處理報酬不確定性之投資組合比較

Portfolio Performance Comparisons under Return Uncertainty

黃銘義(Ming-Yi Huang)

指導教授 ： 余菁蓉

國立暨南國際大學/管理學院/資訊管理學系/碩士(2021年)

https://doi.org/10.6837/ncnu202100030

摘要

穩健最佳化 (Robust Optimization) 和分佈式穩健最佳化 (Distributionally Robust Optimization) 兩種模式常用來處理報酬不確定性的問題。本研究中，因為穩健最佳化有過度保守的特性，所以選擇分佈式穩健最佳化的模式。透過Wasserstein計量為基礎，以資料驅動最佳化 (Data-Driven Optimization) 的方式生成資料，結合Omega模型，提出Omega_Wasserstein模型處理報酬不確定性的問題，最佳解是採用線性混合整數規劃。為了符合現實情況，模型考慮放空及交易成本機制。本研究有四個發現，第一，Omega_Wasserstein模型提升原始Omega模型的績效表現；第二，Omega_Wasserstein模型適用於低波動的國家指數型ETF而不適合股票市場；第三，保守型的投資模式在景氣不好時相對績效表現較好，例如，次級房貸風暴；第四，隨著Wasserstein模糊性半徑的減少，會提升Omega_Wasserstein模型的績效。

關鍵字

模糊性集合 ； 資料驅動最佳化 ； 分佈式穩健最佳化 ； Omega模型 ； Wasserstein計量

並列摘要

Robust optimization (RO) and distributionally robust optimization (DRO) are two methods commonly used to deal with return uncertainty. In this study, we implement DRO while RO is usually considered overconservative. Based on the Wasserstein metric, we propose a new Omega_Wasserstein model where we generate the underlying data by data-driven optimization and combine the Omega model to cope with return uncertainty. The optimal solution is then obtained by linear mixed-integer programming. In our search for a practical outcome, we take short-selling and transaction costs into consideration. There are four major discoveries in the research. First, the Omega_Wasserstein model im-prove the original Omega model; Second, the Omega_Wasserstein model is more suitable for low-volatility market such as the international ETF rather than stock market; Third, the conservative strategy has better performance in bear market such as the subprime mort-gage crisis; Fourth, the lower the Wasserstein radius is, the better the performance of the Omega_Wasserstein model will be.

並列關鍵字

Ambiguity Set ； Data-driven Optimization ； Distributionally Robust Optimization ； Omega Model ； Wasserstein Metric

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