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題名 利用日內交易資料建構市場流動性指標
其他題名 Constructing Market Liquidity Measures with Intraday Trade Data
作者 林信助;郭炳伸
貢獻者 國立政治大學國際貿易學系
行政院國家科學委員會
關鍵詞 經濟;日內交易資料;市場流動性指標
日期 2010
上傳時間 22-Jun-2012 09:49:09 (UTC+8)
摘要 一個金融市場如果容許投資者能夠迅速買進或賣出大量的證券,同時對市場價格產生最小的影響,這個市場普遍會被認為是一個流動性佳的市場。雖然相對容易界定,但在既存文獻中所提出諸多的流動性測量指標中,幾乎每一個指標均只側重於流動性的某一個特定層面的屬性。這是令人困惑的,因為任何良好的管制或投資決策均將有賴於適當流動性指標之建立。在本研究計畫,我們提議建立一個綜合性的流動性指標,同時涵蓋所有三個流動性的屬性,亦即:成交數量、時效性,以及對於價格的影響。具體而言,我們將採用Gourieroux等學者於1999年所提出的無母數核密度方法來估計由成交值加權的交易存續期間之條件機率密度函數。根據這個估計出來的條件機率密度函數,我們至少可以計算出以下兩個有用的測度變數:流動性成本以及交易存續期間之波動程度。本研究計畫的主要的目的,是使我們能夠進一步了解金融市場的流動性。因為截至目前為止,學界尚未能針對市場流動性提出一個一致性的測度。
A market is commonly considered liquid if traders can quickly buy or sell large numbers of securities with little impact on the market price. While relatively easy to define, a wide variety of measurement proxies for liquidity have been proposed in the literature, with each measure focuses only on a particular attribute dimension of liquidity. This is troublesome, as any good regulatory and investment decisions made will be dependent upon an appropriate liquidity measure being used. In this project, we propose to engineer a new composite liquidity measure that incorporates all three attribute dimensions, namely volume, immediacy, and price impact. Specifically, we propose to estimate the density function of the value-weighted duration between trades by following the nonparmatric kernel density estimation approach proposed by Gourieroux et al. (1999). With the estimated density function, we can at least compute the following two useful measures: measures of liquidity costs and measures of volatility of the times to trade. The aim is to further our understanding of financial market liquidity, as no such unanimous measure is yet available.
關聯 基礎研究
學術補助
研究期間:9908~ 10007
研究經費:604仟元
資料類型 report
dc.contributor 國立政治大學國際貿易學系en_US
dc.contributor 行政院國家科學委員會en_US
dc.creator (作者) 林信助;郭炳伸zh_TW
dc.date (日期) 2010en_US
dc.date.accessioned 22-Jun-2012 09:49:09 (UTC+8)-
dc.date.available 22-Jun-2012 09:49:09 (UTC+8)-
dc.date.issued (上傳時間) 22-Jun-2012 09:49:09 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/53121-
dc.description.abstract (摘要) 一個金融市場如果容許投資者能夠迅速買進或賣出大量的證券,同時對市場價格產生最小的影響,這個市場普遍會被認為是一個流動性佳的市場。雖然相對容易界定,但在既存文獻中所提出諸多的流動性測量指標中,幾乎每一個指標均只側重於流動性的某一個特定層面的屬性。這是令人困惑的,因為任何良好的管制或投資決策均將有賴於適當流動性指標之建立。在本研究計畫,我們提議建立一個綜合性的流動性指標,同時涵蓋所有三個流動性的屬性,亦即:成交數量、時效性,以及對於價格的影響。具體而言,我們將採用Gourieroux等學者於1999年所提出的無母數核密度方法來估計由成交值加權的交易存續期間之條件機率密度函數。根據這個估計出來的條件機率密度函數,我們至少可以計算出以下兩個有用的測度變數:流動性成本以及交易存續期間之波動程度。本研究計畫的主要的目的,是使我們能夠進一步了解金融市場的流動性。因為截至目前為止,學界尚未能針對市場流動性提出一個一致性的測度。en_US
dc.description.abstract (摘要) A market is commonly considered liquid if traders can quickly buy or sell large numbers of securities with little impact on the market price. While relatively easy to define, a wide variety of measurement proxies for liquidity have been proposed in the literature, with each measure focuses only on a particular attribute dimension of liquidity. This is troublesome, as any good regulatory and investment decisions made will be dependent upon an appropriate liquidity measure being used. In this project, we propose to engineer a new composite liquidity measure that incorporates all three attribute dimensions, namely volume, immediacy, and price impact. Specifically, we propose to estimate the density function of the value-weighted duration between trades by following the nonparmatric kernel density estimation approach proposed by Gourieroux et al. (1999). With the estimated density function, we can at least compute the following two useful measures: measures of liquidity costs and measures of volatility of the times to trade. The aim is to further our understanding of financial market liquidity, as no such unanimous measure is yet available.en_US
dc.language.iso en_US-
dc.relation (關聯) 基礎研究en_US
dc.relation (關聯) 學術補助en_US
dc.relation (關聯) 研究期間:9908~ 10007en_US
dc.relation (關聯) 研究經費:604仟元en_US
dc.subject (關鍵詞) 經濟;日內交易資料;市場流動性指標en_US
dc.title (題名) 利用日內交易資料建構市場流動性指標zh_TW
dc.title.alternative (其他題名) Constructing Market Liquidity Measures with Intraday Trade Dataen_US
dc.type (資料類型) reporten