臺灣博碩士論文加值系統

針對眾所周知之財務資料波動程度的高度相關性,辨別這種相關性是否就是「緩長記憶」在經濟政策、 財務決策、 實證方法、 及計量理論上都具有非常重要的意義。 本文結合 Hamilton and Susmel (1994) 之狀態變換 (regime switching) SWARCH 模型, 以及 BBM (1996) 和 Chung (1998) 之緩長記憶 FIGARCH 模型, 新創相當一般化之 SW(k)-FIGARCH-L(0,d,0) 模型, 同時估計狀態轉變參數及部分差分係數, 使我們既可在狀態變換的前提下檢定緩長記憶假說, 也能在考慮緩長記憶可能存在的前提下檢定是否有狀態變換發生, 並證明波動程度之狀態變換的確會造成相當嚴重的假性緩長記憶問題。 此新模型不但可避免以單純緩長記憶模型估計狀態變換母體所導致的假性緩長記憶問題, 更解決了單純狀態變換模型殘差項平方值仍具高度跨期相關的窘境, 是一個相對優良的模型, 在財務資料的估計上十分具有發展潛力。 根據實證結果,未考慮狀態變換因素前, 台股指數日報酬率的波動程度的確顯現出緩長記憶特徵, 然而同時考慮狀態變換因素後, 緩長記憶特徵不復存在, 僅具中等記憶。

The strong persistence in the volatility of a variety of financial time series is well-known. To determine whether this persistence can be characterized as ''long memory'''' is obviously important in both financial and econometric modelling. In this paper, after summarizing Hamilton and Susmel''s (1994) regime switching SWARCH model and BBM (1996) and Chung''s (1998) long memory FIGARCH for volatility, I propose a general SW(k)-FIGARCH-L(0,d,0) model that allows the estimation of both regime switching parameters and the long memory parameter. In such a framework I am able to test whether the volatility still has long memory after regime switching has been considered and I show that regime switching in volatility can result in spurious long memory. Furthermore, I find the proposed model also solves one of the problems with the standard SWARCH model that squared residuals obtained from the
SWARCH model estimation usually are highly correlated, which implies the simple regime switching mechanism is not able to characterize all the dynamics in volatility. Based on these encouraging results, I believe the proposed model is a promising tool in analyzing financial data. My empirical analysis of the Taiex data shows that the long memory in their volatility will reduce to intermediate memory after regime switching is considered, which represents an interesting example of the spurious long memory in volatility that caused by the regime switching.

1. 前言......................................................1
2. 緩長記憶模型..............................................5
2.1 緩長記憶設定與自迴歸部分整合移動平均 (ARFIMA) 模型......5
2.2 部分整合一般化自迴歸條件變異數非齊一 (FIGARCH) 模型.....9
2.3 緩長記憶的拉氏乘數檢定 (LM 檢定).......................12
3. 馬可夫變換模型...........................................14
4. 台股指數報酬率與 SW-FIGARCH-L 模型.......................18
4.1 緩長記憶檢定...........................................18
4.2 模型設定...............................................19
4.3 SW($k$)-FIGARCH-L($0,d,0$) 的估計方法..................22
4.4 實證結果...............................................25
5. 狀態變換與假性緩長記憶...................................30
6. 結論與未來研究方向.......................................33
參考文獻....................................................36
圖形........................................................39

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