进口贸易视域下国际粮价对国内粮价的传导路径--来自玉米和大豆的证据

基于玉米和大豆等粮食国际、国内价格历史数据,运用协整及误差修正模型、脉冲响应函数等方法,揭示国际粮价对国内粮价的传导作用及影响路径。理论分析表明,国际粮价通过进口直接路径、进口产品成本路径和进口替代路径等三个子路径传导至国内粮价。实证检验发现,国际大豆价格进口直接路径和进口产品成本路径传导均比较充分,即国际大豆价格会影响大豆进口价格,进而影响国内大豆价格,最终影响国内豆油价格;国际玉米价格进口直接路径和进口产品成本路径传导则不充分,国际玉米价格会影响玉米进口价格,但是对国内玉米价格和国内玉米淀粉价格影响程度较低;而在进口替代路径中,玉米和大豆的国际价格传导均不充分。我国应继续推进以提质增效为目标的农业供给侧结构性改革,加大粮食生产科技投入力度,调动农民种粮积极性,集中力量提高粮食供给质量和效率;坚持粮食进口仅为调剂国内供求余缺的方针,把握粮食安全的主动权;建立健全粮食市场的价格调控体系及风险防控体系,规避国际粮食市场剧烈波动对国内粮食市场的影响。     

领导干部自然资源资产离任审计与企业绿色并购——来自中国重污染型上市公司的经验证据

基于2011~2017年我国A股重污染型上市公司经验数据，实证检验了领导干部自然资源资产离任审计对企业绿色并购行为的影响，研究发现：领导干部自然资源资产离任审计可显著促进辖区内企业实施绿色并购；而当地区市场竞争程度越高时，越会抑制企业在领导干部自然资源资产离任审计试点实施背景下的绿色并购扩张行为；进一步地，企业绿色并购会显著提高企业价值，且这一影响对并购后当年的企业价值影响最为显著；基于异质性分析方面，领导干部自然资源资产离任审计会约束不同产权性质企业的环境治理行为，促进其采取绿色并购，实施绿色发展；同时领导干部自然资源资产离任审计会更有利于东部地区企业实施绿色并购，而对中西部地区的企业影响不明显。     

Analysis of Bitcoin prices using market and sentiment variables

This paper proposes an empirical model for analysing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010–17 and 2010–19. Price discovery is achieved through the Gonzalo–Granger permanent‐transitory decomposition. The pricing factors are endogenous linear combinations of the S&P 500 index, gold price, a Google search variable associated to Bitcoin and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period, a linear combination of four pricing factors describes the efficient Bitcoin price. The S&P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalised by individuals' search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018–19 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.     

现代资产定价理论的研究和发展——基于有效市场假说和资产定价模型的视角

围绕对有效市场假说的联合检验假设难题、资本资产定价模型的检验、“贝塔通缉令”“因子动物园”“多因素模型大战”等重点和核心话题,对现代资产定价理论文献的研究脉络进行梳理和评述,在此基础上对未来资产定价的研究重点和方向提出建议。     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

自然资源资产管理：理论逻辑与改革导向

研究目的:针对自然资源资产管理的理论诠释不清、改革导向不明、自然资源的资产属性尚未充分体现等问题,本文立足新时代党和国家机构改革背景,分析自然资源资产管理的理论逻辑,明确未来自然资源资产管理的改革方向。研究方法:文献研究法,综合分析法。研究结果:新时代自然资源资产管理应当基于"权利—价值—经营—管理"的理论逻辑体系框架,其中自然资源资产权利应当包含自然资源资产所有权、资格权、使用权和管理权等对象内涵,以完整、明晰和稳定作为权利实现要求;自然资源资产价值显化应当基于生态、经济等价值的全面认知,针对不同资产类型分类采用不同价值评估方法,完善市场培育、价值调节和公平分配机制;自然资源资产经营既要保障粮食安全、生态安全和规避可能风险,又要实现高品质利用,因而应当以底量保安全,以存量、数量、质量、差量和流量保协同,优化完善编制资产负债表;自然资源资产管理则应当打破自然资源资产管理割裂的现状,实现系统统一管理并建立全平台、全过程、全资源和全空间的综合监督机制。研究结论:自然资源资产管理应以"三维立体多权化"、"生态价值具象化"、"安全品质六量化"和"系统监管综合化"为改革导向,以期全面推进未来多维、绿色、高效、安全、品质、有序的自然资源资产管理方式的切实实现。     

What determines the long-term correlation between oil prices and exchange rates?

In this study, we obtain the long-term correlation between oil prices and exchange rates by employing the dynamic conditional correlation-mixed data sampling (DCC-MIDAS) model. We then identify the factors that influence the long-term correlation using panel data analysis. We find that the long-run correlations between oil prices and exchange rates are negative for all oil-exchange rate markets except Japan. We also find that both inflation and term spread have negative effects, while the risk-free interest rate has a positive effect on the long-term correlation between oil prices and exchange rates. Importantly, the empirical results show that an increase in inflation will significantly damage the real value of the currency itself.     

Testing vertical price transmission for Vietnam's Robusta coffee

Vietnam has undergone market reforms over the last three decades; and as a consequence, the coffee sector has become increasingly market‐driven. The success of the government's liberalisation policies in terms of market efficiency is investigated by examining the transmission of both positive and negative price changes for Robusta coffee between export and farmgate prices. We used a threshold vector error correction model and high‐frequency daily data. The primary result here is that of a symmetric price transmission between export and farm‐level prices. This result holds when tested with weekly price data, derived from the daily data. Farmgate prices respond faster to decreases than increases in export prices when the long‐run deviation exceeds a certain threshold. These price changes are transmitted within several days. This research also confirms the importance of transaction costs, and other price frictions mostly ignored in prior analyses for coffee.     

On the rebound: estimating direct rebound effects for Australian households

Reducing dependence on fossil fuels by decreasing energy consumption is a common environmental policy. One mechanism used to achieve this is to encourage increased energy efficiency. However, improving efficiency may have an opposing effect and cause an increase in energy consumption if the intensity of use changes. This phenomenon is known as the rebound effect. We estimate direct rebound effects for energy use in Australia based on both aggregate residential energy use data and on household energy expenditure data. Our approach implements a new methodology developed by Hunt and Ryan (2014, Catching on the rebound: Why price elasticities are generally inappropriate measures of rebound effects. Surrey Energy Economics Discussion Paper Series SEEDS 148; 2015, Energy Economics 50, 273) that explicitly relates energy service use with energy source demand and directly incorporates measures of efficiency changes. The results indicate that the rebound effect is relatively high for energy use by Australian households. Due to the unique nature of our household data set, we can examine the influence of demographic and housing characteristics. We find that low‐income households and households with vulnerable members have the largest rebound effects. The relatively large rebound effects found here suggest that consumers gain from efficiency by improved energy services, and thus, policy targeting energy efficiency is not likely to be successful at reducing energy consumption.     

Board directors with foreign experience and stock price crash risk: Evidence from China

This study examines the impact of board directors with foreign experience (BDFEs) on stock price crash risk. We find that BDFEs help reduce crash risk. This association is robust to a series of robustness checks, including a firm fixed effects model, controlling for possibly omitted variables, and instrumental variable estimations. Moreover, we find that the negative association between BDFEs and crash risk is more pronounced for firms with more agency problems, weaker corporate governance, and less overall transparency. Our findings suggest that the characteristics of board directors matter in determining stock price crash risk.