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Table 3 First stage V-IGARCH(1,1) regression model (Equation 2)

From: Derived signals for S & P CNX nifty index futures

Estimators Estimates
\( {\widehat{\beta}}_0 \) −2.2663*
(0.0000)
\( {\widehat{\beta}}_1 \) −0.1117*
(0.0000)
\( {\widehat{\beta}}_2 \) −0.0037**
(0.3800)
\( {\widehat{\alpha}}_0 \) 0.0079*
(0.0000)
\( {\widehat{\alpha}}_1 \) 0.9556*
(0.0000)
\( {\widehat{\alpha}}_2 \) 0.0444**
(0.2130)
\( {\widehat{\alpha}}_3 \) 0.0087*
(0.0026)
Log-Likelihood 745.7772
Skewness −0.2725
Kurtosis 2.2126
  1. Note:* (**) Significant at 0.01 (<0.01) level. Significant at 0.01 level. The first stage V-IGARCH(1,1) estimation considers NSE daily data from December 02, 2002, to November 30, 2004. Mean and conditional variance equations are,
  2. \( {V}_t={\widehat{\beta}}_0+{\widehat{\beta}}_1{M}_t+{\widehat{\beta}}_2{w}_t+{u}_t \)
  3. \( {h}_t={\widehat{\alpha}}_0+{\widehat{\alpha}}_1{\widehat{u}}_{t-1}^2+{\widehat{\alpha}}_2{h}_{t-1}+{\widehat{\alpha}}_3{w}_{t-1}^2 \)
  4. where,V t  = returns, M t.  = trading margins, and w t  = credit availability dummy variable. The results indicate that inefficient returns exist with the existing inefficient trading margins and ineffective credit availability positions. The non-realization of returns is due to conditional probabilistic values, which are explained in second stage V-IGARCH (1, 1) estimations. Here, the conditional variance is 1.02 (\( {\widehat{\alpha}}_0+{\widehat{\alpha}}_1+{\widehat{\alpha}}_2+{\widehat{\alpha}}_3 \)), where Nifty variance returns are negative at around −1.20 (Fig. 1c)