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  • 學位論文

結合多因子與風險平價模型於資產管理的應用

The application of multi-factor model and risk parity model to asset management

指導教授 : 曹承礎
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摘要


資產配置一直為金融市場投資者所關注,經歷多輪的牛市和熊市,如何應對各類資產價格隨著經濟景氣的變化也引起越來越多的重視。在追求收益的同時識別風險,避免資產價格劇烈波動造成的投資策略失誤,是眾多學者研究的重點。 本篇論文研究美國金融市場,依據美林時鐘理論選取包含股票類、債權類、大宗商品類、現金類的8個投資標的在2010年3月到2021年2月的日度價格數據。在收益面向和風險面向分別選取了多因子模型和風險平價模型,並結合金融計量模型和機器學習演算法,構建投資組合。 實證的結果顯示組成的多空風險平價組合和純多風險平價組合,比較其他模型有較小的最大虧損率,模型的回撤控制能力優秀。同時組合也擁有較高的夏普比率,意味著單位風險上所產生的超額收益超過其他模型的組合。結合投資的風險偏好增加槓桿,可以達到增加收益的效果。同時對波動和最大虧損有個良好的預判,避免了因資產價格震蕩引發投資心理的變化,從而減少投資決策的失誤。

並列摘要


Asset allocation has been a concern for financial market investors, and it has been more and more important to deal with the changes in asset prices within the business cycles. It is of great importance avoiding failure in investment decisions in the process of pursuing benefits while identifying risks and avoiding the volatility of asset prices. Based on The Merrill Lynch Investment Clock theory, this paper studies the financial markets of the United States using price data of the eight asset classes that include equity, bond, commodity and cash through February 2010 to February 2021. In this paper, the multi-factor model and the risk parity model are selected, and the econometric model and the machine learning algorithm are used to build investment portfolio. The empirical results show that the investment portfolio base on these models has outstanding control ability of max draw-down compared to others. The portfolio also has a higher sharp ratio, which means that the excess return generated by the unit risk exceeds the portfolio of other models. Increasing leverage can generate more return, with risk aversion degree taken into account. To gain a good pre-judgment of volatility and the biggest loss can avoid failures of investment decision caused by the change of asset price swings.

參考文獻


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