到期日效應與波動率緩長記憶現象對台指期貨動態避險績效的影響

Maturity Effect and Long Memory in Volatility While Dynamic Hedging with TAIEX Futures

林家正(Chia-Cheng Lin)

指導教授 ： 林蒼祥

共同指導教授 ： 蔡蒔銓(Shih-Chuan Tsai)

淡江大學/商管學院/財務金融學系碩士班/碩士(2021年)

https://doi.org/10.6846/TKU.2021.00470

摘要

本研究旨在探討到期日效應與波動率的緩長記憶現象對投資人透過台指期貨做動態避險之績效影響，因此本研究遂以Bollerslev and Mikkelsen (1996) 所提出能夠考量波動緩長記憶現象之FIGARCH模型作為基礎，並參照 Jonathan (2007) 考量基差臨近到期日時之收斂效果，於模型當中加入基差變量因子，且由於實務上金融資產間的相關係數多屬於與時變動的狀態，故採用Engle (2002) 所提出之動態條件相關( Dynamic Conditional Correlation, DCC )模型的概念，形成DCC多變量FIGARCH模型。接著透過模型中到期日效應參數 (λ1和λ2) 與d值的調整，將模型進一步拆分為三組，分別為FIGARCH-mat、GARCH-mat以及GARCH，再改良Johnson(1960) 所提出之避險績效模型，試比較三組模型之個別績效。

根據研究結果顯示，同時考量到期日效應與波動率緩長記憶現象的FIGARCH-mat模型其避險績效最佳，僅考量到期日效應的GARCH-mat次之，若避險模型皆不考量上述兩項因子，其避險績效則最差，此結果亦凸顯基差到期收斂現象(到期日效應)與波動緩長記憶現象對台指期貨動態避險的重要性。

關鍵字

到期日效應 ； 基差收斂 ； 波動率的緩長記憶 ； FIGARCH

並列摘要

This research aims to investigate the impact of maturity effect and long memory in volatility while dynamic hedging with TAIEX futures. Based on the FIGARCH model proposed by Bollerslev and Mikkelsen (1996) which can consider the phenomenon of long memory in volatility. This research also refers to Jonathan (2007) which allows for the convergence effect of basis while the expiry date is approaching. As what he did, putting the basis variable and the maturity effect factors into the model. Besides, due to the correlation coefficients between financial assets are mostly in a state that changes over time in practice, the concept of Dynamic Conditional Correlation (DCC) model proposed by Engle (2002) is much more suitable in this case. After setting up the DCC multivariate FIGARCH model, via the adjustment of the d value and the maturity effect parameters (λ1 and λ2) in the model, the model is further divided into three groups, FIGARCH-mat, GARCH-mat and GARCH. Lastly, improve the hedging performance model proposed by Johnson (1960), and try to compare the individual performance of the three groups of models.

According to the results, the FIGARCH-mat model, which allows for both maturity effect and long memory in volatility, has the best hedging performance. The GARCH-mat, which only considers maturity effect, is the next best. The model which ignores the two factors above has the worst performance. This result also presents the importance of basis convergence (maturity effect) and long memory in volatility while dynamic hedging with TAIEX futures.

並列關鍵字

Maturity Effect ； Basis Convergence ； Long Memory in Volatility ； FIGARCH

參考文獻

國內文獻

1. 張焯然(2001)，「台股指數期貨動態避險效果之探討」，臺灣管理學刊，1，151-164。

國外文獻

1. Bollerslev, T. (1986). "Generalized Autoregressive Conditional Heteroskedasticity." Journal of Econometrics, 31(3), 307-327.

2. Bollerslev, T. (1990). "Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized Arch Model." The Review of Economics and Statistics, 31(3), 498-505.