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Table 5 Second stage V-IGARCH(1,1) regression model (Equation 4)

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

Estimators Estimates
\( {\widehat{\gamma}}_{02} \) −4.0502*
(0.0000)
\( {\widehat{\gamma}}_{12} \) −0.1917**
(0.0424)
\( {\widehat{\gamma}}_{22} \) −0.2083**
(0.0435)
\( {\widehat{\gamma}}_{32} \) 0.0084
(0.3176)
\( {\widehat{\gamma}}_{42} \) 2.3271*
(0.0000)
\( {\widehat{\gamma}}_{521} \) −2.4496*
(0.0000)
\( {\widehat{\gamma}}_{522} \) −1.4117**
(0.0167)
\( {\widehat{\gamma}}_{523} \) −1.4714**
(0.0138)
\( {\widehat{\delta}}_{02} \) 0.0390**
(0.0057)
\( {\widehat{\delta}}_{12} \) 0.8421*
(0.0000)
\( {\widehat{\delta}}_{22} \) 0.1579**
(0.1692)
\( {\widehat{\delta}}_{32} \) 0.0082**
(0.2680)
Log-Likelihood 266.8531
  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. \( {M}_{lt}={\hat{\gamma}}_{02}+{\hat{\gamma}}_{12}\triangle {\hat{u}}_{t-1}+{\hat{\gamma}}_{22}\triangle {\hat{u}}_{t-2}+{\hat{\gamma}}_{32}\triangle {w}_{t-1}+{\hat{\gamma}}_{42}{T}_{pt}+{\hat{\gamma}}_{52}\sum_{i=1}^3{D}_i+{\varepsilon}_{mit} \)
  3. \( {h}_{2t}={\hat{\delta}}_{02}+{\hat{\delta}}_{12}{\hat{\varepsilon}}_{mit-1}^2+{\hat{\delta}}_{22}{h}_{2t-1}+{\hat{\delta}}_{32}\triangle {w}_{t-1}^2 \)
  4. where, M lt  = the number of market lots, T pt  = trading prices, and D i  = dummy variables = time of submission of limit orders. Dummy D 1 is the initial period of the 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 there is an inverse relationship between the number of market lots and risk-neutral trading price in all trading hours causes the returns as inefficient. Since the number of market lots determines the tick value in relation to tick size and hence price discreteness has negative effects on returns