| dc.contributor.advisor | 郭炳伸<br>林信助 | zh_TW |
| dc.contributor.author (Authors) | 張炳善 | zh_TW |
| dc.contributor.author (Authors) | Chang, Ping Shan | en_US |
| dc.creator (作者) | 張炳善 | zh_TW |
| dc.creator (作者) | Chang, Ping Shan | en_US |
| dc.date (日期) | 2009 | en_US |
| dc.date.accessioned | 8-Dec-2010 13:42:40 (UTC+8) | - |
| dc.date.available | 8-Dec-2010 13:42:40 (UTC+8) | - |
| dc.date.issued (上傳時間) | 8-Dec-2010 13:42:40 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0097351002 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/49552 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 國際經營與貿易研究所 | zh_TW |
| dc.description (描述) | 97351002 | zh_TW |
| dc.description (描述) | 98 | zh_TW |
| dc.description.abstract (摘要) | 本文旨在探討具有捕捉交易密集度特性的波動率測度模型是否能幫助投資者改善其資產配置的決策。因此，本文分別考量了利用兩種不同價格抽樣方式所計算出來的實現波動率 (realized volatility) 模型： (1) 日曆時間抽樣法 (calendar time sampling scheme) 與 (2) 交易次數時間抽樣法 (transaction time sampling scheme)。相較於另一廣為應用的一般化自我迴歸條件異質變異 (Generalized AutoregressiveConditional Heteroskedasticity) 模型而言，這兩種實現波動率模型的優點除了在於它們可以捕捉日內資產報酬率的動態變化之外，交易次數時間抽樣法更可以另外捕捉市場的交易密集度。因此利用交易次數間抽樣法所計算出的實現波動率相對提供給投資者較多的訊息。本文利用了West, Edison and Cho (1993) 所提出的資產組合期望效用模型衡量三種波動率測度的預測績效：(1) 實現波動率 - 日曆時間抽樣法 (2) 實現波動率 - 交易次數時間抽樣法 (3) 指數型一般化自我迴歸條件異質變異 (Exponential Generalized Autoregressive Conditional Heteroskedasticity)。我們的實證結果發現，只有在投資者風險趨避係數越小的條件下，此三種波動率測度模型兩兩之間才有較大的期望效用差距；另外，有趣的是，當市場存在異常的交易波動現象時，交易次數時間抽樣法下的實現波動率所產生的期望效用值總是不輸給另外兩種波動率測度模型的結果。 | zh_TW |
| dc.description.abstract (摘要) | This paper examines whether volatility measures that account for trading intensity would help investors make better decisions in their asset allocation. Specifically, we consider two versions of realized volatility (RV), namely, one (RV-C) constructed by regular calendar time sampling, and the other one (RV-T) constructed by transaction time sampling. Comparing to models in the GARCH family, both of these two RVs can capture intraday variations of asset return dynamics. In particular, the RV-T incorporates intraday trading intensity, and hence provides even more valuable information for investors. With the utility-based approach developed by West, Edison, and Cho (1993), we compare the predictive performance of RV-C, RV-T, and the EGARCH model in terms of utility generated with each of these three volatility measures. Our empirical results show that the three measures differ from each other mostly when investors are less risk-averse. Most interestingly, the time-deformed RV-T weakly dominates the RV-C and the EGARCH model when the markets are extremely volatile. | en_US |
| dc.description.tableofcontents | 摘要 i英文摘要 ii第一章 緒論 - 1 -第一節 研究動機 - 1 -第二節 研究方向 - 2 -第三節 研究架構 - 4 -第二章 研究方法 - 5 -第一節 抽樣方法之介紹 - 5 -一. 日曆時間抽樣法 (Calendar Time Sampling Scheme) - 5 -二. 交易次數時間抽樣法 (Transaction Time Sampling Scheme) - 7 -第二節 資產組合之期望效用函數 - 11 -第三節 實現波動率的預測模型 - 14 -第三章 實證資料描述 - 17 -第四章 實證結果 - 19 -第一節 兩種抽樣方式之實現波動率的實證統計性質 - 19 -第二節 期望效用函數的衡量 - 25 -一. 利用 e_(t+1)^2 取代 σ_(t+1)^2 - 25 -二. 利用 RV_(t+1)^2 取代 σ_(t+1)^2 - 29 -第五章 結論與建議 - 33 -附錄 - 35 -第一節 景氣時間抽樣法 (Business Time Sampling Scheme) - 35 -第二節 價格變化差距時間抽樣法 (Tick Time Sampling Scheme) - 36 -第三節 效用差距的檢定結果 - 39 -參考文獻 - 42 - | zh_TW |
| dc.format.extent | 1286512 bytes | - |
| dc.format.extent | 271774 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0097351002 | en_US |
| dc.subject (關鍵詞) | 實現波動率 | zh_TW |
| dc.subject (關鍵詞) | 時間轉換過程 | zh_TW |
| dc.subject (關鍵詞) | 交易次數時間抽樣法 | zh_TW |
| dc.subject (關鍵詞) | 風險趨避 | zh_TW |
| dc.subject (關鍵詞) | realized volatility | en_US |
| dc.subject (關鍵詞) | time-deformation | en_US |
| dc.subject (關鍵詞) | transaction time sampling | en_US |
| dc.subject (關鍵詞) | risk-aversion | en_US |
| dc.title (題名) | 資產配置,波動率與交易密集度 | zh_TW |
| dc.title (題名) | Asset allocation, Volatility and Trading Intensity | en_US |
| dc.type (資料類型) | thesis | en |
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