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Testing Density Forecasts, With Applications to Risk Management

Testing Density Forecasts, With Applications to Risk Management The forecast evaluation literature has traditionally focused on methods of assessing point forecasts. However, in the context of many models of financial risk, interest centers on more than just a single point of the forecast distribution. For example, value-at-risk models that are currently in extremely wide use form interval forecasts. Many other important financial calculations also involve estimates not summarized by a point forecast. Although some techniques are currently available for assessing interval and density forecasts, existing methods tend to display low power in sample sizes typically available. This article suggests a new approach to evaluating such forecasts. It requires evaluation of the entire forecast distribution, rather than a scalar or interval. The information content of forecast distributions combined with ex post realizations is enough to construct a powerful test even with sample sizes as small as 100. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Business & Economic Statistics Taylor & Francis

Testing Density Forecasts, With Applications to Risk Management

Journal of Business & Economic Statistics , Volume 19 (4): 10 – Oct 1, 2001
10 pages

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References (23)

Publisher
Taylor & Francis
Copyright
© American Statistical Association
ISSN
1537-2707
eISSN
0735-0015
DOI
10.1198/07350010152596718
Publisher site
See Article on Publisher Site

Abstract

The forecast evaluation literature has traditionally focused on methods of assessing point forecasts. However, in the context of many models of financial risk, interest centers on more than just a single point of the forecast distribution. For example, value-at-risk models that are currently in extremely wide use form interval forecasts. Many other important financial calculations also involve estimates not summarized by a point forecast. Although some techniques are currently available for assessing interval and density forecasts, existing methods tend to display low power in sample sizes typically available. This article suggests a new approach to evaluating such forecasts. It requires evaluation of the entire forecast distribution, rather than a scalar or interval. The information content of forecast distributions combined with ex post realizations is enough to construct a powerful test even with sample sizes as small as 100.

Journal

Journal of Business & Economic StatisticsTaylor & Francis

Published: Oct 1, 2001

Keywords: Densities; Evaluation; Forecasting; Risk management

There are no references for this article.