【毕业设计外文翻译用----金融市场微观结构外文文献】andersen-bdl99volatility.pdf

(Understanding, Optimizing, Using and Forecasting)
Realized Volatility and Correlation*
Torben G. Andersena, Tim Bollerslevb, Francis X. Dieboldc and Paul Labysd
September 1999
This print/draft: October 26, 1999
__________________
* This work was supported by the National Science Foundation. We are grateful to Olsen &
Associates, Zurich, for data, advice and hospitality.
a Department of Finance, Kellogg Graduate School of Management, Northwestern University,
phone: 847-467-1285, e-mail: t-******@
b Department of Economics, Duke University, and NBER, phone: 919-660-1846, e-mail:
******@
c Department of Finance, Stern School of Business, New York University, Departments of
Economics and Statistics, University of Pennsylvania, and NBER, phone: 610-585-4057,
e-mail: ******@
d Graduate Group in Economics, University of Pennsylvania, 3718 Locust Walk, Philadelphia, PA
19104-6297, phone: 919-929-2549, e-mail: ******@
Volatility is central to many applied issues in finance and financial engineering, ranging
from asset pricing and asset allocation to risk management. Hence, at least since the seminal
contribution by Merton (1980) and later work by Nelson (1992), financial economists have been
intrigued by the very high precision with which volatility can be estimated under the diffusion
assumption routinely invoked in theoretical work. The basic insight follows from the observation
that precise estimation of diffusion volatility does not require a long calendar span of data; rather,
volatility can be estimated arbitrarily well from an arbitrarily short span of data, provided that
returns are sampled sufficiently frequently. This contrasts sharply with precise estimation of the
drift, which generally requires a long calendar span of data, regardless of the frequency with
which returns are sampled.
Consequently, the volatility literature has steadily progressed toward the use of higher-