Experimental and numerical investigation of the reflection coefficient and the distributions of Wigner's reaction matrix for irregular graphs with absorption

We present the results of experimental and numerical study of the distribution of the reflection coefficient P(R) and the distributions of the imaginary P(v) and the real P(u) parts of the Wigner's reaction K matrix for irregular fully connected hexagon networks (graphs) in the presence of strong absorption. In the experiment we used microwave networks, which were built of coaxial cables and attenuators connected by joints. In the numerical calculations experimental networks were described by quantum fully connected hexagon graphs. The presence of absorption introduced by attenuators was modelled by optical potentials. The distribution of the reflection coefficient P(R) and the distributions of the reaction K matrix were obtained from the measurements and numerical calculations of the scattering matrix S of the networks and graphs, respectively. We show that the experimental and numerical results are in good agreement with the exact analytic ones obtained within the framework of random matrix theory (RMT).Comment: 15 pages, 8 figure

Risk-neutral densities and their application in the Piterbarg Framework

Abstract: In this paper we consider two well-known interpolation schemes for the construction of the JSE Shareholder Weighted Top 40 implied volatility surface. We extend the Breeden and Litzenberger formula to the derivative pricing framework developed by Piterbarg post the 2007 financial crisis. Our results show that the statistical moments of the constructed risk-neutral densities are highly dependent on the choice of interpolation scheme. We show how the risk-neutral denity surface can be used to price options and briefly describe how the statistical moments can be used to inform trading strategies

Measuring portfolio performance using a modified measure of risk

This paper reports the results of an investigation into the properties of a theoretical modification of beta proposed by Leland (1999) and based on earlier work of Rubinstein (1976). It is shown that when returns are elliptically symmetric, beta is the appropriate measure of risk and that there are other situations in which the modified beta will be similar to the traditional measure based on the capital asset pricing model. For the case where returns have a normal distribution, it is shown that the criterion either does not exist or reduces exactly to the conventional beta. It is therefore conjectured that the modified measure will only be useful for portfolios that have nonstandard return distributions which incorporate skewness. For such situations, it is shown how to estimate the measure using regression and how to compare the resulting statistic with a traditional estimated beta using Hotelling's test. An empirical study based on stocks from the FTSE350 does not find evidence to support the use of the new measure even in the presence of skewness.Journal of Asset Management (2007) 7, 388-403. doi:10.1057/palgrave.jam.225005

Slowing and cooling molecules and neutral atoms by time-varying electric field gradients

A method of slowing, accelerating, cooling, and bunching molecules and neutral atoms using time-varying electric field gradients is demonstrated with cesium atoms in a fountain. The effects are measured and found to be in agreement with calculation. Time-varying electric field gradient slowing and cooling is applicable to atoms that have large dipole polarizabilities, including atoms that are not amenable to laser slowing and cooling, to Rydberg atoms, and to molecules, especially polar molecules with large electric dipole moments. The possible applications of this method include slowing and cooling thermal beams of atoms and molecules, launching cold atoms from a trap into a fountain, and measuring atomic dipole polarizabilities.Comment: 13 pages, 10 figures. Scheduled for publication in Nov. 1 Phys. Rev.

Embedding mRNA Stability in Correlation Analysis of Time-Series Gene Expression Data

Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R2 that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R2 for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R2 value among genes coding for a transient complex

Can the Consumption-Free Nonexpected Utility Model Solve the Risk Premium Puzzle? An Empirical Study of the Japanese Stock Market

This paper investigates whether the consumption-free two-beta intertemporal capital asset-pricing model developed by Campbell and Vuolteenaho (2004) is able to solve the risk premium puzzle in the Japanese stock market over the period 1984-2002. Using the cash flow and discount rate betas as risk factors, the model is able to explain about half of the market returns by selection of suitable vector autoregression variables. On this basis, the model proposed solves the risk premium puzzle in Japan, thereby suggesting that Japanese investors are less risk averse than US investors. However, a model including only the cash flow beta better explains returns than a model with both betas. The analysis also tests and rejects the simple capital asset-pricing model in Japan

Controls on Open‐Ocean North Atlantic ΔpCO2 at Seasonal and Interannual Time Scales Are Different

The North Atlantic is a substantial sink for anthropogenic CO2. Understanding the mechanisms driving the sink's variability is key to assessing its current state and predicting its potential response to global climate change. Here we apply a time series decomposition technique to satellite and in situ data to examine separately the factors (both biological and nonbiological) that affect the sea‐air CO2 difference (ΔpCO2) on seasonal and interannual time scales. We demonstrate that on seasonal time scales, the subpolar North Atlantic ΔpCO2 signal is predominantly correlated with biological processes, whereas seawater temperature dominates in the subtropics. However, the same factors do not necessarily control ΔpCO2 on interannual time scales. Our results imply that the mechanisms driving seasonal variability in ΔpCO2 cannot necessarily be extrapolated to predict how ΔpCO2, and thus the North Atlantic CO2 sink, may respond to increases in anthropogenic CO2 over longer time scales