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系統識別號 U0002-1506200820414500
中文論文名稱 以微結構模型探討NASDAQ報價價差組成份
英文論文名稱 The Components of Bid-ask Spread for NASDAQ using microstructure model
校院名稱 淡江大學
系所名稱(中) 財務金融學系碩士班
系所名稱(英) Department of Banking and Finance
學年度 96
學期 2
出版年 97
研究生中文姓名 張家銘
研究生英文姓名 Chia -Ming Chang
學號 695530641
學位類別 碩士
語文別 中文
口試日期 2008-06-06
論文頁數 104頁
口試委員 指導教授-段昌文
委員-洪坤
委員-林月能
委員-林允永
中文關鍵字 買賣價差  價差分解  買賣壓  一般動差法  Panel Data模型 
英文關鍵字 bid-ask spread  decomposition of the spread  buying and selling pressure  GMM  panel data model 
學科別分類 學科別社會科學商學
中文摘要 本文分析NASDAQ交易所於2001年4月9日,將1/16進位的分數報價變更為小數報價前後買賣價差組成份的變化情況。本文主要應用Huang and Stoll (1997) 在考量買賣壓力下的價差分解模型,並以GMM估計法進行參數估計。價差分解模型有二維、三維與買賣壓模型,以價差分解為逆選擇成本、存貨持有成本與委託單處理成本三種價差組成份。
實証結果發現,價差於改變小數報價後顯著地縮小,二維模型實證結果顯示價差縮小幅度與個股交易量大小呈正相關,且改變小數報價後逆選擇與存貨持有成本亦為減少。三因子模型實証結果顯示,大與小市值之逆選擇成本在改變小數報價後為減少的,存貨持有成本則為增加,中市值恰好相反;在考慮買賣壓後,逆選擇成本於小數報價後亦為顯著的減少,存貨持有成本則是增加。
最後,Panel Data模型加入虛擬變數探討與五個影響變數來觀察小數報價前後對價差組成份的影響,實證結果發現,在大與小市值中,平均交易間隔時間與成交量對二因子模型所估計出之逆選擇與存貨持有成本的影響程度在事後是減少的,在中市值中影響是增加的,價差對於二因子模型所估計出之逆選擇與存貨持有成本是影響程度減少;在大市值組中,平均交易間隔時間、成交量與價差對三因子模型中所估計出之逆選擇成本影響程度在事後是減少的,在中與小市值組中影響程度則是增加的;平均交易間隔時間對買賣壓模型所估計出之逆選擇成本和存貨持有成本的影響程度在事後是增加的。
英文摘要 The paper estimate the components of bid-ask spread around the decimalization of NASDAQ on April 9, 2001. We apply Huang and Stoll (1997)’s structure models to decompose of bid-ask spread based on two-/three way and buying and selling pressure models, estimating the components of bid-ask spread through the GMM approach. Furthermore, the bid-ask spread can be decomposed into three components including the order-processing, inventory-holding, and adverse selection costs.
We show that the spreads is more decrease after post-decimal pricing. The empirical results of two-way model illustrates that the linkage between the declined level of the spread and their trading volumes are positive relationship. Thus the inventory and adverse selection costs would be decreased during post-decimal pricing. In three-way model, results show that adverse selection costs decreased and inventory holding costs increased after decimalization in the high-/ low- size, and contrary to the mid-size. For structure model considering buying and selling pressure, we find that the adverse selection cost is decreased significantly and the inventory holding cost is increasing after decimal pricing.
Finally, we use Panel Data model with dummy variables and five agency variables to test the influence on the components of bid-ask spread during pre-/post- decimal pricing. The empirical results that the inventory and adverse selection costs are estimated by two-way model effect on average trading between time and trading volume is decreasing for high-/low- size samples during post-decimal pricing, but increased in the mid-size. The effect of spreads on the inventory and adverse selection costs for full sample would be decreasing. The empirical results that the adverse selection costs are estimated by three-way model effect on average trading between time, trading volume and spread is decreasing for high-size samples during post-decimal pricing, but increased in the mid-/low- size. The empirical results that the adverse selection costs are estimated by buying and selling pressure model effect on average trading between time is increasing for full sample during post-decimal pricing.
論文目次 目錄
中文摘要 I
英文摘要 II
目錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第三節 研究架構 4
第四節 研究流程 5
第二章 文獻回顧 6
第一節 NASDAQ交易市場與制度 6
第二節 買賣價差組成份之相關文獻 12
第三節 成交與報價檔次配對之相關文獻 19
第四節 有關交易指示方向設定之相關文獻 20
第五節 探討小數報價前後價差組成份變化之相關文獻 22
第三章 理論模型 24
第一節 價差分解之理論模型 24
第二節 三因子分解價差模型 26
第三節 考慮買賣壓的價差分解模型 30
第四章 研究方法 32
第一節 樣本 32
第二節 交易方向之判斷 33
第三節 一般化動差(GMM)估計法 34
第四節 Panel Data模型設計與分析 41
第五章 實證結果 46
第一節 研究期間與樣本描述 46
第二節 二維分解價差之實證 50
第三節 三因子分解價差之實證 64
第四節 考慮買賣壓的價差分解模型之實證 71
第五節 價差分解模型之結論 79
第六節 Panel Data分析 82
第六章 結論 92
第一節 結論 92
第二節 研究限制 93
參考文獻 94
附錄I NASDAQ-100公司簡表 98
附錄II NASDAQ-100以市值分三組之公司代碼 100
附錄III 研究樣本統計量 101

表目錄
表2.1.1 NASDAQ交易所最小報價單位的改變 11
表2.2.1 討論價差組成份之相關文獻 18
表2.5.1 探討小數報價前後價差組成份的變化 23
表5.1.1 NASDAQ-100以市值分類前、中、後各10家公司與QQQ之統計資料 48
表5.2.1 二維價差分解模型之估計結果 52
表5.2.2 二維價差分解模型之精確性檢定後估計結果 54
表5.2.3 二維分解價差模型以成交量大小分類之估計結果 58
表5.2.4 二維分解價差模型以成交量大小分類之精確性檢定估計結果 61
表5.3.1 三因子分解價差模型之檢定估計結果 67
表5.3.2 三因子分解價差模型之精確性檢定估計結果 69
表5.4.1 買賣壓價差分解模型之估計結果 73
表5.4.2 買賣壓價差分解模型以10家公司為一組之精確性檢定估計結果 75
表5.4.3 買賣壓價差分解模型以30家公司為一組之精確性檢定估計結果 77
表5.5.1 價差分解模型參數顯著個數表 81
表5.6.1 F檢定與Hausman Test檢定結果 84
表5.6.2 Panel Data分析 90
表5.6.3 Panel Data分析統計 91

圖目錄
圖1.4.1 研究流程 5
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中文部份
1. 台灣證券交易所編輯出版,2007年7月,「世界主要證券市場相關制度」。
網站部份
1. 紐約證交所,http://www.nyse.com/
2. 那斯達克交易所,http://www.nasdaq.com/
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