資產報酬的波動程度隱含風險與不確定性，不同的投資者存在不同之風險偏好與風險承受能力，因此瞭解報酬波動之特性有其必要性；又鑑於過去不動產市場對於商用與住宅不動產兩次市場之相關研究較欠缺。因此本研究擬分別探討商用與住宅不動產市場報酬波動特性與差異，並檢視其風險與報酬間之關係。此外，總體經濟環境之變動會影響不動產市場供需關係，進而影響其價格與報酬之波動，因此本研究最後再進一步討論影響其市場報酬之總體經濟因素。 為捕捉不動產市場報酬之波動特性，本研究擬透過GARCH模型分別檢驗商用與住宅不動產市場報酬波動特性與差異；進而應用GARCH-M模型，探討商用與住宅不動產市場報酬與風險之關係；最後透過落遲分配模型實證比較分析顯著影響商用與住宅不動產市場報酬之總體經濟因素。樣本取自台北地區，資料期間為1997年2月至2009年3月之月資料。 實證結果顯示，商用不動產市場中投資人較容易透過自身過去的報酬波動推測未來的波動，反觀住宅不動產市場部分，投資人則傾向注意整體市場消息的散佈，因為其較容易受到外在因素影響而導致報酬波動；由GARCH-M模型實證結果顯示，住宅與商用不動產市場報酬與風險間均呈現顯著正相關，顯示其市場波動風險增加時期，會提供更高之報酬以均衡投資者所面對之較高市場波動風險；由落遲分配模型實證結果顯示，商用與住宅不動產市場報酬確實和總經變數之間有著程度不同的關聯性，所有當期總經變數與不動產報酬間均不存在顯著影響關係，顯示各總經變數對不動產報酬的影響存在時間落差。此外，總經變數對商用報酬的影響程度相對大於對住宅報酬的影響，且總體經濟環境變動對於商用不動產市場報酬之衝擊相對較為迅速。 / This research plans to study the relative volatility characteristic of commercial and residential property returns. In addition, the changing real estate environment can be linked to the macro economy, so we further discusses the relationship between property returns and the macro economy. In order to catch the volatility characteristic of real estate returns, we use GARCH model to examine the volatile behavior of real estate returns of commercial and residential property in the Taipei area during the period of February 1997 to March 2009, and because risk is time-varying in the market, we continue to employ GARCH-M model to observe whether can explain the change in expected returns of commercial and residential property. Furthermore, we use distributed-lag model to explore the relationship between macroeconomic factors and real estate returns. The major findings of this article can be summarized as follows. First, it is easier for investors to infer the future fluctuation through oneself returns in the past in the commercial real estate market, but part on the residential real estate market, the volatility of residential property returns is influenced by external factor more easily. Second, our empirical applications in both commercial and residential real estate markets show that the risk is positively correlated with both property returns and high risk can bring high return. Third, there are different relations of intensity between real estate returns and macroeconomic factors and the impact of macroeconomic factors on real estate returns exist time-lag. In addition, macroeconomic factors’ impact on commercial returns is relatively great, and the environmental change takes place to the impact of the commercial property returns comparatively fast.

本文主要在探討影響台灣股票市場波動的因素，除了考慮以之前學者設定的 VAR(12)模型研究，另外以 SUR(5)模型來討論股市波動與基本面、交易面間的關係；最後，再以自我迴歸異質條件變異數模型來分析股市波動的特性。最重要的是，我們會根據誤差項的各類檢定結果來判定研究股市波動性質的最佳模型。 在聯立方程式的估計中，我們發現代表資訊到達指標的兩變數--週轉率與成交量成長率--會影響股票市場的波動。另外，我們找出交易面（成交量成長率）可能會影響基本面（匯率），這也就是說，在研究股市波動時，我們不需要特別區分變數的屬性。 在 GARCH 模型及 TGARCH 模型中，我們仍然可發現週轉率與成交量成長率會影響股市條件平均數或條件變異數；除此之外，好壞消息對股市日報酬率條件變異數（條件波動）應有不同的影響效果（壞消息的影響力較快反應）。而股市自身風險係數雖然統計檢定上不顯著異於零，但若未加入條件平均數的估計式，則可能會使模型得到較差的誤差項檢定結果，顯見股市自身風險應為影響投資人設定期望報酬率水準的重要因素之一。 從上述估計結果，我們可以知道，若散戶投資人能正確解讀市場上出現的各種新資訊之背後意義，將可使成交量成長率或週轉率（大部份可能代表無意義或不正確的交易行為）的變動幅度降低，進而有效地減少股票市場中股價異常波動的現象。 / My essay's topic focuses on discussing the factors that influence stock market volatility in Taiwan's stock market. Besides VAR(12) model as previous researchers have studied, I tries to set up SUR(5) models analyzing the relationship among the stock market volatility、the foundamental variables'volatilities and trading activities; Then I cited ARCH models ( autoregressive conditional heteroskedisticity models ) to find out the characteristics of stock market volatility. Most important of all, according to each misspecification test ( residual test ), I would specify the better models to describe the stock market volatility. In the estimations of system equations ( VAR(12)and SUR(5)models ), first I found that turnover rate and the growth rate of trading volume, which represent the information arrival indexes, could effect stock return's monthly conditional variance. Second, I especially found out the evidence that trading activities (trading volume growth) would probably have an impact on the macroeconomic variable ( exchange rate volatility ). It shows that we don't need to distinguish the attributes of those factors which could influence stock market volatility. In GARCH and TGARCH model, the positive influences of turnover and trading volume growth on daily stock return's conditional mean and conditional variance ( conditional volatility ) are still obvious, Within these TGARCH model, I discovered that bad news and good news could have different influences on stock market volatility ( the impact of bad news which resulted in downward movements of stock market volatility appeared faster that the good news'which caused upward movements). Stock market's self-risk(σ_t-1^{^2}) is statistically insignificant different from zero in GARCH models, but when I omitted this variable in daily stock return's conditional mean estimation equation, standardized residual might not obey the assumption of normal distribution. It apparently told us that the stock market's self-risk term ( σ_t-1^{^2} ) is one of the critical factors which influences investors to estimate expected return level. From those results above, we realized that if investors could precisely understand the real meanings of new information conveying in the stock market, it might decrease the levels of turnover and trading volume growth ( which could sometimes represent meaningless or inexact trading activities ), then effectively reduce the abnormal volatility phenomenon in stock market.

本研究探討台灣於2011年依據IAS 39進行34號公報之第三次修訂實施，採用已發生損失模型後的兩項議題：（1）放款壞帳費用之提列與盈餘波動性以及資本適足率波動性之關聯性，（2）以歷史成本評價之期末金額及以公允價值評價之期末金額，究竟何者對於未來之帳款沖銷與不良債權較具有關聯性。 實證結果顯示，自2011年採用已發生損失模型後盈餘波動性無顯著之變化，且壞帳費用對於盈餘波動性無解釋能力；而自2011年後資本適足率波動性亦無顯著變化，但壞帳費用對於資本適足率波動性有顯著的影響，顯示銀行明顯透過壞帳費用之提列進行資本管理而非盈餘管理。在未來信用損失預測之部分，以歷史成本評價之期末放款金額對於未來之帳款沖銷及不良債權有顯著的負相關，而以公允價值評價之期末放款金額對於未來之帳款沖銷及不良債權卻無解釋能力，可能係因未來帳款沖銷與未來不良債權之發生與放款之帳齡有顯著的關聯性，而與未來可收取之現金流量無顯著之相關。 / This study aims to investigate how Incurred Loss Model affects the recognition of loan loss provisions and the valuation of loans due to the third revision of SFAS No. 34 which was revised based on IAS 39 in 2011. For the recognition of loan loss provisions, it focuses on the relationship with earnings volatilities and capital adequacy volatilities, and for the valuation of loans, it specializes on whether credit loss predicting is related to historical cost accounting or fair value accounting. The result shows that, since the implementation of Incurred Loss Model in 2011, both the adoption of Incurred Loss Model and the loan loss provisions have no significant impact on earnings volatilities. For capital adequacy volatilities, implementing Incurred Loss Model has no effect on capital adequacy volatilities neither. However, the loan loss provisions since 2011 significantly enhance the volatilities of capital adequacy. It reveals that banks use loan loss provisions to manage capitals instead of earnings. For credit loss predicting, loans evaluated with historical cost accounting have significant negative relations with future charge-offs and non-performing loans while loans evaluated under fair value accounting do not have any explanation power. It may suggests that future charge-offs and non-performing loans are related to the aging of loans, but not the future payoffs of loans.

本文建構代理人基之社會網路新凱因動態隨機一般均衡模型(Social Network-Based DSGE model)，並分別使用權衡性門檻型泰勒法則與一般線型泰勒法則作為代理人基之社會網路新凱因斯動態一般均衡模型中的貨幣政策方程式，模擬產出缺口、通貨膨脹、利率等總體經濟變數資料，接著利用模擬資料，探討不同網路結構對產出缺口、通貨膨脹等總體經濟變數的影響，同時比較權衡性貨幣政策與法則性貨幣政策穩定經濟的有效性。 　　透過產出缺口與通貨膨脹的波動性分析，本研究發現某些特定社會網路結構的影響力大於貨幣政策的影響力，決定了經濟變數的波動程度。在完全連結網路(Fully)的結構下，通貨膨脹與產出缺口的波動度明顯低於其他結構，而無標度網路(Scalefree)的結構會使產出與通膨的波動程度最大。經過驗證，本研究發現群聚度大、平均路徑短的網路結構內節點之間資訊流通速度較快，對穩定經濟有正面助益；相反的，由於無標度網路強大的中心性，使該網路內指標性節點對其餘節點具有龐大影響力，增加節點內決策的不確定性，連帶造成經濟的大幅波動。另外，在相同的網路結構下比較權衡與法則貨幣政策，研究結果指出權衡性政策會造成較大的產出缺口波動，但對抑制通貨膨脹波動的效果較佳；相對的，法則性政策對產出缺口的穩定效果較好，但卻無法兼顧通貨膨脹的波動性。 / We construct an agent-based New Keynesian DSGE model (Dynamic Stochastic General Equilibrium) with different social network structures to investigate the effects of the rule and discretion monetary policy. According to our simulation results, we find the economic stability depends on the specific social network structure rather than the monetary policy basis like rule and discretion. Generally speaking, the more average path length (the less average clustering coefficient) the network structure is, the more economic fluctuation would be. Also, the results show that scalefree network will lead the most dramatic economic fluctuations. These results are ascribed to scale -free’s high centrality. However, if the social network structure is too complicate to control, the central banker can only manipulate the monetary policy to stabilize the economy. With different policy basis, we find the rule monetary policy will lead less output gap volatility.