Asymmetric Dependence in US Financial Risk Factors?

.Asymmetric Dependence; Copulas; Diversification Failure; Risk Factor; Systemic Risk; Time-Varying Downside Risk

Segmentation across International Equity, Bond, and Foreign Exchange Markets

In this paper, we examine the integration of international financial markets. The integration of financial markets across countries and across asset classes is assumed to hold in most empirical studies, but has only been tested for certain countries and certain asset classes. We test for the integration of international equity, bond and foreign exchange markets. Our results indicate that the three classes of assets are segmented. Investigating potential explanations for this segmentation, we find that there are differing degrees of segmentation across these markets and that this is related to the asset returns from each class being explained by different sets of economic risk factors. In pair-wise tests we find that the bond-equity and bond-foreign exchange markets appear to be more segmented than the equity-foreign exchange market.Market integration; GMM; Stochastic discount factor models; Hansen and Jagannathan distance

The Dependence Structure of Macroeconomic Variables in the US

A central role for economic policy involves reducing the incidence of systemic downturns, when key economic variables experience joint extreme events. In this paper, we empirically analyze such dependence using two approaches, correlations and copulas. We document four findings. First, linear correlations and copulas disagree substantially about the nation’s dependence structure, indicating correlation complexity in the US economy. Second, GDP exhibits linear dependence with interest rates and prices, but no extreme dependence with the latter. This is consistent with the existence of liquidity traps. Third, GDP exhibits asymmetric extreme dependence with employment, consumption and investment, with relatively greater dependence during downturns. Fourth, money is neutral, especially during extreme economic conditions.Asymmetric dependence; Copula; Correlation Complexity; Extreme Event; Economic Policy; Money Neutrality; Systemic Downturn

Modeling Asymmetric Volatility Clusters Using Copulas and High Frequency Data

Volatility clustering is a well-known stylized feature of financial asset returns. In this paper, we investigate the asymmetric pattern of volatility clustering on both the stock and foreign exchange rate markets. To this end, we employ copula-based semi-parametric univariate time-series models that accommodate the clusters of both large and small volatilities in the analysis. Using daily realized volatilities of the individual company stocks, stock indices and foreign exchange rates constructed from high frequency data, we find that volatility clustering is strongly asymmetric in the sense that clusters of large volatilities tend to be much stronger than those of small volatilities. In addition, the asymmetric pattern of volatility clusters continues to be visible even when the clusters are allowed to be changing over time, and the volatility clusters themselves remain persistent even after forty days.Volatility clustering, Copulas, Realized volatility, High-frequency data.

Asymmetric Dependence in the US Economy: Application to Money and the Phillips Curve

Abstract A central role for economic policy involves reducing the incidence of systemic downturns, when key economic variables experience joint extreme events. In this paper, we empirically analyze such dependence using two approaches, correlations and copulas. We document four findings. First, linear correlations and copulas disagree substantially about the nation's dependence structure, indicating complexity in the US economy. Second, money appears to be neutral during both normal and extreme economic conditions. Third, GDP exhibits asymmetric dependence with employment and consumption, with relatively greater dependence during downturns. Finally, there is asymmetric dependence between inflation and employment, in the left tail only. This latter finding suggests that historical Phillips curve relations are a property mainly of systemic downturns

Asymmetric Dependence in the US Economy: Application to Money and the Phillips Curve

Abstract A central role for economic policy involves reducing the incidence of systemic downturns, when key economic variables experience joint extreme events. In this paper, we empirically analyze such dependence using two approaches, correlations and copulas. We document four findings. First, linear correlations and copulas disagree substantially about the nation's dependence structure, indicating complexity in the US economy. Second, money appears to be neutral during both normal and extreme economic conditions. Third, GDP exhibits asymmetric dependence with employment and consumption, with relatively greater dependence during downturns. Finally, there is asymmetric dependence between inflation and employment, in the left tail only. This latter finding suggests that historical Phillips curve relations are a property mainly of systemic downturns

An assessment of hepatitis E virus (HEV) in US blood donors and recipients: No detectable HEV RNA in 1939 donors tested and no evidence for HEV transmission to 362 prospectively followed recipients.

BACKGROUND: Hepatitis E virus (HEV) infection has become relevant to blood transfusion practice because isolated cases of blood transmission have been reported and because HEV has been found to cause chronic infection and severe liver disease in immunocompromised patients. STUDY DESIGN AND METHODS: We tested for immunoglobulin (Ig)G and IgM antibodies to the HEV and for HEV RNA in 1939 unselected volunteer US blood donors. Subsequently, we tested the same variables in pre- and serial posttransfusion samples from 362 prospectively followed blood recipients to assess transfusion risk. RESULTS: IgG anti-HEV seroprevalence in the total 1939 donations was 18.8%: 916 of these donations were made in 2006 at which time the seroprevalence was 21.8% and the remaining 1023 donations were in 2012 when the seroprevalence had decreased to 16.0% (p \u3c 0.01). A significant (p \u3c 0.001) stepwise increase in anti-HEV seroprevalence was seen with increasing age. Eight of 1939 donations (0.4%) tested anti-HEV IgM positive; no donation was HEV RNA positive. Two recipients had an apparent anti-HEV seroconversion, but temporal relationships and linked donor testing showed that these were not transfusion-transmitted HEV infections. CONCLUSION: No transfusion-transmitted HEV infections were observed in 362 prospectively followed blood recipients despite an anti-HEV seroprevalence among donations exceeding 16%

Informative scene decomposition for crowd analysis, comparison and simulation guidance

Crowd simulation is a central topic in several fields including graphics. To achieve high-fidelity simulations, data has been increasingly relied upon for analysis and simulation guidance. However, the information in real-world data is often noisy, mixed and unstructured, making it difficult for effective analysis, therefore has not been fully utilized. With the fast-growing volume of crowd data, such a bottleneck needs to be addressed. In this paper, we propose a new framework which comprehensively tackles this problem. It centers at an unsupervised method for analysis. The method takes as input raw and noisy data with highly mixed multi-dimensional (space, time and dynamics) information, and automatically structure it by learning the correlations among these dimensions. The dimensions together with their correlations fully describe the scene semantics which consists of recurring activity patterns in a scene, manifested as space flows with temporal and dynamics profiles. The effectiveness and robustness of the analysis have been tested on datasets with great variations in volume, duration, environment and crowd dynamics. Based on the analysis, new methods for data visualization, simulation evaluation and simulation guidance are also proposed. Together, our framework establishes a highly automated pipeline from raw data to crowd analysis, comparison and simulation guidance. Extensive experiments and evaluations have been conducted to show the flexibility, versatility and intuitiveness of our framework

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead