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A0645
Title: Forecast comparison tests under fat-tails Authors:  Jihyun Kim - Sung Kyun Kwan University (Korea, South) [presenting]
Nour Meddahi - Toulouse School of Economics (France)
Mamiko Yamashita - Toulouse School of Economics (France)
Abstract: Forecast comparison tests are widely implemented to compare the performances of two or more competing forecasts. The critical value is often obtained by the classical central limit theorem (CLT) or by the stationary bootstrap with regularity conditions, including the one where the second moment of the loss difference is bounded. However, the heavy-tailed nature of the financial variables can violate this moment condition. We show that if the moment condition is violated, the size of the test using the classical Normal asymptotics can be heavily distorted. The distortion is large, especially when the tail of the marginal distribution of the loss differences is heavy. As an alternative approach, we propose to use a subsampling method that is robust to fat tails. In the empirical study, we analyze several variance forecast tests. Examining several tail index estimators, we show that the second moment of the loss difference is likely to be unbounded, especially when the popular squared error (SE) function is used as a loss function. We also find that the outcome of the tests may change if the subsampling is used.