Integration of regional electricity markets in Australia: A price convergence assessment

From an electricity market design perspective, it is relevant and practical to know which market structures allow for price convergence, and how long this takes to achieve. This study employs the Phillips and Sul (2007, 2009) methodology to test for the convergence of wholesale electricity prices across the Australian States. We identify a long-run, common price growth pattern that applies to a cluster formed by three Eastern States that share common market characteristics and limited physical interconnection. We also find another cluster with less competitive market structures that, although not interconnected, strongly converge towards their own trend. These findings confirm theoretical expectations while quantifying the rate of convergence. Finally, we also investigate the role that the carbon tax regime has played in the convergence process, with new empirical showing that the previous results are not affected, with the notable exception being the case of South Australia.     

Contribution of green labels in electricity retail markets to fostering renewable energy

In European countries, retailers are obliged to disclose the energy source and the related environmental impacts of their portfolio over the preceding year. The electricity supplied in the Dutch retail market is presented as renewable energy for 34%, but this relatively high share is for 69% based on certificates (Guarantees of Origin) which are imported from in particular Norway. The certificates are used to sell green electricity to consumers. The premium for green electricity which is actually paid by Dutch consumers is no more than a few percentages of the retail price. The low level of this premium is related to the abundant supply of certificates at low marginal costs from Norway. This also means that the premium for green electricity is too low to give an incentive for investments in new capacity. Hence, the current labelling system for renewable electricity is mainly valuable, besides being an instrument for tracking and tracing of renewable energy, as a marketing instrument for electricity retailers. The effectiveness of Guarantees of Origin as a policy instrument to foster renewable electricity sources is weak. This effectiveness can be raised by implementing restrictions on the international trade or the issuance of new certificates.     

江苏知识产权服务业发展环境与发展模式研究

作为现代服务业的一种新型业态,知识产权服务业对区域经济的转型升级有重要促进作用。从四个方面分析了江苏发展知识产权服务业的发展环境,在此基础上,从市场化角度提出了江苏知识产权服务业的发展模式,可为政府和相关管理部门提供决策依据。     

Asymmetric impacts of fundamentals on the natural gas futures volatility: An augmented GARCH approach

We investigated the determinants of daily volatility for natural gas nearby-month futures traded on the NYMEX within a GARCH framework augmented with market fundamentals. Consistent with the previous literature, we found that volatility is much higher on the natural gas and crude oil storage report announcement days, on Mondays and during winters. We also confirmed that high volatility is associated with divergence of storage levels and temperatures from seasonal norms. The asymmetric impact of storage levels on volatility across different seasons is empirically investigated and documented. The mainstream finding in the literature that lower storage levels result in higher volatility is valid only during winter. At other times, it is actually higher storage levels causing higher volatility. Also, time to maturity effect is present only in winters. Additionally, weather shocks have asymmetric impact on volatility depending on the sign of the shock. Finally, we found that augmentation with market fundamentals improves the out-of-sample forecast accuracy of standard GARCH models.     

Influential factors in crude oil price forecasting

This paper identifies factors that are influential in forecasting crude oil prices. We consider six categories of factors (supply, demand, financial market, commodities market, speculative, and geopolitical) and test their significance in the context of estimating various forecasting models. We find that the Least Absolute Shrinkage and Selection Operator (LASSO) regression method provides significant improvements in the forecasting accuracy of prices compared to alternative benchmarks. Relative to the no-change and futures-based models, LASSO forecasts at the 8-step ahead horizon yield significant reductions in Mean Squared Prediction Error (MSPE), with MSPE ratios of 0.873 and 0.898, respectively. We also document substantial improvements in forecasting performance of the factor-based model that employs only a subset of variables chosen by LASSO. Finally, the time-varying nature of the relationship between factors and oil prices is used to explain recent movements in crude oil prices.     

Credit Constraints and Price Expectations of Homeowners

This paper examines the effect of credit constraints on the sale price expectations of homeowners. We extend the results of Genesove and Mayer (1997) by using a sample of mover and non-mover families living in the Netherlands—a country without formal down-payment requirements. We find that homeowners who are more credit constrained expect to sell their house for a higher price. Homeowners already seem to compensate for credit constraints at the very first stages of the transaction process. These results imply that the findings of Genesove and Mayer (1997) are much more generally applicable than previously considered.     

Facebook and people's state self-esteem: The impact of the number of other users' Facebook friends

The present two experimental studies examined the extent to which upward and downward comparison processes on Facebook influence people's state self-esteem. Participants were exposed to mock-up Facebook profiles of female and male targets with many or few Facebook friends. Participant sex was also included in the experimental design. In Study 1, a 2 (number of Facebook friends) × 2 (profile owner sex) × 2 (participant sex) experimental design was employed. Unexpectedly, the manipulation had no significant impact on people's state self-esteem. In Study 2, it was examined whether there would be a curvilinear relationship between the number of other's Facebook friends and participant's self-esteem, in that exposure to others with many or few Facebook friends increases self-esteem compared to others with a moderate number of Facebook friends. Hence, Study 2 employed a 3 (number of Facebook friends) × 2 (profile owner sex) × 2 (participant sex) between-participants experimental design. However, there was neither a linear nor a curvilinear impact of the number of other's Facebook friends on participant's level of state self-esteem. Both studies also revealed that the effects were not more pronounced when participants were exposed to same-sex rather than other-sex Facebook profiles.     

Consumer valuation of the wearables: The case of smartwatches

Wearable devices indicate objects encompassing both mobile computing and fashion characteristics. Although the combination of the two characteristics is relatively new, consumers’ recognition of smartwatches, one type of wearable, is increasing. However, despite the heightened interest in smartwatches, sales are growing more slowly than expected. In order to comprehend this, we should understand potential consumers’ perceptions of smartwatches. This study explored how much potential consumers value various smartwatch attributes by examining their preference structure of the wearable. The preference structure was generated from a conjoint analysis including five smartwatch attributes: brand, price, standalone communication, display shape, and display size. We also compared findings by user group (current wristwatch users vs. non-users). Results showed that display shape and standalone communication are more critical factors influencing respondents’ smartwatch choices than brand and price for both types of users. Results also revealed that a curved display shape is most preferred.     

A comparative study of Artificial Intelligence methods for project duration forecasting

This paper presents five Artificial Intelligence (AI) methods to predict the final duration of a project. A methodology that involves Monte Carlo simulation, Principal Component Analysis and cross-validation is proposed and can be applied by academics and practitioners. The performance of the AI methods is assessed by means of a large and topologically diverse dataset and is benchmarked against the best performing Earned Value Management/Earned Schedule (EVM/ES) methods. The results show that the AI methods outperform the EVM/ES methods if the training and test sets are at least similar to one another. Additionally, the AI methods report excellent early and mid-stage forecasting results. A robustness experiment gradually increases the discrepancy between the training and test sets and demonstrates the limitations of the newly proposed AI methods.