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。     

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.     

股票网络平台中的噪音会影响股价同步性吗*

股价同步性是衡量资本市场信息效率的重要指标。股价同步性的影响机制一直以来都是金融研究的热点问题。本文使用文本分析的方法，通过机器学习度量了东方财富股吧中股民发帖评论中的非理性噪音，检验了股票网络平台中的噪音与股价同步性之间的关系。研究发现：第一，股票网络平台中的噪音会促使投资者做出非理性的投资决策，进而降低股价变动的同步性。噪音评论占全部评论的比例越高，上市公司的股价同步性越低，而且两者之间呈现倒U型的非线性关系。第二，公司个股新闻与公告数量的增加、分析师关注度的提高与外部审计质量的提升，均可以减弱股票网络平台中的噪音对股价同步性的影响。本文关注网络新媒体中的噪音对股价同步性的影响，研究结论对于监管部门加强网络信息监测以及上市公司进一步完善信息披露渠道具有现实意义。     

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.     

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.     

Lowest price guarantees on airline websites: Perceived believability,perceived value,and purchase intentions

Although lowest price guarantees (LPGs) are common on airlines’ official websites, a deeper understanding of how customers evaluate these guarantees is lacking. This study examines the effects of two LPG terms—depth of refund and refund conditions—on customer perceptions and purchase intention. An experiment with a 2 × 2 factorial design was conducted with a sample of 282 adults, and structural equation modelling was used to test the model. The results show that refund depth increases customer purchase intention by enhancing perceptions of the believability and value of the LPG. Strict refund conditions enhance customer purchase intention by increasing the believability of the LPGs.     

The impact of maize price shocks on household food security: Panel evidence from Tanzania

Using three waves (2008/09, 2010/11, 2012/13) of the Tanzanian National Panel Survey, this study investigates the impact of maize price shocks on household food security. Between 2008/09 and 2012/13, calorie intake stagnated for urban households, yet sharply deteriorated for rural households. The latter was driven by a significant decline in the consumption of the major staple maize which showed strongest price hikes among all major food items. Fixed-effects regressions indicate a clear negative relationship between maize prices and average household energy intake. Almost all population groups were found to be negatively affected by maize price shocks, with rural landless households being the most vulnerable group. In particular, a 50 percent rise in maize prices decreases caloric intake for rural (urban) households on average by 4.4 (5.4) percent, and for rural landless households by 12.6 percent. Results further indicate that subsistence agriculture can act as an effective strategy to insure against food price volatility.