Volatility Forecast Comparison Using Imperfect Volatility Proxies

By Patton Andrew J.
Journal of Econometrics (2011)

  • Andrew J. Patton

    Duke University



September 23, 2013

Last update

June 20, 2016










This code compares volatility forecasts from two models (A and B). It produces the t-statistics from Diebold–Mariano–West tests of equal predictive accuracy. The null is expressed as follows H0: Loss(model A) - Loss(model B) = 0. The sign of the t-statistics indicates which forecast performs better for each loss function: a positive t-statistic indicates that model A forecast produces larger average loss than the model B forecast, while a negative sign indicates the opposite. The statistics are displayed for various values of the scale parameter b of the loss function (equation 24, page 252), chosen by the user. The cases b = 0 and b = −2 correspond to the MSE and QLIKE loss functions, respectively.

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