基于模糊层次分析的陶瓷艺术品价格预测模型研究

许多陶瓷艺术品在近十几年时间,价格实现了十几倍,甚至是几十倍的高速增长。本文从影响陶瓷艺术品价格的七种因素,包括年代、欣赏性、珍稀度、美观性、知名度、尺寸、造型复杂度)进行分析,通过层次分析法及模糊权重,定义陶瓷艺术品价格指数。通过检验,所得到的陶瓷艺术品价格指数模型误差比较小。这说明表2所给定的陶瓷艺术品价格评价指标符合定价标准,也为其他陶瓷艺术品价值提供有益参考。     

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.     

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.     

Product design: A pricing framework accounting for product quality and consumer awareness

A systematic framework has been developed to determine the optimal price of a completely new (or existing but improved) chemical product that is being launched (or relaunched) in the presence of a competing product. It has four elements. The first is a pricing model derived from a utility function with constant elasticity of substitution. It accounts for consumers' awareness of the product under consideration and consumer preferences. The second is a set of relationships relating the consumer preferences and the relevant sales data available to a relaunched product to the parameters of the pricing model. In the absence of sales data for a completely new product, the third element is a set of heuristics for choosing a pricing strategy and estimates of the pricing model parameters. The optimal price is finally determined in a profit maximization problem subject to the market size as well as any other constraints. This pricing framework allows simultaneous optimization of product quality and price using product specifications as design variables. It is illustrated with an example on energy drinks. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2462–2471, 2018     

A discrete-time scheduling model for continuous power-intensive process networks with various power contracts

Increased volatility in electricity prices and new emerging demand side management opportunities call for efficient tools for the optimal operation of power-intensive processes. In this work, a general discrete-time model is proposed for the scheduling of power-intensive process networks with various power contracts. The proposed model consists of a network of processes represented by Convex Region Surrogate models that are incorporated in a mode-based scheduling formulation, for which a block contract model is considered that allows the modeling of a large variety of commonly used power contracts. The resulting mixed-integer linear programming model is applied to an illustrative example as well as to a real-world industrial test case. The results demonstrate the model's capability in representing the operational flexibility in a process network and different electricity pricing structures. Moreover, because of its computational efficiency, the model holds much promise for its use in a real industrial setting.     

Energy demand management for process systems through production scheduling and control

Demand response (DR) is an integral part of the Smart Grid paradigm, and has become the focus of growing research, development, and deployment in residential, commercial and industrial systems over the last few years. In process systems, energy demand management through production scheduling is an increasingly important tool that has the potential to provide significant economic and operational benefits by promoting the responsiveness of the process operation and its interactions with the utility providers. However, the dynamic behavior of the underlying process, especially during process transitions, is seldom taken into account as part of the DR problem formulation. Furthermore, the incorporation of energy constraints related to electricity pricing and energy resource availability presents an additional challenge. The goal of this study is to present a novel optimization formulation for energy demand management in process systems that accounts explicitly for transition behaviors and costs, subject to time‐sensitive electricity prices and uncertainties in renewable energy resources. The proposed formulation brings together production scheduling and closed‐loop control, and is realized through a real‐time or receding‐horizon optimization framework depending on the underlying operational scenarios. The dynamic formulation is cast as a mixed‐integer nonlinear programming problem based on a proposed discretization approach, and its merits are demonstrated using a simulated continuous stirred tank reactor where the energy required is assumed to be roughly proportional to the material flow. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3756–3769, 2015     

Estimation of vector error correction models with mixed‐frequency data

Vector autoregressive (VAR) models with error‐correction structures (VECMs) that account for cointegrated variables have been studied extensively and used for further analyses such as forecasting, but only with single‐frequency data. Both unstructured and structured VAR models have been estimated and used with mixed‐frequency data. However, VECMs have not been studied or used with mixed‐frequency data. The article aims partly to fill this gap by estimating a VECM using the expectation‐maximization (EM) algorithm and US data on four monthly coincident indicators and quarterly real GDP and, then, using the estimated model to compute in‐sample monthly smoothed estimates and out‐of‐sample monthly forecasts of GDP. Because the model is treated as operating at the highest monthly frequency and the monthly‐quarterly data are used as given (neither interpolated to all‐monthly data, nor aggregated to all‐quarterly data), the application is expected to be unbiased and efficient. A Monte Carlo analysis compares the accuracy of VECMs estimated with the given mixed‐frequency data vs. with their single‐frequency temporal aggregate.     

Estimation of the long‐memory stochastic volatility model parameters that is robust to level shifts and deterministic trends

I provide conditions under which the trimmed FDQML estimator, advanced by McCloskey (2010) in the context of fully parametric short‐memory models, can be used to estimate the long‐memory stochastic volatility model parameters in the presence of additive low‐frequency contamination in log‐squared returns. The types of low‐frequency contamination covered include level shifts as well as deterministic trends. I establish consistency and asymptotic normality in the presence or absence of such low‐frequency contamination under certain conditions on the growth rate of the trimming parameter. I also provide theoretical guidance on the choice of trimming parameter by heuristically obtaining its asymptotic MSE‐optimal rate under certain types of low‐frequency contamination. A simulation study examines the finite sample properties of the robust estimator, showing substantial gains from its use in the presence of level shifts. The finite sample analysis also explores how different levels of trimming affect the parameter estimates in the presence and absence of low‐frequency contamination and long‐memory.     

A hybrid heuristic for the inventory routing problem under dynamic regional pricing

The inventory routing problem (IRP) seeks to meet the demands of customers during consecutive time periods. Because of the geographical distribution of customers and variations in willingness to pay of the consumers in distinct locations and time, regional and time-based pricing are powerful ways to improve profitability. In this study, a quadratic mixed-integer programming model for single product, multi-period Inventory Routing under the dynamic regional pricing problem (IRDRP) has been proposed. A hybrid heuristic approach is developed to solve it. This algorithm comprises five phases: initialization, demand generation, demand adjustment, inventory routing, and neighborhood search, which are embedded in a simulated annealing framework. Experimental results indicate as the problem size increases, the difference between CPLEX and the proposed heuristic algorithm optimality gap exhibits an upward trend and that the heuristic outperforms CPLEX. A sensitivity analysis demonstrates that by intensifying the scarce capacity, approaching an optimal solution will be more difficult.     

我国原油定价的未来发展趋势

介绍了目前世界和国内部分原油的定价模式 ,分析了我国现行原油定价模式的不足之处 ,并为改进我国的原油定价模式提出了几点措施及建议     

Assessing Persistence In Discrete Nonstationary Time‐Series Models

Abstract. The aim of this paper is to examine the application of measures of persistence in a range of time‐series models nested in the framework of Cramer (1961) . This framework is a generalization of the Wold (1938) decomposition for stationary time‐series which, in addition to accommodating the standard I(0) and I(1) models, caters for a broad range of alternative processes. Two measures of persistence are considered in some detail, namely the long‐run impulse‐response and variance‐ratio functions. Particular emphasis is given to the behaviour of these measures in a range of non‐stationary models specified in discrete time. We document the conflict that arises between different measures, applied to the same model, as well as conflict arising from the use of a given measure in different models. Precisely which persistence measures are time dependent and which are not, is highlighted. The nature of the general representation used also helps to clarify which shock the impulse‐response function refers to in the case of models where more than one random disturbance impinges on the time series.     

关于模型筛选的适应性准则问题——兼与“用序贯法判别在‘A’系催化剂上氨合成反应的速率模型”的作者商榷

<正>一、引言 序贯法是一新的实验设计方法,文献“用序贯法判别在‘A’系催化剂上氨合成反应的速率模型”将序贯法应用到实际中去,并取得较为理想的效果。本文拟对模型筛选的适应性准则这一问题作进一步的探讨。     

Glycerol as a platform chemical: Sweet opportunities on the horizon?

Increased global production of biodiesel since the late‐1990s has created an abundance of crude glycerin that has significantly impacted the glycerin market resulting in a decline in glycerin pricing. Because the economic viability of the biodiesel and oleochemical industries are closely linked to glycerol, the deterioration of glycerol prices negatively affects the cost of materials derived from these industries. Although glycerol has many commercial uses, these markets are generally considered mature making it difficult to absorb the glycerin surpluses. The increasing abundance of glycerin, its renewability, and attractive pricing make glycerol (80% crude glycerin spot price December 2005; US $ = 0.12/lb ($264/tonne), Euro = 140/tonne) an appealing platform chemical to derive a family of commercially valued compounds. To this end, new chemistry to convert glycerol into high volume value‐added products is being developed. This article gives a brief historical background of glycerol and examines recent technological developments to exploit glycerol as a versatile primary chemical building block in an effort to expand glycerol applications and utilize surplus stockpiles of glycerin.     

NORMING RATES AND LIMIT THEORY FOR SOME TIME‐VARYING COEFFICIENT AUTOREGRESSIONS

A time‐varying autoregression is considered with a similarity‐based coefficient and possible drift. It is shown that the random‐walk model has a natural interpretation as the leading term in a small‐sigma expansion of a similarity model with an exponential similarity function as its AR coefficient. Consistency of the quasi‐maximum likelihood estimator of the parameters in this model is established, the behaviours of the score and Hessian functions are analysed and test statistics are suggested. A complete list is provided of the normalization rates required for the consistency proof and for the score and Hessian function standardization. A large family of unit root models with stationary and explosive alternatives is characterized within the similarity class through the asymptotic negligibility of a certain quadratic form that appears in the score function. A variant of the stochastic unit root model within the class is studied, and a large‐sample limit theory provided, which leads to a new nonlinear diffusion process limit showing the form of the drift and conditional volatility induced by sustained stochastic departures from unity. The findings provide a composite case for time‐varying coefficient dynamic modelling. Some simulations and a brief empirical application to data on international Exchange Traded Funds are included. Copyright © 2014 Wiley Publishing Ltd     

Stationarity testing under nonlinear models. Some asymptotic results

Stationarity testing for nonlinear time series models which include several smooth trend components with (possibly) unknown parameters is considered. A pseudo‐Lagrange multiplier stationarity test is proposed and its asymptotic behaviour is derived. The limiting null distribution generally depends on the unknown parameters of the model. A bootstrap approach permits this problem to be circumvented and consistency of the bootstrapped test is obtained. The theoretical analysis is complemented with a simulation study which allows us to check the performance of the test in finite samples. The article ends with an empirical application.     

Two‐Phase Sequential Simulation of a Fluidized Bed Reformer

A two‐phase flow model is adapted in order to predict the performance of a fluidized bed reformer using the sequential modular simulator. Since there are physical and chemical phenomena interacting in the reformer, two sub‐models appear to be necessary to describe the overall model. These are the hydrodynamic and reaction sub‐models. The hydrodynamic sub‐model is based on the dynamic two‐phase model and the reaction sub‐model is derived from the literature. In the overall model, the bed is divided into several sections. At each section, the flow of the gas is considered as plug flow through the bubble phase and to be perfectly mixed through the emulsion phase. Two sets of experimental data from the literature at different hydrodynamic regimes were used in order to validate the proposed model. A close agreement was observed between the model predictions and the experimental data. The model proposed in this work may be used as a framework for the development of sophisticated models for non‐ideal reactors inside process simulators.     

Modelling the Dynamic Dependence Structure in Multivariate Financial Time Series

Abstract. The dependence structure in multivariate financial time series is of great importance in portfolio management. By studying daily return histories of 17 exchange‐traded index funds, we identify important features of the data, and we propose two new models to capture these features. The first is an extension of the multivariate BEKK (Baba, Engle, Kraft, Kroner) model, which includes a multivariate t‐type error distribution with different degrees of freedom. We demonstrate that this error distribution is able to accommodate different levels of heavy‐tailed behaviour and thus provides a better fit than models based on a multivariate t‐with a common degree of freedom. The second model is copula based, and can be regarded as an extension of the standard and the generalized dynamic conditional correlation model [Engle, Journal of Business and Economics Statistics (2002) Vol. 17, 425–446; Cappiello et al. (2003) Working paper, UCSD] to a Student copula. Model comparison is carried out using criteria including the Akaike information criteria and Bayesian information criteria. We also evaluate the two models from an asset‐allocation perspective using a three‐asset portfolio as an example, constructing optimal portfolios based on the Markowitz theory. Our results indicate that, for our data, the proposed models both outperform the standard BEKK model, with the copula model performing better than the extension of the BEKK model.     

石油焦价格对CFB热电厂效益影响分析

通过对国内外石油焦的市场、价格波动情况进行研究，指出石油焦价格具有较大波动性，总体趋势与动力用煤价格和原油价格变化趋势一致。烧石油焦CFB锅炉由于对燃料选择具有一定灵活性，可以改善石油焦价格波动引起的效益下滑。提出了国内燃料级石油焦的合理定价建议及将来企业选择烧石油焦CFB锅炉应注意的问题。     

中国现行天然气价格比较分析

介绍了我国天然气消费中存在的问题,指出气价偏高、特别是消费结构和气价结构不够合理是其中的核心问题。对比了国内外天然气价格的差距、天然气价格与可替代燃料价格的差距,分析了国外天然气定价方法、价格变化走向,以及当前天然气的价格构成,提出了降低我国天然气价格的几点措施。     

Options analysis for long-term capacity design and operation of a lignocellulosic biomass refinery

The growth of the lignocellulosic fuels has been hindered by technological and market uncertainty. This paper optimizes strategic investment decisions by prospective biobased fuel and chemical enterprises. A real options-based stochastic integer programming model is developed in this paper. We model a hypothetical, vertically integrated lignocellulosic enterprise that produces cellulosic ethanol and biosuccinic acid. Uncertainty is represented in bioproduct demands and prices. Strategic options including investment in research and development, investments in a flexible production platform and deferral of project investment are modeled. A hypothetical market model is also developed to correlate crude oil prices with the evolution of bioproduct markets. The discounted value of equity free cash flows is optimized. The optimal results include multiple capacity design plans based on the long term evolution of bioproduct markets. Monte Carlo simulations are also conducted to quantify the risk adjusted NPV's and returns on investment for the optimal capacity design trajectories.