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.

The correct measure and definition of liquidity in finance literature remains an unresolved empirical issue. The main objective of the present study was to develop, validate and test the liquidity mismatch index (LMI) developed by Brunnermeier, Krishnamurthy and Gorton (2012) empirically. Building on the work of these prior studies, the study undertook to develop a measure of liquidity that integrates both market liquidity and funding liquidity within a context of asset liability management. Liquidity mismatch indices were developed and then tested empirically to validate them by regressing them against the known determinants of liquidity. Furthermore, the study investigated the nexus between liquidity and profitability. The unit of analysis was a panel of 12 South African banks over the period 2005–2015. The study developed two liquidity measures – the bank liquidity mismatch index (BLMI) and the aggregate liquidity mismatch index (ALMI) – whose performances were compared to and contrasted with the Basel III liquidity measures and traditional liquidity measures using a generalised method of moments (GMM) model. Overall, the two constructed liquidity indices performed better than other liquidity measures. Significantly, the ALMI provided a better macro-prudential liquidity measure that can be utilised in dynamic stochastic general equilibrium (DSGE) models, thus presenting a major contribution to the body of knowledge. Unlike the LMI, the BLMI and ALMI can be used to evaluate the liquidity of a given bank under liquidity stress events, which are scaled by theoretically motivated and empirically supported liquidity weights. The constructed BLMI contains information regarding the liquidity risk within the context of asset liability mismatches, and the measure used comprehensive data from bank balance sheets and from financial market measures. The newly developed liquidity measures are based on portfolio management theory as they account for the significance of liquidity spirals. Empirical results show that banks increase their liquidity buffers during times of turmoil as both BLMI and ALMI improved during the period 2007–2009. Subsequently, the improvement in economic performance resulted in a rise in ALMI but a decrease in BLMI. We found no evidence to support the theory that banks, which heavily depend on external funding, end up in serious liquidity problems. The findings imply that any policy implemented with the intention of increasing bank capital is good for bank liquidity since the financial fragility–crowding-out hypothesis is outweighed by the risk absorption hypothesis because the relationship between capital and bank liquidity is positive. / Finance, Risk Management and Banking / D. Phil. (Management Studies)

This project focused on developing the ideal Asset / Liability Model for credit unions with assets between one hundred million and five hundred million dollars. Ideally the model should be closely aligned with that of a successful credit union at the high end of this range. SELCO Community Credit Union of Eugene Oregon was used in creating the model.