債券型ETF之長期記憶及預測

Long-term Memory and Forecast for The Bond ETFs

蔡易成(Yi-Chen Tsai)

指導教授 ： 陳若暉

中原大學/商學院/企業管理研究所/碩士(2015年)

https://doi.org/10.6840/CYCU.2015.00069

摘要

隨著經濟環境和金融工具的不斷變化，投資者更願意投資在低成本，高透明度和高回報率的金融產品上。相較於傳統的金融產品，ETF具有以上特點。甚至諾貝爾經濟學獎得主羅伯特•恩格爾在2003年曾經稱讚ETF是一個偉大的創新。
　　此篇研究主要有兩個主題。首先，這項研究利用ARFIMA-FIGARCH提供更多的證據，分析有關在經濟的非線性時間序列的長期記憶性的報酬和波動性，可揭露長期記憶的參數是否是非整數值。
　　其次，本文採用神經網絡，如倒傳遞式神經網絡（BPN）、遞迴式類神經網路（RNN）和延時遞歸式神經網絡（TDRNN）模型來預測債券ETF與六變數之間的關係。六個變數包括股票價格、指數之波動性、買入-出售比率、匯率、倫敦銀行同業拆借利率和CRB指數。
　　結果指出，只有新興市場債券ETF具有長期記憶。而在神經網絡的結果，在做大部分的ETF分析上，BPN具有最好的預測能力，除了BUND和AGG必須使用TDRNN才具有更好的預測效果。
　　本研究結果提供的投資策略在未來將可作為投資者或發行者提供參考，其結果也能提供學術界作為潛在途徑的研究，並且將有利於在投資界創造利潤的潛在機會。

關鍵字

債券型ETF

並列摘要

With the changing of the economic environment and financial instruments, original investment market is no longer applicable. Investors prefer to the low cost, high formation transparency and high reward instruments. Comparison to conventional financial products, ETF has above characteristics and even Nobel laureate in economics Robert F. Engle in 2003 once praised ETF is a great innovation .
There are two main themes. First, this study provides additional evidence of nonlinearities in economic time-series from the long-term memory properties in return and volatility by using ARFIMA-FIGARCH which could reveal that long memory parameters are non-integer values or not.
Second, this paper uses neural networks such as Back propagation Neural Network (BPN), Recurrent Neural Network (RNN) and Time-delay Recurrent Neural Network (TDRNN) models to predict bond ETFs with the six variables that including stock price, volatility indice (VIX), Put-Call Ratio, exchange rate, LIBOR and commodity research bureau (CRB) index.
The result shows that only iShares J.P. Morgan USD Emerging Markets Bond ETF (EMB) is completed with long-term memory. The long-term memory is worth to not on part of integration process associated with a sequence of recession velocity and impulse response coefficients. In the results of neural network, the best forecasting performance is BPN, while except BUND and AGG have better predictions by using TDRNN.
The result of this paper will to provide an investment strategy in the future to be a reference for investors or issuers. And the results can also provide the academic community potential avenues for research that will benefit the investing community in creating potential opportunity to create profit.

並列關鍵字

Long-term Memory ； Neural Networks ； ARFIMA-FIGARCH

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