COMPARING THE PERSISTENCY OF DIFFERENT FREQUENCIES OF STOCK RETURNS VOLATILITY IN AN EMERGING MARKET: A CASE STUDY OF PAKISTAN

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Abstract

This study aims at comparing the variance structure of high (daily) and low (weekly, monthly) frequencies of data. By employing ARCH (1) and GARCH (1, 1) models, the study finds evidence that the intensity of the shocks are not equal for all the series. The study first finds that statistical properties of the three data series of returns are substantially different from one another and the persistence of conditional volatility is also different for the three series. The presence of persistency are more in the daily stock returns as compared to other data sets, which shows that the volatility models are sensitive to the frequencies of data series. In simple the results reveal that the variance structure of high frequency data is
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Rizwan and Khan (2007) studied the volatility of the Pakistani stock market and found volatility clustering which signifies inefficiency in the stock market. They found that lagged returns are significant in explaining current returns. The volatility persistence measures the time period for which any shock has significant impact on variance.
Volatility has different phenomenon when it is measured on short, medium and long term basis. So the different frequencies must be examined to see the short, medium and long term affects of the volatility. Dawood (2007) investigated volatility in the Karachi stock exchange and found that in 1990’s the market has become more volatile both on short term (daily) and medium term (monthly) basis. He found that stock market reacts too actively to economic shocks but these reaction take place on daily basis and die away within a month.
High frequency data series is considered to be the most volatile series than the low frequencies of data. As Chang (2006) investigated the mean reversion behavior of different series of data and concluded that the daily, weekly, and monthly returns are negatively auto correlated in both the short and the long term and mean reversion situation exists in the low frequency data and not in the high frequency data. He found that though the three frequencies have the mean reversion behavior, but the behavior is different for high frequency

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