Large‐scale volatility models: theoretical properties of professionals’ practice

Abstract. This article examines the way in which GARCH models are estimated and used for forecasting by practitioners in particular using the highly popular Riskmetrics^TM approach. Although it permits sizable computational gains and provide a simple way to impose positive semi‐definitiveness of multivariate version of the model, we show that this approach delivers non‐consistent parameter’ estimates. The novel theoretical result is corroborated by a set of Monte Carlo exercises. A set of empirical applications suggest that this could cause, in general, unreliable forecasts of conditional volatilities and correlations.     

Local Likelihood for non‐parametric ARCH(1) models

Abstract. We propose a non‐parametric local likelihood estimator for the log‐transformed autoregressive conditional heteroscedastic (ARCH) (1) model. Our non‐parametric estimator is constructed within the likelihood framework for non‐Gaussian observations: it is different from standard kernel regression smoothing, where the innovations are assumed to be normally distributed. We derive consistency and asymptotic normality for our estimators and show, by a simulation experiment and some real‐data examples, that the local likelihood estimator has better predictive potential than classical local regression. A possible extension of the estimation procedure to more general multiplicative ARCH(p) models with p > 1 predictor variables is also described.     

Improved prediction intervals for stochastic process models

Abstract.  This paper reviews some recent results on the construction of improved prediction limits for time series models and presents a simple solution based on a fully conditional approach. A prediction limit, expressed as a modification of the estimative one, is obtained so that its conditional and unconditional coverage probability equals the target value to third-order accuracy. Although the specification of the ancillary statistic is not required, it respects the conditionality principle, to the relevant order of approximation. Moreover, the corresponding predictive density is specified in a relatively simple closed form. Simple examples show the usefulness of this conditional approach to prediction.     

Testing for EGARCH Against Stochastic Volatility Models

Abstract. It is shown that the EGARCH model is the degenerate case of Danielsson's [Journal of Econometrics (1994) Vol. 61, pp. 375–400] stochastic volatility model where the disturbance of the transition equation of conditional volatility has zero variance. The Lagrange multiplier test statistic is obtained for the EGARCH model against the stochastic volatility model by expressing the degenerate density under the null hypothesis by the Dirac delta function. The finite sample performance of the test is studied in a small Monte Carlo experiment.     

Apricot Kernel Oil Ameliorates Cyclophosphamide-Associated Immunosuppression in Rats

The effects of dietary apricot kernel oil (AKO), which contains high levels of oleic and linoleic acids and lower levels of α‐tocopherol, were evaluated in a rat model of cyclophosphamide‐induced immunosuppression. Rats had intraperitoneal injection with cyclophosphamide to induce immunosuppression and were then infused with AKO or normal saline (NS) for 4 weeks. Enzyme‐linked immunosorbent assays were used to detect antimicrobial factors in lymphocytes and anti‐inflammatory factors in hepatocytes. Hematoxylin & eosin staining was conducted prior to histopathological analysis of the spleen, liver, and thymus. Significant differences were observed between the immune functions of the healthy control group, the normal saline group, and the AKO group. Compared to the normal saline‐treated group, lymphocytes isolated from rats administered AKO showed significant improvement in immunoglobulin (Ig)A, IgM, IgG, interleukin (IL)‐2, IL‐12, and tumor necrosis factor‐α (TNF‐α) levels (p < 0.01). Liver tissue levels of malondialdehyde and activities of superoxide dismutase and glutathione peroxidase indicated reduced oxidative stress in rats treated with AKO (p < 0.01). Dietary AKO positively affected rat growth and inhibited cyclophosphamide‐associated organ degeneration. These results suggested that AKO may enhance the immune system in vivo. These effects may reflect the activities of intermediate oleic and linoleic acid metabolites, which play a vital role in the immune system, and the α‐tocopherol in AKO may further enhance this phenomenon. Thus, the use of AKO as a nutritional supplement can be proposed to ameliorate chemotherapy‐associated immunosuppression.     

嵌入式Linux内核的编译与移植

嵌入式系统是一个高起点的技术领域,而嵌入式Linux以多方面的优势已成为嵌入式系统领域研究的一个热点．本文首先介绍了嵌入式Linux,然后着重介绍了嵌入式Linux内核的编译过程,并最后描述了嵌入式Linux内核的移植过程。     

Mechanism of the effects of microwave irradiation on the relative volatility of binary mixtures

The use of microwave irradiation to enhance distillation processes has been reported recently. However, there is an ongoing debate in the scientific community on whether the observed enhancement is mainly a consequence of the shift of the “equilibrium” of vapor–liquid mass transfer. In this article, a developed instrument was used to determine the relative volatility of various binary mixtures under microwave irradiation. By comparing the relative volatility in the presence/absence of microwave irradiation, the shift of the “equilibrium” of vapor–liquid mass transfer was observed for certain binary mixtures under microwave irradiation. The effects of microwave irradiation on the relative volatility of binary mixtures (in addition to the mechanisms involved therein) were analyzed using the non‐equilibrium thermodynamic principle. The results demonstrate that differences in the dielectric properties, microwave field intensity, intermolecular forces, and boiling point play dominant roles in determining the effects of microwaves on the relative volatility. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1328–1337, 2017     

Comparison of parameter estimation methods in stochastic chemical kinetic models: Examples in systems biology

Stochastic chemical kinetics has become a staple for mechanistically modeling various phenomena in systems biology. These models, even more so than their deterministic counterparts, pose a challenging problem in the estimation of kinetic parameters from experimental data. As a result of the inherent randomness involved in stochastic chemical kinetic models, the estimation methods tend to be statistical in nature. Three classes of estimation methods are implemented and compared in this paper. The first is the exact method, which uses the continuous‐time Markov chain representation of stochastic chemical kinetics and is tractable only for a very restricted class of problems. The next class of methods is based on Markov chain Monte Carlo (MCMC) techniques. The third method, termed conditional density importance sampling (CDIS), is a new method introduced in this paper. The use of these methods is demonstrated on two examples taken from systems biology, one of which is a new model of single‐cell viral infection. The applicability, strengths and weaknesses of the three classes of estimation methods are discussed. Using simulated data for the two examples, some guidelines are provided on experimental design to obtain more information from a limited number of measurements. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1253–1268, 2014     

基于RKHS的小波核在化工软测量中的仿真研究

由可分Hilbert空间与L2(R)的等价性,利用内积同构的线性算子,可以把L2(R)中子空间的小波尺度函数折算为Hilbert空间中子空间的小波尺度函数.基于支持向量机核函数的条件和小波多分辨率理论,在Hilbert空间构造出Modet小波核函数.通过仿真实验,与传统的RBF核函数相比较,该尺度再生核函数具有更高的精度和更好的泛化能力,充分体现了支持向量机较好的推广性能.     

山核桃仁油中未知成分的确定及含量分析

采用色 -质联用仪对山核桃仁油中 14%的未知成分及含量进行了确定和分析。研究结果表明 :山核桃仁油中的不饱和脂肪酸 w(不饱和脂肪酸 ) =89 0 % ,未知成分是十九醇、8 己基 十五烷、10 甲基 二十烷、11,14 二十碳二烯酸、1 溴 8 十七炔、9 己基 十七烷、二十四烷、13 二十二碳烯酸、1 溴代 7 十九炔、15 二十四碳烯酸、二十六酸、7 己基 二十烷、二十七烷共 13种 ,其中13 二十二碳烯酸、15 二十四碳烯酸、十九醇等具有较强的生理活性和药用价值     

Non-Gaussian Filter and Smoother Based on the Pearson Distribution System

The Pearson distribution system can represent wide class of distributions with various skewness and kurtosis. We develop a practical approach of using all types of its distribution system including the type-IV distribution which was difficult to implement.
We propose an easily implemented algorithm which uses less-memory and performs at a higher speed than other typical methods: using analytic approximation of successive conditional probability density functions for prediction and filtering by the Pearson distribution system in the case of both the system and observation noise being one-dimensional. By using the approximated probability density function and the numerical integration, we obtain mean, variance, skewness and kurtosis of the next distribution. We decide the next approximated distribution from the Pearson distribution system. We adopt these steps for the prediction, filtering and smoothing recursively. Our framework makes it possible to construct time series models with various noise distributions.
We apply our non-Gaussian filter to the estimation of non-Gaussian stochastic volatility models of the stock returns. We compare our method with the typical method.     

Planning and scheduling of flexible process networks under uncertainty with stochastic inventory: MINLP models and algorithm

The tactical planning and scheduling of chemical process networks consisting of both dedicated and flexible processes under demand and supply uncertainty is addressed. To integrate the stochastic inventory control decisions with the production planning and scheduling, a mixed‐integer nonlinear programming (MINLP) model is proposed that captures the stochastic nature of the demand variations and supply delays using the guaranteed‐service approach. The model takes into account multiple tradeoffs and simultaneously determines the optimal selection of production schemes, purchase amounts of raw materials, sales of final products, production levels of processes, detailed cyclic production schedules for flexible processes, and working inventory and safety stock levels of all chemicals involved in the process network. To globally optimize the resulting nonconvex MINLP problems with modest computational times, the model properties are exploited and a tailored branch‐and‐refine algorithm based on the successive piecewise linear approximation is proposed. To handle the degeneracy of alternative optima in assignment configurations of production scheduling, three symmetry breaking cuts are further developed to accelerate the solution process. The application of the model and the performance of the proposed algorithm are illustrated through three examples with up to 25 chemicals and 16 processes including at most 8 production schemes for each flexible process. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1511–1532, 2013     

Maximum Likelihood Estimates of a Class of One-Dimensional Stochastic Differential Equation Models From Discrete Data

The problem of computing the maximum likelihood estimate of the parameters of a specific class of stochastic differential equation (SDE) models with linear drift whose sample paths are observed at discrete time points is considered. This estimate is obtained as in Cleur and Manfredi (1999) by discretizing the explicit expressions for the estimates which maximize the likelihood function in continuous time, by discretizing the likelihood function through a quadrature approximation before maximizing it, and by maximizing the likelihood function of the Euler scheme approximation to the underlying continuous process. Simulation results indicate that, for the constellation of parameter values considered, all three approaches lead to very similar results.     

KOH活化芒果核制备活性炭及其吸附性能研究

以芒果核为原料,氢氧化钾溶液为活化剂制备芒果核基活性炭。结果表明,芒果核基活性炭的最佳制备工艺条件为:活化剂氢氧化钾溶液浓度2 mol/L,活化时间80 min、活化温度600℃、碳化温度350℃,在此工艺条件下制备的芒果核活性炭的碘吸附值和亚甲基蓝吸附值分别为1 489.26和193.71 mg/g。芒果核活性炭吸附剂对重金属Cd~(2+)和Cu~(2+)具有一定的吸附能力,其饱和吸附量分别为26.15和38.25 mg/g。采用扫描电镜对产品的表面形态进行分析,发现其具有丰富的不规则孔隙结构。     

基于稀疏特征空间的核方法建模研究

为提高支持向量机在建模方面的拟合性能,针对核函数方法中单个核函数的局限性,尝试融合核支持向量机建模方法以提高模型的泛化能力和精度。为避免在进行核融合时,支持向量机稀疏性的缺失,提出将数据映射到稀疏特征空间进行研究。通过仿真研究表明,所建模型在保证稀疏性的前提下,能较好地提高建模精度,从而验证了算法的有效性。     

Enzymatic Glycerolysis of Palm and Palm Kernel Oils

Glycerolysis of palm and palm kernel oils were carried out using commercial lipases from Candida antarctica (Novozym 435) and Mucor miehei (Novozym 388) as catalyst (500 units lipase/g oil) at 40°C and with an oil:glycerol molar ratio of 1:2 in a solvent-free system. Novozym 435 catalyzed the glycerolysis of palm and palm kernel oils giving reaction products in similar compositions. Partial acylglycerols contents of the glycerolysis products obtained from palm and palm kernel oils were 64% (wt) and 66% (wt), respectively. However, partial acylglycerols contents of the glycerolysis products obtained from palm and palm kernel oils conducted with Novozym 388 as catalyst at the same conditions were 44% (wt) and 56% (wt), respectively. On the other hand, free fatty acid contents of the glycerolysis products of palm and palm kernel oils obtained using Novozym 388 were higher, 25-30% (wt), than those obtained by Novozym 435, 4-5% (wt). The monoacylglycerols fraction with the highest content of oleic acid, 62.7% (wt), was obtained from the palm kernel oil glycerolysis reaction catalyzed by Novozym 435.     

基于多向核熵成分分析的微生物发酵过程多阶段划分及故障监测

针对多向核主元分析法(MKPCA)在监控动态非线性和多模态间歇生产过程故障的不足,提出一种基于物理信息熵的多阶段多向核熵成分分析(multiple sub-stage multi-way kernel entropy component analysis,MSMKECA)的新方法用于故障监控。该方法首先通过核映射将数据从低维空间映射到高维特征空间;其次在高维特征空间依据熵结构信息计算每个时刻数据矩阵的相似度指标进行阶段划分,将间歇过程划分为各稳定阶段和各过渡阶段,并在过渡阶段用时变的协方差代替固定协方差;最后在划分的阶段里分别建立模型进行间歇过程监测解决间歇过程的动态非线性和多阶段特性;将所提出的算法应用于青霉素发酵仿真系统的在线监测,验证了该方法的有效性。     

山苍子核仁油酯交换制备生物柴油的研究

山苍子核仁油是一种丰富的植物油资源,目前尚未得到很好的开发利用。本文考察了以山苍子核仁油为原料,对甲苯磺酸作为催化剂制备生物柴油。结果表明制备生物柴油的最佳工艺条件是：反应温度80℃,催化剂用量为原料油质量的5%,醇油摩尔比为9：1,反应时间4h。在此条件下生物柴油的产率可达75%以上。