Asset pricing and energy consumption risk

This paper proposes energy consumption in the US as a new measure for the consumption capital asset pricing model. We find that (i) industrial energy growth produces reasonable values for the relative risk aversion coefficient and the implied risk-free rate; (ii) compared to alternative consumption measures, industrial energy performs well in explaining the cross-sectional variation in stock returns with the lowest implied risk aversion and pricing errors; (iii) the industrial energy consumption risk model performs equally well as the Fama–French three-factor model in the cross-sectional asset pricing tests; and (iv) total energy consumption risk is priced in the presence of the Fama–French factor risks.     

Factor state–space models for high-dimensional realized covariance matrices of asset returns

We propose a dynamic factor state–space model for high-dimensional covariance matrices of asset returns. It makes use of observed risk factors and assumes that the latent integrated joint covariance matrix of the assets and the factors is observed through their realized covariance matrix with a Wishart measurement density. For the latent integrated covariance matrix of the assets we impose a strict factor structure allowing for dynamic variation in the covariance matrices of the factors and the residual components as well as in the factor loadings. This factor structure translates into a factorization of the Wishart measurement density which facilitates statistical inference based on simple Bayesian MCMC procedures making the approach scalable w.r.t. the number of assets. An empirical application to realized covariance matrices for 60 NYSE traded stocks using the Fama–French factors and sector-specific factors represented by Exchange Traded Funds (ETFs) shows that the model performs very well in- and out of sample.     

Economics of household food waste

Food waste has drawn increasing public attention, and the high levels of estimated waste are largely considered to be a failure of our current food system. Recently, economists have begun to weigh in, showing food waste can emerge as the result of a complex equilibrium affected by consumers’ preferences for convenience; expectations about future food prices and availability; food safety concerns; producers’ costs of holding inventory, transportation, and storage; government regulation; and technology. If food waste is a form of inefficiency, there are either strong economic motivations to reduce waste, or unmeasured costs or preferences affecting waste decisions. If consumers have behavioral biases, suffer from information asymmetries, or do not pay the full cost of their waste, there may be a role for government intervention to reduce waste, but most empirical models in the literature have not articulated or quantified the extent of the deadweight loss from the market failures in relation to food waste. In some cases, waste reduction efforts could harm producers if overall demand for food is reduced or harm consumers if overconsumption is encouraged, quality or safety degrades, or supply disruptions occur. Technological innovations, which lower the cost of storage or extend shelf life have the potential to improve both consumer and producer welfare.     

基于DEA-SBM模型的中国地板上市企业环境效率研究

考虑非期望产出,基于企业层面,根据2011-2016年数据,采用DEA-SBM模型与DEA-CCR模型对中国12家地板上市公司的环境效率与经济效率进行测算与比较。结果表明:2016年环境效率和经济效率测算结果显示不同地板上市企业运行效率存在差别;与经济效率相比,一些企业如北新建材、扬子地板,环境效率保持不变,一些企业如宜华生活、吉林森工和丰林集团,环境效率得到提升;还有一些企业如兔宝宝、升达林业和大自然家居等,环境效率则出现下降。考虑了非期望产出,中国地板上市企业整体环境效率均值低于经济效率均值,总体处于0.79~0.84之间。提出了政府应加强监管力度,地板上市企业应提高技术改进能力,建立完善社会责任信息披露机制的建议。     

Islamic and conventional portfolios optimization under investor sentiment states: Bayesian vs Markowitz portfolio analysis

This paper investigates the portfolio optimization under investor’s sentiment states of Hidden Markov model and over a different time horizon during the period 2004–2016. To compare the efficient portfolios of the Islamic and the conventional stock indexes, we have employed two approaches: the Bayesian and Markowitz mean-variance. Our findings reveal that the Bayesian efficient frontier of Islamic and conventional stock portfolios is affected by the investor’s sentiment state and the time horizon. Our findings also indicate that the investor’s sentiment regimes change the Islamic and the conventional optimal diversified portfolios.Moreover, the results show that the potential diversification benefits seem to be more important when using the Bayesian approach than when applying the Markowitz approach. This finding is valid for the bearish, depressed, bullish and calm states in Islamic stock markets. However, the diversification of potential portfolios is significant only for the bullish and the bubble states in the conventional financial markets.The findings of the study provided additional evidence for investors to exploit googling investor sentiment states to evaluate the portfolio performance and make an optimal portfolio allocation.     

Modelling distribution of aggregate expenditure on tourism

The aim of this article is to obtain a statistical distribution that describes the aggregate expenditure of tourists related to their length of stay at a given location. This distribution enables us to estimate two parameters simultaneously; one controls the length of stay and the other, the expense incurred. We propose two distribution models, for which closed-form expressions are obtained. Covariates are introduced in order to study the factors that affect the aggregate expenditure. The results obtained indicate that the models achieve a reasonably good fit, with and without covariates.     

Analysis of the effect of charging infrastructure design on electric taxi driving profiles: A case study approach on the example of Singapore

Replacing conventional vehicle taxis with electric vehicles would be an efficient measure to reduce greenhouse gas emissions. Due to the limited range and long charging times of current battery electric vehicles, it is of utmost importance to provide sufficient charging facilities. This article analyses the impact of the placement and charging power of charging stations on potential mileage and revenue of electric taxis on the example of Singapore. Therefore, we developed an agent-based electric taxi simulation model to investigate electric taxis’ driving profiles with respect to different vehicle types and charging infrastructure designs. This model is also capable of simulating conventional taxi driving profiles. The validation of these simulation results with real taxi data showed that the model is reproducing taxi driving profiles with high accuracy in great detail. We found out that electric taxis could reach the same mileage and revenue as conventional taxis if charging with a power of 160?kW is possible. Furthermore, we discovered that waiting times for available charging stations have a stronger effect on revenue than the length of detours to reach charging stations. Based on these findings, we concluded that it is more important to reduce waiting times by placing sufficient numbers of charging stations at each location before expanding the charging network by installing small numbers of charging stations at many locations.     

The effect of diplomatic representation on trade: A panel data analysis

This study addresses current gaps in the empirical literature regarding the effect of diplomatic representation on trade using a panel data set for 100 countries with 5‐year interval data from 1985 to 2005 and four‐digit level industry data. The results indicate that the effect of diplomatic representation on exports in differentiated goods is positive and significant and larger than on exports in homogeneous goods on average, but not statistically different from it. Furthermore, diplomatic representation only increases trade along the extensive margin and not along the intensive margin. The results indicate that diplomatic representation is effective in performing its function as a network search intermediary and that it is a useful policy tool to alleviate market failure.     

From disruptively digital to proudly analog: A holistic typology of digital transformation strategies

Recently booming academic interest in digital transformation aims to provide continuous support to managers in dealing with this important issue. However, as with all new fields, the scholarly literature is characterized by increased variability and diversity of topics covered, constructs used, and the relationships between them, resulting in an unclear and blurry understanding of the whole of digital transformation. Our article closes this gap by identifying distinctive digital transformation strategies in terms of two critical dimensions: usage of digital technologies and readiness of a business model for digital operation. The result is a typology of four generic digital transformation strategies that essentially differ in the primary motivation and target of transformation, leadership style, importance of skills like creativity and entrepreneurial spirit among employees, risks and challenges faced in the process, consequences of potential failure, and available tactics for improvement. By providing heuristics and a systematic basis for comparison of different strategies, it is our belief that the proposed framework will be useful to researchers and managers responsible for and interested in digital transformation.     

Steady state adjusting trends using a data-driven local polynomial regression

Economic variables usually follow a dynamic trend pattern. However, it is difficult to estimate this trend precisely as numerous economically- and statistically-based estimation methods exist. This contribution proposes a data-driven nonparametric trend that is local polynomial, to improve arbitrary trend estimations of commonly used methods concerning the selection of the smoothing parameter and the dependence structure. An iterative plug-in (IPI) algorithm determines the bandwidth endogenously and allows a theory-based interpretation of the length of growth processes. This length of the bandwidth reflects the lengths of the steady state periods. Consequently, the bandwidth identifies the time period of stable economic conditions and can detect economic changes. To demonstrate the power of this estimation approach, an extensive simulation study is performed. Furthermore, examples using US and UK GDP data along with a guide for the optimal choice of algorithms for empirical applications are provided. This proposed method yields new insights for growth dynamics, cyclical movements and their dependence.