Stability conditions for heteroscedastic factor models with conditionally autoregressive betas

We characterize the stability properties of a heteroscedastic multi‐factor model of financial asset returns, with conditionally known factors and beta coefficients driven by general conditionally autoregressive processes. These processes generalize existing structures and address a number of empirical issues of current concern. Our analysis derives closed‐form sufficient conditions for the existence of strict stationary solutions for the composite asset conditional variances and covariances, not known previously in the literature. It is shown that stability is guaranteed when individual‐process and cross‐process restrictions hold simultaneously. Our results are also applicable to the study of the co‐movement between volatility and beta coefficients as well as between beta coefficients themselves.     

Efficient estimation and particle filter for max‐stable processes

Extreme values are often correlated over time, for example, in a financial time series, and these values carry various risks. Max‐stable processes such as maxima of moving maxima (M3) processes have been recently considered in the literature to describe time‐dependent dynamics, which have been difficult to estimate. This article first proposes a feasible and efficient Bayesian estimation method for nonlinear and non‐Gaussian state space models based on these processes and describes a Markov chain Monte Carlo algorithm where the sampling efficiency is improved by the normal mixture sampler. Furthermore, a unique particle filter that adapts to extreme observations is proposed and shown to be highly accurate in comparison with other well‐known filters. Our proposed algorithms were applied to daily minima of high‐frequency stock return data, and a model comparison was conducted using marginal likelihoods to investigate the time‐dependent dynamics in extreme stock returns for financial risk management.     

Implicit Bayesian Inference Using Option Prices

Abstract. A Bayesian approach to option pricing is presented in which posterior inference about the underlying returns process is conducted implicitly via observed option prices. A range of models allowing for conditional leptokurtosis, skewness and time‐varying volatility in returns are considered, with posterior parameter distributions and model probabilities backed out from the option prices. Models are ranked according to several criteria, including out‐of‐sample predictive and hedging performance. The methodology accommodates heteroscedasticity and autocorrelation in the option pricing errors, as well as regime shifts across contract groups. The method is applied to intraday option price data on the S&P500 stock index for 1995. While the results provide support for models that accommodate leptokurtosis and skewness, no one model dominates when all criteria are considered.     

Derivation of a color space for image color difference measurement

In digital image reproduction, it is often desirable to compute image difference of reproductions and the original images. The traditional CIE color difference formula, designed for simple color patches in controlled viewing conditions, is not adequate for computing image difference for spatially complex image stimuli. Zhang and Wandell [Proceedings of the SID Symposium, 1996; p 731–734] introduced the S‐CIELAB model to account for complex color stimuli using spatial filtering as a preprocessing stage. Building on S‐CIELAB, iCAM was designed to serve as both a color appearance model and also an image difference metric for complex color stimuli [IS&T/SID 10th Color Imaging Conference, 2002; p 33–38]. These image difference models follow a similar image processing path to approximate the behavior of human observers. Generally, image pairs are first converted into device‐independent coordinates such as CIE XYZ tristimulus values or approximate human cone responses (LMS), and then further transformed into opponent‐color channels approximating white‐black, red‐green, and yellow‐blue color perceptions. Once in the opponent space, the images are filtered with approximations of human contrast sensitivity functions (CSFs) to remove information that is invisible to the human visual system. The images are then transformed back to a color difference space such as CIELAB, and pixel‐by‐pixel color differences are calculated. The shape and effectiveness of the CSF spatial filters used in this type of modeling is highly dependent on the choice of opponent color space. For image difference calculations, the ideal opponent color space would be both linear and orthogonal such that the linear filtering is correct and any spatial processing on one channel does not affect the others. This article presents a review of historical opponent color spaces and an experimental derivation of a new color space and corresponding spatial filters specifically designed for image color difference calculations. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010     

Influence diagnostics for multivariate GARCH processes

This article presents diagnostics for identifying influential observations when estimating multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models. We derive influence diagnostics by introducing minor perturbations to the conditional variances and covariances. The derived diagnostics are applied to a bivariate GARCH model of daily returns of the S&P500 and IBM. We find that univariate diagnostic procedures may be unable to identify the influential observations in a multivariate model. Importantly, the proposed curvature‐based diagnostic identified influential observations where the correlation between the two series had a major change. These observations were not identified as influential using the univariate diagnostics for each asset separately. When estimating the bivariate GARCH model allowing for weights at influential observations, we found that the time‐varying correlations behaved differently from that implied by the model ignoring influential observations. The application therefore highlights the importance of extending univariate diagnostic procedures to multivariate settings.     

Testing for parameter stability in nonlinear autoregressive models

In this article we develop testing procedures for the detection of structural changes in nonlinear autoregressive processes. For the detection procedure, we model the regression function by a single layer feedforward neural network. We show that CUSUM‐type tests based on cumulative sums of estimated residuals, that have been intensively studied for linear regression, can be extended to this case. The limit distribution under the null hypothesis is obtained, which is needed to construct asymptotic tests. For a large class of alternatives, it is shown that the tests have asymptotic power one. In this case, we obtain a consistent change‐point estimator which is related to the test statistics. Power and size are further investigated in a small simulation study with a particular emphasis on situations where the model is misspecified, i.e. the data is not generated by a neural network but some other regression function. As illustration, an application on the Nile data set as well as S&P log‐returns is given.     

An Organocatalytic Synthesis of cis‐N‐Alkyl‐ and N‐Arylaziridine Carboxylates

An extremely mild protocol that employs readily available starting materials, i.e., aldehyde, amine and alkyl diazoacetate, returns structurally diverse N‐substituted‐C‐2/3‐difunctionalised aziridines in excellent yields and stereoselectivities when pyridinium triflate is incorporated as an organocatalyst. The reaction process is environmentally benign affording water and nitrogen as the only by‐products. This racemic protocol paves the way for the development of novel asymmetric organocatalysts capable of generating optically active aziridines.     

Boundary control synthesis for a lithium‐ion battery thermal regulation problem

The thermal regulation problem for a lithium ion (Li‐ion) battery with boundary control actuation is considered. The model of the transient temperature dynamics of the battery is given by a nonhomogeneous parabolic partial differential equation (PDE) on a two‐dimensional spatial domain which accounts for the time‐varying heat generation during the battery discharge cycle. The spatial domain is given as a disk with radial and angular coordinates which captures the nonradially symmetric heat‐transfer phenomena due to the application of the control input along a portion of the spatial domain boundary. The Li‐ion battery model is formulated within an appropriately defined infinite‐dimensional function space setting which is suitable for spectral controller synthesis. The key challenges in the output feedback model‐based controller design addressed in this work are: the dependence of the state on time‐varying system parameters, the restriction of the input along a portion of the battery domain boundary, the observer‐based optimal boundary control design where the separation principle is utilized to demonstrate the stability of the closed loop system, and the realization of the outback feedback control problem based on state measurement and interpolation of the temperature field. Numerical results for simulation case studies are presented. © 2013 American Institute of Chemical Engineers AIChE J, 59: 3782–3796, 2013     

Least tail‐trimmed squares for infinite variance autoregressions

We develop a robust least squares estimator for autoregressions with possibly heavy tailed errors. Robustness to heavy tails is ensured by negligibly trimming the squared error according to extreme values of the error and regressors. Tail‐trimming ensures asymptotic normality and super‐‐convergence with a rate comparable to the highest achieved amongst M‐estimators for stationary data. Moreover, tail‐trimming ensures robustness to heavy tails in both small and large samples. By comparison, existing robust estimators are not as robust in small samples, have a slower rate of convergence when the variance is infinite, or are not asymptotically normal. We present a consistent estimator of the covariance matrix and treat classic inference without knowledge of the rate of convergence. A simulation study demonstrates the sharpness and approximate normality of the estimator, and we apply the estimator to financial returns data. Finally, tail‐trimming can be easily extended beyond least squares estimation for a linear stationary AR model. We discuss extensions to quasi‐maximum likelihood for GARCH, weighted least squares for a possibly non‐stationary random coefficient autoregression, and empirical likelihood for robust confidence region estimation, in each case for models with possibly heavy tailed errors.     

Subsampling inference for the autocovariances and autocorrelations of long‐memory heavy‐ tailed linear time series

We provide a self‐normalization for the sample autocovariances and autocorrelations of a linear, long‐memory time series with innovations that have either finite fourth moment or are heavy‐tailed with tail index 2 < α < 4. In the asymptotic distribution of the sample autocovariance there are three rates of convergence that depend on the interplay between the memory parameter d and α, and which consequently lead to three different limit distributions; for the sample autocorrelation the limit distribution only depends on d. We introduce a self‐normalized sample autocovariance statistic, which is computable without knowledge of α or d (or their relationship), and which converges to a non‐degenerate distribution. We also treat self‐normalization of the autocorrelations. The sampling distributions can then be approximated non‐parametrically by subsampling, as the corresponding asymptotic distribution is still parameter‐dependent. The subsampling‐based confidence intervals for the process autocovariances and autocorrelations are shown to have satisfactory empirical coverage rates in a simulation study. The impact of subsampling block size on the coverage is assessed. The methodology is further applied to the log‐squared returns of Merck stock.     

Dictionary‐based estimation of spectra for wide‐gamut color imaging

With the widespread use of commercialized wide‐gamut displays, the demand for wide‐gamut image content is increasing. To acquire wide‐gamut image content using camera systems, color information should be accurately reconstructed from recorded image signals for a wide range of colors. However, it is difficult to obtain color information accurately, especially for saturated colors, if conventional color cameras are used. Spectrum‐based color image reproduction can solve this problem; however, bulky spectral imaging systems are required for this purpose. To acquire spectral images more conveniently, a new spectral imaging scheme has been proposed that uses two types of data: high spatial‐resolution red, green, and blue (RGB) images and low spatial‐resolution spectral data measured from the same scene. Although this method estimates spectral images with high overall accuracy, the error becomes relatively large when multiple different colors, especially those with high saturation, are arranged in a small region. The main reason for this error is that the spectral data are utilized as low‐order spectral statistics of local spectra in this method. To solve this problem, in this study, a nonlinear estimation method based on sparse and redundant dictionaries was used for spectral image estimation—where the dictionary contains a number of spectra—without loss of information from the low spatial‐resolution spectral data. The estimated spectra are represented by a mixture of a few spectra included in the dictionary. Therefore, the respective feature of every spectrum is expected to be preserved in the estimation, and the color saturation is also preserved for any region. Experiments performed using the simulated data showed that the dictionary‐based estimation can be used to obtain saturated colors accurately, even when multiple colors are arranged in a small region. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2013     

Carbohydrate–PNA and Aptamer–PNA Conjugates for the Spatial Screening of Lectins and Lectin Assemblies

Nucleic acid architectures offer intriguing opportunities for the interrogation of structural properties of protein receptors. In this study, we performed a DNA‐programmed spatial screening to characterize two functionally distinct receptor systems: 1) structurally well‐defined Ricinus communis agglutinin (RCA₁₂₀), and 2) rather ill‐defined assemblies of L‐selectin on nanoparticles and leukocytes. A robust synthesis route that allowed the attachment both of carbohydrate ligands—such as N‐acetyllactosamine (LacNAc), sialyl‐Lewis‐X (sLe^X), and mannose—and of a DNA aptamer to PNAs was developed. A systematically assembled series of different PNA–DNA complexes served as multivalent scaffolds to control the spatial alignments of appended lectin ligands. The spatial screening of the binding sites of RCA₁₂₀ was in agreement with the crystal structure analysis. The study revealed that two appropriately presented LacNAc ligands suffice to provide unprecedented RCA₁₂₀ affinity (K_D=4 μM ). In addition, a potential secondary binding site was identified. Less dramatic binding enhancements were obtained when the more flexible L‐selectin assemblies were probed. This study involved the bivalent display both of the weak‐affinity sLe^X ligand and of a high‐affinity DNA aptamer. Bivalent presentation led to rather modest (sixfold or less) enhancements of binding when the self‐assemblies were targeted against L‐selectin on gold nanoparticles. Spatial screening of L‐selectin on the surfaces of leukocytes showed higher affinity enhancements (25‐fold). This and the distance–activity relationships indicated that leukocytes permit dense clustering of L‐selectin.     

Bias Correction Estimation for a Continuous‐Time Asset Return Model with Jumps

In this article, local linear estimators are adapted for the unknown infinitesimal coefficients associated with continuous‐time asset return models with jumps, which can correct the bias automatically due to their simple bias representation. The integrated diffusion models with jumps, especially infinite activity jumps, are mainly investigated. In addition, under mild conditions, the weak consistency and asymptotic normality are provided through the conditional Lindeberg theorem as the time span T→∞ and the sample interval Δ _n →0. Furthermore, our method presents advantages in bias correction through simulation whether jumps belong to the finite activity case or infinite activity case. Finally, the estimators are illustrated empirically through the returns of stock index under 5‐minute high sampling frequency for real application.     

Specular gloss versus surface topography for oil‐filled nanoparticle coatings on paper

The appearance or printing quality of paper surfaces is mostly characterized by their glossiness, measured with a glossmeter as specular reflectance. The gloss properties of a base paper substrate can be improved after application of a poly(styrene‐co‐maleimide) nanoparticle coating under pure conditions or in the presence of different vegetable oils. The specular gloss properties of 11 different nanoparticle paper coatings have been determined under 75° and 85° incident light angles, with good relation between values along parallel (machine) and perpendicular (transverse) direction. The gloss properties for the different coatings have been further related to the surface topography. Therefore, the statistical and spatial surface roughness parameters have been studied in detail at two length scales including non‐contact profilometry (1 × 1 mm²) and atomic force microscopy (2 × 2 μm²). Based on values of the Rayleigh parameter for non‐contact profilometry, the surfaces can be considered as optically rough. The gloss values cannot be directly related to statistical surface roughness parameters. Otherwise, an experimental power‐law model for gloss has been proposed as a function of (β/Sq) with correlation length β and root‐mean‐square roughness Sq. A best‐fit model illustrates that gloss properties of various nanoparticle paper coatings mainly relate to the spatial surface roughness parameters determined from non‐contact profilometry. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 596–610, 2016     

Dynamic spatial Bayesian models for radioactivity deposition

Dynamic spatial Bayesian (DSB) models are proposed for the analytical modelling of radioactivity deposition after a nuclear accident. The proposed models are extensions of the multi‐variate time‐series dynamic linear models of West and Harrison (1997) to Markov random field processes. They combine the outputs from a long‐range atmospheric dispersal model with measured data (and prior information) to provide improved deposition prediction in space and time. Two versions of a Gaussian DSB model were applied to the radioactivity deposition in Bavaria over a 15 days period during the Chernobyl nuclear accident. One version had fixed functional forms for its spatial variances and covariances while the other allowed those to adapt and ‘learn’ from data in the conjugate Bayesian paradigm. There were two main sources of information for radioactivity deposition in our application: radioactivity measurements at a sparse set of 13 monitoring stations, and the numerical deposition evaluation of the atmospheric dispersal K‐model for the points of a 64 × 64 regular grid. We have analysed the temporal predictions (one‐step‐ahead forecasting) of those DSB models to show that the dispersal K‐model tended in general to underestimate the deposition levels at all times while the DSB models corrected for that although with different degrees of adjustment.     

A globally convergent method for finding all steady‐state solutions of distillation columns

A globally convergent method is proposed that either returns all solutions to steady‐state models of distillation columns or proves their infeasibility. Initial estimates are not required. The method requires a specific but fairly general block‐sparsity pattern; in return, the computational efforts grow linearly with the number of stages in the column. The well‐known stage‐by‐stage (and the sequential modular) approach also reduces the task of solving high‐dimensional steady‐state models to that of solving a sequence of low‐dimensional ones. Unfortunately, these low‐dimensional systems are extremely sensitive to the initial estimates, so that solving them can be notoriously difficult or even impossible. The proposed algorithm overcomes these numerical difficulties by a new reparameterization technique. The successful solution of a numerically challenging reactive distillation column with seven steady‐states shows the robustness of the method. No published software known to the authors could compute all solutions to this difficult model without expert tuning. © 2013 American Institute of Chemical Engineers AIChE J 60: 410–414, 2014     

Temporal clustering for order reduction of nonlinear parabolic PDE systems with time‐dependent spatial domains: Application to a hydraulic fracturing process

A temporally‐local model order‐reduction technique for nonlinear parabolic partial differential equation (PDE) systems with time‐dependent spatial domains is presented. In lieu of approximating the solution of interest using global (with respect to the time domain) empirical eigenfunctions, low‐dimensional models are derived by constructing appropriate temporally‐local eigenfunctions. Within this context, first of all, the time domain is partitioned into multiple clusters (i.e., subdomains) by using the framework known as global optimum search. This approach, a variant of Generalized Benders Decomposition, formulates clustering as a Mixed‐Integer Nonlinear Programming problem and involves the iterative solution of a Linear Programming problem (primal problem) and a Mixed‐Integer Linear Programming problem (master problem). Following the cluster generation, local (with respect to time) eigenfunctions are constructed by applying the proper orthogonal decomposition method to the snapshots contained within each cluster. Then, the Galerkin's projection method is employed to derive low‐dimensional ordinary differential equation (ODE) systems for each cluster. The local ODE systems are subsequently used to compute approximate solutions to the original PDE system. The proposed local model order‐reduction technique is applied to a hydraulic fracturing process described by a nonlinear parabolic PDE system with the time‐dependent spatial domain. It is shown to be more accurate and computationally efficient in approximating the original nonlinear system with fewer eigenfunctions, compared to the model order‐reduction technique with temporally‐global eigenfunctions. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3818–3831, 2017     

Detecting Tail Risk Differences in Multivariate Time Series

We derive functional central limit theory for tail index estimates in multivariate time series under mild conditions on the extremal dependence between the components. We use this result to also derive convergence results for extreme value‐at‐risk and extreme expected shortfall estimates. This allows us to construct tests for equality of ‘tail risk’ in multivariate data, which can be useful in a number of empirical contexts. In constructing test statistics, we avoid estimating long‐run variances by using self‐normalization. Size and power of the tests for equal ‘tail risk’ are assessed in simulations. An empirical application to exchange returns illustrates the practical usefulness of the tests.     

磷复肥“三内技术”研究与开发(五)

介绍磷复肥喷浆造粒“三内技术”中的料幕特性的研究 ,探讨物料量、料幕密度与扬料板之间的关系 ,提出扬料板计算为升举式扬料板设计提供理论依据。     

Optimization of dry powder deposition parameters for production of large substrates using functionally graded ceramics

The ability to control the spatial variation in three dimensions of dielectric properties is known to play a crucial role in achieving novel electromagnetic performance such as miniaturization and high gain of broadband antennas. To address this need, the objective in this paper is to use earlier proposed Dry Powder Deposition to produce large substrates of Functionally Graded Materials with spatially controlled CaTiO₃‐MgTiO₃‐Mg₂TiO₄ dielectrics. We present an in‐depth analysis and optimization of critical processing parameters such as compaction pressure, sintering temperature, and dwell time. Using the optimized compaction and co‐sintering process, spatially varying large (8.2 cm × 8.2 cm) substrates were produced without the presence of any significant cracks and warping. In addition to these structural functionalities, sintered ceramic constituents of the designed substrate display targeted dielectric permittivity values of a miniaturized broadband Satellite Communication Ultra High Frequency antenna at the optimized conditions. This optimized route opens up possibilities for multifunctional metrics to be addressed for other applications calling for different spatial distributions of large Functionally Graded Materials possibly with the same family of ceramic constituents.