Comparison of Volatility Measures: A Risk Management Perspective
Posted: 28 Dec 2009
Date Written: Winter 2010
In this paper we address the issue of forecasting Value-at-Risk (VaR) using different volatility measures: realized volatility, bipower realized volatility, two-scales realized volatility, realized kernel, as well as the daily range. We propose a dynamic model with a flexible trend specification bonded with a penalized maximum likelihood estimation strategy: the P-spline multiplicative error model. Exploiting ultra-high-frequency data (UHFD) volatility measures, VaR predictive ability is considerably improved upon relative to a baseline GARCH but not so relative to the range; there are gains from modeling volatility trends and from using realized kernels that are robust to dependent microstructure noise.
Keywords: C22, C51, C52, C53, GARCH, MEM, P-splines, VaR, volatility measures
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