Application of a Log Likelihood Object in GARCH with T-Distributed Errors and EGARCH with Generalised Error Distribution Model to the Monthly Returns of an Equity Hedge Fund

In this article, we have tested the volatility of the monthly returns of an equity hedge fund for changing conditional variances by using a log likelihood model. Generalized autoregressive conditional heteroskedastic models, (GARCH) with t-distributed errors, and exponential generalized autoregressive conditional heteroskedastic model, (EGARCH) with generalised error distribution take into account the non-linearity that arises in the financial time series. The aim is to compare and select the maximum value of the log likelihood estimation of the monthly returns of an equity hedge fund. The log likelihood model take into account autoregressive, (AR), moving average, (MA), and monthly seasonal moving average, (SMA) factors that could better explain volatility clusters. We have selected the model with the best forecasting ability in terms of the lowest value of the Akaike information criterion, the Schwarz criterion, the Hannan – Quinn criterion. The best model will help the arbitrageurs to better craft their investment strategy in terms of holding, buying or selling portfolios of equity hedge funds. The software that we have used is EViews 6