Usefulness of resale price maintenance under different levels of sales-effort cost and system-parameter uncertainties

This paper on “resale price maintenance” (RPM) has three main parts:

(i)

Using a simple and parsimonious model, we show that even with only one retailer, a “supplier” or “manufacturer” (hereafter “Manu”) should impose minimum-RPM under some circumstances but maximum-RPM in others. These two sets of circumstances are defined by a very simple formula.     

贝叶斯向量自回归分析方法及其应用

由于经济环境的多变,使得经济预测面临数据量少的建模难题,贝叶斯方法对小样本数据建模问题具有明显优势。本文在共轭条件似然函数"矩阵正态-Wishart分布"意义下,首先讨论了向量自回归模型的贝叶斯分析方法,得到了模型参数的后验分布与一步预测分布。其次,给出了分量方程的对应结果,说明了模型阶数的推断方法。最后,列出了计算步骤,并作为应用,对上海房地产价格指数数据进行预测建模,取得了较好效果。     

不同路径下能源价格对碳价格的传导关系研究

针对已有文献大多探讨能源价格与碳价格间的影响程度,本文从建模的角度寻找碳价格与能源价格间的联动关系。首先,使用随机微分方程刻画碳与能源的价格走势,得到碳——能源联动模型解析解。其次,在解析解的基础上,分别讨论能源价格如何通过影响碳市场价格均值、波动率路径从而影响整个碳价格。第三,分析重大能源政策产生的跳跃对碳价格的影响。结果显示,随着能源与碳市场关联程度的增加,相较于能源价格通过均值路径影响碳价格,其通过波动率路径影响碳价格的效果更为明显;且跳跃路径下的碳价格震荡小于波动率路径下能源价格传导引起的碳价格震荡。本文的结果对理清碳——能源价格联动关系具有积极意义,为投资者规避碳市场风险提供必要理论指导。     

需求依赖价格下港口存货质押业务中质押率研究

港口与银行合作办理质押贷款业务,不仅能增加港口的存储收入和金融收入,同时也能吸引更多的客户,从而增加其主营业务收入。在银行统一授信港口情况下,本文分析了质押物品的需求依赖价格条件下,风险中性的港口质押贷款业务中的最优质押率计算方法。并分别分析了在乘积型和加型随机需求函数条件下,质押产品价格、企业违约概率和质押物品数量以及质押期限对最优质押率和港口最大期望收益的影响,并通过数值分析验证了所有结论。     

Ornstein–Uhlenbeck processes in Hilbert space with non-Gaussian stochastic volatility

We propose a non-Gaussian operator-valued extension of the Barndorff-Nielsen and Shephard stochastic volatility dynamics, defined as the square-root of an operator-valued Ornstein–Uhlenbeck process with Lévy noise and bounded drift. We derive conditions for the positive definiteness of the Ornstein–Uhlenbeck process, where in particular we must restrict to operator-valued Lévy processes with “non-decreasing paths”. It turns out that the volatility model allows for an explicit calculation of its characteristic function, showing an affine structure. We introduce another Hilbert space-valued Ornstein–Uhlenbeck process with Wiener noise perturbed by this class of stochastic volatility dynamics. Under a strong commutativity condition between the covariance operator of the Wiener process and the stochastic volatility, we can derive an analytical expression for the characteristic functional of the Ornstein–Uhlenbeck process perturbed by stochastic volatility if the noises are independent. The case of operator-valued compound Poisson processes as driving noise in the volatility is discussed as a particular example of interest. We apply our results to futures prices in commodity markets, where we discuss our proposed stochastic volatility model in light of ambit fields.     

Slopes of shadow prices and Lagrange multipliers

Many economic models and optimization problems generate (endogenous) shadow prices—alias dual variables or Lagrange multipliers. Frequently the “slopes” of resulting price curves—that is, multiplier derivatives—are of great interest. These objects relate to the Jacobian of the optimality conditions. That particular matrix often has block structure. So, we derive explicit formulas for the inverse of such matrices and, as a consequence, for the multiplier derivatives.     

ON THE INCREMENTS DISTRIBUTION OF STOCK PRICES

Abstract. In this paper, the models of increment distributions of stock price are constructed with two approaches. The first approach is based on limit theorems of random summation. The second approach is based on the statistical analysis of the increment distribution of the logarithms of stock prices.     

Extremal Behavior of Diffusion Models in Finance

We investigate the extremal behavior of a diffusion X _t given by the SDE , where W is standard Brownian motion, μ is the drift term and σ is the diffusion coefficient. Under some appropriate conditions on X _t we prove that the point process of ε -upcrossings converges in distribution to a homogeneous Poisson process. As examples we study the extremal behavior of term structure models or asset price processes such as the Vasicek model, the Cox–Ingersoll–Ross model and the generalized hyperbolic diffusion. We also show how to construct a diffusion with pre-determined stationary density which captures any extremal behavior. As an example we introduce a new model, the generalized inverse Gaussian diffusion. This revised version was published online in July 2006 with corrections to the Cover Date.     

一种基于局部回归的多尺度碳市场价格预测模型研究

碳市场价格呈现非线性、非平稳的复杂特性,准确预测具有较大的挑战。基于“分而治之”的思想,提出了一种基于局部回归的多尺度碳市场价格预测模型。提出的模型利用集成经验模态分解(EEMD)对碳市场价格时间序列进行分解。启发于EEMD局部特征分解的特点,对分解后的分量采用局部回归方法进行预测,然后将分量预测结果进行集成。采用的局部回归方法包括局部线性回归(LLP)、局部多项式回归、局部岭回归、局部主成分回归、局部偏最小二乘回归和局部套索回归。实验结果表明基于局部回归的多尺度预测模型具有优异的预测性能。在提出的模型中,EEMD-LLP结构简单且性能更为突出,进一步对EEMD-LLP参数的适应性进行探讨。与新近提出模型的对比结果表明了EEMD-LLP在碳市场价格预测中的有效性。     

Detection of Mutual Exciting Structure in Stock Price Trend Dynamics

We investigated a comprehensive analysis of the mutual exciting mechanism for the dynamic of stock price trends. A multi-dimensional Hawkes-model-based approach was proposed to capture the mutual exciting activities, which take the form of point processes induced by dual moving average crossovers. We first performed statistical measurements for the crossover event sequence, introducing the distribution of the inter-event times of dual moving average crossovers and the correlations of local variation (LV), which is often used in spike train analysis. It was demonstrated that the crossover dynamics in most stock sectors are generally more regular than a standard Poisson process, and the correlation between variations is ubiquitous. In this sense, the proposed model allowed us to identify some asymmetric cross-excitations, and a mutually exciting structure of stock sectors could be characterized by mutual excitation correlations obtained from the kernel matrix of our model. Using simulations, we were able to substantiate that a burst of the dual moving average crossovers in one sector increases the intensity of burst both in the same sector (self-excitation) as well as in other sectors (cross-excitation), generating episodes of highly clustered burst across the market. Furthermore, based on our finding, an algorithmic pair trading strategy was developed and backtesting results on real market data showed that the mutual excitation mechanism might be profitable for stock trading.