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Return volatility and trading volume...

Andersen, Torben Gustav.

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Return volatility and trading volume in financial markets: An information flow interpretation of stochastic volatility.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Return volatility and trading volume in financial markets: An information flow interpretation of stochastic volatility./
作者:	Andersen, Torben Gustav.
面頁冊數:	281 p.
附註:	Adviser: Peter C. B. Phillips.
Contained By:	Dissertation Abstracts International54-01A.
標題:	Economics, Finance. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9314781

Return volatility and trading volume in financial markets: An information flow interpretation of stochastic volatility.
Andersen, Torben Gustav.

Return volatility and trading volume in financial markets: An information flow interpretation of stochastic volatility. - 281 p.

Adviser: Peter C. B. Phillips.

Thesis (Ph.D.)--Yale University, 1992.

This dissertation studies the dynamic relation between return volatility and trading volume under the assumption that the flow of information drives both returns and volume. In this scenario both series may assist in the construction of measures of volatility. Moreover, the investigation brings evidence to bear on the workings of financial markets in general and the return generating mechanism in particular.Subjects--Topical Terms:

626650
Economics, Finance.

Return volatility and trading volume in financial markets: An information flow interpretation of stochastic volatility.
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100 1 $a Andersen, Torben Gustav. $3 1005930
245 1 0 $a Return volatility and trading volume in financial markets: An information flow interpretation of stochastic volatility.
300 $a 281 p.
500 $a Adviser: Peter C. B. Phillips.
500 $a Source: Dissertation Abstracts International, Volume: 54-01, Section: A, page: 0267.
502 $a Thesis (Ph.D.)--Yale University, 1992.
520 $a This dissertation studies the dynamic relation between return volatility and trading volume under the assumption that the flow of information drives both returns and volume. In this scenario both series may assist in the construction of measures of volatility. Moreover, the investigation brings evidence to bear on the workings of financial markets in general and the return generating mechanism in particular.
520 $a Chapter 1 demonstrates how the information flow interpretation of return volatility is compatible with formal models of market microstructure in which informational asymmetries and exogenous liquidity needs motivate trade. The specification is consistent with the "mixed distribution hypothesis" for daily returns since it is governed by the mixture of distributions characterizing respectively the return innovations associated with the arrival of news and the interdaily flow of information arrivals. However, the latter distribution is exogenous so the model allows a derivation of the contemporaneous joint distribution of returns and volume, but is silent on intertemporal aspects.
520 $a Chapter 2 is concerned with modeling systems of equations involving stochastic volatility processes. Stochastic volatility is defined and shown to represent a natural extension of the popular ARCH processes. The distinguishing feature is the measurability conditions imposed on the conditional variance process. Next, a class of Stochastic (Transformed) AutoRegressive Volatility models, (STARV) models, is identified. A direct characterization of the marginal distributions is impossible but conditions for strict stationarity are established and the unconditional moments are determined. Hence, estimation by Generalized Method of Moments techniques is feasible.
520 $a Finally, chapter 3 combines chapters 1 and 2 by assuming that the information flow belongs to the STARV class. This model of the return-volume relation is investigated empirically for IBM common stocks. Moreover, a method for generating estimates and forecasts of volatility is developed. Actual estimates and forecasts are contrasted to those obtained from an ARCH model. The bivariate model predicts substantially lower persistence of volatility than do ARCH models. This is consistent with results in the literature that find a "robustifying" impact of including volume in the forecasting procedure.
590 $a School code: 0265.
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791 $a Ph.D.
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