In this article, we have tested the application of a log likelihood object of an ARMA(p,q) model in five hedge funds categories. We have applied an autoregressive moving average, ARMA(2,2) model of order AR(1), AR(2), MA(1), MA(2) and SMA(12) to test the natural logarithmic monthly market...

In this article, we have applied an autoregressive moving average, ARMA(2,2) model of order AR(1), AR(2), MA(1), MA(2) and SMA(12) to test the natural logarithmic monthly market returns of the of the five stock call options contracts of well-known Swedish companies. The companies are Electrolux...

This paper aims to compare the effectiveness of two different univariate time series forecasting models, i.e. Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heterocedasticity (GARCH) in forecasting Khartoum Stock Exchange Index (KSE 30). Using monthly...

The Indian stock market has a significant place in the global market. It is known that performances of Indian stock market affect the nearby share markets as well as the far away markets. The Bombay Stock Exchange (BSE) is the first Indian stock market exchange. As such predictions on BSE were...

Volatility has been used as an indirect means for predicting risk accompanied with an asset. Volatility explains the variations in returns. Forecasting volatility has been a stimulating problem in the financial systems. This study examined the different volatility estimators and determined the...