Do Realistic Distribution Assumptions Improve Risk Estimates? A Long-Term Out-of-Sample Perspective on Risk Management

34 Pages Posted: 19 May 2003

See all articles by Robert Neumann

Robert Neumann

Danske Bank - Danske Markets

Aron Akesson

Danske Bank - Danske Markets

Date Written: February 11, 2003

Abstract

Empirical studies suggest that the normal distribution is inadequate to describe the fat tails associated with returns from financial data series. However, using 26,115 daily out-of-sample observations from the Dow Jones Industrial Average over the period 1898-2002, we show that it is difficult to dismiss the normal distribution as an out-of-sample assumption. Contrary to popular belief, the findings suggest that powerful statistical methods such as the flexible multi-parameter Normal Inverse Gaussian (NIG) distribution function do not transfer their superior in-sample properties to out-of-sample observations. This indicates that there is a trade-off between realism and robustness, whereby realism in-sample may introduce instability out-of-sample.

Keywords: VaR back-test, Normal Inverse Gaussian distribution, GARCH

JEL Classification: C16, C52

Suggested Citation

Neumann, Robert and Akesson, Aron, Do Realistic Distribution Assumptions Improve Risk Estimates? A Long-Term Out-of-Sample Perspective on Risk Management (February 11, 2003). Available at SSRN: https://ssrn.com/abstract=392580 or http://dx.doi.org/10.2139/ssrn.392580

Robert Neumann (Contact Author)

Danske Bank - Danske Markets ( email )

Holmens Kanal 2-12
DK-1092 Copenhagen K
Denmark

Aron Akesson

Danske Bank - Danske Markets ( email )

Holmens Kanal 2-12
DK-1092 Copenhagen K
Denmark

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