With the rising importance of China’s role in the world economy, the Chinese economic fluctuation has become a more and more significant factor that influences the world economy. Therefore, it is an interesting issue for all circles as well as academicians that whether the real economic interconnection leads to volatility spillover between China’s and international stock markets. In this paper,...

The evolution of financial markets is a complicated real-world phenomenon that ranks at the top in terms of difficulty of modeling and/or prediction. One reason for this difficulty is the well-documented nonlinearity that is inherently at work. The state-of-the-art on the nonlinear modeling of financial returns is given by the popular ARCH (Auto-Regressive Conditional Heteroscedasticity) models...

A new technique based on diffusion tensor imaging and computational neuroanatomy was developed to efficiently and quantitatively characterize the three-dimensional morphology of the developing brains. The technique was used to analyze the phenotype of conditional Bcl-x knock-out mice, in which the bcl-x gene was deleted specifically in neurons of the cerebral cortex and hippocampus beginning at...

Post-season harvest surveys provide data used in the management of Missouri wildlife. These surveys provide information on the number of animals harvested, hunting pressure and hunter success rate. These estimates provide unbiased results at the statewide level due to the large sample size. However, if this survey information is used to make county estimates, poor results often occur due to sma...

Article history: Received October 1, 2011 Received in Revised form November, 14, 2011 Accepted 30 January 2012 Available online 20 February 2012 In this study, the backpropagation neural network (BPNN) is tested for the ability to forecast the daily volatility of two stock market indices from the Middle East and North Africa (MENA) region using volume; namely Morocco and Saudi Arabia. Volatilit...

In this work we consider the task of constructing prediction intervals in an inductive batch setting. We present a discriminative learning framework which optimizes the expected error rate under a budget constraint on the interval sizes. Most current methods for constructing prediction intervals offer guarantees for a single new test point. Applying these methods to multiple test points results...