Half-Skyrmions and Spike-Vortex Solutions of Two-Component Nonlinear Schrodinger Systems

Recently, skyrmions with integer topological charges have been observed numerically but have not yet been shown rigorously on two-component systems of nonlinear Schrodinger equations (NLSE) describing a binary mixture of Bose-Einstein condensates. Besides, half-skyrmions characterized by half-integer topological charges can also be found in the nonlinear sigma model which is a model of the Bose-Einstein condensate of the Schwinger bosons. Here we prove rigorously the existence of half-skyrmions which may come from a new type of soliton solutions called spike-vortex solutions of two-component systems of NLSE on the entire plane. These spike-vortex solutions having spikes in one component and a vortex in the other component may form half-skyrmions. By Liapunov-Schmidt reduction process, we may find spike-vortex solutions of two-component systems of NLSE.Comment: to appear in J.Math.Phy

A quantitative assessment of empirical magnetic field models at geosynchronous orbit during magnetic storms

[1] We evaluate the performance of recent empirical magnetic field models (Tsyganenko, 1996, 2002a, 2002b; Tsyganenko and Sitnov, 2005, hereafter referred to as T96, T02 and TS05, respectively) during magnetic storm times including both pre- and post-storm intervals. The model outputs are compared with GOES observations of the magnetic field at geosynchronous orbit. In the case of a major magnetic storm, the T96 and T02 models predict anomalously strong negative Bz at geostationary orbit on the nightside due to input values exceeding the model limits, whereas a comprehensive magnetic field data survey using GOES does not support that prediction. On the basis of additional comparisons using 52 storm events, we discuss the strengths and limitations of each model. Furthermore, we quantify the performance of individual models at predicting geostationary magnetic fields as a function of local time, Dst, and storm phase. Compared to the earlier models (T96 and T02), the most recent storm-time model (TS05) has the best overall performance across the entire range of local times, storm levels, and storm phases at geostationary orbit. The field residuals between TS05 and GOES are small (≤3 nT) compared to the intrinsic short time-scale magnetic variability of the geostationary environment even during non-storm conditions (∼24 nT). Finally, we demonstrate how field model errors may affect radiation belt studies when estimating electron phase space density

Modeling radiation belt radial diffusion in ULF wave fields: 2. Estimating rates of radial diffusion using combined MHD and particle codes

[1] Quantifying radial transport of radiation belt electrons in ULF wave fields is essential for understanding the variability of the trapped relativistic electrons. To estimate the radial diffusion coefficients (DLL), we follow MeV electrons in realistic magnetospheric configurations and wave fields calculated from a global MHD code. We create idealized pressure-driven MHD simulations for controlled solar wind velocities (hereafter referred to as pressure-driven Vx simulations) with ULF waves that are comparable to GOES data under similar conditions, by driving the MHD code with synthetic pressure profiles that mimic the pressure variations of a particular solar wind velocity. The ULF wave amplitude, in both magnetic and electric fields, increases at larger radial distance and during intervals with higher solar wind velocity and pressure fluctuations. To calculate DLL as a function of solar wind velocity (Vx = 400 and 600 km/s), we follow 90 degree pitch angle electrons in magnetic and electric fields of the pressure-driven Vx simulations. DLL is higher at larger radial distance and for the case with higher solar wind velocity and pressure variations. Our simulated DLL values are relatively small compared to previous studies which used larger wave fields in their estimations. For comparison, we scale our DLL values to match the wave amplitudes of the previous studies with those of the idealized MHD simulations. After the scaling, our DLL values for Vx = 600 km/s are comparable to theDLL values derived from Polar measurements during nonstorm intervals. This demonstrates the use of MHD models to quantify the effect of pressure-driven ULF waves on radiation belt electrons and thus to differentiate the radial diffusive process from other mechanisms

Modeling radiation belt radial diffusion in ULF wave fields: 1. Quantifying ULF wave power at geosynchronous orbit in observations and in global MHD model

[1] To provide critical ULF wave field information for radial diffusion studies in the radiation belts, we quantify ULF wave power (f = 0.5–8.3 mHz) in GOES observations and magnetic field predictions from a global magnetospheric model. A statistical study of 9 years of GOES data reveals the wave local time distribution and power at geosynchronous orbit in field-aligned coordinates as functions of wave frequency, solar wind conditions (Vx, ΔPd and IMF Bz) and geomagnetic activity levels (Kp, Dst and AE). ULF wave power grows monotonically with increasing solar wind Vx, dynamic pressure variations ΔPd and geomagnetic indices in a highly correlated way. During intervals of northward and southward IMF Bz, wave activity concentrates on the dayside and nightside sectors, respectively, due to different wave generation mechanisms in primarily open and closed magnetospheric configurations. Since global magnetospheric models have recently been used to trace particles in radiation belt studies, it is important to quantify the wave predictions of these models at frequencies relevant to electron dynamics (mHz range). Using 27 days of real interplanetary conditions as model inputs, we examine the ULF wave predictions modeled by the Lyon-Fedder-Mobarry magnetohydrodynamic code. The LFM code does well at reproducing, in a statistical sense, the ULF waves observed by GOES. This suggests that the LFM code is capable of modeling variability in the magnetosphere on ULF time scales during typical conditions. The code provides a long-missing wave field model needed to quantify the interaction of radiation belt electrons with realistic, global ULF waves throughout the inner magnetosphere

Econometric Analysis of Financial Derivatives

__Abstract__ One of the fastest growing areas in empirical finance, and also one of the least rigorously analyzed, especially from a financial econometrics perspective, is the econometric analysis of financial derivatives, which are typically complicated and difficult to analyze. The purpose of this special issue of the journal on “Econometric Analysis of Financial Derivatives” is to highlight several areas of research by leading academics in which novel econometric, financial econometric, mathematical finance and empirical finance methods have contributed significantly to the econometric analysis of financial derivatives, including market-based estimation of stochastic volatility models, the fine structure of equity-index option dynamics, leverage and feedback effects in multifactor Wishart stochastic volatility for option pricing, option pricing with non-Gaussian scaling and infinite-state switching volatility, stock return and cash flow predictability: the role of volatility risk, the long and the short of the risk-return trade-off, What’s beneath the surface? option pricing with multifrequency latent states, bootstrap score tests for fractional integration in heteroskedastic ARFIMA models, with an application to price dynamics in commodity spot and futures markets, a stochastic dominance approach to financial risk management strategies, empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction, non-linear dynamic model of the variance risk premium, pricing with finite dimensional dependence, quanto option pricing in the presence of fat tails and asymmetric dependence, smile from the past: a general option pricing framework with multiple volatility and leverage components, COMFORT: A common market factor non-Gaussian returns model, divided governments and futures prices, and model-based pricing for financial derivative

What Do Experts Know About Forecasting Journal Quality? A Comparison with ISI Research Impact in Finance

Experts possess knowledge and information that are not publicly available. The paper is concerned with forecasting academic journal quality and research impact using a survey of international experts from a national project on ranking academic finance journals in Taiwan. A comparison is made with publicly available bibliometric data, namely the Thomson Reuters ISI Web of Science citations database (hereafter ISI) for the Business - Finance (hereafter Finance) category. The paper analyses the leading international journals in Finance using expert scores and quantifiable Research Assessment Measures (RAMs), and highlights the similarities and differences in the expert scores and alternative RAMs, where the RAMs are based on alternative transformations of citations taken from the ISI database. Alternative RAMs may be calculated annually or updated daily to answer the perennial questions as to When, Where and How (frequently) published papers are cited (see Chang et al. (2011a, b, c)). The RAMs include the most widely used RAM, namely the classic 2-year impact factor including journal self citations (2YIF), 2-year impact factor excluding journal self citations (2YIF*), 5-year impact factor including journal self citations (5YIF), Immediacy (or zero-year impact factor (0YIF)), Eigenfactor, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, PI-BETA (Papers Ignored - By Even The Authors), 2-year Self-citation Threshold Approval Ratings (2Y-STAR), Historical Self-citation Threshold Approval Ratings (H-STAR), Impact Factor Inflation (IFI), and Cited Article Influence (CAI). As data are not available for 5YIF, Article Influence and CAI for 13 of the leading 34 journals considered, 10 RAMs are analysed for 21 highly-cited journals in Finance. The harmonic mean of the ranks of the 10 RAMs for the 34 highly-cited journals are also presented. It is shown that emphasizing the 2-year impact factor of a journal, which partly answers the question as to When published papers are cited, to the exclusion of other informative RAMs, which answer Where and How (frequently) published papers are cited, can lead to a distorted evaluation of journal impact and influence relative to the Harmonic Mean rankings. A linear regression model is used to forecast expert scores on the basis of RAMs that capture journal impact, journal policy, the number of high quality papers, and quantitative information about a journal. The robustness of the rankings is also analysed

Coercive Journal Self Citations, Impact Factor, Journal Influence and Article Influence

This paper examines the issue of coercive journal self citations and the practical usefulness of two recent journal performance metrics, namely the Eigenfactor score, which may be interpreted as measuring “Journal Influence”, and the Article Influence score, using the Thomson Reuters ISI Web of Science (hereafter ISI) data for 2009 for the 200 most highly cited journals in each of the Sciences and Social Sciences. The paper also compares the two new bibliometric measures with two existing ISI metrics, namely Total Citations and the 5-year Impact Factor (5YIF) (including journal self citations) of a journal. It is shown that the Sciences and Social Sciences are different in terms of the strength of the relationship of journal performance metrics, although the actual relationships are very similar. Moreover, the journal influence and article influence journal performance metrics are shown to be closely related empirically to the two existing ISI metrics, and hence add little in practical usefulness to what is already known, except for eliminating the pressure arising from coercive journal self citations. These empirical results are compared with existing results in the bibliometrics literature

Ranking Journal Quality by Harmonic Mean of Ranks: An Application to ISI Statistics & Probability

As the preponderance of journal rankings becomes increasingly more frequent and prominent in academic decision making, such rankings in broad discipline categories is taking on an increasingly important role. The paper focuses on the robustness of rankings of academic journal quality and research impact using on the widely-used Thomson Reuters ISI Web of Science citations database (ISI) for the Statistics & Probability category. The paper analyses 110 ISI international journals in Statistics & Probability using quantifiable Research Assessment Measures (RAMs), and highlights the similarities and differences in various RAMs, which are based on alternative transformations of citations and influence. Alternative RAMs may be calculated annually or updated daily to determine When, Where and How (frequently) published papers are cited (see Chang et al. (2011a, b, c), Chang et al. (2012)). The RAMs are grouped in four distinct classes that include impact factor, mean citations and non-citations, journal policy, number of high quality papers, and journal influence and article influence. These classes include the most widely used RAMs, namely the classic 2-year impact factor including journal self citations (2YIF), 2-year impact factor excluding journal self citations (2YIF*), 5-year impact factor including journal self citations (5YIF), Eigenfactor (or Journal Influence), Article Influence, h-index, PI-BETA (Papers Ignored - By Even The Authors), 5YD2 (= 5YIF/2YIF) as a measure of citations longevity, and Escalating Self Citations (ESC) as a measure of increasing journal self citations. The paper highlights robust rankings based on the harmonic mean of the ranks of RAMs across the 4 classes. It is shown that focusing solely on the 2-year impact factor (2YIF) of a journal, which partly answers the question as to When published papers are cited, to the exclusion of other informative RAMs, which answer Where and How (frequently) published papers are cited, can lead to a distorted evaluation of journal quality, impact and influence relative to the more robust harmonic mean of the ranks

Connecting VIX and Stock Index ETF

As stock market indexes are not tradeable, the importance and trading volume of Exchange Traded Funds (ETFs) cannot be understated. ETFs track and attempt to replicate the performance of a specific index. Numerous studies have demonstrated a strong relationship between the S&P500 Composite Index and the Volatility Index (VIX), but few empirical studies have focused on the relationship between VIX and ETF returns. The purpose of the paper is to investigate whether VIX returns affect ETF returns by using vector autoregressive (VAR) models to determine whether daily VIX returns with different moving average processes affect ETF returns. The ARCH-LM test shows conditional heteroskedasticity in the estimation of ETF returns, so that the diagonal BEKK model is used to accommodate multivariate conditional heteroskedasticity in the VAR estimates of ETF returns. Daily data on ETF returns that follow different stock indexes in the USA and Europe are used in the empirical analysis. The estimates show that daily VIX returns have: (1) significant negative effects on European ETF returns in the short run; (2) stronger significant effects on single market ETF returns than on European ETF returns; and (3) lower impacts on the European ETF returns than on S&P500 returns

What do Experts Know About Ranking Journal Quality? A Comparison with ISI Research Impact in Finance

Experts possess knowledge and information that are not publicly available. The paper is concerned with the ranking of academic journal quality and research impact using a survey of experts from a national project on ranking academic finance journals. A comparison is made with publicly available bibliometric data, namely the Thomson Reuters ISI Web of Science citations database (hereafter ISI) for the Business - Finance category. The paper analyses the leading international journals in Finance using expert scores and quantifiable Research Assessment Measures (RAMs), and highlights the similarities and differences in the expert scores and alternative RAMs, where the RAMs are based on alternative transformations of citations taken from the ISI database. Alternative RAMs may be calculated annually or updated daily to answer the perennial questions as to When, Where and How (frequently) published papers are cited (see Chang et al. (2011a, b, c)). The RAMs include the most widely used RAM, namely the classic 2-year impact factor including journal self citations (2YIF), 2-year impact factor excluding journal self citations (2YIF*), 5-year impact factor including journal self citations (5YIF), Immediacy (or zero-year impact factor (0YIF)), Eigenfactor, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, PI-BETA (Papers Ignored - By Even The Authors), 2-year Self-citation Threshold Approval Ratings (2Y-STAR), Historical Self-citation Threshold Approval Ratings (H-STAR), Impact Factor Inflation (IFI), and Cited Article Influence (CAI). As data are not available for 5YIF, Article Influence and CAI for 13 of the leading 34 journals considered, 10 RAMs are analysed for 21 highly-cited journals in Finance. Harmonic mean rankings of the 10 RAMs for the 34 highly-cited journals are also presented. It is shown that emphasizing the 2-year impact factor of a journal, which partly answers the question as to When published papers are cited, to the exclusion of other informative RAMs, which answer Where and How (frequently) published papers are cited, can lead to a distorted evaluation of journal impact and influence relative to the Harmonic Mean rankings. A simple regression model is used to predict expert scores on the basis of RAMs that capture journal impact, journal policy, the number of high quality papers, and quantitative information about a journal