住宅市場因其內容包括各類住宅次市場，又因為實質住宅單位難以衡量，以及實證資料難以蒐集等原因，使得住宅需求分析較為複雜。 本研究主要在探討何種函數形式較適合使用於住宅需求模型，而該種函數形式必須能對實際的住宅需求變動情形充分說明，並非如一般住宅需求模型之建立忽略了函數形式與模型之間的關連性；以外本文對不同所得階層、不同區位對位宅需求是否有明顯的影響之課題作一深入探討。而值得注意的是，不論對不同所得或位於不同區位的住宅需求來說，利率對住宅消費性需求的影響並不顯著，有別於一般利率對住宅需求有明顯影響力的印象。 本研究共分為五章，摘要內容如下： 第一章：介紹本文的動機、目的，研究限制與架構，並界定本研究之研究對象為住宅消費性、有效需求。 第二章：針對發展較成熟的國外文獻作一回顧整理，藉以發現一般在研究住宅需求相關課題時可能遇到的問題；此外介紹建構本文之基礎理論。 第三章：在對國外文獻進行回顧之後，本研究尚對國內住宅需求模型作驗證分析，探討造成各模型差異甚大的原因；並特別針對住宅價格資料之課題作比較分析。 第四章：在以Stone-Geary 效用函數以及目的變數建立住宅需求模型之後，以台北市為實證範圍，進行縱斷面的迴歸分析，發現以Stone-Geary 效用函數建立的住宅需求模型，頗能說明台北市住宅需求的變動情形。 第五章：針對國內外文獻處理以及實證分析結果，提出本研究之結論建議與後續研究。

本研究探討長期信託基金（諸如退休基金，人壽保險公司等）之固定收益債券多期資產配置，利用時間可加性之效用函數描述投資者於投資期限時對財富大小之風險偏好程度，滿足基金之長期最適效益目標，為避免模型過於複雜，本文假設於動態完備市場中針對基金所持有之資產執行動態資產配置，建立財務動態調整機制以評量基金到期之獲利表現。為實際反應市場之風險程度，持有資產將利用隨機擴散過程表示，短期市場利率採用單因子Vasicek隨機模型表示，本文以給定金融市場之情境假設，說明不同到期日之債券為適當之獲利投資及避險工具，本研究之多期資產配置模型主要參考Cox與Huang (1989, 1991)與Sorensen (1999)，將未來財富過程利用平賭過程表示，給定不同投資限制條件、風險偏好程度與市場系統風險，以擬似動態規劃實際計算與比較每期之最適資產配置。 / This study attempts to investigate the hedging behavior through multi-period asset allocation strategy for the long-term fund manager, i.e., pension fund managers, life insurers, etc. Time additive utility function is employed to depict the risk preference of the investors during his investment time horizon. Based on their long-duration liabilities, assets held by the fund manager are employed in hedging and speculating under dynamic complete market assumption. To fully reflect the financial risks from the market, a risk management mechanism is implemented to monitor the long-term financial soundness. Short-term interest rate model proposed by Vasicek is employed to characterize the diffusion pattern of the invested assets. Current financial market information are incorporated and investigated to portray the hedging strategy through fixed income securities with various maturities. The quasi-dynamic approach proposed in Cox and Huang (1989, 1991) and Sorensen (1999) are implemented to construct the optimal asset allocation model. The optimal strategy is examined through maximizing the indirect utility function through the optimal growth portfolio. Finally, the hedging behaviors are compared and fully explored under various market scenarios.

附最低保證投資型保險商品的特色在於無論投資者的投資績效好壞，保險金額皆享有一最低投資保證，過去關於此類商品的研究皆假設標的資產為單一資產，或依固定比例之投資組合，並沒有考慮到投資人自行配置投資組合的效果，但大部分市售商品中，投資人可以自行配置投資標，此情況之下，保險公司如何衡量適當的保證成本即為一相當重要之課題。 本研究假設投資人風險偏好服從冪次效用函數，並假設與保單所連結之投資標的有兩種資產，一為具有高風險高報酬的資產，另一為具有低風險低報酬之資產，在每個保單年度之初，投資人可以選擇配置在兩種資產之比例，我們運用黃迪揚(2009)所提出的動態規劃數值解之方法，計算出在考慮投資人自行配置資產之下，保證成本將會比固定比例之投資高出12個百分點。 此外，為了瞭解在不同資產報酬率的模型之下，保證成本是否會有不一樣的結論，除了對數常態模型之外，我們假設高風險資產與低風險資產服從ARIMA-GARCH(Autoregressive Integrated Moving Average-Generalized Autoregressive Conditional Heteroscedastic )模型，並得到較高的保證成本。 / The main characteristic of variable annuities (VA) with minimum benefits is that the benefit will be guaranteed. Previous literatures assume a specific underling asset return process when considering the guaranteed cost of VA; but they do not consider the portfolio choice opportunity of the policyholders. However, it is common for policyholders to rebalance his portfolio in many types of VA products. Therefore it’s important for insurance companies to apply an approximate method to measure the guaranteed cost. In this research, we assume that there are two potential assets in policyholders’ portfolio; one with high risk and high return and the other one with low risk and low return. The utility function of the policyholder is assumed to follow a power utility. We consider the asset allocation effect on the guaranteed cost for a VA with guaranteed minimum withdrawal benefits, finding that the guaranteed cost will increase 12% compared with a specific underling asset. The model effect of the asset return process is also examined by considering two different asset processes, the lognormal model and ARIMA-GARCH model. The solution of dynamic programming problem is solved by the numerical approach proposed by Huang (2009). Finally we get the conclusion which the guaranteed cost given by the ARIMA-GARCH model is greater than the lognormal model.