Expanding airport capacity of cities under uncertainty: Strategies to mitigate congestion

Airport capacity constraints are increasingly challenging the growth of air traffic. At the same time, decision-making about airport capacity investments is extremely complex, involving trade-offs. This paper’s objective is to optimise a privately owned airport system’s capacity investment decision in a city under demand uncertainty. Next to the investment size, our real options model incorporates the timing of the investment, as well as the cost of congestion. The results reveal that the larger a city’s initial airport capacity, the smaller its investment will relatively be and the lower the occupancy rate threshold at which investment will take place. We also show that, in case of a higher demand growth combined with more demand uncertainty, the city will benefit from a significantly larger investment, but made later at a higher occupancy rate. In this case, cities with a small initial capacity will sometimes even more than double current capacity. Higher airport charges and an increase in non-aeronautical revenues both lead to a later investment in more capacity, due to the increased project attractiveness. An increase in congestion costs results in a larger investment made earlier, in order to eliminate delays. Airport operational cost and capacity holding cost increases both lead to smaller investments.     

Unemployment and econometric learning

We apply well-known results of the econometric learning literature to the Mortensen and Pissarides real business cycle model. Agents can always learn the unique rational expectations equilibrium (REE), for all possible well-defined sets of parameter values, by using the minimum-state-variable solution to the model and decreasing gain learning. From this perspective the assumption of rational expectations in the model could be seen as reasonable. But using a parametrisation with UK data, simulations show that the speed of convergence to the REE is slow. This type of learning dampens the cyclical response of unemployment to small structural shocks.     

Impact of the 2014 Suspension Agreement on sugar between the United States and Mexico

In December 2014, the U.S. and Mexico agreed to a suspension agreement that set a $22.25/cwt import price floor on U.S. sugar imports from Mexico. A partial equilibrium trade model was developed to estimate the economic impact the agreement would have had if it had been in effect from 2008 to 2014. In years when the price floor would have been binding, on average, U.S. producers would have gained $138 million and Mexican producers would have lost $218 million. However, total Mexican welfare would have actually increased by $11.5 million. Furthermore, the average price floor that would have maximized total Mexican welfare over that period is $22.76/cwt. Also, under certain supply and demand elasticity conditions, the average price floor that would have maximized joint U.S. and Mexican producer welfare over that period is $21.91/cwt. The latter two estimates are both close to the actual price floor agreed to in the 2014 Suspension Agreement.     

股价信息含量与投资-股价敏感性——基于融资融券的准自然实验

本文利用2006-2015年间的数据研究了融资融券对投资-股价敏感性的影响。利用融资融券作为股价信息含量的一个外生冲击变量,本文研究发现,我国A股市场确实存在反馈效应,融资融券政策的实施增强了标的公司投资-股价敏感性,这个结论在采用倾向得分模型(PSM)配对后依然成立,说明融资融券使股价融入了更多有利于管理层投资决策的信息。其次,融资融券对投资-股价敏感性的影响在机构投资者比例高、流动性高、处于新兴行业的这类管理层反馈效应更强的股票组中更显著。参照以往研究考虑了融资约束对反馈效应的调节作用,同样发现融资融券对投资-股价敏感性的影响在国有企业和规模较大的企业组中更显著。最后,融资融券交易规模越大,投资对股价的敏感性越强。     

Price and RevPAR determinants of Airbnb listings: Convergent and divergent evidence

This paper explores the performance determinants of Airbnb listings, analyzing three research questions. First, the study investigates the different effects generated by the antecedents on price and revenue; second, it ranks different groups of variables; third, it distinguishes between private rooms and entire homes or apartments. These research questions are addressed by analyzing Airbnb listings in Milan, a business city where the sharing economy is growing fast. In particular, the study will use the monthly data of all Airbnb listings in Milan recorded by AirDNA during the period from November 2014 to June 2019, which consists of 323,184 total observations. Some hedonic price models are calculated, adding the Shapley value approach. Empirical findings show some important differences between price and revenue determinants. Furthermore, listing type and size, along with location and seasonality, are by far the most important factors that explain performance differentials among Airbnb properties.     

股票价格波动的信息渠道影响研究——基于媒体报道和分析师预测的视角

文章手工收集、整理了2006-2015年中国A股上市公司的媒体报道倾向数据，实证研究了媒体报道正向倾向与分析师乐观预测偏差对上市公司负收益偏态系数的影响。研究表明，虽然媒体报道正向倾向、分析师乐观预测均分别对上市公司股价负收益偏态系数产生显著的负向影响，但是两者的交互项对股价负收益偏态系数的影响则是显著为正，即当媒体报道正面倾向与分析师乐观预测两者共同作用时，将会触发"信息过度关注偏差"机制，从而降低了上市公司股票收益。文章研究还发现，媒体报道、分析师预测和"信息过度关注偏差"机制的作用强度，在市场化程度高地区要明显大于市场化程度低地区，可能的原因是市场化程度高地区的企业更加容易被媒体报道和分析师关注，进而产生更强的关注效应。     

Big data hedonic pricing: Econometric insights into room rates’ determinants by hotel category

This research uses a big dataset of online prices published on Booking.com by three-, four- and five-star hotels located in Madrid (Spain). Data is broadened by other sources, resulting in a rich set of context-, hotel- and offer-based variables. This research aims to determine the impact of these variables on the online room prices set by one representative sample, featuring the total pricing behaviour as well as per hotel categories. The variables considered, their extent and the insight per category represent a novelty and complement the literature on demand forecasting and hedonic pricing, enabling the improvement of optimisation techniques. The models, based on regression analysis with random effects, reveal a significant impact of the variables on room prices and a clearly distinct behaviour for five-star hotels. This research contributes to the scientific community and practitioners by showing the pricing dynamics behaviour of an important urban destination by star rating.     

Contagion between real estate and financial markets: A Bayesian quantile-on-quantile approach

We study contagion between Real Estate Investment Trusts (REITs) and the equity market in the U.S. over four sub-samples covering January, 2003 to December, 2017, by using Bayesian nonparametric quantile-on-quantile (QQ) regressions with heteroskedasticity. We find that the spillovers from the REITs on to the equity market has varied over time and quantiles defining the states of these two markets across the four sub-samples, thus providing evidence of shift-contagion. Further, contagion from REITs upon the stock market went up during the global financial crisis particularly, and also over the period corresponding to the European sovereign debt crisis, relative to the pre-crisis period. Our main findings are robust to alternative model specifications of the benchmark Bayesian QQ model, especially when we control for omitted variable bias using the heteroskedastic error structure. Our results have important implications for various agents in the economy namely, academics, investors and policymakers.     

Trading activity and price discovery in Bitcoin futures markets

This study examines the impact of trading activities on price discovery in the Bitcoin futures markets. We find that trades of hedgers are positively correlated with the modified information shares in both CME and CBOE futures markets, suggesting that their trading promotes futures market efficiency. Retailers’ trading activity relates negatively to the price discovery of the CME Bitcoin futures and thus destabilizes the market. Speculators exert positive (negative) impact on the price discovery in the CME (CBOE) Bitcoin futures. Our finding that CME’s Bitcoin futures exhibit superior price discovery than CBOE’s provides plausible justification for CBOE’s decision in March 2019 to suspend further listings of Bitcoin futures contracts.     

减排与增长:服务业如何实现绿色均衡发展?

服务业是中国经济转型和产业结构升级的重要抓手,在资源与环境"双重"约束日益趋紧的背景下,有必要研究服务业绿色发展问题。能否在减排成本最小的前提下提高服务业增长率,走出一条绿色发展路径?为此,文章创新性地将排放权交易市场引入服务业部门,采用方向性距离函数、非线性规划等实证研究方法测算2004-2012年中国服务业14个细分行业的绿色效率和碳排放影子价格。研究表明:中国服务业发展方式并非"绿色",服务业减排成本和碳排放量逐年增长,减排面临巨大的经济压力;在文章设计的排放权交易模型下,服务业细分行业能够形成一个统一的影子价格,从而实现一条绿色发展路径,该路径满足帕累托有效和投入产出技术有效;在均衡路径上,流通服务业应出售排放权,而其他减排成本较高的服务业购买排放权。