世界豆油期货市场关联性研究:基于中美两国的实证分析

期货市场国际关联性是同一期货合约的价格在各国市场上的相互关系。本文通过采用协整检验、方差分解和脉冲响应等技术对世界最大两个豆油期货市场的关联性研究发现:大连、芝加哥交易所豆油期货价格和中国豆油现货平均价格之间存在长期均衡关系。大连豆油期货市场的影响力与权威性都比芝加哥市场强大。     

中国住房市场波动的影响因素研究——基于租金收益率的方差分解

基于资产定价的视角,本文通过将标准的动态戈登增长模型和传统的住房使用成本模型相结合,建立了一个关于住房市场租金收益率的动态住房使用成本模型。该模型将租金收益率分解为购房的预期资金成本、预期购房相对于租房的风险溢价和预期未来租金增长率三个部分的现值之和。进一步,本文将该模型应用于京沪广深四大城市的季度数据,并使用方差分解方法来考察国内住房市场动态波动的影响因素及其相对重要性。本文结果表明,资金成本变动在四大城市的住房市场波动中为最重要的影响因素,而租金在住房市场波动中虽然存在着一定的影响作用,但并不如资金成本显著。此外,本文还发现,不能直接观测得到的购房相对于租房的风险溢价也是影响国内住房市场的一个不可忽视的重要因素。值得注意的是,近年四大城市居民租房面临的风险相对于购房正日益上升。     

随机波动模型下定价方差互换的一类控制变量

本文从风险中性定价的角度出发,给出了在随机波动模型下定价方差互换的一类控制变量,从而大大提高了使用蒙特卡罗方法计算方差互换价格时的效率,并在波动率的平方满足几何布朗运动(GBM)和波动率满足Ornstein-Uhlen-beck(OU)过程这两种随机波动情况下给出了控制变量的具体形式。特别对于GBM型随机波动模型,可以得到一系列控制变量,从而进行多元控制。     

中国出口贸易与环境污染互动关系研究——基于广义脉冲响应函数的实证分析

本文通过运用广义脉冲响应函数法与方差分解法,考察我国1990-2006年期间4类环境污染指标与出口贸易之间的长期动态影响特征。分析结果表明,出口贸易是影响我国污染排放的重要原因,环境污染对出口贸易也存在着反向作用,并且在短期内比较明显。方差分解结果显示出口贸易是解释各类污染排放的预测方差的重要变量,而污染排放对出口贸易的预测方差贡献度则相对较小。     

江西省农民收入结构及地区差异分析

随着我国经济的飞速发展,农民收入差距也在不断扩大。由于受客观环境和地理位置等因素的影响,农业生产总体水平不同,农民收入构成中,转移性收入和财产性收入所占比重低,地区差异不显著,差异突出体现在工资性收入和家庭经营性收入上。要从根本上解决农民增收问题,不仅要改革传统的农业生产方式,而且关键是要把农业剩余劳动力由农业转移到非农业部门就业,这样才能产生良好效应。     

Jump-diffusion volatility models for variance swaps: An empirical performance analysis

This paper studies a class of tractable jump-diffusion models, including stochastic volatility models with various specifications of jump intensity for stock returns and variance processes. We employ the Markov chain Monte Carlo (MCMC) method to implement model estimation, and investigate the performance of all models in capturing the term structure of variance swap rates and fitting the dynamics of stock returns. It is evident that the stochastic volatility models, equipped with self-exciting jumps in the spot variance and linearly-dependent jumps in the central-tendency variance, can produce consistent model estimates, aptly explain the stylized facts in variance swaps, and boost pricing performance. Moreover, our empirical results show that large self-exciting jumps in the spot variance, as an independent risk source, facilitate term structure modeling for variance swaps, whilst the central-tendency variance may jump with small sizes, but signaling substantial regime changes in the long run. Both types of jumps occur infrequently, and are more related to market turmoils over the period from 2008 to 2021.     

A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks

The unexpected emergence of the COVID-19 pandemic has changed how grocery shopping is done. The grocery retail stores need to ensure hygiene, quality, and safety concerns in-store shopping by providing “no-touch” smart packaging solutions for agri-food products. The benefit of smart packaging is to inform consumers about the freshness level of a packaged product without having direct contact. This paper proposes a data-driven decision support system that uses smart packaging as a smart product-service system to manage the sustainable grocery store supply chain during outbreaks to prevent food waste. The proposed model dynamically updates the price of a packaged perishable product depending on freshness level while reducing food waste and the number of rejected customers and maximising profit by increasing the inventory turnover rate of grocery stores. The model was tested on a hypothetical but realistic case study of a single product. The results of this study showed that stock capacities, freshness discount rate, freshness period, and quantity discounts significantly affect the performance of a grocery store supply chain during outbreaks.     

Analytically pricing variance and volatility swaps under a Markov-modulated model with liquidity risks

This paper determines strike prices of discretely sampled variance/volatility swaps taking into account stochastic liquidity risks and the switching of economic conditions. We adopt nonlinear regime switching volatility to reflect how asset prices are affected by economic cycles, and market prices of assets are discounted according to the level of market liquidity. We then establish a risk-neutral measure under regime switching Esscher transform, so that analytical valuation of variance/volatility swaps can be completed based on the closed-form forward characteristic function. The limiting behavior of discretely sampled variance/volatility swaps is also considered through the investigation of pricing continuously sampled variance/volatility swaps. Finally, based on the results from numerical implementation, we confirm that the new model is very flexible in reflecting different influence associated with common real market observations.     

Improving variance forecasts: The role of Realized Variance features

In this paper, we effectively extend the Realized-EGARCH (R-EGARCH) framework by allowing the conditional variance process to incorporate exogenous variates related to different observable features of Realized Variance (RV). The choice of these features is well motivated by recent studies on the Heterogeneous Autoregressive (HAR) class of models. We examine several specifications nested within our augmented R-EGARCH representation, and we find that they perform significantly better than the standard R-EGARCH model. These specifications incorporate realized semi-variances, heterogeneous long-memory effects of RV, and jump variation. We also show that the performance of our framework further improves if we allow for skewness and excess kurtosis for asset return innovations, instead of assuming normality. This can better filter the true distribution of the return innovations, and thus can more accurately estimate their effects on the variance process. This is also supported by a Monte Carlo simulation exercise executed in the paper.