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.

Comparison of volatility measures: a risk management perspective / C.T. Brownlees; G.M. Gallo. - In: JOURNAL OF FINANCIAL ECONOMETRICS. - ISSN 1479-8409. - STAMPA. - 8(2010), pp. 29-56. [10.1093/jjfinec/nbp009]

Comparison of volatility measures: a risk management perspective

GALLO, GIAMPIERO MARIA
2010

Abstract

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.
8
29
56
C.T. Brownlees; G.M. Gallo
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2158/371660
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