Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model

Accurate probabilistic forecasting of wind power output is critical to maximizing network integration of this clean energy source. There is a large literature on temporal modeling of wind power forecasting, but considerably less work combining spatial dependence into the forecasting framework. Through the careful consideration of the temporal modeling component, complemented by support vector regression of the temporal model residuals, this work demonstrates that a DVINE copula model most accurately represents the residual spatial dependence. Additionally, this work proposes a complete set of validation mechanisms for multi-h-step forecasts that, when considered together, comprehensively evaluate accuracy. The model and validation mechanisms are demonstrated in two case studies, totaling ten wind farms in the Texas electric grid. The proposed method outperforms baseline and competitive models, with an average Continuous Ranked Probability Score of less than 0.15 for individual farms, and an average Energy Score of less than 0.35 for multiple farms, over the 24-hour-ahead horizon. Results show the model’s ability to replicate the power output dynamics through calibrated and sharp predictive densities.     

The cost of capital and optimal financing policy in a dynamic setting

This paper revisits the Modigliani–Miller propositions on the optimal financing policy and cost of capital in a dynamic setting. In an environment without taxes and bankruptcy costs, the results are generally consistent with the Modigliani–Miller Propositions 1 and 2. However, the first proposition should be presented and interpreted more carefully, as given firm characteristics, there is only one optimal capital structure. Thus, a firm’s capital structure is relevant. A relaxation of assumptions about either taxes or bankruptcy costs leads to conclusions that are generally different from those in Modigliani and Miller (1958). The model predicts that leverage and sales-to-capital ratios decrease but firm size and capital stock increase with the subjective discount factor of the firm’s manager if there are taxes and bankruptcy costs. The empirical analysis supports these predictions.     

Destabilizing role of futures markets on North American hard red spring wheat spot prices

The objective of the paper is to determine if the futures prices of hard red spring wheat (HRSW) have stabilizing or destabilizing impact on spot HRSW price in North America. Several important results emerge from thorough empirical analysis. First, both Granger causality tests and directed acyclic graph algorithms (DAGs) point to two-way causality between futures and spot HRSW prices and thus endogeneity in both prices formation. To the contrary, both procedures suggest that ending stocks are exogenous to spot and futures HRSW prices. Both vector error correction model and impulse response functions point to a large and long-lasting impact of a shock to futures price on spot price level. Finally, variance decomposition analysis indicates that futures prices are responsible for the bulk of spot price volatility in both short and long run. Our result is consistent with those of theoretical models suggesting that when production (supply side) is the dominant disturbance, spot price is destabilized in both the short and the long run by futures prices. An important implication of this research is the need for alternative market mechanisms or alternative farm policy measures that would mitigate price risk and ensure sustainable farming of American HRSW farmers.     

A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth

This paper estimates a three-frequency dynamic factor model for nowcasting the Canadian provincial gross domestic product (GDP). The Canadian provincial GDP at market prices is released by Statistics Canada annually with a significant lag (11 months). This necessitates a mixed-frequency approach that can process timely monthly data, the quarterly national accounts, and the annual target variable. The model is estimated on a wide set of provincial, national and international data. In a pseudo real-time exercise, we find that the model outperforms simple benchmarks and is competitive with more sophisticated mixed-frequency approaches (MIDAS models). We also find that variables from the Labour Force Survey are important predictors of real activity. This paper expands previous work that has documented the importance of foreign variables for nowcasting Canadian GDP. This paper finds that including national and foreign predictors is useful for Ontario, while worsening the nowcast performance for smaller provinces.     

Feelings of ownership and volunteering: Examining psychological ownership as a volunteering motivation for nonprofit service organisations

Given many nonprofit service organisations rely on volunteers to provide valued services to their communities, examining the factors that impact the retention and recruitment of volunteers is of central importance for the success and longevity of many community-based services. Psychological ownership is a phenomenon whereby objects that are not physically or legally possessed can be the target of ownership feelings. While such psychological ownership has been examined in the paid-employment literature as an antecedent for prosocial behaviours, there has been no examination of psychological ownership within the volunteering literature. As such, this paper examines the role of psychological ownership as a factor in volunteer retention for predominantly community-based nonprofit service organisations by examining how the sense of ownership over the nonprofit service provider affects volunteering attitudes and intentions. Alongside ownership, previous volunteering behaviours, and time pressures are also examined as contributing factors. Results indicate that volunteering does increase ownership perceptions, and those ownership perceptions have positive outcomes for volunteering behaviours. However, time pressure is a significant moderator of these relationships and different volunteering behaviours can be observed for high and low time-pressured volunteers.     

Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

The forecast of the real estate market is an important part of studying the Chinese economic market. Most existing methods have strict requirements on input variables and are complex in parameter estimation. To obtain better prediction results, a modified Holt's exponential smoothing (MHES) method was proposed to predict the housing price by using historical data. Unlike the traditional exponential smoothing models, MHES sets different weights on historical data and the smoothing parameters depend on the sample size. Meanwhile, the proposed MHES incorporates the whale optimization algorithm (WOA) to obtain the optimal parameters. Housing price data from Kunming, Changchun, Xuzhou and Handan were used to test the performance of the model. The housing prices results of four cities indicate that the proposed method has a smaller prediction error and shorter computation time than that of other traditional models. Therefore, WOA-MHES can be applied efficiently to housing price forecasting and can be a reliable tool for market investors and policy makers.     

Modelling the iron ore price index: A new perspective from a hybrid data reconstructed EEMD-GORU model

As iron ore is the fundamental steel production resource, predicting its price is strategically important for risk management at related enterprises and projects. Based on a signal decomposition technology and an artificial neural network, this paper proposes a hybrid EEMD-GORU model and a novel data reconstruction method to explore the price risk and fluctuation correlations between China’s iron ore futures and spot markets, and to forecast the price index series of China’s and international iron ore spot markets from the futures market. The analysis found that the iron ore futures market in China better reflected the price fluctuations and risk factors in the imported and international iron ore spot markets. However, the forward price in China’s iron ore futures market was unable to adequately reflect the changes in the domestic iron ore market, and was therefore unable to fully disseminate domestic iron ore market information. The proposed model was found to provide better market risk perceptions and predictions through its combinations of the different volatility information in futures and spot markets. The results are valuable references for the early-warning and management of the related enterprise project risks.     

基于模糊时间序列的预测模型——以上证指数为例

模糊理论使用语义变量本身所蕴含的特性,能减少处理问题时的不确定性所带来的困扰,被广泛的应用于各种领域的研究。首先回顾了基于模糊理论的模糊时间序列定义,对现有的模糊时间序列模型进行分析;在此基础上提出了一种新的模糊时间序列预测方法,以上证指数为对象进行了拟合。从结果看,新的基于模糊时间序列预测方法在MSN、平均误差(%)和标准误差(%)等指标上要优于现有的的预测方法。     

我国货币政策对房价作用时滞的实证分析

本文应用向量自回归模型的脉冲响应函数和预测方差分解的方法．对我国货币政策影响房价的外部时滞进行分析。利用2004年1月至2008年6月的月度数据测算出的结果是：信贷规模、狭义货币供应量和利率对房价的作用时滞分别为7个月、3个月和11个月。信贷规模和利率水平度对房价的冲击效应较小。而货币供应量对房价的冲击效应较为明显。央行在制定货币政策时应综合考虑货币工具的特点、作用时滞、经济形势和理性预期的影响。     

Profiling high-frequency equity price movements in directional changes

Market prices are traditionally sampled in fixed time intervals to form time series. Directional change (DC) is an alternative approach to record price movements. Instead of sampling at fixed intervals, DC is data driven: price changes dictate when a price is recorded. DC provides us with a complementary way to extract information from data. It allows us to observe features that may not be recognized in time series. The argument is that time series and DC-based analysis complement each other. With data sampled at irregular time intervals in DC, however, some of the time series indicators cannot be used in DC-based analysis. For example, returns must be time adjusted and volatility must be amended accordingly. A major objective of this paper is to introduce indicators for profiling markets under DC. We analyse empirical high-frequency data on major equities traded on the UK stock market, and through DC profiling extract information complementary to features observed through time series profiling.