A Short Tale of Long Tail Integration

Integration of the form $\int_a^\infty {f(x)w(x)dx}$, where $w(x)$ is either $\sin (\omega {\kern 1pt} x)$ or $\cos (\omega {\kern 1pt} x)$, is widely encountered in many engineering and scientific applications, such as those involving Fourier or Laplace transforms. Often such integrals are approximated by a numerical integration over a finite domain $(a,\,b)$, leaving a truncation error equal to the tail integration $\int_b^\infty {f(x)w(x)dx}$ in addition to the discretization error. This paper describes a very simple, perhaps the simplest, end-point correction to approximate the tail integration, which significantly reduces the truncation error and thus increases the overall accuracy of the numerical integration, with virtually no extra computational effort. Higher order correction terms and error estimates for the end-point correction formula are also derived. The effectiveness of this one-point correction formula is demonstrated through several examples

The Evolution of Cross-Region Price Distribution in Russia

The behavior of the entire cross-section distribution of prices in Russian regions is analyzed from 1992 through 2000, using non-parametric techniques. The cost of a staples basket is used as a price representative. Price dispersion measured as the standard deviation of prices is found to be diminishing since about 1994; and the shape of the cross-region distribution of prices tends to be more regular over time. To characterize intra-distribution mobility, a transition probability function (stochastic kernel) is estimated. It is also used to derive a long-run limit of the price distribution. Overall, the results suggest that, excluding a few years following the price liberalization, price convergence has been happening among Russian regions.http://deepblue.lib.umich.edu/bitstream/2027.42/40102/3/wp716.pd

Computing Tails of Compound Distributions Using Direct Numerical Integration

An efficient adaptive direct numerical integration (DNI) algorithm is developed for computing high quantiles and conditional Value at Risk (CVaR) of compound distributions using characteristic functions. A key innovation of the numerical scheme is an effective tail integration approximation that reduces the truncation errors significantly with little extra effort. High precision results of the 0.999 quantile and CVaR were obtained for compound losses with heavy tails and a very wide range of loss frequencies using the DNI, Fast Fourier Transform (FFT) and Monte Carlo (MC) methods. These results, particularly relevant to operational risk modelling, can serve as benchmarks for comparing different numerical methods. We found that the adaptive DNI can achieve high accuracy with relatively coarse grids. It is much faster than MC and competitive with FFT in computing high quantiles and CVaR of compound distributions in the case of moderate to high frequencies and heavy tails

Bacteriophages and their structural organisation

Viruses are extremely small infectious particles that are not visible in a light microscope, and are able to pass through fine porcelain filters. They exist in a huge variety of forms and infect practically all living systems: animals, plants, insects and bacteria. All viruses have a genome, typically only one type of nucleic acid, but it could be one or several molecules of DNA or RNA, which is surrounded by a protective stable coat (capsid) and sometimes by additional layers which may be very complex and contain carbohydrates, lipids, and additional proteins. The viruses that have only a protein coat are named “naked”, or non- enveloped viruses. Many viruses have an envelope (enveloped viruses) that wraps around the protein capsid. This envelope is formed from a lipid membrane of the host cell during the release of a virus out of the cell

Calculation of aggregate loss distributions

Estimation of the operational risk capital under the Loss Distribution Approach requires evaluation of aggregate (compound) loss distributions which is one of the classic problems in risk theory. Closed-form solutions are not available for the distributions typically used in operational risk. However with modern computer processing power, these distributions can be calculated virtually exactly using numerical methods. This paper reviews numerical algorithms that can be successfully used to calculate the aggregate loss distributions. In particular Monte Carlo, Panjer recursion and Fourier transformation methods are presented and compared. Also, several closed-form approximations based on moment matching and asymptotic result for heavy-tailed distributions are reviewed

The Evolution of Cross-Region Price Distribution in Russia

The behavior of the entire cross-section distribution of prices in Russian regions is analyzed from 1992 through 2000, using non-parametric techniques. The cost of a staples basket is used as a price representative. Price dispersion measured as the standard deviation of prices is found to be diminishing since about 1994; and the shape of the cross-region distribution of prices tends to be more regular over time. To characterize intra-distribution mobility, a transition probability function (stochastic kernel) is estimated. It is also used to derive a long-run limit of the price distribution. Overall, the results suggest that, excluding a few years following the price liberalization, price convergence has been happening among Russian regions.price convergence, price dispersion, distribution dynamics, market integration, Russia