Small scale aspects of warm dark matter : power spectra and acoustic oscillations

We provide a semi-analytic study of the small scale aspects of the power spectra of warm dark matter (WDM) candidates that decoupled while relativistic with arbitrary distribution functions. These are characterized by two widely different scales $k_{eq} \sim 0.01\,(\mathrm{Mpc})^{-1}$ and k_{fs}= \sqrt{3}\,k_{eq}/2\,^{1/2} with $^{1/2} \ll 1$ the velocity dispersion at matter radiation equality. Density perturbations evolve through three stages: radiation domination when the particle is relativistic and non-relativistic and matter domination. An early ISW effect during the first stage leads to an enhancement of density perturbations and a plateau in the transfer function for $k \lesssim k_{fs}$. An effective fluid description emerges at small scales which includes the effects of free streaming in initial conditions and inhomogeneities. The transfer function features \emph{WDM-acoustic oscillations} at scales $k \gtrsim 2 \,k_{fs}$. We study the power spectra for two models of sterile neutrinos with $m \sim \,\mathrm{keV}$ produced non-resonantly, at the QCD and EW scales respectively. The latter case yields acoustic oscillations on mass scales $\sim 10^{8}\,M_{\odot}$. Our results reveal a \emph{quasi-degeneracy} between the mass, distribution function and decoupling temperature suggesting caveats on the constraints on the mass of a sterile neutrino from current WDM N-body simulations and Lyman-$\alpha$ forest data. A simple analytic interpolation of the power spectra between large and small scales and its numerical implementation is given.Comment: 47 pages, 17 figures, section with comparison with Boltzmann code

Forecasting Realized Volatility Using A Nonnegative Semiparametric Model

This paper introduces a parsimonious and yet flexible nonnegative semiparametric model to forecast financial volatility. The new model extends the linear nonnegative autoregressive model of Barndorff-Nielsen & Shephard (2001) and Nielsen & Shephard (2003) by way of a power transformation. It is semiparametric in the sense that the dependency structure and distributional form of its error component are left unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Simulation studies validate the new estimation method and suggest that it works reasonably well in finite samples. The out-of-sample performance of the proposed model is evaluated against a number of standard methods, using data on S&P 500 monthly realized volatilities. The competing models include the exponential smoothing method, a linear AR(1) model, a log-linear AR(1) model, and two long-memory ARFIMA models. Various loss functions are utilized to evaluate the predictive accuracy of the alternative methods. It is found that the new model generally produces highly competitive forecasts.Autoregression, nonlinear/non-Gaussian time series, realized volatility, semiparametric model, volatility forecast

Forecasting Realized Volatility Using A Nonnegative Semiparametric Model

This paper introduces a parsimonious and yet flexible nonnegative semiparametric model to forecast financial volatility. The new model extends the linear nonnegative autoregressive model of Barndorff-Nielsen & Shephard (2001) and Nielsen & Shephard (2003) by way of a power transformation. It is semiparametric in the sense that the dependency structure and distributional form of its error component are left unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Simulation studies validate the new estimation method and suggest that it works reasonably well in finite samples. The out-of-sample performance of the proposed model is evaluated against a number of standard methods, using data on S&P 500 monthly realized volatilities. The competing models include the exponential smoothing method, a linear AR(1) model, a log-linear AR(1) model, and two long-memory ARFIMA models. Various loss functions are utilized to evaluate the predictive accuracy of the alternative methods. It is found that the new model generally produces highly competitive forecasts.Autoregression, nonlinear/non-Gaussian time series, realized volatility, semiparametric model, volatility forecast.

Bribery Among the Korean Elite: Putting an End to a Cultural Ritual and Restoring Honor

On August 26, 1996, the criminal bribery convictions of two former South Korean Presidents sent shockwaves throughout the nation of South Korea. The court found former Presidents Chun Doo Hwan and Rof Tae Woo guilty of amassing hundreds of millions of dollars in bribes during their respective presidential terms. The court also found corporate executives of major Korean conglomerates guilty of bribing the former Presidents in exchange for government contracts or political favors. Such events invite a look into South Korea\u27s difficult past, revealing a history of remarkable industrial progress tarnished by pervasive government corruption. This Note first explores South Korea\u27s sociocultural and political history in order to assess the modern practice of bribery among public officials. The Note then analyzes the Korean antibribery laws and evaluates the legal machinery against corruption. The author determines that the poor enforcement of the antibribery laws allowed bribery to spread among public officials. Next, the author describes a theoretical model for optimal law enforcement, premised on efficiency, and applies its principles to the present context. In the process, the author proposes solutions to resolve the current problem of government corruption, emphasizing the need for optimal enforcement of the Korean antibribery laws. Although meaningful reform will be difficult to achieve, the author concludes that the laws against bribery can ultimately provide a government of integrity for South Korea