Oil price markets can benefit from a better considerate of how shocks can affect volatility through time. This study assesses the impact of structural changes and outliers on volatility persistence of two crude oil markets WTI and Brent oil price between January 1, 1996 and March 17, 2014. First, we identify the FIGARCH process proposed by Baillie et al. (1996) [Baillie, R.T., Bollerslev, T., & Mikkelsen, H.O., (1996), Fractionally integrated generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 74, 3-30.] and investigate some of its statistical proprieties and then incorporate this information into the volatility modelling. We also show that outliers can bias the estimation of the persistence of the volatility. Taking into account outliers on the volatility modelling process improve the understanding of volatility in crude oil markets.

In this paper, we examine and forecast the House Price Index (HPI) and mortgage market rate in terms of the description of the subprime crisis. We use a semi-parametric local polynomial Whittle estimator proposed by Shimotsu et al. (2005) [Shimotsu, K., & Phillips, P.C.B. (2005), Exact local Whittle estimation of fractional integration. The Annals of Statistics, 33(4), 1890-1933.] in a long memory parameter time series. Empirical investigation of HPI and mortgage market rate shows that these variables are more persistent when the d estimates are found on the Shimotsu method than on the one of Künsch (1987) [Künsch, H.R. (1987). Statistical aspects of self-similar processes. In Y. Prokhorov and V.V. Sazanov (eds.), Proceedings of the First World Congress of the Bernoulli Society, VNU Science Press, Utrecht, 67-74.]. The estimating forecast values are more realistic and they strongly reflect the present US economy actuality in the two series as indicated by the forecast evaluation topics.

This paper investigates the contribution of index of the speculative pressure to the persistence of currency crises by identifying the determinants of high persistence in the exchange market pressure index for twenty countries affected by crisis. The Exact Local Whittle model is utilized to identify the high persistence of crisis. Our results show the persistence of the currency crisis.

The aim of this paper is to study the dynamics of the real exchange rate deviations of G7 countries by capturing nonlinearity and long memory features. In this context, we used fractionally integrated STAR (FISTAR) models proposed by Van Dijk et al. (2002) [Van Dijk, D., Franses, P.H., Paap, R., (2002), A nonlinear long-memory model with an application to US unemployment, Journal of Econometrics, 110, 135-165.] for a case with an exponential transition function. Indeed, this study can take into account procedures characterized by several dynamic regimes and persistence phenomena. Empirically, the elements of both fractional long memory and threshold non-linearity are present for the real exchange rates of the G-7 countries against the US, notably in the EU countries.

The Purchasing Power Parity (PPP) theory, which serves as a key to the determination of several models of exchange rates, suggests a long-term relationship between exchange rates and relative prices. It states that the price levels in all the countries are the same when measured in terms of a single currency. The purpose of this study is to model the behavior of the exchange rates of five partner countries of Tunisia, namely, (Germany, the United States, France, Italy, the UK, Morocco and Libya) relative to its fundamentals over the period 1990-1999. Beyond the traditional linear cointegration, we use the approaches based on fractional cointegration. We are trying to discriminate between the adjustment dynamics with long memory (but linear) and a dynamics of a short memory (nonlinear). Given the important role of the exchange rates in the successful experience of open economies, we are interested, in this work, in analyzing the dynamics of the exchange rates in the long run. The econometric results obtained through the GPH tests, make us consider the PPP as an event in the long run if significant short-term deviations from the PPP cannot exist. Therefore, the analysis of the fractional cointegration makes the deviations, regarding equilibrium, follow a slightly integrated process and therefore capture a much wider group of research parity or mean-reverting behavior.

In the area of financial stock market forecasting, many studies have focused on application of Artificial Neural Networks (ANNs). Due to its high rate of uncertainty and volatility, the stock markets returns forecasting by conventional methods became a difficult task. The ANNs is a relatively new and have been reported as good methods to predict financial stock market levels and can model flexible linear or non-linear relationship among variables. The aim of the study is to employ an ANN models to estimate and predict the dynamic volatility of the daily of S & P500 market returns. Results of ANN models will be compared with time series model using GARCH family models. The use of the novel model for conditional stock markets returns volatility can handle the vast amount of nonlinear data, simulate their relationship and give a moderate solution for the hard problem. The forecasts of stock index returns in the paper will be evaluated and compared, considering the MSE, RMSE and MAE forecasts statistic.