Can energy prices predict stock returns? An extreme bounds analysis

We assess the predictive abilities of energy prices for future US stock market returns using Sala-i-Martin's (1997) extreme bounds analysis (EBA). The EBA results reveal that the predictive power of energy prices varies substantially across the regression models with different combinations of conditioning variables. Energy prices are not robust predictors for the stock returns in the whole sample period from June 1987 to April 2015. However, before the 2008 global financial crisis, energy prices exerted a moderate negative effect on future stock returns and their effects have become strongly positive afterwards. In general, the predictive power declines with the increase in forecast horizon and it varies considerably over time.     

随机限制市场中不定权益的对冲成本

本文是2004年陈典发工作([1])的继续,我们研究随机限制市场中欧式不定权益的对冲问题,获得了它们的上对冲成本以及对冲交易策略的风险溢价表达式。     

面向行业应用的软件开发检查工具

现有的自动化测试和检查工具能够解决一些常规的测试和检查问题,但是对于行业应用软件开发过程的某些个性化测试和检查要求,没有提供很好的支持.因此针对行业应用软件的个性化要求,提出了一种面向行业应用的软件开发检查工具,在通用测试和检查工具的基础上,从代码检查、数据结构检查、接口检查和日志检查4个方面,对系统进行进一步的测试和检查.实验结果表明,该工具能有效地提高软件系统的开发效率和正确性,降低软件系统的开发成本.     

Compact yet efficient hardware implementation of artificial neural networks with customized topology

There are several neural network implementations using either software, hardware-based or a hardware/software co-design. This work proposes a hardware architecture to implement an artificial neural network (ANN), whose topology is the multilayer perceptron (MLP). In this paper, we explore the parallelism of neural networks and allow on-the-fly changes of the number of inputs, number of layers and number of neurons per layer of the net. This reconfigurability characteristic permits that any application of ANNs may be implemented using the proposed hardware. In order to reduce the processing time that is spent in arithmetic computation, a real number is represented using a fraction of integers. In this way, the arithmetic is limited to integer operations, performed by fast combinational circuits. A simple state machine is required to control sums and products of fractions. Sigmoid is used as the activation function in the proposed implementation. It is approximated by polynomials, whose underlying computation requires only sums and products. A theorem is introduced and proven so as to cover the arithmetic strategy of the computation of the activation function. Thus, the arithmetic circuitry used to implement the neuron weighted sum is reused for computing the sigmoid. This resource sharing decreased drastically the total area of the system. After modeling and simulation for functionality validation, the proposed architecture synthesized using reconfigurable hardware. The results are promising.     

Enhanced default risk models with SVM+

Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM.     

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.     

Modeling spot price dependence in Australian electricity markets with applications to risk management

We examine the dependence structure of electricity spot prices across regional markets in Australia. One of the major objectives in establishing a national electricity market was to provide a nationally integrated and efficient electricity market, limiting market power of generators in the separate regional markets. Our analysis is based on a GARCH approach to model the marginal price series in the considered regions in combination with copulae to capture the dependence structure between the marginals. We apply different copula models including Archimedean, elliptical and copula mixture models. We find a positive dependence structure between the prices for all considered markets, while the strongest dependence is exhibited between markets that are connected via interconnector transmission lines. Regarding the nature of dependence, the Student-t copula provides a good fit to the data, while the overall best results are obtained using copula mixture models due to their ability to also capture asymmetric dependence in the tails of the distribution. Interestingly, our results also suggest that for the four major markets, NSW, QLD, SA and VIC, the degree of dependence has decreased starting from the year 2008 towards the end of the sample period in 2010. Examining the Value-at-Risk of stylized portfolios constructed from electricity spot contracts in different markets, we find that the Student-t and mixture copula models outperform the Gaussian copula in a backtesting study. Our results are important for risk management and hedging decisions of market participants, in particular for those operating in several regional markets simultaneously.     

Energy-saving generation dispatch toward a sustainable electric power industry in China

Energy shortages, climate change and environmental pollution are critical issues that the entire world is faced with currently. To tackle the challenge and realize sustainable development, the Chinese government launched the Energy-Saving Generation Dispatch (ESGD) in 2007. In the ESGD scheme, generating units are dispatched based on fuel consumption rates and pollutant emission intensities from low to high. However, annual generation quotas still widely exist. With the mandatory shutdown of small-capacity and low-efficiency thermal generating units in 2006–2010, most of the currently running thermal generating units are large-capacity and highly efficient units. The additional improvement of the overall energy efficiency under this situation is a key problem for the Chinese electric power industry. To this end, a new type of ESGD framework is designed in this paper. Sequential coordination among yearly, monthly, day-ahead and real-time generation schedules is proposed. Based on the framework, the corresponding models are formulated. Empirical analysis is conducted using the realistic data obtained from the Guangdong Power Grid Corporation. Four generation dispatch modes are compared. The results indicate that the proposed ESGD mode can further reduce energy consumption and pollutant emissions. Hopefully, this paper can provide a valuable reference for policy making in the Chinese power sector.     

Energy trade efficiency and its determinants: A Malmquist index approach

This paper adopts the Malmquist index approach to investigate multi-product energy trade efficiency and its determinants from an empirical perspective. Using trade statistics of coal, oil and gas of 40 countries over the period of 1995 to 2008, we found that the efficiency of bilateral energy trade ranged between 0.26 and 0.35 when imperfect substitution between different energy products is taken into account. This measure is significantly lower than those obtained from traditional gravity models. It suggests that the ability of cross-product substitution affects trade efficiency improvement which results from regional market integration and related trade policy. The results provide useful insights on predicting the pattern of future energy trade and hence have important implications for relevant countries to prioritize product-specific trade policies.     

Predictability of price movements in deregulated electricity markets

In this paper we investigate predictability of electricity prices in the Canadian provinces of Alberta and Ontario, as well as in the US Mid-C market. Using scale-dependent detrended fluctuation analysis, spectral analysis, and the probability distribution analysis we show that the studied markets exhibit strongly anti-persistent properties suggesting that their dynamics can be predicted based on historic price records across the range of time scales from 1 h to one month. For both Canadian markets, the price movements reveal three types of correlated behavior which can be used for forecasting. The discovered scenarios remain the same on different time scales up to one month as well as for on- and off-peak electricity data. These scenarios represent sharp increases of prices and are not present in the Mid-C market due to its lower volatility. We argue that extreme price movements in this market should follow the same tendency as the more volatile Canadian markets. The estimated values of the Pareto indices suggest that the prediction of these events can be statistically stable. The results obtained provide new relevant information for managing financial risks associated with the dynamics of electricity derivatives over time frame exceeding one day.