company by using Excel. Through the adoption of financial and actuarial theories and the application of stochastic method, we want to provide a rudiment analysis framework of life dynamic financial model that combines theoretical basis and practical application. This analysis framework includes seven categories of assets. The simulation models or related issues for each category will be discussed accordingly. – 1. Bonds and mortgage loans: providing CIR and Vesicek interest rate model for users to generate the interest term structure. 2. Stocks: applying CAPM method to simulate the stock prices and stock returns. 3. Real estate and rental income: using Geometric Brownian Motion to simulate the price of real estate and the rental income. 4. Foreign investment assets: using Geometric Brownian Motion to simulate the movement of exchange rate. 5. Cash and Deposits. 6. Account Receivable: after considering bad loans, we amortize the residual account receivables for a specific period. On the liability side, we use three types of products - term life, whole life endowment, and deferred annuity - to generate the business profile as well as the cash flows patterns of the life insurance company. By integrating the asset and liability sides of the model, we can simulate the revenue of the company for the following ten years and enable the users to predict the future cash flows under uncertain financial conditions. Finally, applications of this model are presented as thoroughly as possible to educate the users about how to make the optimal asset allocation decisions and sensitive scenario analysis. The application results show that the model reasonably fits the desired results. Since the model presented here is not a complete DFA model, future researches may consider adding more refined component into the analysis framework like using programming language.

本文主要是針對保險業及退休基金的資產負債管理議題為研究重心，延續Huang (2004)的研究，其研究是以理論求解的方式求出多期最適資產配置的唯一解，而其研究也衍生出兩個議題：首先是文中允許資產買賣空;再者其模型僅解決單期挹注資金的問題，而不考慮多期挹注資金。但這對於實際市場操作上會有一些的問題。因此本文延續了其研究，希望解決這兩個議題，讓模型更能解出一般化的資產負債管理問題。 本文所選擇的投資的標的是以一般退休基金與保險業所採用，分別是短債(short-term bonds)、永續債卷(consols)、指數連結型債券(index-linked gilts(ILG))、股票(equity)為四種投資標的，以蒙地卡羅模型模擬出4000組Wilkie 投資模型(1995)下的四種標的年報酬率以及負債年成長率，利用這些預期的模擬值找出最適的投資比例以及應該挹注的金額。而本文主要將問題化為決策變數的二次函數，並以一般化最小平方法(generalized least square,GLS)來求出決策變數，而用此方法最大的優點在於一般化最小平方法具有唯一解，且在利用軟體求解的速度相當快，因此是非常有效率的。本文探討的問題可以分成兩個部分。我們首先討論「單期挹注資金」的問題，只考慮在期初挹注資金。接著我們考慮「多期挹注資金」的問題，是在計畫期間內能將資金分成多期投入。兩者都能將目標函數化為最小平方的形式，因此本文除了找出合理的資產配置以及解決多期挹注資金的問題之外，也將重點著重於找一個能快速且精準的方法來解決資產配置的問題。 / This paper deals with the insurance and pension asset liability management issue. Huang (2004) derives a theoretical close solution of multi-period asset allocation. However, there are two further problems in his paper. First, short selling is allowable. Second, multi-period investing is not acceptable. These two restrictions sometimes are big problems in practice. This paper extends his paper and releases these two restrictions. In other words, we intend to find a solution of multi-period asset allocation so that we can invest money and change proportion of investment in each period without problems of short selling. In this paper, we use the standard asset classes used by pension or insurance funds such as short-term bonds, consols, index-linked gilts and equities. We generate thousand times of Monte Caro simulations of Wilkie investment model (1995) to predict future asset returns. Furthermore, in order to improve time-efficiency and accuracy, we derive a quadratic objective function and obtain a unique solution using sequential quadratic programming.