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題名：台灣短期利率動態行為之實證研究
作者：連春紅
作者(外文)：Chun-Hung Lien
校院名稱：國立中山大學
系所名稱：財務管理學系研究所
指導教授：廖四郎
徐守德
學位類別：博士
出版日期：2006
主題關鍵詞：隨機波動模型；CKLS模型；預測準確性檢定；預測涵蓋性檢定；Stochastic Volatility Model；Regime Switching；forecast encompassing；predictive accuracy；CKLS；Mean Reversion
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短期利率在財務理論與實證佔有相當重要的地位，利率變動會影響金融商品的價格行為，因此明瞭短期利率之動態行為將有助於金融商品之風險管理。本論文由三篇文章構成，利用台灣商業本票初級市場發行利率，由實證角度探討台灣短期利率之動態行為。
第一篇使用不同的計量技巧估計並比較連續短期利率模型在台灣短期利率之實證表現。在參數估計方面，使用兩種近似方式將連續的利率過程寫成間斷模型，再以一般動差法(GMM)與準最大概似法(QML)估計，以評估模型在資料配適上的優劣；此外，亦比較不同資料頻率(月、週資料)對模型估計與評估是否有顯著的不同。實證結果指出，不同近似方法所得到的估計結果並無太大差異，不過估計方法與資料頻率會影響估計結果，使用週資料並以準最大概似法估計所得到之結果較具穩定性與效率性；其次，台灣短期利率存在均數復歸現象，且短期利率波動性受利率水準值影響，惟敏感度係數估計值小於1，而在模型評估方面，相較於一般化的未受限制模型，以CIR-SR模型在資料的配適上表現最好。
第二篇則由樣本外預測準確性與預測涵蓋性角度，比較連續時間短期利率模型在預測台灣利率水準值與波動性的表現。在樣本外預測方面，採用遞迴迴歸方式，計算不同預測長度的利率水準值與波動性之預測值。為衡量模型預測誤差間之差異是否具有統計上的顯著性，本文採用預測準確性檢定與預測涵蓋性檢定，以評估模型樣本外的預測績效，同時，為求檢定的穩健性，分別考慮不同期間下之預測績效。實證結果發現，利率模型在樣本內/外的表現不甚相同。較複雜的模型在樣本內有優異的配適表現，然在樣本外預測表現則不盡相同。在預測利率平均數方面，有均數復歸的模型，在某些期間內表現較佳；而設定 的異質變異數模型，在預測利率波動性上，於某些期間內，表現優於同質變異數模型；至於其它形式的異質變異數模型，預測表現均不如同質變異數模型。
第三篇則針對實證上尚未有定論之利率波動性設定做探討，估計並檢定三大類利率波動模型，分別為確定的波動模型、隨機的波動模型與狀態轉換的波動模型，希冀由實證資料決定最適合的波動形式。實證結果顯示，台灣資料存在均數復歸的現象與水準效果。但以模型而言，同時考慮GARCH效果與水準效果之模型較適合台灣的資料。
This study includes three issues about the dynamic of 30-days Taiwan Commercial Paper rate (CP2).The first issue focuses on the estimation of continuous-time short-term interest rate models. We discretize the continuous-time models by using two different approaches, and then use weekly and monthly data to estimate the parameters. The models are evaluated by data fit. We find that the estimated parameters are similar for different discretization approaches and would be more stable and efficient under quasi-maximum likelihood (QML) with weekly data. There exists mean reversion for Taiwan CP rate and the relationship between the volatility and the level of interest rates are less than 1 and smaller than that of American T-Bill rates reported by CKLS (1992) and Nowman (1997). We also find that CIR-SR model performs best for Taiwan CP rate.
The second issue compares the continuous-time short-term interest rate models empirically both by predictive accuracy test and encompassing test. Having the estimated parameters of the models by discretization of Nowman(1997) and QML, we produce the forecasts on conditional mean and volatility for the interest rate over multiple-step-ahead horizons. The results indicate that the sophisticated models outperform the simpler models in the in-sample data fit, but have a distinct performance in the out-of-sample forecasting. The models equipped with mean reversion can produce better forecasts on conditional means during some period, and the heteroskedasticity variance model with outperform counterparts in volatility forecasting in some periods.
The third issue concerns the persistent and massive volatility of short-term interest rates. This part inquires how the realizations on Taiwan short-term interest rates can be best described empirically. Various popular volatility specifications are estimated and tested. The empirical findings reveal that the mean reversion is an important characteristic for the Taiwan interest rates, and the level effect exists. Overall, the GARCH-L model fits well to Taiwan interest rates.
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