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系統識別號 U0002-1906201315361300
中文論文名稱 台灣債券型基金之風險管理、擇時能力與門檻效果之實證研究
英文論文名稱 An Empirical Study on the Risk Management, Market Timing Ability, and Threshold Effect of Bond Funds in Taiwan
校院名稱 淡江大學
系所名稱(中) 財務金融學系博士班
系所名稱(英) Department of Banking and Finance
學年度 101
學期 2
出版年 102
研究生中文姓名 李喬銘
研究生英文姓名 Joe-Ming Lee
學號 898530026
學位類別 博士
語文別 英文
口試日期 2013-05-31
論文頁數 65頁
口試委員 指導教授-李沃牆
委員-李沃牆
委員-古永嘉
委員-林景春
委員-何宗武
委員-韓千山
委員-洪明欽
委員-郭嘉祥
中文關鍵字 債券型基金  基金分流政策  Copula Function  ARMAX-GARCH Model  門檻效果 
英文關鍵字 Bond Fund  Fund Segregation Policy  Copula Function  ARMAX-GARCH Model  Threshold Effect 
學科別分類
中文摘要 本論文探討台灣債券型基金之風險管理、並檢定其擇時能力與門檻效果。首先,我們使用五個COPULA函數檢驗債券型基金實施分流政策對於風險與收益之影響。接著使用ARMAX-GARCH 模型檢驗債券市場的完整性和債券型基金擇時能力。最後,本文應用門檻自我迴歸模型(Threshold Autoregressive Model) 檢驗台灣債券型基金分流政策前後的基金流量和投資風險之間的門檻效果關係。
實證結果顯示,在台灣債券型基金之風險管理方面,持有OS&OP的比例與風險值呈正相關,這意味著OS&OP是影響債券型基金的絕對關鍵因素,實施債券型基金分流政策後,解決基金市場面臨的流動性危機並降低市場風險值。
而在擇時能力的實證結果顯示,由於台灣債券市場結構完整性和缺乏流動性,影響到債券經理人認為難以靈活調整投資組合的分配和系統性風險,造成除HM模型外,無論是在TM模型,TM-ARMAX-GARCH模型或HM ARMAX-GARCH模型下,大部分債券型基金均不具備選股能力和顯著的系統性風險及擇時能力。因此,本文建議台灣的債券市場應該開發出更多的投資產品,提高市場的流動性,並擴大基金經理人的操作空間。
在門檻效果檢定方面,實證顯示債券型基金的投資者只關心的投資回報,而忽略了投資風險。特別是當規模不斷擴大的債券型基金,基金投資者相信該基金穩賺不賠。為了滿足投資者,債券基金經理人追逐短期績效以吸引投資者,而忽略了風險。本文建議投資者應注意風險,而基金經理人除了追求利潤之外,應該去善盡管理風險的專業與義務。
英文摘要 This dissertation discusses an empirical study on the risk management, market timing ability, and threshold effect of bond funds in Taiwan. First, we apply five widely used copula functions to understand the correlation between the OS&OP ratios and the mean return rate of the net value, as well as the VaR of Taiwan’s bond segregation policy. Second, we investigate bond market integrity and market timing ability in Taiwan’s bond market via the ARMAX-GARCH model. Third and finally, this paper constructs the threshold autoregressive model to investigate the relationship between bond funds’ net flows and the investment risk before and after the bond segregation policy.
From examining the VaR of bond funds in Taiwan, findings show that the OS&OP ratio has a positive correlation with their VaR. This implies that the OS&OP ratio serves as an absolute key factor for bond funds. In fact, after Taiwan’s bond segregation policy in year 2007, investors had to immediately deal with a bond market of scarce liquidity. We thus conclude that the bond fund segregation policy significantly reduced the risk for investing in bond funds.
Taking a look at the market timing ability of bond funds in Taiwan, the results show that due to market integrity and the lack of liquidity in Taiwan’s bond market, a bond manager finds it difficult to flexibly adjust for any portfolio allocation and systemic risk. No matter in the T-M model, T-M ARMAX-GARCH model, or H-M ARMAX-GARCH model, this study’s results present that most bond funds do not have selective ability, timing ability, nor significant systemic risk, except for the H-M model. Hence, we recommend that Taiwan’s bond market should develop more investment products, in order to improve liquidity in the market and to enlarge the operating space of bond fund managers.
For the measurement of the relationship between bond funds’ net flow and the investment risk of bond funds in Taiwan, the findings herein show that bond fund investors are concerned about their investment return while neglecting investment risk. In particular, when a bond fund expands its size, investors believe that the fund cannot lose any money on investment products. In order to satisfy such a belief, bond fund managers only target short-term returns so as to attract more or new investors, while ignoring any risk. Thus, this paper recommends that investors pay attention to risk and that fund managers should fulfill their obligations in addition to the pursuit of profit. Moreover, bond funds should include risk management professionals to help run the funds.
論文目次 Table of Content Page
LIST OF TABLES VII
LIST OF FIGURES VIII
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 OBJECTIVES 6
1.3 THE FLOW CHART 8
CHAPTER 2 LITERATURE REVIEW 9
2.1 RISK MANAGEMENT 9
2.2 MARKET TIMING ABILITY 11
2.3 THRESHOLD EFFECT 12
CHAPTER 3 METHODOLOGY 14
3.1 DATA RESOURCE 14
3.2 COPULA MODEL 15
3.3 ARMAX-GARCH BASED CAPM MODEL 19
3.4 THE THRESHOLD AUTOREGRESSIVE MODEL 21
CHAPTER 4 EMPIRICAL RESULTS AND ANALYSIS 30
4.1 RISK MANAGEMENT 30
4.2 MARKET TIMING ABILITY 41
4.3 THE THRESHOLD EFFECT 47
CHAPTER 5 CONCLUSION AND REMARKS 54
5.1 RISK MANAGEMENT 55
5.2 MARKET TIMING ABILITY 56
5.3 THE THRESHOLD EFFECT 57
REFERENCES 59

LIST OF TABLES
TABLE4.1-1 SUMMARY STATISTICS OF BOND FUNDS - OS&OP, RP, S-CD, AND THE SCALE OF BOND FUND SALES 31
TABLE4.1-2. STUDENT’S PAIR T-TEST RESULTS 33
TABLE4.1-3. KENDALL’S TAU OF COPULA FUNCTIONS 34
TABL4.1-4. SUMMARY STATISTICS OF VOLATILITY 37
TABLE 4.1-4. SUMMARY STATISTICS OF VAR 39
TABLE 4.1-5. KENDALL’S TAU OF COPULA FUNCTIONS 40
TABLE4.2-1. SUMMARY STATISTICS OF BOND FUNDS’ PERFORMANCES 42
TABLE4.2-2. THE T-M MODEL AND T-M ARMAX-GARCH MODEL RESULTS 43
TABLE4.3-1. SUMMARY STATISTICS OF BOND FUNDS 47
TABLE4.3-2. PANEL UNIT ROOT TEST RESULTS 48
TABLE4.3-3. ESTIMATED COEFFICIENTS OF THE FIXED EFFECT RESULTS 50
TABLE4.3-4. TESTS FOR THE RESULTS OF THE THRESHOLD EFFECTS 50
TABLE4.3-4. THRESHOLD AUTOREGRESSIVE MODEL’S RESULTS 52







LIST OF FIGURES
FIGURE 1.3-1 THE FLOW CHAT 8
FIGURE (4.1-1A). VARIATION OF OP&OS RATIO - BEFORE AND AFTER BOND SEGREGATION POLICY 32
FIGURE (4.1-1B). VARIATION OF RP RATIO - BEFORE AND AFTER BOND SEGREGATION POLICY 32
FIGURE (4.1-1C). VARIATION OF ST-D - BEFORE AND AFTER BOND SEGREGATION POLICY 32
FIGURE (4.1-1D). VARIATION OF THE SCALE - BEFORE AND AFTER BOND SEGREGATION POLICY 32
FIGURE (4.1-1E). OS&OP RATIO VERSUS MEAN RETURN RATE OF BOND FUNDS 35
FIGURE (4.1-1F). P RATIO VERSUS MEAN RETURN RATE OF BOND FUNDS 36
FIGURE (4.1-1G). S -CD RATIO VERSUS MEAN RETURN RATE OF BOND FUNDS 36
FIGURE (4.1-1H). THE SCALE OF BOND FUND SALES VERSUS MEAN RETURN RATE OF BOND FUNDS 36
FIGURE (4.1-1I). HISTORICAL VOLATILITY OF NET VALUE RETURN - BEFORE AND AFTER THE POLICY 38
FIGURE (4.1-1J). GARCH VOLATILITY OF NET VALUE RETURN - BEFORE AND AFTER THE POLICY 38
FIGURE (4.1-1K). OS&OP RATIO VERUS VAR_HIS_ALL OF BOND FUNDS 40
FIGURE (4.1-1L). OS&OP RATIO VERUS VAR_GARCH_ALL OF BOND FUNDS 41

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