GESTIÓN HACIA EL DESARROLLO DEL PERSONAL CON FALENCIAS EN LA

Model diagnostic

Following Diebold and Lopez [32], after fitting the volatility equation it is necessary to carefully check whether the estimated model is the most appropriate, if necessary the model can be refined by removing the parameters that are not statistically significant.

The most commonly used test of significance for each parameter individually is the called t-Student test, with the null hypothesis. H0: β = 0 against H1: β , 0.

The test statistic is:

t = βb

Se(β)b

(2.26)

The test follows a t-distribution with T-p degrees of freedom, where T is the number of observations and p the number of estimated parameters. The null hy- pothesis is rejected if β is too far from zero, that is, if observed statistic in absoluteb value is greater than the critical value.

The test for the whole model or joint test for significance of all coefficients except the intercept is based on the F -distribution with the null hypothesis H0 : ∀βi= 0

for i = 1, ..., p against the alternative H1: ∃βi, 0. The F - statistic is given by:

F = (SSRr− SSRu)/m SSRu/(T − p)

∼ F (m, T − p) (2.27)

where SSRu and SSRr indicate the sum of squared of residuals for unrestricted and

restricted models, T the sample size, p the number of parameters of the unrestricted model and m is the number of parameter restrictions. The null hypothesis is rejected if F -statistic is greater than the correspondent critical value.

By default, for parameter diagnostic is showing the standard errors, p-value and

t-statistic from the output of statistical packages.

Forecasting volatility

According to Zivot and Wang [123], one of the objectives of analysing conditional volatility models is to make predictions of the future value of volatility. Since the GARCH model in equation 2.8 has a representation of the ARMA process, the predictions can be made similar to the procedure used in ARMA models. Predicting out-of-sample conditional variance over time using GARCH models can often yield accurate forecasts of future volatility, especially over short horizons.

For simplicity, consider the GARCH(1,1) model and assume that the forecast origin is h. For 1-step ahead forecast, we have: σ_h+12 = ω + α1u2h+ β1σh2, where

u2_h and σ_h2 are known at the time index h. Therefore, the 1-step ahead forecast is

σ_h2(1) = ω + α1u2h+ β1σh2.

For multi-step ahead forecast, we can use u2_t = σ_t2z_t2 and rewrite the volatility equation 2.9 as σ_t+12 = ω + α1σt2zt2+ β1σ2t. Now adding and subtracting α1σt2, and

realized few algebra operations we get σ_t+12 = ω + (α1+ β1)σt2+ α1σt2(z2t − 1). When

t=h+1, the last equation becomes: σ_h+22 = ω + (α1+ β1)σ_h+12 + α1σ_h+12 (z2_h+1− 1).

Since E(z_h+12 − 1|Fh) = 0, the 2-step ahead volatility forecast at the origin h satisfies

the equation σ_h2(2) = ω + (α1+ β1)σh2(1). In general, we have

σ2_h(k) = ω +

k−1

X

i=1

(α1+ β1)i−1+ (α1+ β1)k−1σh2(1) ; k ≥ 2 (2.28)

Notice that, as k → ∞ the volatility forecast in equation 2.28 approaches the unconditional variance ω/(1 − α1− β1), if the GARCH process is stationary, that is

α1+ β1< 1.

Model evaluation and news impact curves

According to Zivot [122], the fit evaluation of a GARCH model can be done using graphical representation or through more formal statistics tests. If the GARCH is correctly specified, the estimated series of standardized residuals zt= ut/σtand their

squared z_t2should not display serial correlation, conditional heteroskedasticity or any type of nonlinear dependence. The modified Ljung-Box statistics 2.4 can be used to test the null hypothesis of no autocorrelation up to specific lag and Engle’s LM statistics described in subsection 2.4.1, can be used to test the null of no remaining ARCH effects. If it is assumed that the errors are Gaussian, then a plot of zt against

time should have approximately 95% of its values between ±2; a normal qq-plot should look approximately linear; and JB statistics, equation 2.5, should not be too much larger than six.

According to Yu [120], among the measures used to evaluate the accuracy of prediction stands out the root mean square error (RMSE), the mean absolute error (MAE), the U-Thail statistic and mean absolute percentage error (MAPE).

M AE = 1 T T X i=1 |σ_t2−σb 2 t| ; RM SE = v u u t 1 T T X i=1  σ_t2−σb 2 t 2 (2.29)

Measurements of MAE and RMSE are the most popular measures used to check whether a given model makes good predictions. In terms of interpretation, small values or near to zero, indicate the model is well specified and therefore can present

2 . 5 . A P P L I C AT I O N O F U N I VA R I AT E G A RC H M O D E L S

the best estimates within the sample, consequently better predictions.

M AP E = 1 T T X i=1     σ2_t −σb 2 t σ_t2     ; U −T hail = PT i=1  σ_t2−σb 2 t 2 PT i=1  σ2_t−1− σ2 t 2 (2.30) The value of U-Thail is an indication of systematic error since it measures the extent to which the average of the forecast variance and actual series deviates from each other. The U-Thail statistics take values between 0 and 1. Values close to zero, indicate that the model has good performance in forecasting, but values close to one, is not necessarily an indication of bad forecasting performance.

If more than one GARCH models are all capable to modelling leverage effects, such as EGARCH, TGARCH and APGARCH, the choice of appropriate model can be made by using a model selection criterion such as the Bayesian information criterion (BIC), Akaike Information Criterion (AIC). Alternatively, Engle and Ng [44], proposed to use the news impact curve (NIC) introduced by Pagan and Schwert [95], to compare different models.

Following Engle and Ng [44], the news impact curve is a graph of the conditional variance at time t as a function of the error term at time t-1, assuming that all previews information are constant. If the news impact curve is symmetrical about zero, then the shocks of the same absolute value have the same impact on future conditional variance, otherwise, the shocks of the same magnitude have different effects on future conditional variance.

In next section 2.5, we present an application of univariate GARCH model, especially the asymmetric GARCH models, which is a replication of Faias et al. [48] paper, but here we explore a wider range of asymmetric GARCH models and with accrued validation in order to get the best possible model specification.