scholarly journals Market Efficiency, Role of Earnings Information, and Stock Returns: A Vector Autoregressive Model Approach

2011 ◽  
Vol 1 (2011) ◽  
pp. 17-37 ◽  
Author(s):  
Keiichi Kubota ◽  
Hitoshi Takehara
2021 ◽  
Vol 13 (1) ◽  
pp. 35
Author(s):  
Mihir Dash ◽  
Rita S.

This study proposes a vector autoregressive form for the market model and tests its significance against the market model for information technology (IT) sector stocks in the Indian stock market. The analysis was performed for a sample of nineteen IT sector stocks listed on the National Stock Exchange of India, of which nine stocks were large-cap, six were mid-cap, and four were small-cap. The study period considered was Jan. 1, 2018 – Dec. 31, 2018. The key contribution of the study was the finding that the vector autoregressive model is a better model of stock returns than the market model for IT sector stocks. Thus, IT sector stocks seem to react more to market movements from the previous day than on the day itself. The implication for asset pricing modelling is that systematic risk may be further decomposed into a component corresponding to sensitivity to market movements on the day and a component corresponding to sensitivity to market movements on the previous day. The asset pricing model would be extended to include market risk premia for both of these components of systemic risk. Keywords: market model, vector autoregressive model, IT sector, asset pricing modelling, systematic risk.


Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 883
Author(s):  
Yaqing Liu ◽  
Hongbing Ouyang ◽  
Xiaolu Wei

The existing spatial panel structural vector auto-regressive model can effectively capture the time and spatial dynamic dependence of endogenous variables. However, the hypothesis that the common factors have the same effect for all spatial units is unreasonable. Therefore, incorporating time effects, spatial effects, and time-individual effects, this paper develops a more general spatial panel structural vector autoregressive model with interactive effects (ISpSVAR) that can reflect the different effects of common factors on different spatial units. Additionally, based on whether or not the common factors can be observed, this paper proposes procedures to estimate ISpSVAR separately and studies the finite sample properties of estimators by Monte Carlo simulation. The simulation results show the effectiveness of the proposed ISpSVAR model and its estimation procedures.


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