1399 Words6 Pages

The EGARCH model has a number of advantages over the GARCH (p,q) model. The most important one is its logarithmic specification, which allows for relaxation of the positive constraints among the parameters. Another advantage of the EGARCH model is that it incorporates the asymmetries in stock return volatilities. Another advantage of the EGARCH model is that it successfully captures the persistence of volatility shocks. Based on these advantages, we apply the EGARCH model for estimating the volatility of the Emerging equity market.
Table 11 and 12: Here the null hypothesis states that coefficients are not significant. From the above two tables it can be seen that all Coefficient are significant for India, China, Mexico and Turkey. For Brazil*…show more content…*

VaR models were developed to estimate the exposure of a portfolio to market risk (Jorion, 2007). VaR has also emerged as standard quantitative measure of market risk within most financial institutions; moreover, this method also forms the basis for a host of risk controls (e.g., position limits and margin requirements) (IMF, 2007). There are various methods, or approaches, to measure VaR. Differences among these approaches arise from the model applied to the estimation of the expected changes in prices. Table 16 in annexure presents the VaR failure rates for the emerging equity markets, reported at the 95 percent probability*…show more content…*

Ten years data of six emerging equity markets namely Brazil, china, India, Mexico, turkey and Russia has been taken for study purpose. VaR is calculated to estimate the probability of risk in the portfolio of these nations. Initially for data checking purpose, ADF and PP tests were applied to check the stationary of data, the result of which indicated that the data was stationary at level. Further descriptive statistics were applied to check the normality of data. This included Skweness, kurtosis and Jarque Bera. The ARCH (LM) test i.e. Lagrange multiplier (LM) test for autoregressive conditional heteroskedasticity in residuals was applied. The data was normally distributed. The arch effect was found in all the emerging equity markets which implied that there was impact of previous day’s returns on the present

VaR models were developed to estimate the exposure of a portfolio to market risk (Jorion, 2007). VaR has also emerged as standard quantitative measure of market risk within most financial institutions; moreover, this method also forms the basis for a host of risk controls (e.g., position limits and margin requirements) (IMF, 2007). There are various methods, or approaches, to measure VaR. Differences among these approaches arise from the model applied to the estimation of the expected changes in prices. Table 16 in annexure presents the VaR failure rates for the emerging equity markets, reported at the 95 percent probability

Ten years data of six emerging equity markets namely Brazil, china, India, Mexico, turkey and Russia has been taken for study purpose. VaR is calculated to estimate the probability of risk in the portfolio of these nations. Initially for data checking purpose, ADF and PP tests were applied to check the stationary of data, the result of which indicated that the data was stationary at level. Further descriptive statistics were applied to check the normality of data. This included Skweness, kurtosis and Jarque Bera. The ARCH (LM) test i.e. Lagrange multiplier (LM) test for autoregressive conditional heteroskedasticity in residuals was applied. The data was normally distributed. The arch effect was found in all the emerging equity markets which implied that there was impact of previous day’s returns on the present

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## Gap Analysis Model

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## ARCH/GARCH Model

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## Fraud Triangle Case Summary

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