  We analyze the performance of RiskMetrics, a widely used methodology for
measuring market risk. Based on the assumption of normally distributed returns,
the RiskMetrics model completely ignores the presence of fat tails in the
distribution function, which is an important feature of financial data.
Nevertheless, it was commonly found that RiskMetrics performs satisfactorily
well, and therefore the technique has become widely used in the financial
industry. We find, however, that the success of RiskMetrics is the artifact of
the choice of the risk measure. First, the outstanding performance of
volatility estimates is basically due to the choice of a very short (one-period
ahead) forecasting horizon. Second, the satisfactory performance in obtaining
Value-at-Risk by simply multiplying volatility with a constant factor is mainly
due to the choice of the particular significance level.
