Forecast on silver futures linked with structural breaks and day-of-the-week effect

Silver future is crucial to global financial markets. However, the existing literature rarely considers the impacts of structural breaks and day-of-the-week effect simultaneously on the volatility of silver future price. Based on heterogeneous autoregressive (HAR) theory, we establish six new type heterogeneous autoregressive (HAR) models by incorporating structural breaks and day-of-the-week effect to forecast the volatility. The empirical results indicate that new models’ accuracy is better than the original HAR model. We find that structural breaks and the day-of-the-week effect contain much forecasting information on silver forecasting. In addition, structural breaks have a positive effect on the silver futures’ volatility. Day-of-the-week effect has a significantly negative influence on silver futures’ price volatility, especially in the mid-term and the long-term. Our works is the first to combine the structural breaks and day-of-the-week effect to identify more market information. This paper provides a better forecasting method to predict silver future volatility.     

Comparative performances of measures of consumer and economic sentiment in forecasting consumption: a multi-country analysis

ABSTRACT

This paper seeks to compare the capabilities of assorted measures of consumer and economic sentiment in predicting the growth of household expenditure. An analysis of quarterly data on five European countries shows that for none of these can the model which incorporates the EU’s headline consumer confidence indicator be deemed to be significantly inferior to any of its seven rivals. However, the rankings of the sentiment variables are seen to be influenced by: the proportion of total spending by households that is devoted to durable goods; and the nature of the behaviour of consumption over the forecast interval.     

航空静电的防与放

本文对静电的形成原因进行简单讲述,并列举在民航客机中某些部位容易形成静电和该部位中静电的负作用,我们在工作中应如何预防静电的聚集和释放静电。     

Business strategies and the transition to low‐carbon cities

Cities are key drivers of global climate change, with the majority of greenhouse gas (GHG) emissions being tied to urban life. Local actions to mitigate and adapt to climate change are essential for stabilization of the global climate and can also help to address other urban ecological problems such as pollution, decreasing biodiversity, etc. Companies are important urban actors in the development of low‐carbon cities because they provide a multitude of goods and services to city populations and directly influence urban carbon dioxide (CO₂) emissions. This is a new area of research. While studies on corporate sustainability are numerous, there is little, if any, existing research that examines the role of companies in climate change adaptation and mitigation within specific urban areas. Urban ecologists also have not examined how corporate activity affects urban systems. Taking a multi‐disciplinary systems approach, we present a conceptual model of the role of companies in managing urban interactions with the climate system. We also present empirical findings illustrating how one company ‘partners’ with the city of Rotterdam to test electric vehicles as a pilot project for urban climate adaptation and mitigation. Copyright © 2010 John Wiley & Sons, Ltd and ERP Environment.     

Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data

We utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting.     

Design and development of inventory knowledge discovery system

Inventory management (IM) performance is affected by the forecasting accuracy of both demand and supply. In this paper, an inventory knowledge discovery system (IKDS) is designed and developed to forecast and acquire knowledge among variables for demand forecasting. In IKDS, the TREes PArroting Networks (TREPAN) algorithm is used to extract knowledge from trained networks in the form of decision trees which can be used to understand previously unknown relationships between the input variables so as to improve the forecasting performance for IM. The experimental results show that the forecasting accuracy using TREPAN is superior to traditional methods like moving average and autoregressive integrated moving average. In addition, the knowledge extracted from IKDS is represented in a comprehensible way and can be used to facilitate human decision-making.     

Electrification of a city bus network—An optimization model for cost-effective placing of charging infrastructure and battery sizing of fast-charging electric bus systems

The deployment of battery-powered electric bus systems within the public transportation sector plays an important role in increasing energy efficiency and abating emissions. Rising attention is given to bus systems using fast charging technology. This concept requires a comprehensive infrastructure to equip bus routes with charging stations. The combination of charging infrastructure and bus batteries needs a reliable energy supply to maintain a stable bus operation even under demanding conditions. An efficient layout of the charging infrastructure and an appropriate dimensioning of battery capacity are crucial to minimize the total cost of ownership and to enable an energetically feasible bus operation. In this work, the central issue of jointly optimizing the charging infrastructure and battery capacity is described by a capacitated set covering problem. A mixed-integer linear optimization model is developed to determine the minimum number and location of required charging stations for a bus network as well as the adequate battery capacity for each bus line. The bus energy consumption for each route segment is determined based on individual route, bus type, traffic, and other information. Different scenarios are examined in order to assess the influence of charging power, climate, and changing operating conditions. The findings reveal significant differences in terms of required infrastructure. Moreover, the results highlight a trade-off between battery capacity and charging infrastructure under different operational and infrastructure conditions. This paper addresses upcoming challenges for transport authorities during the electrification process of the bus fleets and sharpens the focus on infrastructural issues related to the fast charging concept.     

Cointegration as an explanation for the Meese–Rogoff puzzle

Some economists suggest that the failure of exchange-rate models to outperform the random walk in exchange rate forecasting out of sample can be attributed to failure to take into account cointegration when it is present. We attempt to find out if cointegration matters for forecasting accuracy by examining the relation between the stationarity and size of the forecasting error. Results based on three macroeconomic models of exchange rates do not provide strong support for the proposition that cointegration matters for forecasting accuracy. The simulation results show that while stationary errors tend to be smaller than non-stationary errors, this is not a universal rule. Irrespective of the presence or absence of cointegration, none of the three models can outperform the random walk in out-of-sample forecasting, which means that cointegration cannot solve the Meese–Rogoff puzzle.     

Looking into the black box of boosting: the case of Germany

This article looks into the ‘fine print’ of boosting for economic forecasting. By using German industrial production for the period from 1996 to 2014 and a data set consisting of 175 monthly indicators, we evaluate which indicators get selected by the boosting algorithm over time and four different forecasting horizons. It turns out that a number of hard indicators like turnovers, as well as a small number of survey results, get selected frequently by the algorithm and are therefore important to forecasting the performance of the German economy. However, there are indicators such as money supply that never get chosen by the boosting approach at all.     

A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting

Air transport demand forecasting is receiving increasing attention, especially because of intrinsic difficulties and practical applications. Total passengers are used as a proxy for air transport demand. However, the air passenger time series usually has a complex behavior due to their irregularity, high volatility and seasonality. This paper proposes a new hybrid approach, combining singular spectrum analysis (SSA), adaptive-network-based fuzzy inference system (ANFIS) and improved particle swarm optimization (IPSO), for short-term air passenger traffic prediction. The SSA is used for identifying and extracting the trend and seasonality of air transport demand and the artificial intelligence technologies, including ANFIS and IPSO, are utilized to deal with the irregularity and volatility of the demand. The HK air passenger data are collected to establish and validate the forecasting model. Empirical results clearly points to the enormous potential that the proposed approach possesses in air transport demand forecasting and can be considered as a viable alternative.