指數型債券報酬與波動性之緩長記憶和預測研究

Long-memory Modeling and Forecasting of the Returns and Volatility of Exchange-traded Notes (ETNs)

李爾政(Argel-Sawali Masa)

指導教授 ： 狄 強

中原大學/商學院/國際商學碩士學位學程/碩士(2014年)

https://doi.org/10.6840/cycu201400517

摘要

本研究實證分析指數交易型債卷(ETNs)波動的可預測性。以商品期貨、貨幣及金融債卷ETNs資料爲主，檢測採用三種緩長記憶模式："自迴歸移動部分整合模式" (ARFIMA)與"非均齊條件變異模式 "(GARCH)、”部分整合自迴歸條件異質變異數” (FIGARCH)及” 双曲线记忆广义自回归条件异方差模型” (HYGARCH)；三種橫向預測模式：1-、5-、及20-的橫向測試，來預測ETNs的報酬率及波動率。本研究在數項ETNs中發現了長期殘差，但報酬率及波動率中兼未分析出任何的雙重長期殘差。另外，本研究也基於Fama在1970年發表的假設在判定未來價值，尤其是波動率上，缺少變化而提出疑問。除此之外，基於ETNs相近的特性及不明顯的效果，商品期貨、貨幣及金融債卷特質上的差異也并未被總結。然而，中期記憶的出現，應該被投資者們視爲忽依靠“非持久性”及“非長期投資”的跡象。最後，ARFIMA-FIGARCH模式比ARFIMA-GARCH模式及ARFIMA-HYGARCH模式有些微的優勢，尤其是在1-、5-及20-的橫向預測模式中。本研究的優先在ETNs上實行了更爲廣大的部分整合模式。這些研究資料能夠提供貿易商、基金經理、基金發行、及基金投資者更爲齊全的交易策略。本研究也提供了ETNs基礎上更爲新穎的認識并開拓了未來學生及研究員的研究平臺。

關鍵字

外樣本預測分析 ； 指數交易型債卷 ； 緩長記憶模式

並列摘要

This research provides evidence in determining the predictability of exchange traded notes (ETNs). This study utilizes commodity, currency, and equity ETNs as data samples, and examines the performance of three combinations of long memory models, i.e., autoregressive fractionally-integrated moving average and generalized autoregressive conditional heteroskedasticity (ARFIMA-GARCH); autoregressive fractionally-integrated moving average and fractionally integrated generalized autoregressive conditional heteroskedasticity (ARFIMA-FIGARCH); and autoregressive fractionally-integrated moving average and hyperbolic generalized autoregressive conditional heteroskedasticity (ARFIMA-HYGARCH); and three forecasting horizons, i.e., 1-, 5-, and 20-step-ahead horizons to model ETNs returns and volatilities. The paper finds long memory processes in several ETNs, however, dual long-memory process in returns and volatilities is not verified. The research also poses a challenge to the weak-form efficiency hypothesis of Fama (1970), because lagged changes is identified to determine future values, especially volatilities. Also, differences in the characteristics of commodity, currency and equity ETNs are not concluded because of similarities in ETN traits and insignificant results. However, the presence of intermediate memory should serve as a warning sign for investors not to depend on their anti-persistence and not to keep investments in the long-run. Lastly, the ARFIMA-FIGARCH models has a slight edge over the ARFIMA-GARCH and ARFIMA-HYGARCH specifications using 1-, 5-, and 20-forecast horizons. The strength of this research is being a pioneer in applying a wider number of fractionally-integrated models in ETNs. These evidences can provide traders, fund managers, issuers and investors in creating well-defined trading strategies. Results can also provide fresh understanding in terms of ETNs, and open future channels of research to academicians and researchers.

並列關鍵字

Exchange-traded Notes ； Long Memory models ； Out-of-sample Forecasting Analysis

參考文獻

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