期貨市場動態：不對稱波動，槓桿效應和長期記憶特徵

Futures Markets Dynamics: Asymmetric Volatility, Leverage Effects and Long-memory Characteristics

吳皆宏(Kai Hong Goh)

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(2018年)

https://doi.org/10.6840/cycu201800189

摘要

本研究計劃通過比較五種類型的期貨合約來檢驗其中的長期記憶，不對稱波動性和槓桿效應的收益率和波動率模型的表現。每日數據將來自2000年1月至2018年3月的二級數據庫Quandl.com。該提議利用兩個短內存模型，即自回歸移動平均 - 指數廣義自回歸條件異方差（ARMA-EGARCH）;和自回歸移動平均數 - 不對稱冪自回歸條件異方差（ARMA-APARCH）;和兩個長記憶模型，自回歸分數整合移動平均 - 分數整合指數廣義自回歸條件異方差（ARFIMA-FIEGARCH）;和自回歸分數積分移動平均 - 分數整合不對稱冪自回歸條件異方差（ARFIMA-FIAPARCH）。期貨時間序列數據的正確建模可以為交易員，基金經理和投資者提供明確的交易策略。這些發現表明，長記憶模型都能夠進行準確的預測，特別是波動性。然而，在這項研究遇到結構性變化之後，最佳擬合模型可能會從一個期貨和期間變為另一期。調查結果還可以提供對這類金融時間序列數據屬性的更多了解，並為學者和研究人員開放未來的研究渠道。未來的研究人員正在繼續使用這些模型進行研究，可以使用其他長記憶模型，如BEC-FIGARCH來實現更詳細的比較和準確性或預測。此外，這些模型也可用於預測其他可能的數據，如股票，ETF和相關的宏觀經濟數據。

關鍵字

期貨市場 ； 長期記憶 ； 槓桿效應 ； 波動性不對稱

並列摘要

This research proposal examines the performance of return and volatility models containing long-memory, asymmetric volatility, and leverage effects by comparing five types of futures contracts. The daily data will be sourced from the secondary database Quandl.com from January 2000 to March 2018. This proposal utilizes two short-memory models, the autoregressive moving average – exponential generalized autoregressive conditional heteroskedasticity (ARMA-EGARCH); and autoregressive moving average – asymmetric power autoregressive conditional heteroskedasticity (ARMA-APARCH); and two long-memory models, autoregressive fractionally-integrated moving average – fractionally-integrated exponential generalized autoregressive conditional heteroskedasticity (ARFIMA-FIEGARCH); and autoregressive fractionally-integrated moving average – fractionally-integrated asymmetric power autoregressive conditional heteroskedasticity (ARFIMA-FIAPARCH). The proper modelling of futures time-series data can provide traders, fund managers and investors in creating well-defined trading strategies. These findings suggest that both long-memory models are capable of accurate forecast, especially on the volatility. However, the best fitted models might change from one futures and period to another after this study encountered structural changes. Findings can also offer more understanding in the properties of this type of financial time-series data, and open future channels of research to academicians and researchers. Future researchers who are continuing such study using these models can utilize other long memory models such as Bivariate Error Correction Fractionally Integrated GARCH (BEC-FIGARCH) to achieve a more detailed comparison and accuracy or forecasting. In addition, these models can also be implemented to forecast other possible data such as stocks, ETFs and related macroeconomic data.

並列關鍵字

Futures market ； Long-memory ； Leverage Effects ； Volatility Asymmetric

參考文獻

References

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