Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models

In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model

Nonlinear time series and neural-network models of exchange rates between the US dollar and major currencies

This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non- linear models, including smoot

Cointegrated Dynamics for A Generalized Long Memory Process

Recent developments in econometric methods enable estimation and testing of general long memory process, which include the general Gegenbauer process. This paper considers the error correction model for a vector general long memory process, which encompasses the vector autoregressive fractionally-integrated moving average and general Gegenbauer process. We modify the tests for unit roots and cointegration, based on the concept of heterogeneous autoregression. The Monte Carlo simulations show that the finite sample properties of the modified tests are satisfactory, while the conventional tests suffer from size distortion. Empirical results for interest rates series for the U.S.A. and Australia indicate that: (1) the modified unit root test detected unit roots for all series, (2) after differencing, all series favour the general Gegenbauer process, (3) the modified test for cointegration found only two cointegrating vectors, and (4) the zero interest rate policy in the U.S.A. has no effect on the cointegrating vector for the two countrie

Avian Influenza Viruses Infect Primary Human Bronchial Epithelial Cells Unconstrained by Sialic Acid α2,3 Residues

Avian influenza viruses (AIV) are an important emerging threat to public health. It is thought that sialic acid (sia) receptors are barriers in cross-species transmission where the binding preferences of AIV and human influenza viruses are sias α2,3 versus α2,6, respectively. In this study, we show that a normal fully differentiated, primary human bronchial epithelial cell model is readily infected by low pathogenic H5N1, H5N2 and H5N3 AIV, which primarily bind to sia α2,3 moieties, and replicate in these cells independent of specific sias on the cell surface. NHBE cells treated with neuraminidase prior to infection are infected by AIV despite removal of sia α2,3 moieties. Following AIV infection, higher levels of IP-10 and RANTES are secreted compared to human influenza virus infection, indicating differential chemokine expression patterns, a feature that may contribute to differences in disease pathogenesis between avian and human influenza virus infections in humans

Cancer stem cell metabolism: A potential target for cancer therapy

© 2016 The Author(s). Cancer Stem cells (CSCs) are a unipotent cell population present within the tumour cell mass. CSCs are known to be highly chemo-resistant, and in recent years, they have gained intense interest as key tumour initiating cells that may also play an integral role in tumour recurrence following chemotherapy. Cancer cells have the ability to alter their metabolism in order to fulfil bio-energetic and biosynthetic requirements. They are largely dependent on aerobic glycolysis for their energy production and also are associated with increased fatty acid synthesis and increased rates of glutamine utilisation. Emerging evidence has shown that therapeutic resistance to cancer treatment may arise due to dysregulation in glucose metabolism, fatty acid synthesis, and glutaminolysis. To propagate their lethal effects and maintain survival, tumour cells alter their metabolic requirements to ensure optimal nutrient use for their survival, evasion from host immune attack, and proliferation. It is now evident that cancer cells metabolise glutamine to grow rapidly because it provides the metabolic stimulus for required energy and precursors for synthesis of proteins, lipids, and nucleic acids. It can also regulate the activities of some of the signalling pathways that control the proliferation of cancer cells. This review describes the key metabolic pathways required by CSCs to maintain a survival advantage and highlights how a combined approach of targeting cellular metabolism in conjunction with the use of chemotherapeutic drugs may provide a promising strategy to overcome therapeutic resistance and therefore aid in cancer therapy