Branching ratios of BcB_c Meson Decaying to Vector and Axial-Vector Mesons

We investigate the weak decays of $B_c$ mesons in Cabibbo-Kobayashi-Maskawa favored and suppressed modes. We present a detailed analysis of the $B_c$ meson decaying to vector meson (V) and axial-vector meson (A) in the final state. We also give the form factors involving $B_c \to A$ transition in the Isgur-Scora-Grinstein-Wise II framework and consequently, predict the branching ratios of $B_c \to V A$ and $AA$ decays.Comment: 26 Pages, Version to appear in Phys. Rev. D(2013

Near Infrared Spectroscopic Monitoring During Cardiopulmonary Exercise Testing Detects Anaerobic Threshold

Cardiopulmonary exercise testing (CPET) provides assessment of the integrative responses involving the pulmonary, cardiovascular, and skeletal muscle systems. Application of exercise testing remains limited to children who are able to understand and cooperate with the exercise protocol. Near-infrared spectroscopy (NIRS) provides a noninvasive, continuous method to monitor regional tissue oxygenation (rSO2). Our specific aim was to predict anaerobic threshold (AT) during CPET noninvasively using two-site NIRS monitoring. Achievement of a practical noninvasive technology for estimating AT will increase the compatibility of CPET. Patients without structural or acquired heart disease were eligible for inclusion if they were ordered to undergo CPET by a cardiologist. Data from 51 subjects was analyzed. The ventilatory anaerobic threshold (VAT) was computed on VCO2 and respiratory quotient post hoc using the standard V-slope method. The inflection points of the regional rSO2 time-series were identified as the noninvasive regional NIRS AT for each of the two monitored regions (cerebral and kidney). AT calculation made using an average of kidney and brain NIRS matched the calculation made by VAT for the same patient. Two-site NIRS monitoring of visceral organs is a predictor of AT

The Variance Ratio Statistic at Large Horizons

We make three contributions to using the variance ratio statistic at large horizons. Allowing for general heteroscedasticity in the data, we obtain the asymptotic distribution of the statistic when the horizon k is increasing with the sample size n but at a slower rate so that k=n ! 0. The test is shown to be consistent against a variety of relevant mean reverting alternatives when k=n ! 0. This is in contrast to the case when k=n ! – > 0; where the statistic has been recently shown to be inconsistent against such alternatives. Secondly, we provide and justify a simple power transformation of the statistic which yields almost perfectly normally distributed statistics in finite samples, solving the well known right skewness problem. Thirdly, we provide a more powerful way of pooling information from different horizons to test for mean reverting alternatives. Monte Carlo simulations illustrate the theoretical improvements provided. --Mean reversion,Frequency domain,Power transformation

Estimation of Mis-Specified Long Memory Models

We study the asymptotic behaviour of frequency domain maximum likelihood estimators of mis-specified models of long memory Gaussian series. We show that even if the long memory structure of the time series is correctly specified, mis-specification of the short memory dynamics may result in parameter estimators which are slower than pn consistent. The conditions under which this happens are provided and the asymptotic distribution of the estimators is shown to be non-Gaussian. Conditions under which estimators of the parameters of the mis-specified model have the standard pn consistent and asymptotically normal behaviour are also provided. --

Forecasting Realised Volatility using a Long Memory Stochastic Volatility Model: Estimation, Prediction and Seasonal Adjustment

We study the modelling of large data sets of high frequency returns using a long memory stochastic volatility (LMSV) model. Issues pertaining to estimation and forecasting of datasets using the LMSV model are studied in detail. Furthermore, a new method of de-seasonalising the volatility in high frequency data is proposed, that allows for slowly varying seasonality. Using both simulated as well as real data, we compare the forecasting performance of the LMSV model for forecasting realised volatility to that of a linear long memory model fit to the log realised volatility. The performance of the new seasonal adjustment is also compared to a recently proposed procedure using real data. --