Journal of Global Economics

Modeling, analyzing, and forecasting volatility has been the subject of extensive research among academics, practitioners and portfolio managers. This paper estimates a variety of GARCH models using weekly closing price (in USD/barrel) of Brent crude oil and weekly closing prices (in USD/pound) of coffee Arabica, and compares the forecasting performance of these models based on a high frequency intra-day data which allows for a more precise realized volatility measurement. The study used weekly price data to explicitly model volatility, and employed high-frequency intra-day data to assess model forecasting performance. The analysis points to the conclusion that GARCH (1,1) for Arabica coffee and GARCH (1,2) crude oil returns were best models, respectively with Student’s t distributed innovation terms is the most accurate volatility forecasting models in the context of our empirical setting. We recommend and encourage future researchers studying the forecasting performance of GARCH models to pay particular attention to the measurement of realized volatility, and employ high-frequency data whenever feasible.

Trends in the top Initial Public Offers (IPOs) of several popular private companies in India has shown to great effect that the market quotes a higher than usual value for the these private companies due to a frenzy among the public. This phenomenon is hypothesised by tracking various top IPOs which have taken place over the years with respect to popularity and the number of shares that have been issued in the market. Different other theories are also accounted for and have been encompassed along, the reasons for this frenzy widely differs from price offered during the opening bell to media releases.