本研究旨在提供簡單且實用的投資組合動態調整程序。本程序透過偵測市場及經濟狀況提出調整方法，能大幅降低損失。偵測方法主要是透過市場資料在大幅波動時所呈現之緩長記憶特性。我們提供準確穩定的緩長記憶參數估計法、設計可信的結構轉換檢定、找到準確的投資組合報酬預測法、投資組合配置產業指標。透過蒙地卡羅模擬，我們發現(1)橫斷面相關的資料在整合後會有緩長記憶的特性(2)整合資料的預測結果比個別預測的結果準確。實證部分，我們選用NASDAQ每個產業前20大市值的公司股票組成投資組合，結果顯示我們所提出的程序在風險控管方面有很好的表現。

This paper provides a practical and easy-to-implement procedure to update portfolio allocations in real time in response to volatile market conditions. The advantage of this procedure allows to greatly reduce the loss arising from market and economics uncertainty in that it could be achieved through a stable estimating method for long memory parameter, together with a reliable predictive test for structural breaks in the aggregated series, an accurate portfolio-forecasting method and particularly, an industry-specific criterion in deciding portfolio distribution, based on our new findings: (i) the aggregation of correlated time series displays the long memory property; (ii) the forecast of aggregated time series outperforms the aggregation of the forecast of each time series when speaking of the portfolio forecasting. Simulations confirm the theoretical justification and show promising finite sample performance of the proposed methodologies. As an illustration, we form a NASDAQ-Industry 20 portfolio which demonstrates the feasibility and superiority of our new real time portfolio updating procedure from risk management point of view.

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