Some researchers, for example, Koop [1], and Sims [2], have advocated for Bayesian alternatives to unit-root testing over the classical approach using the augmented Dickey-Fuller test (ADF). This paper studies the power of what Koop [1] has called the Objectiveʺ Bayesian approach to unit-root...

In this study, a vector autoregression (VAR) model with time-varying parameters (TVP) to predict the daily Indian rupee (INR)/US dollar (USD) exchange rates for the Indian economy is developed. The method is based on characterization of the TVP as an optimal control problem. The methodology is a...

This study examines the impact of volatility shifts on volatility persistence for three major sector indices of Istanbul Stock Exchange (ISE) and ISE National 100 index over the period beginning from 1997 and ending in 2009. The exponential generalized autoregressive conditional...

Persistent current account deficits were observed in some developing countries that are received substantial foreign capital in the last decades. This has raised the issue of sustainability and increased the volume of studies about the measures of sustainable current account deficits in the...

In this study, we treat the seasonal variation in monthly time series in the context of the Western-European tourism demand for Tunisia, by presenting different techniques of detection of seasonality and the parametric and non-parametric approaches of seasonal adjustment. Then, we compare the...

We study optimal hedging design for returns on an Italian equity mutual fund index since 2008. Alternative hedging instruments include one-month futures contracts for FTSE-MIB, FTSE100 and Xetra DAX. We use bivariate models of our Italian equity mutual fund index and each hedging instrument to...

This paper presents a study of Artificial Neural Network (ANN) and Bayesian Network (BN) for use in stock index prediction. The data from Nigerian Stock Exchange (NSE) market are applied as a case study. Based on the rescaled range analysis, the neural network was used to capture the...

This paper introduces a new parametric measure of productivity change using the hyperbolic distance function. More specifically, the paper first estimates a stochastic translog hyperbolic distance function, which satisfies regularity conditions, using the Bayesian approach. Then it derives an...