| dc.contributor.advisor | 饒秀華 | zh_TW |
| dc.contributor.advisor | Rau,Hsiu Hau | en_US |
| dc.contributor.author (作者) | 王英明 | zh_TW |
| dc.contributor.author (作者) | Wang,Ying Ming | en_US |
| dc.creator (作者) | 王英明 | zh_TW |
| dc.creator (作者) | Wang,Ying Ming | en_US |
| dc.date (日期) | 2006 | en_US |
| dc.date.accessioned | 18-九月-2009 14:12:56 (UTC+8) | - |
| dc.date.available | 18-九月-2009 14:12:56 (UTC+8) | - |
| dc.date.issued (上傳時間) | 18-九月-2009 14:12:56 (UTC+8) | - |
| dc.identifier (其他 識別碼) | G0094351014 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/35122 | - |
| 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) | 94351014 | zh_TW |
| dc.description (描述) | 95 | zh_TW |
| dc.description.abstract (摘要) | 本文以 GJR-GARCH 為分析模型，針對所選八家台灣上市公司股價所計算之每日對數報酬率(daily log returns)，對於各種不斷到達的新增訊息所引起的波動反應。所納入條件變異數方程式的訊息到達(解釋變數)分別為：(1)同日成交數量(2)成交量變動率(3)星期一與星期五之日曆效應(4)不同權值規模(size-based)投資組合間的波動外溢效果。研究結果發現(1)同日成交量對於台股權值較低的小公司，有能力捕捉其波動性，但是對於權值偏高的大公司，其解釋能力顯有不足(2)成交量變化普遍會導致公司報酬率的波動(3)臺灣股市波動性並不具有星期五效應，至於星期一效應也只出現在部分的小公司(4)不同規模的投資組合間雖然互有波動外溢現象，但其不對稱性非常明顯，亦即訊息到達後，先造成大公司股價的波動，此波動再進而影響到小公司，引起小公司股價的波動。 | zh_TW |
| dc.description.abstract (摘要) | Applying the GJR-GARCH model to the daily returns of eight selected firms from Taiwan stock market, this paper examines response of variance volatility to various information arrivals which separately include (1) concurrent trading volume (2) change in trading volume (3) calendar effects, especially Modnay and Friday effects,and (4) asymmetric volatility spillover between two sized-based portfolios. The results find that concurrent trading volume as a proxy of information arrival dramatically reduces volatility persistence of the small firm`s conditional variance, but has little influence on large firm`s,and change in trading volume cause significant change in conditional variance.Although there is a conjecture that the volatility in stock markets may be higher on Monday and Friday,it can`t be found in this study.The results also strongly support that the volatility spillover effect from larger to small portfoliois more significant than that from smaller to large portfolio. | en_US |
| dc.description.tableofcontents | 1前言 72文獻回顧 93研究方法 144實證分析 204.1資料來源與敘述統計 204.2波動性與同日成交量 254.3成交量變化導致波動性變化 334.4波動性與日曆效應 364.5波動外溢效果的不對稱性 385結論與建議 46A 附錄一：樣本公司明細資料 48B 附錄二：樣本公司在研究期間內的正負衝擊日數 48表目錄1 EGARCH與GJR-GARCH 模型的選擇 162報酬率原始資料敘述統計 213報酬率原始資料定態檢驗 244 GJR-GARCH 誤差項分配型態的選擇 255 Engle與Ng訊息衝擊不對稱性診斷 276個設(八家公司)GJR-GARCH-t 配適結果 287成交量定態與自我相關檢定 298樣本公司每日成交量序列獨立性檢定(BDS test) 309加入成交量前後之條件波動比較(1) 3310加入成交量前後之條件波動比較(2) 3411 2006年12月份平均每日成交量(單位:千張) 3512以成交量變化為解釋變數之參數估計 3613一週內不同交易日之平均報酬率(%)比較 3714以星期一及星期五為解釋變數之參數估計 3715大小兩投資組合原始資料彙總統計 4016大小兩投資組合 GJR-GARCH-t配適結果 4217大小兩投資組合波動外溢係數 4518兩投資組合加入外溢效果後之波動持續性變化(1) 4519兩投資組合加入外溢效果後之波動持續性變化(2) 45圖目錄1樣本公司原始報酬序列資料分配型態 222樣本公司配適 GJR-GARCH(1,1)-t 後之標準化殘差 263小公司報酬波動與成交量之比較(2006年) 314大公司報酬波動與成交量之比較(2006年) 325八家樣本公司成交量走勢(2006年) 356四家小公司每星期一報酬波動比較(五年內計247個交易日) 397大小兩投資組合捕捉不對稱性之能力比較 43 | zh_TW |
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| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0094351014 | en_US |
| dc.subject (關鍵詞) | GARCH 模型 | zh_TW |
| dc.subject (關鍵詞) | 波動性 | zh_TW |
| dc.subject (關鍵詞) | 訊息到達 | zh_TW |
| dc.subject (關鍵詞) | 成交量 | zh_TW |
| dc.subject (關鍵詞) | 日曆效應 | zh_TW |
| dc.subject (關鍵詞) | 外溢效果 | zh_TW |
| dc.subject (關鍵詞) | 不對稱性 | zh_TW |
| dc.subject (關鍵詞) | GARCH | en_US |
| dc.subject (關鍵詞) | Volatility | en_US |
| dc.subject (關鍵詞) | Information arrival | en_US |
| dc.subject (關鍵詞) | Trading volume | en_US |
| dc.subject (關鍵詞) | Calendar effects | en_US |
| dc.subject (關鍵詞) | Spillover | en_US |
| dc.subject (關鍵詞) | Asymmetry | en_US |
| dc.title (題名) | 台股報酬波動與訊息到達之關係研究 | zh_TW |
| dc.title (題名) | Relationship between Return Volatility and Information Arrival in the Taiwan Stock Market | en_US |
| dc.type (資料類型) | thesis | en |
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