Characterizing the Distribution of High Frequency Returns by Realized Quantile-based Measures Preliminary draft Please do not cite without permission

نویسنده

Charles S. Bos

چکیده

The risk associated with financial returns is commonly measured through an estimator of the variance. Variance estimators based on high frequency returns are susceptible to the influence of jumps, and these should be accounted for. This article proposes to measure the uncertainty and other characteristics of return series through a quantile-based approach, which automatically is robust to outliers in the data. The quantile-based measures for location, dispersion, skewness and kurtosis can be used for testing whether underlying returns follow a prespecified distribution as well. A high frequency time series of returns on IBM stock is used for clarifying the possibilities of a quantile-based approach for measuring and predicting risk.

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