Properties of realized variance for a pure jump process: calendar time sampling versus business time sampling
Oomen, Roel C. A. (2004) Properties of realized variance for a pure jump process: calendar time sampling versus business time sampling. Working Paper. Coventry: Warwick Business School, Financial Econometrics Research Centre. Working papers (Warwick Business School. Financial Econometrics Research Centre) (No.04-).
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In this paper we study the impact of market microstructure effects on the properties of realized variance using a pure jump process for high frequency security prices. Closed form expressions for the bias and mean squared error of realized variance are derived under alternative sampling schemes. Importantly, we show that business time sampling is generally superior to the common practice of calendar time sampling in that it leads to a reduction in mean squared error. Using IBM transaction data we estimate the model parameters and determine the optimal sampling frequency for each day in the data set. The empirical results reveal a downward trend in optimal sampling frequency over the last 4 years with considerable day-to-day variation that is closely related to changes in market liquidity.
|Item Type:||Working or Discussion Paper (Working Paper)|
|Subjects:||H Social Sciences > HB Economic Theory|
|Divisions:||Faculty of Social Sciences > Warwick Business School > Financial Econometrics Research Centre
Faculty of Social Sciences > Warwick Business School
|Library of Congress Subject Headings (LCSH):||Sampling (Statistics), Multilevel models (Statistics), Analysis of variance, Diffusion processes, Accounting and price fluctuations|
|Series Name:||Working papers (Warwick Business School. Financial Econometrics Research Centre)|
|Publisher:||Warwick Business School, Financial Econometrics Research Centre|
|Place of Publication:||Coventry|
|Official Date:||March 2004|
|Number of Pages:||27|
|Status:||Not Peer Reviewed|
|Access rights to Published version:||Open Access|
Andersen, P. K., Ø. Borgan, R. Gill, and N. Keiding, 1993, Statistical Models Based on Counting Processes.
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