Yasemin Ulu

Publications (3)0.78 Total impact

• Article: A Comparison of the Runs Test for Volatility Forecastability and the LM Test for GARCH Using Aggregated Returns

  Yasemin Ulu
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  ABSTRACT: Christoffersen and Diebold (2000) have introduced a runs test for forecastable volatility in aggregated returns. In this note, we compare the size and power of their runs test and the more conventional LM test for GARCH by Monte Carlo simulation. When the true daily process is GARCH, EGARCH, or stochastic volatility, the LM test has better power than the runs test for the moderate-horizon returns considered by Christoffersen and Diebold. For long-horizon returns, however, the tests have very similar power. We also consider a qualitative threshold GARCH model. For this process, we find that the runs test has greater power than the LM test. Theresults support the use of the runs test with aggregated returns.
  Econometric Reviews 01/2007; 26(5):557-566. · 0.78 Impact Factor
• Article: Sampling properties of criteria for evaluating GARCH volatility forecasts

  Yasemin Ulu
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  ABSTRACT: There is considerable evidence that GARCH models do not forecast financial volatility well out of sample when evaluated by the R2 from the Mincer and Zarnowitz (1969) regression. Andersen and Bollerslev (1998) argued that although the R2s tend to be small, they are consistent with the population value of the criterion for a correctly specified GARCH model. We extend the Andersen and Bollerslev result and derive the population moments of the mean squared error, the mean absolute error and a heteroscedasticity adjusted mean square error for the GARCH volatility forecasts. We state existence conditions for the moments. The criteria and their population values are illustrated with empirical examples. Using Monte Carlo simulation, we analyse the sampling properties of these criteria. When volatility is highly persistent, we find that the sampling distribution of the R2 is highly skewed to the right, which indicates that the majority of the realized R2s lie below the population R2. Among the accuracy criteria, we find the heteroscedasticity adjusted mean-squared error is preferable because it has the weakest existence condition and its sampling distribution is reflective of the population value. 'A Good Volatility Model Forecasts Volatility' Engle and Patton (2001)
  Applied Financial Economics. 01/2007; 17(8):671-681.
• Article: Out-of-sample forecasting performance of the QGARCH model

  Yasemin Ulu
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  ABSTRACT: The population value of the R 2 is derived from the Mincer-Zarnowitz volatility forecast regression for a QGARCH(1,1). The study shows that the population R 2 exceeds that of the standard GARCH(1,1). This indicates that accounting for asymmetry in the conditional variance process can increase the predictive power of volatility forecasts. As with the standard GARCH(1,1) model, however, the R 2 is still bounded by the reciprocal of the innovation kurtosis. As a result, small values of the R 2 should be anticipated when using the QGARCH(1,1) in empirical work.
  Applied Financial Economics Letters 02/2005; 1(6):387-392.