No abstract

Reliable forecasts of the price of oil are of interest for a wide range of applications. For example, central banks and private sector forecasters view the price of oil as one of the key variables in generating macroeconomic projections and in assessing macroeconomic risks. Of particular interest is the question of the extent to which the price of oil is helpful in predicting recessions. This paper presents a statistical learning method (SLM) based on combined fuzzy system (FS), artificial neural network (ANN), and support vector regression (SVR) to cope with optimum long-term oil price forecasting in noisy, uncertain, and complex environments. A number of quantitative factors were discovered from this model and used as the input. For verification and testing, the West Texas Intermediate (WTI) crude oil spot price is used to test the effectiveness of the proposed learning methodology.Empirical results reveal that the proposed SLM-based forecasting can model the nonlinear relationship between the input variables and price very well. Furthermore, in-sample and out-of-sample prediction performance also demonstrates that the proposed SLM model can produce more accurate prediction results than other nonlinear models.Keywords: crude oil price, fuzzy system (FS), artificial neural networks (ANNs), support vector regression (SVR) IntroductionCrude oil has been playing an increasingly important role in the world economy since nearly two thirds of the world's energy demands are met from crude oil (Alvarez-Ramirez, Soriano, Cisneros, & Suarez, 2003). It is said that crude oil is also the world's largest and most actively traded commodity, accounting for over 10% of total world trade (Verleger, 1993). Like most commodities, crude oil price is mainly influenced by international politics, economy, military affairs, diplomacy, and other factors. The frequent change of these factors makes oil price show uncertainty, mutagenicity, and randomness. In addition, crude oil products are one of the world's major commodities with a high volatility level. They are traded in New York Mercantile Exchange (NYMEX) market together with other energy and mineral commodities. Simultaneously, the volatility of this crude oil price depends on demand and supply of the commodity, level of inventories, and economic indicators.In the past decades, traditional statistical and econometric techniques, such as linear regression (LinR), co-integration analysis, Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, naive (2001) suggested a semi-parametric statistical method for short-term oil price forecasting based on the GARCH properties of crude oil price. Similarly, Barone-Adesi, Bourgoin, and Giannopoulos (1986) suggested a semi-parametric approach for oil price forecasting. Gulen (1998) used a co-integration analysis to predict the West Texas Intermediate (WTI) price.Ye, Zyren, and Shore (2006) presented a simple econometric model of WTI prices, using OECD petroleum inventory levels, relative inventories, and high-and low-invento...

No abstract

The objective of this study is to identify the determinants of bank lending policy in the context of Yemen. To achieve this goal we try to study the probability of lending investment companies through a number of explanatory variables included in the multivariate model (Discriminate Analysis Model), and in the artificial intelligence model (Artificial Neural Network). To study our problem, we collected forty (40) actual cases of investment credit from eight Yemeni banks (28 agreement cases, 12 rejection cases). The two models have showed that the variable of guarantee has a significant influence on the decision-making process of bank lending policy. Decision makers in credit are based on a process of anticipation in the future. The findings indicated that most banks considered the type of guarantee as a key element to the success of lending decision, because, the guarantee is the best way for the bank to mulch the information asymmetries and to guard against the risk of non-repayment of loans.

No abstract