Bio-inspired algorithm optimization of neural network for the prediction of Dubai crude oil price

Previous studies proposed several bio-inspired algorithms for the optimization of Neural Network (NN) to avoid local minima and to improve accuracy and convergence speed. To advance the performance of NN, a new bio-inspired algorithm called Flower Pollination Algorithm (FPA) is used to optimize the weights and bias of NN due to its ability to explore very large search space and frequent chosen of similar solution. The FPA optimized NN (FPNN) was applied to build a model for the prediction of Dubai crude oil price unlike previous studies that mainly focus on theWest Texas Intermediate and Brent crude oil price benchmarks. Result

Impact of crude oil price volatility on economic activities: An empirical investigation in the Thai economy

This paper empirically examines the impact of oil price volatility on key macroeconomic indicators of Thailand. Following Andersen et al. [2004. Analytical evaluation of volatility forecasts. International Economic Review 45(4), 1079-1110], quarterly oil price volatility is measured by using the realized volatility (RV). The impact of the oil price volatility is investigated using the vector auto-regression (VAR) system. The Granger causality test, impulse response functions, and variance decomposition show that oil price volatility has significant impact on macroeconomic indicators, such as unemployment and investment, over the period from 1993Q1 to 2006Q4. Perron's [1997. Further evidence on breaking trend functions in macroeconomic variables. Journal of Econometrics 80(2), 355-385] test identifies structural breaks in all the concerned variables during the time of the Asian Financial Crisis (1997-1998). A VAR for the post-crisis period shows that the impact of oil price volatility is transmitted to budget deficit. The floating exchange rate regime introduced after the crisis may be the key contributor to this new channel of impact.Oil price volatility Thailand Granger causality test Impulse response function Variance decomposition