:::

詳目顯示

回上一頁
題名:長期投資組合管理之權變避險模式:理論與台灣金融市場之實證
作者:蔡孟哲
作者(外文):TSAI, MENG-CHE
校院名稱:國立高雄第一科技大學
系所名稱:財務金融學院博士班
指導教授:許溪南
楊德源
學位類別:博士
出版日期:2018
主題關鍵詞:權變避險模式避險比率ETF技術分析台指期貨台指選擇權Contingent Hedging ModelHedge RatiosETFTechnical AnalysisTAIEX FuturesTAIEX Options
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:2
投資組合管理伴隨著有效避險可視為整體投資組合管理思維的一環,它是一個動態的過程,不是隨興注入式的避險活動。本論文旨在提出一個長期投資組合管理之權變避險模式,並以台灣金融市場資料驗證此投資組合管理避險模式之優越性。此避險策略包括三大決策:避險時機、避險工具及避險比率的選擇。元大台灣50是一個相當多角化的ETF,成分股涵蓋台股各產業的龍頭股,是非常熱門的投資商品,尤其在大盤長期看漲的前提之下,此項商品可以當作一個優良的長期投資組合標的。
本文藉由技術分析方法(如KD與MACD指標(合稱KMD模式)及濾嘴法則)判斷避險時機點,並以元大台灣50反1、台指期及台指選擇權當作避險的工具。此模式好處為一方面可享受標的物上漲時的利益並且也可鎖定下方風險,使整個投資報酬率大為提升。實證結果發現KMD模式運用在日資料上,並以台指期貨避險,得到的年化報酬率為最高,可達13.33%;若採用不避險的買進持有(Buy and hold)策略僅為9.51%。由此可知,權變避險模式策略報酬率遠大於買進持有策略,適合運用於長期投資組合管理上。
Portfolio management along with effective hedging can be viewed as a portion of the thought of portfolio management. It is a dynamic process, not a casually injected hedging activity. The purposes of this dissertation are to propose a contingent hedging model for the long-term portfolio management and to empirically test the superiority of this portfolio management model using the data for the Taiwan’s financial markets. The contingent hedging model contains three major decisions:the selection of hedging timing, hedging vehicles and hedging ratios. Yuanta Taiwan 50 is a well-diversified ETF, which is a stock portfolio consisting of Taiwan's blue chips in various industries, and has already become a very popular investment product. Under the premise of long-term bullish stock market, this product can be viewed as an excellent object of long-term portfolio.
This dissertation uses technical analysis methods such as KD and MACD indicators, collectively referred to as the KMD model, and filter rules to determine the timing of hedging, and Yuanta Daily Taiwan 50 Bear -1X ETF, TAIEX Futures and TAIEX Options as hedging vehicles. Empirical results show that the KMD model used for the selection of hedging timing in Yuanta Taiwan 50 daily data and the TAIEX Futures used as a hedging vehicle achieves the highest annualized rate of return of 13.33%, largely outperformed the buy and hold strategy of 9.51% only. Thus we conclude that the performance of the contingent hedging model strategy is far superior to the buy and hold strategy, suitable for long-term portfolio management.
中文參考文獻

1.王邵佑,2000,隨機指標 (KD值)投資績效之實證研究,國立台北大學企業管理研究所,碩士論文。

2.林天運,2007,大盤未來走勢預測-KD指標的實證分析,國立成功大學國際企業研究所,碩士論文。

3.周怡貞,2004,台灣進出口商最適避險時機之探討-以新台幣對美元為例,國立成功大學企業管理研究所,碩士論文。

4.洪志豪,1999,技術指標KD、MACD、RSI與WMS%R之操作績效實證,國立台灣大學國際企業學研究所,碩士論文。

5.洪國興,2011,不同外匯避險部位、策略、工具及期間之避險效果,國立中央大學財務金融研究所,碩士論文。

6.胡嘉琪,2012,隨機指標、乖離率指標與指數股票型基金之報酬,南台科技大學財務金融研究所,碩士論文。

7.許溪南,賴彌煥,2000,“權變投資組合保險在台灣股市之應用”,風險管理學報,2卷,2期,頁89-118。

8.許溪南,何怡滿,劉玉琦,2009,“權變避險模式在台灣股市之應用”,台灣管理學刊,9卷,1期,頁23-46。

9.許溪南,何怡滿,劉泰山,2011,“KD及MACD在避險時機之應用:以台指期貨避險為例”,東吳經濟商學學報,72期,頁109-138。

10.許溪南,何怡滿,張瓊如,2012,“KD 與MA技術指標在避險時機的應用:以台股選擇權為例”,輔仁管理評論,19卷,1期,頁27-46。

11.黃彥聖,1995,“移動平均法的投資績效”,管理評論,14卷,1期,頁47-67。

12.張清良,2008,“股票市場買賣研判指標的應用”,國立中正大學財務金融研究所,碩士論文。

13.John J. Murphy著,2000,金融市場技術分析(上),初版,黃嘉斌譯,寰宇財金出版社,台北。

英文參考文獻

1.Akhigbe, A., Makar, S., Wang, L. and Whyte, A. M., 2018, “Interest Rate Derivatives Use in Banking:Market Pricing Implications of Cash Flow Hedges”, Journal of Banking and Finance, vol. 86, no. c, pp. 113-126.

2.Alexander, S. S., 1961, “Price Movements in Speculative Markets:Trends or Random Walks”, Industrial Review , vol. 2, no. 2, pp.199-218.

3.Bansal, V. K. and Marshall, J. F., 2015, “A Tracking Error Approach to Leveraged ETFs:Are They Really That Bad?” Global Finance Journal , vol. 26, no. c, pp.47-63.

4.Brock, W., Lakonishok, J. and Lebaron, B., 1992, “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns”, Journal of Finance, vol. 47, no. 5, pp.1731-1764.

5.Corrado, C. J. and Lee, S. H., 1992, “Filter Rule Tests of the Economic Significance of Serial Dependencies in Daily Stock Return”, Journal of Financial Research, vol. 15, no. 4, pp.369-387.

6.Chang, S. L. and Hsu, Y. W., 2017, “An Investigation of Short- and Long-Term Tracking Performance and Portfolio Simulation of Leveraged and Inverse ETFs”, Journal of Futures and Options, vol. 10, no. 3, pp.85-165.

7.Coqueret, G., Martellini, L. and Milhau, V., 2017, “Equity Portfolios with Improved Liability-Hedging Benefits”, Journal of Portfolio Management, vol. 43, no. 2, pp.37-49.

8.Ederington, L.H., 1979, “The Hedging Performance of the New Futures Markets”, Journal of Finance, vol. 34, no. 1, pp.157-170.

9.Fama, E. F., 1970, “Efficient Capital Market:A Review of Theory and Empirical Works”, Journal of Finance, vol. 25, no. 2, pp.383-417.

10.Ghosh, A., 1993, “Hedging with Stock Index Futures:Estimation and Forecasting with Error Correction Model”, Journal of Futures Markets, vol. 13, no. 7, pp.743-752.

11.Hsu, P. H. and Kuan, C. M., 2005, “Reexamining the Profitability of Technical Analysis with Data Snooping Checks”, Journal of Financial Econometrics, vol. 3, no. 4, pp. 606-628.

12.Hsu, H., 2013, “The Return Distribution, Properties, and Optimal Strike Price for the Portfolio Insurance Strategy”, Journal of Futures and Options, vol. 6, no. 2, pp. 73 -103.

13.Johnson, L. L., 1960, “The Theory of Hedging and Speculation in Commodity Futures”, Review of Economic Studies, vol. 27, no. 3, pp. 139-151.

14.Jorion, P., 2003, “Portfolio Optimization with Tracking-Error Constraints”, Financial Analysts Journal, vol. 59, no. 5, pp.70-82.

15.Kenourgios, D., 2008, “Hedge Ratio Estimation and Hedging Effectiveness:the Case of the S&P500 Stock Index Futures Contract”, International Journal of Risk Assessment and Management, vol. 9, no. 1-2, pp.121-134.

16.Kwon, K. Y. and Kish, R. J., 2002, “Technical Strategies and Return Predictability: NYSE”, Applied Financial Economics, vol. 12, no. 9, pp.639-653.

17.Lee, T. S. and Lin, C. C., 2017, “The Factors Affecting Tracking Errors of Leveraged and Inverse ETF in Taiwan, Japan and Korea”, Journal of Futures and Options, vol. 10, no. 2, pp.51-87.

18.Lo, W. A., Mamaysky, M. and Wang, J., 2000, “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation”, Journal of Finance, vol. 55, no. 4, pp.1705-1765.

19.Markowitz, H. M., 1952, “Portfolio selection”, Journal of Finance, vol. 7, no. 1, pp.77-91.

20.Park, C. H. and Irwin, S. H., 2004, “The Profitability of Technical Analysis:A Review”, AgMAS Project Research Report, no. 2004-04, Available at SSRN:https://ssrn.com/abstract=603481.

21.Park, C. H. and Irwin, S. H., 2007, “What Do We Know about the Profitability of Technical Analysis”, Journal of Economic Surveys, vol. 21, no. 4, pp.786-826.

22.Pruitt, S. W. and White, R. E., 1998, “The CRISMA Trading System:Who Says Technical Analysis Can’t Beat the Market?” Journal of Portfolio Management, vol. 14, no. 3, pp.55-58.

23.Szakmary, A. C., Davidson III, W. N. and Schwarz, T. V., 1999, “Filter Tests in NASDAQ Stocks”, The Financial Review, vol. 34, no.1, pp.45-70.

23.Working, H., 1962, “New Concepts Concerning Futures Markets and Prices”, The American Economic Review, vol. 52, no. 3, pp.431-459.


 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE