-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions …

Bayesian forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of … leads to gains in forecasting accuracy for some time series. …

-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions …

forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of volatility … leads to gains in forecasting accuracy for some time series. …

For forecasting volatility of futures returns, the paper proposes an indirect method based on the relationship between … futures and the underlying asset for the returns and time-varying volatility. For volatility forecasting, the paper considers … that the new method based on stochastic volatility models with the asymmetry and long memory outperforms the forecasting …

the Nigeria stock market. The EGARCH model is found to be the most efficient for forecasting volatilities and has the … years. The forecasting performance shows the volatility in the Nigeria stock market to be on the increase for the next four …

on some high frequency basis has spurred the research in the field of volatility modeling and forecasting into new …

Alternate Models for Forecasting Hedge Fund ReturnsMichael HoldenFaculty Sponsor: Gordon Dash, Finance and Decision … forecast methods in producing one-period ahead change-of-direction when forecasting the expected returns of various hedge fund …