A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from high-frequency returns coupled with simple modeling procedures. Building on recent theoretical results in Barndorff-Nielsen and Shephard (2004a, 2005) for related bi-power variation measures, the present paper provides a practical and robust framework for non-parametrically measuring the jump component in asset return volatility. In an application to the DM/ $ exchange rate, the S&P500 market index, and the 30-year U.S. Treasury bond yield, we find that jumps are both highly prevalent and distinctly less persistent than the continuous sample path variation process. Moreover, many jumps appear directly associated with specific macroeconomic news announcements. Separating jump from non-jump movements in a simple but sophisticated volatility forecasting model, we find that almost all of the predictability in daily, weekly, and monthly return volatilities comes from the non-jump component. Our results thus set the stage for a number of interesting future econometric developments and important financial applications by separately modeling, forecasting, and pricing the continuous and jump components of the total return variation process.

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This paper investigates the statistical properties of realized variance in the presence of market microstruc-ture noise. Different from the existing literature, the analysis relies on a pure jump process for high frequency security prices and explicitly distinguishes among alternative sampling schemes, including calendar time sam-pling, business time sampling, and transaction time sampling. The main finding of this paper is that transac-tion time sampling is generally superior to the common practice of calendar time sampling in that it leads to a lower mean squared error of realized variance. The benefits of sampling in transaction time are particularly pronounced when the trade intensity pattern is volatile. Based on IBM transaction data over the period 2000– 2004 the empirical analysis finds (i) an average optimal sampling frequency of about 3 minutes with a steady downward trend and significant day-to-day variation related to market liquidity and (ii) a consistent reduction in mean squared error of realized variance due to sampling in transaction time that is about 5 % on average but

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informs doi 10.1287/mnsc.1060.0520

Comments are welcome 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 de-termine 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.

This paper is a short review on the application of continuous-time random walks to Econophysics in the last five years.

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