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Table 6 Second stage V-IGARCH(1,1) regression model (Equation 5)

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

Estimators Estimates
\( {\widehat{\gamma}}_{03} \) −0.3041*
(0.0000)
\( {\widehat{\gamma}}_{13} \) 0.0021
(0.2431)
\( {\widehat{\gamma}}_{23} \) 0.0015
(0.3027)
\( {\widehat{\gamma}}_{33} \) −0.0003
(0.3708)
\( {\widehat{\gamma}}_{43} \) −0.3738*
(0.0000)
\( {\widehat{\gamma}}_{531} \) −0.1027*
(0.0000)
\( {\widehat{\gamma}}_{532} \) 0.2324*
(0.0000)
\( {\widehat{\gamma}}_{533} \) −0.0278*
(0.0000)
\( {\widehat{\delta}}_{03} \) −0.000006**
(0.0116)
\( {\widehat{\delta}}_{13} \) 1.0000*
(0.0000)
\( {\widehat{\delta}}_{23} \) −0.000000*
(0.0000)
\( {\widehat{\delta}}_{33} \) 0.0005*
(0.0000)
Log-Likelihood 1089.7317
  1. Note:* (**) Significant at 0.01 (<0.01) level. The second 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. \( {C}_t={\hat{\gamma}}_{03}+{\hat{\gamma}}_{13}\triangle {\hat{u}}_{t-1}+{\hat{\gamma}}_{23}\triangle {\hat{u}}_{t-2}+{\hat{\gamma}}_{33}\triangle {w}_{t-1}+{\hat{\gamma}}_{43}{T}_{pt}+{\hat{\gamma}}_{53}\sum_{i=1}^3{D}_i+{\varepsilon}_{ct} \)
  3. \( {h}_{3t}={\hat{\delta}}_{03}+{\hat{\delta}}_{13}{\hat{\varepsilon}}_{ct-1}^2+{\hat{\delta}}_{23}{h}_{1t-1}+{\hat{\delta}}_{33}\triangle {w}_{t-1}^2 \)
  4. where, C t  = total money supply, T pt  = trading prices, and D i  = dummy variables = time of submission of limit orders. Dummy D 1 is the initial period of th e Nifty trading, D 2 is in between the initial and the last periods of trading, and D 3 is the last period of the Nifty trading. \( \triangle {\hat{u}}_{t-1} \), \( \triangle {\hat{u}}_{t-2} \), ∆w t − 1, and \( \triangle {w}_{t-1}^2 \) = first and second differenced residuals and first differenced credit availability dummy values are derived from the first stage V-IGARCH (1, 1) estimation. This result indicates that impact costs are higher in the entire trading time-period (trading hours from 09:55 IST to15:30 IST) of submission of limit orders and hence reduction effects on returns exist