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...

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 tested the full information maximum likelihood, (FIML) of the natural logarithmic monthly returns of the CAD/USD, DKK/USD, CHF/USD and JPY/USD spot exchange rates. We have applied a system of four equations of the spot exchange rates to test and spot volatility...

In this article, we have tested the volatility of the returns of the spot exchange rate of EURO/USD, the returns of a real exchange rate index and the money supply, (M1), for changing conditional variances. Autoregressive Conditional Heteroskedastic models (ARCH), Generalized Autoregressive...

In this article, we are investigating the effects of macroeconomic variables in terms of natural logarithmic yearly returns of general government revenues and general government total expenditures of Greece. We have applied a Vector Error Correction model, (VEC) a Granger causality and Johansen...

In this article, we have tested the volatility of the returns of the spot exchange rate of AUD/USD 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 models (ARCH), and Generalized Autoregressive Conditional Heteroskedastic models, (GARCH) take into account the non-linearity that arises in the financial time series. Well known anomalies such as the calendar effects, January effect and seasonality's...

In this article, we test a linear Gaussian space model and the Kalman filter ARMA(1,3) model to estimate logarithmic monthly returns of US corporate bonds. The purpose of this article is to estimate expectations that arise from the interaction of arbitrageurs and noise traders. An arbitrageur...

In this article, we test a linear Gaussian space model and the Kalman filter ARMA(2,4) model to estimate logarithmic monthly returns of UK general and life insurance companies. This fact motivates us to use state space model that will reveal to us the final one – step ahead values of the...