Book Description

Investors step into the stock market with the objective of earning smart returns on their investments. The stock market can help in realising these goals of the investors, however, all investments are subject to risks. The origin of the risk is the uncertainty of realising the desired returns on the investment. This aspect is known as risk of the investment. This paper aims to search the best model to estimate and forecast volatility of Indian and Chinese stock market. The data for the paper is related to the two main indices of Indian Stock Market namely, SENSEX and NIFTY and two indices of Chinese stock market, namely, Shenzhen composite index and Shanghai composite index for the period July 2003 to June 2013. We applied symmetrical as well as asymmetrical GARCH models to the data. Among all the three models i.e. GARCH, EGARCH and TARCH, we found the GARCH (1,1) model as the best model to estimate and forecast the volatility of Chinese stock market for both the daily and weekly return series. For the Indian stock market, the recommended volatility estimation and forecasting model is EGARCH model that captures the leverage effect. We did not find volatility clustering and leverage effect for the monthly return series for both Indian and Chinese stock market. Thus, it is suggested to use the traditional time invariant volatility models for the monthly return series.