Is pass‑through of the exchange rate to restaurant and hotel prices asymmetric in the US?Role of monetary policy uncertainty

This study examines the exchange rate pass-through to the United States(US)restaurant and hotel prices by incorporating the effect of monetary policy uncertainty over the period 2001:M12 to 2019:M01.Using the nonlinear autoregressive distributed lag(NARDL)model,empirical evidence indicates asymmetric pass-through of exchange rate and monetary policy uncertainty.Moreover,a stronger pass-through effect is observed during depreciation and a negative shock in monetary policy uncertainty,corroborating asymmetric pass-through predictions.Our results further show that a positive shock in energy prices leads to an increase in restaurant and hotel prices.Furthermore,asymmetric causality indicates that a positive shock in the exchange rate causes a positive shock to restaurant and hotel prices.We found feedback causal effects between positive and negative shocks in monetary policy uncertainty and positive and negative shocks in the exchange rate.Additionally,we detected a one-way asymmetric causality,flowing from a positive(negative)shock to a positive(negative)shock in energy prices.Therefore,these findings provide insights for policymakers to achieve low and stable prices in the US restaurant and hotel industry through sound monetary policy formulations.Highlights.The drivers of restaurant and hotel business in tourism destinations are examined.There is asymmetric pass-through of exchange rate and monetary policy uncertainty.A stronger pass-through is observed during appreciation and a negative shock to monetary policy uncertainty.There is asymmetric causality from positive shock in exchange rate to postive shock in restaurant and hotel prices.

The interaction between stock prices and interest rates in Turkey:empirical evidence from ARDL bounds test cointegration

This paper demonstrates a significant,long-running relationship between stock prices and domestic interest rates in Turkey’s financial markets for the period of 2001 M1-2017 M4.Cointegration analysis is investigated using the autoregressivedistributed lag bounds(ARDL Bounds)test and vector autoregressive cointegration.Additionally,cointegrating equations such as the fully modified ordinary least square,dynamic ordinary least squares,and canonical cointegrating regression are applied to check the long-run elasticities in the concerned relationship.The ARDL Bounds and Johansen Cointegration test results show that,dynamically,both prices are significantly related to each other.The cointegrating equation outcomes demonstrate elasticities whereby both coefficients have negative signs.Additionally,the same results are corroborated by the impulse response where all variables respond negatively to each other.

Exploring Patent Effects on Higher Stock Price and Stock Return Rate-A Study in China Stock Market

Based on the valid patent data and stock price data of China A-shares,the patent effects of four patent species including the invention publication,the invention grant,the utility model grant,and the design grant,on the stock price and the stock return rate were analyzed via analysis of variance(ANOVA).It was proved that the A-shares having new patents of any patent species shown the higher stock price mean and the higher stock return rate mean than those A-shares having no new patents did.The A-shares having new design grants were found to show the highest stock price mean among the A-shares having new patents of any patent species.The A-shares in the group of top 25%patent count of either the invention publication or the invention grant shown the highest stock return rates mean than those A-shares in other groups of less patent count did.The invention grant,following the general concept,showed its excellent patent effect.The design grant,beyond the expectation,also showed patent effects on the higher stock price and the higher stock return rate.The finding would improve the state of the art in the patent valuation and the listing company evaluation.

Do the RMB exchange rate and global commodity prices have asymmetric or symmetric effects on China’s stock prices?

With the rapid expansion of the RMB exchange rate’s floating range,the effects of the RMB exchange rate and global commodity price changes on China’s stock prices are likely to increase.This study uses both auto regressive distributed lag(ARDL)and nonlinear ARDL(NARDL)approaches to explore the symmetric and asymmetric effects of the RMB exchange rate and global commodity prices on China’s stock prices.Our findings show that without considering the critical variable of global commodity prices,there is no cointegration relationship between the RMB exchange rate and China’s stock prices,and the coefficient of the RMB exchange rate is not statistically significant.However,when we introduce global commodity prices into the NARDL model,the result shows that the RMB exchange rate has a negative effect on China’s stock prices,that there indeed exists a long-run cointegration relationship among the RMB exchange rate,global commodity prices,and stock prices in the NARDL model,and that global commodity price changes have an asymmetric effect on China’s stock prices in the long run.Specifically,China’s stock prices are more sensitive to increases than decreases in global commodity prices.Thus,increases in global commodity prices cause China’s stock prices to decline sharply.In contrast,the same magnitude of decline in global commodity prices induces a smaller increase in China’s stock prices.

Evaluating the exchange rate and commodity price nexus in Malaysia: evidence from the threshold cointegration approach

This paper examines the long-and short-run dynamics of asymmetric adjustment between the nominal exchange rate and commodity prices,namely oil,palm oil,rubber,and natural gas prices,in Malaysia using monthly data from January 1994 to December 2017.The relationship between exchange rate and each commodity price is examined in terms of Engle-Granger and threshold cointegrations.The estimated results provide evidence of long-run threshold cointegration and show that the adjustments towards the long-run equilibrium position are asymmetric in the short run.Furthermore,this study finds evidence of a unidirectional causal relationship running from the nominal exchange rate to oil price in the long and short run using a spectral frequency domain causality application.There is also empirical evidence of bidirectional causality between the nominal exchange rate and palm oil price,rubber price,and natural gas price in the long and short run.Overall,the findings have significant implications for the current debate on the future of primary commodities in Malaysia.

The Empirical Analysis of the Relationship between our Country Steel Price Index and the International Iron ore Freight Rate

The use of econometric methods to analyze the relationship between our country steel price index and the international iron ore freight rate,time series stationarity test,cointegration test,Granger test of causality and model parameter estimation tools use,find that there is Granger causality between our country steel price index and the international iron ore freight rate,China' s steel price fluctuations to some extent affect the international iron ore freight.

Bitcoin:Exploring Price Predictability and the Impact of Investor Sentiment

This article addresses the predictability of Bitcoin’s price by examining relationships between Bitcoin and financial and emotional variables such as the Fear and Greed Index(FGI),the American Interest Rate(FED),and the Stock Market Index(NASDAQ).Through the use of statistical techniques such as the Johansen Cointegration Test and Granger Causality,as well as forecasting models,the study reveals that,despite the notorious volatility of the cryptocurrency market,it is possible to identify consistent behavioral patterns that can be successfully used to predict Bitcoin returns.The approach that combines VAR models and neural networks stands out as an effective tool to assist investors and analysts in making informed decisions in an ever-changing market environment.

网约车平台算法个性化定价、乘客履约率及其监管对策

网约车平台算法个性化定价产生了复杂的市场影响,相比传统出租车服务,网约车乘客违约率达30%左右,因此算法个性化定价对乘客违约率的影响机制以及乘客是否履约的关键特征值得探索。文中尝试运用矩形Hotelling模型建立算法个性化定价与乘客违约率的因果关联机制,以两个网约车平台之间的Stackelberg博弈模型揭示歧视性定价、乘客违约率与平台之间竞争强度的关系。进一步运用网约车平台订单大数据,以Bhattacharyya距离、提升决策树及改进拉斯维加斯方法(包裹法)等归纳学习工具对网约车平台百万量级订单进行数据挖掘,找出决定乘客是否履约的关键特征。分析结果表明,平台进行个性化定价时乘客的最终消费选择主要取决于价格因素;而改进车辆匹配、派单策略及减少乘客候车时间可显著提高订单履约率。研究结果对网约车平台完善定价及运营策略以维持双边市场用户数量、保证平台持续成功运营具有重要参考价值,也为反垄断部门干预平台个性化定价提供了理论依据。

资本脱实向虚矫正新思路:基于市场结构的非对称性

资本“脱实向虚”严重制约着实体经济发展,优化资本配置成为实体经济高质量发展的重要着力点。从“脱实向虚”现象的成因机制展开分析,探讨治理资本“脱实向虚”的新思路。运用动态优化方法推导出满足社会福利最大化目标的最优垄断加价率计算公式,并对比分析1998—2020年中国、美国、德国多部门最优垄断加价率与实际加价率间的量化关系。研究发现:(1)加入世界贸易组织(WTO)是中国资本“脱实向虚”的起点;(2)中国、美国、德国实体经济产品的实际利润率普遍低于最优利润率,并且中国制造业产品的实际利润率低于其他国家;(3)美国和德国虚拟经济实际利润率的增长并未导致其实体经济实际利润率的相对(相对于最优利润率)下降,虚拟经济的发展并未以牺牲实体经济为代价,而中国存在生产性资本逃逸至虚拟经济现象。因此,应深化改革当前金融体系,调整市场结构非对称性,以更好地服务实体经济。

严控“溢价率”条件下住宅用地定价影响因素研究

土地市场是全国统一大市场的有机组成部分,地价管理是土地市场建设的核心工作之一。文章从土地的商品属性和特征价格出发,阐述了不完全竞争状态下的地价波动现象,强调了建立要素市场价格异常波动调节机制的必要性。基于评估方法交叉混淆、评估参数偏差不当、权重选取主观随意、土地评估对未来房价走势及库存和销售情况不敏感等问题,分析了土地评估的局限性。结合严控“溢价率”政策对住宅用地定价的更高要求,指出政府在确定土地出让价格时需一并考虑土地出让底价与15%“溢价率”之间的上下限关系。研究认为:确定住宅用地出让价格是一项复杂而庞大的系统工程,并非由单一因素决定,除参考土地评估价格以外,还应重视土地储备成本、土地出让计提、土地隐性成本、出让年度计划、土地使用强度、地价房价关系、房屋去化周期等7种因素对土地出让价格的影响。在此基础上,将土地出让成本的硬性约束作用、土地供应的政策导向作用、房地联动的产业关联作用纳入住宅用地价格形成机制之中。

考虑消费者情绪效用下新款产品上市策略

随着企业创新投入的增加,企业对产品更新换代频率加快。由于上市的新款产品会导致市场中同类产品的市场价值贬值,故消费者对购买的产品预期贬值与实际贬值存在偏差,从而导致失望或欣喜的情绪变化所产生的情绪效用影响其购买决策。考虑一般耐用品的市场价值具有贬值特征以及因贬值产生消费者情绪效用的情况下,通过构建新旧两款产品连续型定价模型,得到了新旧两款产品最优定价以及新款产品最优上市时间。分析得到:上市的新款产品对旧款产品市场价值的冲击影响企业对新款产品的定价;新款产品上市后,旧款产品定价应考虑旧款产品市场价值和新款产品市场贬值率;新款产品上市后,新旧两款产品的最优定价受消费者对产品市场价值贬值预期判断所产生的偏差影响;新款产品的上市定价与旧款产品的定价比值影响新款产品对旧款产品市场价值的冲击特征;新款产品的上市时间应取决于旧款产品的市场价值、市场自然价值贬值率以及消费者的正负情绪落差大小,并且新款产品的最优上市策略由新品的价值决定。

供应链核心企业财务公司的双边利率定价模型研究

供应链核心企业财务公司的目标是提升链内企业总体资金能力。本文以最优控制理论为指导,基于已有的定价策略模型,以资金成本、预期损失、信誉水平为影响因素构建供应链核心企业财务公司的双边利率定价模型,求解财务公司双边利率的最优值,通过算例分析和图像模拟验证了结果的可行性。结果表明:财务公司的最优贷款利率和最优存款利率之间存在相互影响的函数关系;最优贷款利率随单位成本和信誉水平增加而增加,随预期损失的增加而减小;最优存款利率随单位成本和预期损失的增加而减小,随信誉水平增加而增加,旨在有效指导供应链财务公司进行合理的利率定价。

基建投资、工业土地价格与城投债发行利率

以2011-2019年中国155个城市为样本,采用面板数据模型、中介效应模型和GMM方法探讨了基建投资、工业土地价格对城投债发行利率的影响。研究结果表明:基础设施建设投资规模和工业土地出让价格对城投债发行利率分别具有正向和负向影响,且低碳试点城市和非试点城市存在显著的区域异质性;基建投资的总体效益与技术效益的提高有助于融资平台以较低的利率发行城投债。

国有大型油气企业新能源发电项目经济评价探讨

国有大型油气企业基于减排降碳和可持续健康发展考量,积极探索新能源发电业务,投资项目经济评价方法和参数,成为影响新能源发电业务投资决策和高质量发展的决定性因素。基于经济评价基本原理,根据新能源发电项目技术经济特征,构建新能源发电项目经济评价方法和参数体系。折现现金流法仍然是新能源发电项目经济评价的主要方法,内部收益率和平准化度电成本是衡量项目盈利能力的重要指标。不同类型新能源发电项目经济评价方法具有较高的一致性,但应在投资构成、成本费用估算、收入与税金估算等方面体现差异,重点在营业收入估算中体现项目真正价值。在规制性配置储能要求以及储能收益政策有待落实的情况下,新能源发电项目效益有待提高,建议现阶段适当降低投资回报要求,尤其是出于市场拓展和协同发展目的的战略性新能源发电项目。项目电价和有效利用小时数对新能源发电项目的影响至关重要,应视情况合理选取参数。风光气电融合项目应分业务估算成本。国有大型油气企业应积极开展宏观配套政策和商业模式创新研究,以支撑新能源发电业务的高质量发展。

2014-2021年中国鸡蛋市场交易价格统计数据集

本数据集通过查阅全国农产品商务信息公共服务平台、中华人民共和国商务部等信息平台采集到全国及12个省份鸡蛋市场交易价格数据,遵循数据的一致性、连续性、完整性等原则对原始数据进行筛选、处理、加工以及数据校对后得到2014–2021年中国鸡蛋市场交易价格统计数据集。本数据集包含了2014–2021年全国及12个省份鸡蛋市场日度、月度批发价格数据和全国鸡蛋市场周度零售价格数据,并对其分析拓展得到了鸡蛋价格变化率和变化率折线图等数据信息。本数据集对鸡蛋市场价格的预测分析和波动研究提供有力的数据支撑。

考虑服务供应能力和服务质量的众包物流服务定价策略

通过考虑订单需求量与服务供应能力的关系,分别构建基于服务供应能力以及基于服务供应能力和服务质量的众包物流服务定价模型,提出最优的服务价格与服务质量决策;在此基础上,比较不同定价模式的最优决策和企业利润,并分析众包配送员报酬率对最优决策和企业利润的影响。研究发现,众包物流企业在基于服务供应能力和服务质量的定价模型中能获得更高的利润;此外,不同条件下服务价格与报酬率成反比或随着报酬率的增加先上升再下降,而服务质量和众包物流企业利润都随着报酬率的增加先上升后下降。

住房价格与养老保障水平对人口出生率的影响——基于2019~2023年我国省际面板数据的实证分析

目的:基于孩子成本——效益理论,分析住房价格与养老保障水平对人口出生率的影响,为提高我国生育水平,推动我国人口长期均衡发展、优化人口结构提出对策建议。方法:利用2019年~2023年31个省的宏观数据,采用房价收入比和基本养老保险覆盖率作为住房价格和养老保障水平的衡量指标,通过面板固定效应模型进行实证分析,考察住房价格与养老保障水平对人口出生率的影响。结果:基准回归结果表明,作为生育成本的房价收入比(β=-0.201,P<0.001)和作为生育保障收益替代的基本养老保险覆盖率(β=-11.832,P<0.001)对人口出生率都有显著的负向影响,加入双向固定效应后的结果与基准回归结果保持一致。异质性回归结果表明,住房价格与养老保障对人口出生率的影响会因城镇化率和养老金发放标准的不同存在差异。结论:我国应当通过调控房价收入比、改革基本养老保险制度、加强正确婚恋观和家庭观引导以及均衡教育与医疗资源等措施,增大生育收益与成本之差,提高生育意愿和人口出生率。

稳健外交关系如何影响企业出口国内增加值率

本文利用2000~2013年中国工业企业数据库与海关贸易数据库的合并数据测度了企业出口国内增加值率,在此基础上研究双边外交关系发展对中国企业出口国内增加值率的影响。研究发现,双边外交关系发展会提高企业出口国内增加值率,该结果经过变量替换、更换回归方法等稳健性检验后依旧成立。机制检验表明,双边外交关系发展对企业出口国内增加值率的影响主要通过降低国内外中间品相对价格和提高企业加成率两条途径实现。异质性分析发现,双边外交关系发展会显著提高低技术企业出口国内增加值率,但对高技术企业出口国内增加值率却表现为抑制作用;与“一带一路”沿线国家相比,双边外交关系发展对企业出口国内增加值率的提升作用在非“一带一路”沿线国家更大。进一步分析表明,金融危机、加征关税和经济政策不确定性会削弱双边外交关系发展对企业出口国内增加值率的提升作用。

现货市场中实现保供目标的容量补偿定价方法

为解决电力现货市场环境下的电力系统装机充裕度不足的问题,提出一种实现电力系统保供目标的容量补偿定价方法。首先,根据保供要求的电力系统可靠性标准,构建基于内部收益率评估的机组投资、退役决策模型;其次,通过“机组电能市场收入评估、机组容量补偿收入评估、投资退役决策”的迭代,确定目标年份市场中分类型机组的预期装机;然后,计算目标年份的可靠性指标,根据可靠性指标与标准的偏差调整容量补偿价格,使得发电充裕度能够满足目标年份系统的可靠性标准。算例结果表明,所提定价方法能够在保证电力系统可靠性的基础上,降低电力用户的综合购电成本。

Used car price prediction based on XGBoost and retention rate

In order to improve the accuracy of used car price prediction,a machine learning prediction model based on the retention rate is proposed in this paper.Firstly,a random forest algorithm is used to filter the variables in the data.Seven main characteristic variables that affect used car prices,such as new car price,service time,mileage and so on,are filtered out.Then,the linear regression classification method is introduced to classify the test data into high and low retention rate data.After that,the extreme gradient boosting(XGBoost)regression model is built for the two datasets respectively.The prediction results show that the comprehensive evaluation index of the proposed model is 0.548,which is significantly improved compared to 0.488 of the original XGBoost model.Finally,compared with other representative machine learning algorithms,this model shows certain advantages in terms of mean absolute percentage error(MAPE),5%accuracy rate and comprehensive evaluation index.As a result,the retention rate-based machine learning model established in this paper has significant advantages in terms of the accuracy of used car price prediction.