Modeling Long Memory in The Indian Stock Market using Fractionally Integrated Egarch Model

Abstract

Abstract-The weak form of market efficiency assumes that prediction of asset returns based on historical information's is not possible. Nevertheless, a great number of studies show that asset returns exhibit significant autocorrelation between observations widely separated in time. This is one of the stylized facts of financial markets which is known as long memory. The presence of long memory can be defined in term of persistence of autocorrelation. This paper studies the presence of long memory property in the Indian stock market. Using data from BSE500 stock index, this study found evidence of long memory property in the Indian stock market as seen in developed stock markets and some other emerging markets. It is found that the FIEGARCH (1, d, 1) is the best fit model and it outperforms other ARCH-type models in modelling volatility in the Indian stock market.

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