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

Abstract This research aims to compare the distributed structure of high (daily) and low (weekly, monthly) data frequency. Using the ARCH (1) and GARCH (1,1) models, this study found evidence that the impact strength of all series is not equal. In this study, we first found that the statistical properties of the three data series regression are significantly different from each other, and that the sustainability of the conditional variation also differs among the three series. The daily price-earnings ratio is more sustainable than the other data sets, and the volatility model is sensitive to the frequency of the data series.

Volatility analysis of the stock market is an important research field. There are various academic researches on the effectiveness of time series models in estimating and forecasting stock market volatility in developed and developing countries. Some scholars choose to use all ARCH and non - ARCH models to simulate stock price fluctuations, but some scholars use only one model. The data used by these scholars also varies in composition. Analyze data of one country or model and compare data of multiple stock exchanges according to the purpose of the survey

Forbes and Rigobon (2002), Bekaert, Harvey and Lumsdaine (2002a, b), Edwards (2000) etc have focused on the volatility of the stock market, focusing on the volatility between different economies. During the subsequent financial crisis. Bakaert and Harvey 2000 (quoted in Batra 2004) analyzes the price-earnings ratio of emerging market groups before and after financial reform. According to Aggarwal, Inclan and Leal 1999 (cited in Batra 2004), local events and events make the stock market of emerging economies unstable. To reach this conclusion, they analyzed the volatility of the emerging stock market during the period 1985-95, and when the timing of a particular event or stock market volatility fluctuated significantly, by using the ICSS algorithm We identified a sudden change in. They calculated the variance of each point

Emerging markets are more vulnerable than the developed markets. This is mainly due to the strong positive correlation between local emerging stock market volatility and local stock market and currency fluctuations. The spring of 2018 is a classic example, the strength of the dollar is causing a major new weakness. Emerging markets are showing strong momentum, so momentum effects may not be achieved after transaction costs, but it is not surprising that the weakness in the next quarter will continue. On the other hand, as a result of analyzing the fundamentals of emerging markets, I was convinced that the assumptions behind the forecast are reasonable. Evaluation is easier to predict emerging market returns