Forecast quality improvement with Action Research: A success story at PharmaCo

There is a gap in the forecasting research surrounding the theory of integrating and improving forecasting in practice. The number of academically affiliated consultancies and knowledge transfer projects that there are around, due to a need for improvements in forecast quality, would suggest that many interventions and actions are taking place. However, the problems that surround practitioner understanding, learning and usage are rarely documented. This article takes the first step toward trying to rectify this situation by using the specific case study of a fully engaged company. A successful action research intervention in the Production Planning and Control work unit improved the use and understanding of the forecast function, contributing to substantial savings, enhanced communication and improved working practices.     

Risk aversion,prudence and temperance: An experiment in gain and loss

We characterize the individual's attitude towards risk, prudence and temperance in the gain and loss domains. We analyze the links between the three features of preferences for a given domain and between domains for each feature of preferences. Consequently, the reflection effect, the mixed risk aversion and the risk apportionment, are key concepts of our study. We also display some determinants for risk aversion, prudence and temperance in each domain. To do this, we conducted a lab experiment with students eliciting risk aversion, prudence and temperance in the two domains, and collected information about each subject's characteristics.     

Forecasting unknown-unknowns by boosting the risk radar within the risk intelligent organisation

This theoretical perspective paper interprets (un)known-(un)known risk quadrants as being formed from both abstract and concrete risk knowledge. It shows that these quadrants are useful for categorising risk forecasting challenges against the levels of abstract and concrete risk knowledge that are typically available, as well as for measuring perceived levels of abstract and concrete risk knowledge available for forecasting in psychometric research. Drawing on cybersecurity risk examples, a case is made for refocusing risk management forecasting efforts towards changing unknown-unknowns into known-knowns. We propose that this be achieved by developing the ‘boosted risk radar’ as organisational practice, where suitably ‘risk intelligent’ managers gather ‘risk intelligence information’, such that the ‘risk intelligent organisation’ can purposefully co-develop both abstract and concrete risk forecasting knowledge. We also illustrate what this can entail in simple practical terms within organisations.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Pharmaceutical cost‐sharing systems and savings for healthcare systems from parallel trade

This paper studies the consequences of parallel trade in a two‐country model. It compares a coinsurance scheme (consumers pay a percentage of the drug price) and an indemnity insurance scheme (reimbursement is independent of the drug price) with respect to changes in copayments and public health expenditure. In the destination country, copayments for patients decrease to a larger extent under indemnity insurance, whereas reductions in public health expenditure occur only under coinsurance. In the source country, copayments increase less under coinsurance, whereas health expenditure is reduced more under indemnity insurance. In both countries, total expenditure under parallel trade is lower.     

Hedging and temporal permit issuances in cap-and-trade programs: The Market Stability Reserve under risk aversion

Cap-and-trade programs such as the European Union's Emissions Trading System (EU ETS) expose firms to considerable risks, to which the firms can respond with hedging. We develop an intertemporal stochastic equilibrium model to analyze the implications of hedging by risk-averse firms. We show that the resulting time-varying risk premium depends on the size of the permit bank. Applying the model to the EU ETS, we find that hedging can lead to a U-shaped price path, because prices initially fall due to negative risk premiums and then rise as the hedging demand declines. The Market Stability Reserve (MSR) reduces the permit bank and thus, increases the hedging value of the permits. This offers an explanation for the recent price hike, but also implies that prices may decline in the future due to more negative risk premiums. In addition, we find higher permit cancellations through the MSR than previous analyses, which do not account for hedging.     

What is happening in Paris? Airbnb,hotels, and the Parisian market: A case study

Airbnb has been the subject of heated discussions among tourism and hospitality scholars. In order to understand the true impact of P2Psharing on the tourism and hospitality landscape, it is important to understand that market in detail first. Therefore, in this paper, we explore the development of the Airbnb market over the last seven years in Paris, the most popular destination for Airbnb guests with more than 40,000 accommodation rentals. This research note is a summary of our findings on the Paris market. Our study shows different growth and seasonality patterns for Airbnb and hotels and dissimilarities in the geographical location of the offers. The findings indicate that the two products are not in direct competition and that their relationship might be more complex than previously thought. We trust that exploring the nature of the competitive relationship would be a valuable avenue for further investigation.     

政策不确定性、财政扩张与区域商业银行风险——基于中国地方官员变更和银行业的证据

文章利用2007-2017年我国93家区域商业银行的面板数据，并结合省级宏观经济数据和地方官员变更数据，实证考察了中国地方官员变更引起的经济政策不确定性对区域商业银行风险的重要影响。研究结果表明：（1）地方官员变更引起的政策不确定性增大了区域商业银行风险；（2）地方官员变更引起的政策不确定性，通过提高银行的资产收益率降低了商业银行风险；而不确定性时期的财政扩张，通过降低银行资产收益率增大了商业银行风险；并且财政扩张的负面影响大于政策不确定性的正面影响；（3）各省的市委书记发生职位更替引起的政策不确定性对区域商业银行风险的影响更大。文章研究为新时代背景下的金融供给侧结构性改革、政府宏观经济政策的制定以及财政体制与金融体制之间的联系提供参考。     

中国股市行业风险与宏观经济之间的风险传导机制

本文采用行业收益率溢出指数度量股市行业风险,并进一步研究中国股市行业风险与宏观经济的相互影响,同时引入股息率和利率两个中介渠道深入挖掘其传导机制。我们运用GARCH-in-Mean模型对股市行业风险和宏观经济变量之间的一阶矩和二阶矩相互关系同时进行分析,结果发现,股市行业风险和宏观经济变量之间水平值和波动率都存在双向影响,对外溢出效应较大的行业起主导作用。此外,股市行业风险对宏观经济变量的影响方面,股息率和利率均起到中介渠道作用;宏观经济变量对股市行业风险的影响方面,只是利率起到中介渠道作用。股市行业风险与宏观经济的传导效应在不同时期差异显著。本文研究结论有助于深刻理解金融与实体经济之间的风险传导机制,对防范系统性风险、防止金融和实体经济“风险共振”以及提升金融服务实体经济能力等具有参考意义。     

Change-point detection for expected shortfall in time series

Expected shortfall (ES) is a popular risk measure and plays an important role in risk and portfolio management. Recently, change-point detection of risk measures has been attracting much attention in finance. Based on the self-normalized CUSUM statistic in Fan, Glynn and Pelger (2018) and the Wild Binary Segmentation (WBS) algorithm in Fryzlewicz (2014), this paper proposes a variant WBS procedure to detect and estimate change points of ES in time series. The strengthened Schwarz information criterion is also introduced to determine the number of change points. Monte Carlo simulation studies are conducted to assess the finite-sample performance of our variant WBS procedure about ES in time series. An empirical application is given to illustrate the usefulness of our procedure.