博碩士論文 104225024 詳細資訊




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姓名 李宛柔(Wan-Rou Lee)  查詢紙本館藏   畢業系所 統計研究所
論文名稱
(A Dynamic Rebalancing Strategy for Portfolio Allocation)
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摘要(中) 投資組合的重新分配或調整權重是投資組合管理中不可或缺的一部分。 在實務中,定期重新分配策略(calendar rebalancing)是基本的重新分配策略,可利用資產重分配策略以獲得最佳投資效果。在定期重新分配策略中,投資組合經理以固定的時間間隔,定期重新分配其投資組合,並使用傳統時間的歷史數據來計算合適的權重。每當重新平衡投資組合,支付稅款和交易費用是不可避免的。因此,重新分配投資組合並不總是得到相應的回報。

在這項研究中,我們著重研究在定期的重新分配之前,先檢驗重新平衡的必要性。在product partition model假設下,通過changepoint detection來進行檢測。 我們提出一種新的重分配策略dynamic rebalancing strategy with optimal training period(DRO)進行動態的重新平衡,以改善定期重新分配策略。我們通過回測測試來檢驗我們的DRO策略的效果並與定期重新分配策略的成果進行比較。 最後,我們發現DRO策略在較長的時間間隔假設下,復合年增長率(CAGR)方面有更大的回報。另外,當經濟形勢穩定時,DRO策略的表現優於
定期重新分配策略。
摘要(英)
Reallocation, or adjust weights of portfolio is an indispensable part in portfolio management. In the practice, calendar rebalancing is a basic rebalancing strategy that either retail or institutional investors can utilize to create an optimal investment process. In calendar rebalancing, portfolio managers reallocate their portfolio at predefined intervals and use the historical data over the pass fixed time to calculate the suitable weights. It′s known that each time you rebalance the portfolio, paying for the tax and transaction fee is inevitable.However, reallocating the portfolio does not always get the relevant return.

In this study, we focus on examining the necessity of rebalancing before the regular reallocation by using changepoint detection under a product partition model. We propose a dynamic rebalancing with optimal training period (DRO) to improve the calendar rebalancing. We examine the efficiency of our rebalancing strategy by using backtesting procedure and compare with the calendar rebalancing. As a result, we discover that the DRO strategy has greater reward in terms of compound annual growth rate when the rolling window is longer. Besides, the representation of the DRO strategy is better than the calendar rebalancing in general when the economic situation is steady.
關鍵字(中) ★ 投資組合分配
★ 重新平衡
關鍵字(英) ★ Portfolio allocation
★ Rebalancing
★ Markowitz
★ Changepoint detection
論文目次 1 Introduction 1

2 Our methodology 5
2.1 The mean-variance analysis . . . . . . . . . . . . 5
2.2 Overview of the product partition model . . . . . . 6
2.3 Applying the Bayesian product partition model for dynamic rebalancing portfolio . . . . . . . . . . . . . 9

3 Simulation studies in Bayesian changepoint detection
11
3.1 Study plan for the simulations . . . . . . . . . . 11
3.2 Simulation results . . . . . . . . . . . . . . . . 12

4 Empirical analysis in Bayesian changepoints detection
15

5 Applications to dynamic rebalanced portfolio 17
5.1 Study plan . . . . . . . . . . . . . . . . . . . . 17
5.2 Summarizing the historical data . . . . . . . . . .20
5.3 Backtesting Results . . . . . . . . . . . . . . . .27

6 Conclusion and Future Extensions 34
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . 34
6.2 Future extensions . . . . . . . . . . . . . . . . .35

A Review on The Bayesian product partition changepoint detection 39
A.1 Recursive run length estimation . . . . . . . . . .39
A.2 The changepoint prior . . . . . . . . . . . . . . .40
A.3 Conjugate-exponential Models . . . . . . . . . . . 41
B Tables of backtesting results 44
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指導教授 傅承德、鄧惠文 審核日期 2017-7-11
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