多组分间歇精馏工艺计算探讨

介绍用“微元时段”计算多组分间歇精馏的方法。     

Simulation-based sequential analysis of Markov switching stochastic volatility models

We propose a simulation-based algorithm for inference in stochastic volatility models with possible regime switching in which the regime state is governed by a first-order Markov process. Using auxiliary particle filters we developed a strategy to sequentially learn about states and parameters of the model. The methodology is tested against a synthetic time series and validated with a real financial time series: the IBOVESPA stock index (São Paulo Stock Exchange).     

Double auction mechanisms on Markovian networks

This paper studies the double auction (DA) mechanism in Ma and Li (2011) for a class of exchange economies. We extend their results to more general cases where sellers and buyers each form a complex time non-homogeneous Markovian chain, as specified in Ram et al. (2009), in the communication of their private information. A numerical example is also provided. Both bubbles and crashes are observed in the example, consistent with results of our theorems. Our example and theoretical results provide new evidence that a DA mechanism, widely utilized in real exchange markets, may contribute to the excess volatility identified in Shiller (1981) and LeRoy and Porter (1981).     

On a Mixture GARCH Time‐Series Model

Abstract. Recently, there has been a lot of interest in modelling real data with a heavy‐tailed distribution. A popular candidate is the so‐called generalized autoregressive conditional heteroscedastic (GARCH) model. Unfortunately, the tails of GARCH models are not thick enough in some applications. In this paper, we propose a mixture generalized autoregressive conditional heteroscedastic (MGARCH) model. The stationarity conditions and the tail behaviour of the MGARCH model are studied. It is shown that MGARCH models have tails thicker than those of the associated GARCH models. Therefore, the MGARCH models are more capable of capturing the heavy‐tailed features in real data. Some real examples illustrate the results.     

Volatility of Simulated High-Level Nuclear Waste Glass by Thermogravimetric Analysis

The volatilities of simulated, high-level nuclear waste glasses have been measured using thermogravimetric analysis (TGA). These volatilities were measured in the region of the glass transition temperature ( T _g) of the waste glasses, which is between 450° and 500°C. These data were obtained because the Waste Acceptance Preliminary Specifications require that no foreign materials be released into the canistered waste form upon heating of the canister to this glass transition temperature. In fact, all of the waste glass samples studied actually exhibited a net weight gain upon heating. This weight gain was shown to be due to oxygen uptake through oxidation of FeO. Powdered glass samples did show a small weight loss which was smaller in magnitude than the weight gain and was associated with water desorption. No true volatility was detected to the level of sensitivity (0.01 wt%) of the TGA instrument. This converts to a sensitivity of 330 μ g/m² of glass surface and a corresponding minimum value of 41 mg of volatiles for each Defense Waste Processing Facility conistered waste form. TGA experiments carried out at higher temperatures (800°C) revealed that organic concentrations in the waste glasses are less than 0.01 wt%. Thus, these results demonstrate that the Defense Waste Processing Facility will be able to comply with the Waste Acceptance Preliminary Specifications on the exclusion of foreign materials from the canistered waste forms, after exposure to T _g.     

基于R软件的金融资产收益波动率建模分析

金融资产收益波动率是金融计量分析的核心议题,该文考虑对其建模分析的计算机实现问题。利用R软件的强大计算与绘图功能,给出金融资产收益波动率各个模型的模拟实现以及对实际金融市场数据的各种建模分析。     

基于季节长记忆双自回归模型的日径流模拟

传统时间序列模型无法同时考虑径流序列的长记忆性和时变波动性,且模型参数限制严格,从而使日径流序列模拟受到限制,影响径流模拟预测精度。本文提出了同时考虑非平稳性、季节性、长记忆性和时变波动性的新型双自回归模型(WOA-SFIDAR),并与经典长记忆波动率模型(SFIAR-GARCH)进行对比,选取渭河流域4个水文站日径流序列进行模拟验证。结果表明：WOA-SFIDAR模型的模拟能力优于SFIAR-GARCH模型,模拟结果很好地保持了日径流过程的统计特性。7、8月份模拟均值误差相对较大,WOA-SFIDAR模型的误差范围(5.72～32.56)低于SFIAR-GARCH模型(7.42～48.02)。WOA-SFIDAR模拟逐月变差系数(C_v)和偏态系数(C_s)与实测序列统计值间偏差范围为0～0.51和0.02～1.31,优于SFIAR-GARCH模型(0.02～0.56和0.06～1.52);模拟结果能够保持日径流序列自相关系数(ACF)的变化趋势,且随滞时的增加,实测序列与模拟序列的ACF差距减小。文中模型扩展了水文随机模拟方法,可为日径流模拟...     

基于灰色马尔科夫模型预测金融波动

引入灰色模型和符号时间序列分析方法,与马尔科夫模型方法相结合,提出了一种新的预测金融波动的方法。首先将波动序列符号化,然后建立灰色马尔科夫模型,不仅能减小影响预测精度的误差,而且能利用马尔科夫模型来调整误差,使结果更加精准。采用上海证券交易所综合指数2007--2010年间隔为5分钟的高频数据为样本,对已实现波动序列进行实证分析,成功预测了下一时点波动值所处的区间,并验证了该方法的可行性和有效性。     

多阶段复合期权模型在新药研发中的应用研究

考虑到新药研发周期长和高度的不确定性的特点,在实物期权理论的基础上建立了多阶段复合期权的评价模型,针对二阶段定价模型的不足,提出了各阶段波动率不同的三阶段定价模型,并得到其封闭解。最后用改进的三阶段变波动率复合期权模型来评估一个新药研发项目的价值,计算结果表明模型具有较好的实用性。     

低挥发性脱漆剂的研究

以苯甲醇(BA)、丙二醇乙醚(MPA)和二丙酮醇(DKA)为主溶剂制成低挥发性脱漆剂，考察了主溶剂、促进剂的种类、用量等因素对脱漆效果的影响。实验表明：促进剂甲酸、苯酚的用量为9％时，脱漆效率可提高2～3倍；加入1．5％的液体石蜡可以降低其挥发速率，增加脱漆剂的有效作用时间。