Stock Return Autocorrelation and Volatility in Emerging Nations

<div><p><em>This research has been conducted to estimate the Value at Risk of nations and volatility of returns of indices by using GARCH based models in the emerging equity markets of the world. </em></p><p><em>For the study six emerging markets were taken into consideration viz. china, India, turkey, Mexico, Indonesia, Russia, and Brazil. The data these emerging stock indices of the world have been taken for the research purpose. Different GARCH (auto regressive) based models were applied to estimate the volatility in markets , further different Garch based models were compared for best fit and VaR was calculated to estimate the risk. The results of Garch (1,1) model shows that there is no serial correlation in Brazil and Mexico and for India, China, Russia and Turkey there exist positive serial correlation. E-garch which is superior model than garch reported that there is no serial correlation in Brazil, china, Mexico and Russia but for India and Turkey there exist positive serial correlation. Further a stronger garch model that is Pgarch was applied which showed that there is positive serial correlation in all the Emerging equity markets viz. India, China, Russia and Turkey. The generalized results can be of positive correlation between India, China, Russia and Turkey markets.</em></p></div>

Estimating an Autoregressive Current Effects Model of Sales Response when Observations are Aggregated over Time: Least Squares versus Maximum Likelihood

Estimating autoregressive current effects models is not straightforward when observations are aggregated over time. The author evaluates a familiar iterative generalized least squares (IGLS) approach and contrasts it to a maximum likelihood (ML) approach. Analytic and numerical results suggest that (1) IGLS and ML provide good estimates for the response parameters in instances of positive serial correlation, (2) ML provides superior (in mean squared error) estimates for the serial correlation coefficient, and (3) IGLS might have difficulty in deriving parameter estimates in instances of negative serial correlation.