Clustering of discretely observed diffusion processes

In this paper a new dissimilarity measure to identify groups of assets dynamics is proposed. The underlying generating process is assumed to be a diffusion process solution of stochastic differential equations and observed at discrete time. The mesh of observations is not required to shrink to zero. As distance between two observed paths, the quadratic distance of the corresponding estimated Markov operators is considered. Analysis of both synthetic data and real financial data from NYSE/NASDAQ stocks, give evidence that this distance seems capable to catch differences in both the drift and diffusion coefficients contrary to other commonly used metrics

Empirical L2L^2-distance test statistics for ergodic diffusions

The aim of this paper is to introduce a new type of test statistic for simple null hypothesis on one-dimensional ergodic diffusion processes sampled at discrete times. We deal with a quasi-likelihood approach for stochastic differential equations (i.e. local gaussian approximation of the transition functions) and define a test statistic by means of the empirical $L^2$-distance between quasi-likelihoods. We prove that the introduced test statistic is asymptotically distribution free; namely it weakly converges to a $\chi^2$ random variable. Furthermore, we study the power under local alternatives of the parametric test. We show by the Monte Carlo analysis that, in the small sample case, the introduced test seems to perform better than other tests proposed in literature

Nonparametric methods for volatility density estimation

Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and review several nonparametric methods for estimation of the density of the volatility process. Both models based on discretely sampled continuous time processes and discrete time models will be discussed. The key insight for the analysis is a transformation of the volatility density estimation problem to a deconvolution model for which standard methods exist. Three type of nonparametric density estimators are reviewed: the Fourier-type deconvolution kernel density estimator, a wavelet deconvolution density estimator and a penalized projection estimator. The performance of these estimators will be compared. Key words: stochastic volatility models, deconvolution, density estimation, kernel estimator, wavelets, minimum contrast estimation, mixin

Direct observations of nucleation in a nondilute multicomponent alloy

The chemical pathways leading to gamma-prime(L1_2)-nucleation from nondilute Ni-5.2 Al-14.2 Cr at.%, gamma(f.c.c.), at 873 K are followed with radial distribution functions and isoconcentration surface analyses of direct-space atom-probe tomographic images. Although Cr atoms initially are randomly distributed, a distribution of congruent Ni3Al short-range order domains (SRO), =0.6 nm, results from Al diffusion during quenching. Domain site occupancy develops as their number density increases leading to Al-rich phase separation by gamma-prime-nucleation, =0.75 nm, after SRO occurs.Comment: 5 pages, 4 figure

Volatility forecasting

Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1

X-ray Emission of Baryonic Gas in the Universe: Luminosity-Temperature Relationship and Soft-Band Background

We study the X-ray emission of baryon fluid in the universe using the WIGEON cosmological hydrodynamic simulations. It has been revealed that cosmic baryon fluid in the nonlinear regime behaves like Burgers turbulence, i.e. the fluid field consists of shocks. Like turbulence in incompressible fluid, the Burgers turbulence plays an important role in converting the kinetic energy of the fluid to thermal energy and heats the gas. We show that the simulation sample of the $\Lambda$CDM model without adding extra heating sources can fit well the observed distributions of X-ray luminosity versus temperature ($L_{\rm x}$ vs. $T$) of galaxy groups and is also consistent with the distributions of X-ray luminosity versus velocity dispersion ($L_{\rm x}$ vs. $\sigma$). Because the baryonic gas is multiphase, the $L_{\rm x}-T$ and $L_{\rm x}-\sigma$ distributions are significantly scattered. If we describe the relationships by power laws $L_{\rm x}\propto T^{\alpha_{LT}}$ and $L_{\rm x}\propto \sigma^{\alpha_{LV}}$, we find $\alpha_{LT}>2.5$ and $\alpha_{LV}>2.1$. The X-ray background in the soft $0.5-2$ keV band emitted by the baryonic gas in the temperature range $10^5<T<10^7$ K has also been calculated. We show that of the total background, (1) no more than 2% comes from the region with temperature less than $10^{6.5}$ K, and (2) no more than 7% is from the region of dark matter with mass density $\rho_{\rm dm}<50 \bar{\rho}_{\rm dm}$. The region of $\rho_{\rm dm}>50\bar{\rho}_{\rm dm}$ is generally clustered and discretely distributed. Therefore, almost all of the soft X-ray background comes from clustered sources, and the contribution from truly diffuse gas is probably negligible. This point agrees with current X-ray observations.Comment: 32 pages including 14 figures and 2 tables. Final version for publication in Ap