Robustness of alternative non‐linearity tests for SETAR models

In recent years there has been a growing interest in exploiting potential forecast gains from the non‐linear structure of self‐exciting threshold autoregressive (SETAR) models. Statistical tests have been proposed in the literature to help analysts check for the presence of SETAR‐type non‐linearities in an observed time series. It is important to study the power and robustness properties of these tests since erroneous test results might lead to misspecified prediction problems. In this paper we investigate the robustness properties of several commonly used non‐linearity tests. Both the robustness with respect to outlying observations and the robustness with respect to model specification are considered. The power comparison of these testing procedures is carried out using Monte Carlo simulation. The results indicate that all of the existing tests are not robust to outliers and model misspecification. Finally, an empirical application applies the statistical tests to stock market returns of the four little dragons (Hong Kong, South Korea, Singapore and Taiwan) in East Asia. The non‐linearity tests fail to provide consistent conclusions most of the time. The results in this article stress the need for a more robust test for SETAR‐type non‐linearity in time series analysis and forecasting. Copyright © 2004 John Wiley & Sons, Ltd.     

Alternative splicing and genome complexity.

Alternative splicing of mRNA allows many gene products with different functions to be produced from a single coding sequence. It has recently been proposed as a mechanism by which higher-order diversity is generated. Here we show, using large-scale expressed sequence tag (EST) analysis, that among seven different eukaryotes the amount of alternative splicing is comparable, with no large differences between humans and other animals.     

RNAi-mediated gene silencing in non-human primates

The opportunity to harness the RNA interference (RNAi) pathway to silence disease-causing genes holds great promise for the development of therapeutics directed against targets that are otherwise not addressable with current medicines. Although there are numerous examples of in vivo silencing of target genes after local delivery of small interfering RNAs (siRNAs), there remain only a few reports of RNAi-mediated silencing in response to systemic delivery of siRNA, and there are no reports of systemic efficacy in non-rodent species. Here we show that siRNAs, when delivered systemically in a liposomal formulation, can silence the disease target apolipoprotein B (ApoB) in non-human primates. APOB-specific siRNAs were encapsulated in stable nucleic acid lipid particles (SNALP) and administered by intravenous injection to cynomolgus monkeys at doses of 1 or 2.5 mg kg(-1). A single siRNA injection resulted in dose-dependent silencing of APOB messenger RNA expression in the liver 48 h after administration, with maximal silencing of >90%. This silencing effect occurred as a result of APOB mRNA cleavage at precisely the site predicted for the RNAi mechanism. Significant reductions in ApoB protein, serum cholesterol and low-density lipoprotein levels were observed as early as 24 h after treatment and lasted for 11 days at the highest siRNA dose, thus demonstrating an immediate, potent and lasting biological effect of siRNA treatment. Our findings show clinically relevant RNAi-mediated gene silencing in non-human primates, supporting RNAi therapeutics as a potential new class of drugs.     

Improving moving average trading rules with boosting and statistical learning methods

We present a system for combining the different types of predictions given by a wide category of mechanical trading rules through statistical learning methods (boosting, and several model averaging methods like Bayesian or simple averaging methods). Statistical learning methods supply better out‐of‐sample results than most of the single moving average rules in the NYSE Composite Index from January 1993 to December 2002. Moreover, using a filter to reduce trading frequency, the filtered boosting model produces a technical strategy which, although it is not able to overcome the returns of the buy‐and‐hold (B&H) strategy during rising periods, it does overcome the B&H during falling periods and is able to absorb a considerable part of falls in the market. Copyright © 2008 John Wiley & Sons, Ltd.     

Coupling superconducting qubits via a cavity bus

Superconducting circuits are promising candidates for constructing quantum bits (qubits) in a quantum computer; single-qubit operations are now routine, and several examples of two-qubit interactions and gates have been demonstrated. These experiments show that two nearby qubits can be readily coupled with local interactions. Performing gate operations between an arbitrary pair of distant qubits is highly desirable for any quantum computer architecture, but has not yet been demonstrated. An efficient way to achieve this goal is to couple the qubits to a 'quantum bus', which distributes quantum information among the qubits. Here we show the implementation of such a quantum bus, using microwave photons confined in a transmission line cavity, to couple two superconducting qubits on opposite sides of a chip. The interaction is mediated by the exchange of virtual rather than real photons, avoiding cavity-induced loss. Using fast control of the qubits to switch the coupling effectively on and off, we demonstrate coherent transfer of quantum states between the qubits. The cavity is also used to perform multiplexed control and measurement of the qubit states. This approach can be expanded to more than two qubits, and is an attractive architecture for quantum information processing on a chip.     

Ultralow-power organic complementary circuits

The prospect of using low-temperature processable organic semiconductors to implement transistors, circuits, displays and sensors on arbitrary substrates, such as glass or plastics, offers enormous potential for a wide range of electronic products. Of particular interest are portable devices that can be powered by small batteries or by near-field radio-frequency coupling. The main problem with existing approaches is the large power consumption of conventional organic circuits, which makes battery-powered applications problematic, if not impossible. Here we demonstrate an organic circuit with very low power consumption that uses a self-assembled monolayer gate dielectric and two different air-stable molecular semiconductors (pentacene and hexadecafluorocopperphthalocyanine, F16CuPc). The monolayer dielectric is grown on patterned metal gates at room temperature and is optimized to provide a large gate capacitance and low gate leakage currents. By combining low-voltage p-channel and n-channel organic thin-film transistors in a complementary circuit design, the static currents are reduced to below 100 pA per logic gate. We have fabricated complementary inverters, NAND gates, and ring oscillators that operate with supply voltages between 1.5 and 3 V and have a static power consumption of less than 1 nW per logic gate. These organic circuits are thus well suited for battery-powered systems such as portable display devices and large-surface sensor networks as well as for radio-frequency identification tags with extended operating range.     

Forecasting Government Bond Yields with Neural Networks Considering Cointegration

This paper discusses techniques that might be helpful in predicting interest rates and tries to evaluate a new hybrid forecasting approach. Results of examining government bond yields in Germany and France reported in this study indicate that a hybrid forecasting approach which combines techniques of cointegration analysis with neural network (NN) forecasting models can produce superior results to the use of NN forecasting models alone. The findings documented in this paper could be a consequence of the fact that examining differenced data under certain conditions will lead to a loss of information and that the inclusion of the error correction term from the cointegration model can help to cope with this problem. The paper also discusses some possibly interesting directions for further research. Copyright © 2015 John Wiley & Sons, Ltd.     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

Cross‐Section Stock Return and Implied Covariance between Jump and Diffusive Volatility

I examine the information content of option‐implied covariance between jumps and diffusive risk in the cross‐sectional variation in future returns. This paper documents that the difference between realized volatility and implied covariance (RV‐ICov) can predict future returns. The results show a significant and negative association of expected return and realized volatility–implied covariance spread in both the portfolio level analysis and cross‐sectional regression study. A trading strategy of buying a portfolio with the lowest RV‐ICov quintile portfolio and selling with the highest one generates positive and significant returns. This RV‐Cov anomaly is robust to controlling for size, book‐to‐market value, liquidity and systematic risk proportion. Copyright © 2015 John Wiley & Sons, Ltd.