Dominant regions in noncrystallographic hyperplane arrangements

For a crystallographic root system, dominant regions in the Catalan hyperplane arrangement are in bijection with antichains in a partial order on the positive roots. For a noncrystallographic root system, the analogous arrangement and regions have importance in the representation theory of an associated graded Hecke algebra. Since there is also an analogous root order, it is natural to hope that a similar bijection can be used to understand these regions. We show that such a bijection does hold for type $H_3$ and for type $I_2(m)$, including arbitrary ratio of root lengths when $m$ is even, but does not hold for type $H_4$. We give a criterion that explains this failure and a list of the 16 antichains in the $H_4$ root order which correspond to empty regions.Comment: 29 pages, 5 figure

Immuno-Anti-Infective Drug Design Using BioAI

According to the World Health Organization, antibiotic resistance is one of the biggest threats to global health, food security, and development today. A growing number of infections, like Methicillin-resistant Staphylococcus aureus, are becoming harder to treat as the antibiotics used to treat them become less effective. As a result, the primary concern for infections in the hospital setting is due to the S. aureus’s growing resistance to antibiotics. Therefore, in response to this global health threat, our project focuses on furthering the research in developing a drug that S. aureus will not develop resistance to. In this paper, we assess NPY-Y2 as a potential immuno-anti-infective drug target to prevent the activation of Sortase A on S. aureus. We have shown that NPY-Y2 is a potential drug target; however, further invasion assay experiments need to be conducted for more reliable verification. In a larger scheme, our hope is that the approach of this research will allow for the development of other anti-infective drugs for other bacteria

Assessment of selected soil parameters in a long-term Western Canadian organic field experiment

A long-term field study was used to compare soil nitrogen and phosphorous status, and soil aggregate stability in organic and conventional cropping systems. Two rotations were tested: a grain only and a grain-alfalfa hay rotation. The organic systems had a lower nitrate leaching potential than the same rotations under conventional management. After 13 years, one organic system (the grain-alfalfa; no manure return) is suffering serious soil P depletion. However, the grain only and the grain-alfalfa with manure return to land systems had soil P levels similar to the prairie grass control treatment and showed no signs of P deficiency. Despite having lower levels of organic carbon, the organic soils had higher levels of wet aggregate stability than conventionally managed soils

Semi-parametric Expected Shortfall Forecasting

Intra-day sources of data have proven effective for dynamic volatility and tail risk estimation. Expected shortfall is a tail risk measure, that is now recommended by the Basel Committee, involving a conditional expectation that can be semi-parametrically estimated via an asymmetric sum of squares function. The conditional autoregressive expectile class of model, used to indirectly model expected shortfall, is generalised to incorporate information on the intra-day range. An asymmetric Gaussian density model error formulation allows a likelihood to be developed that leads to semiparametric estimation and forecasts of expectiles, and subsequently of expected shortfall. Adaptive Markov chain Monte Carlo sampling schemes are employed for estimation, while their performance is assessed via a simulation study. The proposed models compare favourably with a large range of competitors in an empirical study forecasting seven financial return series over a ten year period

Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range

Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We propose some novel nonlinear threshold conditional autoregressive VaR (CAViaR) models that incorporate intra-day price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the Skewed-Laplace distribution. We examine how a range of risk models perform during the 2008-09 financial crisis, and evaluate how the crisis affects the performance of risk models via forecasting VaR. Empirical analysis is conducted on five Asia-Pacific Economic Cooperation stock market indices as well as two exchange rate series. We examine violation rates, back-testing criteria, market risk charges and quantile loss function values to measure and assess the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more efficiently than other models, across the series considered, which should be useful for financial practitioners.Value-at-Risk; CAViaR model; Skewed-Laplace distribution; intra-day range; backtesting; Markov chain Monte Carlo

Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range

Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We pro- pose some novel nonlinear threshold conditional autoregressive VaR (CAViaR) models that incorporate intra-day price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the Skewed-Laplace distribution. We examine how a range of risk models perform during the 2008-09 financial crisis, and evaluate how the crisis aects the performance of risk models via forecasting VaR. Empirical analysis is conducted on five Asia-Pacific Economic Cooperation stock market indices as well as two exchange rate series. We examine violation rates, back-testing criteria, market risk charges and quantile loss function values to measure and assess the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more eficiently than other models, across the series considered, which should be useful for financial practitioners.Value-at-Risk; CAViaR model; Skewed-Laplace distribution; intra-day range; backtesting, Markov chain Monte Carlo.

A novel tool for individual haplotype inference using mixed data

© 2009 Lin and Fann; licensee BioMed Central Ltd

Sentiment-induced bubbles in the cryptocurrency market

Cryptocurrencies lack clear measures of fundamental values and are often associated with speculative bubbles. This paper introduces a new way of testing for speculative bubbles based on StockTwits sentiment, which is used as the transition variable in a smooth transition autoregression. The model allows for conditional heteroskedasticity and fat tails of the conditional distribution of the error term, and volatility may depend on the constructed sentiment index. We apply the model to the CRIX index, for which several bubble periods are identified. The detected locally explosive price dynamics, given the specified bubble regime controlled by a smooth transition function, are more akin to the notion of speculative bubble that is driven by exuberant sentiment. Furthermore, we find that volatility increases as the sentiment index decreases, which is analogous to the commonly called leverage effect