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.     

Multi‐variate stochastic volatility modelling using Wishart autoregressive processes

A new multi‐variate stochastic volatility estimation procedure for financial time series is proposed. A Wishart autoregressive process is considered for the volatility precision covariance matrix, for the estimation of which a two step procedure is adopted. The first step is the conditional inference on the autoregressive parameters and the second step is the unconditional inference, based on a Newton‐Raphson iterative algorithm. The proposed methodology, which is mostly Bayesian, is suitable for medium dimensional data and it bridges the gap between closed‐form estimation and simulation‐based estimation algorithms. An example, consisting of foreign exchange rates data, illustrates the proposed methodology.     

Integration of CARMA processes and spot volatility modelling

Continuous‐time autoregressive moving average (CARMA) processes with a non‐negative kernel and driven by a non‐decreasing Lévy process constitute a useful and very general class of stationary, non‐negative continuous‐time processes which have been used, in particular for the modelling of stochastic volatility. In the celebrated stochastic volatility model of Barndorff‐Nielsen and Shephard (2001) , the spot (or instantaneous) volatility at time t, V(t), is represented by a stationary Lévy‐driven Ornstein‐Uhlenbeck process. This has the shortcoming that its autocorrelation function is necessarily a decreasing exponential function, limiting its ability to generate integrated volatility sequences, , with autocorrelation functions resembling those of observed realized volatility sequences. (A realized volatility sequence is a sequence of estimated integrals of spot volatility over successive intervals of fixed length, typically 1 day.) If instead of the stationary Ornstein–Uhlenbeck process, we use a CARMA process to represent spot volatility, we can overcome the restriction to exponentially decaying autocorrelation function and obtain a more realistic model for the dependence observed in realized volatility. In this article, we show how to use realized volatility data to estimate parameters of a CARMA model for spot volatility and apply the analysis to a daily realized volatility sequence for the Deutsche Mark/ US dollar exchange rate.     

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.     

Sequential robust estimation for nonparametric autoregressive models

We construct a robust truncated sequential estimator for the pointwise estimation problem in nonparametric autoregression models with smooth coe?cients. For Gaussian models we propose an adaptive procedure based on the constructed sequential estimators. The minimax nonadaptive and adaptive convergence rates are established. It turns out that in this case these rates are the same as for regression models.     

A family of multivariate non-gaussian time series models

In this article, we propose a class of multivariate non-Gaussian time series models which include dynamic versions of many well-known distributions and consider their Bayesian analysis. A key feature of our proposed model is its ability to account for correlations across time as well as across series (contemporary) via a common random environment. The proposed modeling approach yields analytically tractable dynamic marginal likelihoods, a property not typically found outside of linear Gaussian time series models. These dynamic marginal likelihoods can be tied back to known static multivariate distributions such as the Lomax, generalized Lomax, and the multivariate Burr distributions. The availability of the marginal likelihoods allows us to develop efficient estimation methods for various settings using Markov chain Monte Carlo as well as sequential Monte Carlo methods. Our approach can be considered to be a multivariate generalization of commonly used univariate non-Gaussian class of state space models. To illustrate our methodology, we use simulated data examples and a real application of multivariate time series for modeling the joint dynamics of stochastic volatility in financial indexes, the VIX and VXN.     

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.     

On the Relationship between Uhlig Extended and beta-Bartlett Processes

Stochastic volatility processes are used in multi-variate time series analysis to track time-varying patterns in covariance matrices. Uhlig extended (UE) and beta-Bartlett (BB) processes are especially convenient for analyzing high-dimensional time series because they are conjugate with Wishart likelihoods. In this article, we show that UE and BB are closely related, but not equivalent: their hyperparameters can be matched so that they have the same forward-filtered posteriors and one-step ahead forecasts, but different joint (smoothed) posterior distributions. Under this circumstance, Bayes factors cannot discriminate the models and alternative approaches to model comparison are needed. We illustrate these issues in a retrospective analysis of volatilities of returns of foreign exchange rates. Additionally, we provide a backward sampling algorithm for the BB process, for which retrospective analysis had not been developed.     

Minimum α‐divergence estimation for arch models

Abstract. This paper considers a minimum α‐divergence estimation for a class of ARCH(p) models. For these models with unknown volatility parameters, the exact form of the innovation density is supposed to be unknown in detail but is thought to be close to members of some parametric family. To approximate such a density, we first construct an estimator for the unknown volatility parameters using the conditional least squares estimator given by Tjøstheim [Stochastic processes and their applications (1986) Vol. 21, pp. 251–273]. Then, a nonparametric kernel density estimator is constructed for the innovation density based on the estimated residuals. Using techniques of the minimum Hellinger distance estimation for stochastic models and residual empirical process from an ARCH(p) model given by Beran [Annals of Statistics (1977) Vol. 5, pp. 445–463] and Lee and Taniguchi [Statistica Sinica (2005) Vol. 15, pp. 215–234] respectively, it is shown that the proposed estimator is consistent and asymptotically normal. Moreover, a robustness measure for the score of the estimator is introduced. The asymptotic efficiency and robustness of the estimator are illustrated by simulations. The proposed estimator is also applied to daily stock returns of Dell Corporation.     

A FLEXIBLE STATE SPACE MODEL AND ITS APPLICATIONS

The standard state space model treats observations as imprecise measurement of the Markovian states. Our flexible model handles the states and observations symmetrically, which are simultaneously determined by past observations and up to first‐lagged states. The only distinction between the states and observations is the observability. When it is applied to the autoregressive moving average, dynamic factor and stochastic volatility models, the state space form is both parsimonious and intuitive, for low‐dimension states are constructed simply by stacking all the relevant but unobserved components in the structural model.     

基于随机配置网络的机载电子吊舱多工况热模型

机载电子吊舱是搭载多功能机载电子设备的主要平台,能够显著提升战机性能。然而,不断增加的电子设备功率和高度低气压力飞行环境会造成吊舱内恶劣的热环境,严重影响电子设备的可靠性。因此,十分有必要建立准确的电子吊舱热模型,用于预测不同飞行工况下的电子设备热响应。综合热网络分析思想和随机网络算法思想,提出一种基于随机配置网络的热模型建立方法,并通过采用冲压空气冷却系统的电子吊舱实验数据加以验证。为了建立准确的舱内温度响应模型,通过传热机制将高温贮存、高温工作、低温贮存、低温故障和低温工作工况下的实验数据分为3组并分别用于建立设备贮存热模型、设备工作热模型和综合热模型,利用热网络分析获取随机配置网络的有效输入,采用四折交叉验证和灰度图分析综合确定了3个热模型的超参数。建模结果表明：3个热模型的范围序列可统一为[1～40],最大隐含层节点数可分别设为6、9、11,设备温度拟合效果较好,仅在边界条件约束下进行多工况全过程的电子设备温度预测,预测误差在3.512℃内。总体看来,该热建模方法从数据挖掘的角度较为简单、准确、快速地描述了电子设备热关系,可用于开展预期飞行环境下的机载电子吊舱温度预测,用于评估热管理系统的性能。     

Inference for asymmetric exponentially weighted moving average models

The exponentially weighted moving average (EWMA) model in ‘Risk-Metrics’ has been a benchmark for controlling and forecasting risks in financial operations. However, it is incapable of capturing the asymmetric volatility effect and the heavy-tailed innovation, which are two important stylized features of financial returns. We propose a new asymmetric EWMA model driven by the Student's t-distributed innovations to take these two stylized features into account and study its maximum likelihood estimation and model diagnostic checking. The finite-sample performance of the estimation and diagnostic test statistic is examined by the simulated data.     

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。     

稀疏核局部保持投影及其在非线性过程故障检测中的应用(英文)

Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.     

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     

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     

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.     

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.