Search results

1 – 10 of over 27000
To view the access options for this content please click here
Article
Publication date: 29 July 2014

Wen-Tsao Pan

When facing a clouded global economy, many countries would increase their gold reserves. On the other hand, oil supply and demand depends on the political and economic…

Abstract

Purpose

When facing a clouded global economy, many countries would increase their gold reserves. On the other hand, oil supply and demand depends on the political and economic situations of oil producing countries and their production technologies. Both oil and gold reserve play important roles in the economic development of a country. The paper aims to discuss this issue.

Design/methodology/approach

This paper uses the historical data of oil and gold prices as research data, and uses the historical price tendency charts of oil and gold, as well as cluster analysis, to discuss the correlation between the historical data of oil and gold prices. By referring to the technical index equation of stocks, the technical indices of oil and gold prices are calculated as the independent variable and the closing price as the dependent variable of the forecasting model.

Findings

The findings indicate that there is no obvious correlation between the price tendencies of oil and gold. According to five evaluating indicators, the MFOAGRNN forecast model has better forecast ability than the other three forecasting models.

Originality/value

This paper explored the correlation between oil and gold prices, and built oil and gold prices forecasting models. In addition, this paper proposes a modified FOA (MFOA), where an escape parameter Δ is added to Si. The findings showed that the forecasting model that combines MFOA and GRNN has the best ability to forecast the closing price of oil and gold.

Details

Kybernetes, vol. 43 no. 7
Type: Research Article
ISSN: 0368-492X

Keywords

To view the access options for this content please click here
Article
Publication date: 21 September 2020

Christof Naumzik and Stefan Feuerriegel

Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they…

Abstract

Purpose

Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, the purpose of this paper is to provide accurate forecasts..

Design/methodology/approach

This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, this paper implements a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis.

Findings

This study disentangles the respective relevance of each predictor. This study shows that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior.

Research limitations/implications

The performance gain from including more predictors might be larger than from a better model. Future research should place attention on expanding the data basis in electricity price forecasting.

Practical implications

When developing pricing models, practitioners can achieve reasonable performance with a simple model (e.g. seasonal-autoregressive moving-average) that is built upon a wide range of predictors.

Originality/value

The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.

Details

International Journal of Energy Sector Management, vol. 15 no. 1
Type: Research Article
ISSN: 1750-6220

Keywords

To view the access options for this content please click here

Abstract

Details

Modern Management in the Global Mining Industry
Type: Book
ISBN: 978-1-78973-788-2

To view the access options for this content please click here
Book part
Publication date: 12 November 2014

Tiziana Assenza, Te Bao, Cars Hommes and Domenico Massaro

Expectations play a crucial role in finance, macroeconomics, monetary economics, and fiscal policy. In the last decade a rapidly increasing number of laboratory…

Abstract

Expectations play a crucial role in finance, macroeconomics, monetary economics, and fiscal policy. In the last decade a rapidly increasing number of laboratory experiments have been performed to study individual expectation formation, the interactions of individual forecasting rules, and the aggregate macro behavior they co-create. The aim of this article is to provide a comprehensive literature survey on laboratory experiments on expectations in macroeconomics and finance. In particular, we discuss the extent to which expectations are rational or may be described by simple forecasting heuristics, at the individual as well as the aggregate level.

Details

Experiments in Macroeconomics
Type: Book
ISBN: 978-1-78441-195-4

Keywords

To view the access options for this content please click here
Article
Publication date: 26 March 2020

Luyao Wang, Jianying Feng, Xiaojie Sui, Xiaoquan Chu and Weisong Mu

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

Abstract

Purpose

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

Design/methodology/approach

This paper reviews the main research methods and their application of forecasting of agricultural product prices, summarizes the application examples of common forecasting methods, and prospects the future research directions.

Findings

1) It is the trend to use hybrid models to predict agricultural products prices in the future research; 2) the application of the prediction model based on price influencing factors should be further expanded in the future research; 3) the performance of the model should be evaluated based on DS rather than just error-based metrics in the future research; 4) seasonal adjustment models can be applied to the difficult seasonal forecasting tasks in the agriculture product prices in the future research; 5) hybrid optimization algorithm can be used to improve the prediction performance of the model in the future research.

Originality/value

The methods from this paper can provide reference for researchers, and the research trends proposed at the end of this paper can provide solutions or new research directions for relevant researchers.

To view the access options for this content please click here
Article
Publication date: 21 November 2019

Xiaoquan Chu, Yue Li, Dong Tian, Jianying Feng and Weisong Mu

The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the…

Abstract

Purpose

The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price prediction issue of China’s table grape.

Design/methodology/approach

The approaches follows the framework of “decomposition and ensemble,” using ensemble empirical mode decomposition (EEMD) to optimize the conventional price forecasting methods, and, integrating the multiple linear regression and support vector machine to build a hybrid model which could be applied in solving price series predicting problems.

Findings

The proposed EEMD-ADD optimized hybrid model is validated to be considered satisfactory in a case of China’ grape price forecasting in terms of its statistical measures and prediction performance.

Practical implications

This study would resolve the difficulties in grape price forecasting and provides an adaptive strategy for other agricultural economic predicting problems as well.

Originality/value

The paper fills the vacancy of concerning researches, proposes an optimized hybrid model integrating both classical econometric and artificial intelligence models to forecast price using time series method.

To view the access options for this content please click here
Article
Publication date: 13 November 2017

Anupam Dutta

While numerous empirical studies have tried to model and forecast the oil price volatility over the years, such attempts using the crude oil volatility index (OVX) rarely…

Abstract

Purpose

While numerous empirical studies have tried to model and forecast the oil price volatility over the years, such attempts using the crude oil volatility index (OVX) rarely exist. In order to conceal this void, the purpose of this paper is to investigate whether including OVX in the realized volatility (RV) models improve the accuracy of predictions.

Design/methodology/approach

At the empirical stage, the authors employ several measures to frame the RV of crude oil futures returns. In particular, the authors use three different range-based RV estimators recommended by Parkinson (1980), Rogers and Satchell (1991) and Alizadeh et al. (2002), respectively.

Findings

The findings reveal that the information content of crude OVX helps to provide more accurate volatility predictions in comparison to the base-line RV model which contains only historical oil volatilities. Besides, the forecast encompassing test further suggests that the modified RV model (when OVX is introduced in the base-line RV model) forecast encompasses the conventional RV forecast in majority of the cases.

Practical implications

Since forecasting oil price volatility plays a vital role in portfolio optimization, derivatives pricing, optimum asset allocation decisions and risk management, the findings of this study thus carry important implications for energy economists, investors and policymakers.

Originality/value

This paper adds to the existing literature, since it is one of the initial studies to explore whether OVX is informative about the realized variance of the US oil market returns. The findings recommend that the information content of oil implied volatilities should be taken into account when modeling the US oil market volatility. In addition, range-based measures should be utilized while estimating the RV.

Details

Journal of Economic Studies, vol. 44 no. 6
Type: Research Article
ISSN: 0144-3585

Keywords

To view the access options for this content please click here
Article
Publication date: 5 May 2015

Ling T. He

The purpose of this paper is to create an endurance index of housing investor sentiment and use it to forecast housing stock returns. This study performs not only…

Abstract

Purpose

The purpose of this paper is to create an endurance index of housing investor sentiment and use it to forecast housing stock returns. This study performs not only in-sample and out-of-sample forecasting, like many previous studies did, but also a true forecasting by using all lag terms of independent variables. In addition, an evaluation procedure is applied to quantify the quality of forecasts.

Design/methodology/approach

Using a binomial probability distribution model, this paper creates an endurance index of housing investor sentiment. The index reflects the probability of the high or low stock price being the close price for the Philadelphia Stock Exchange Housing Sector Index. This housing investor sentiment endurance index directly uses housing stock price differentials to measure housing investor reactions to all relevant news. Empirical results in this study suggest that the index can not only play a significant role in explaining variations in housing stock returns but also have decent forecasting ability.

Findings

Results of this study reveal the considerable forecasting ability of the index. Monthly forecasts of housing stock returns have an overall accuracy of 51 per cent, while the overall accuracy of 8-quarter rolling forecasts even reaches 84 per cent. In addition, the index has decent forecasting ability on changes in housing prices as suggested by the strong evidence of one-direction causal relations running from the endurance index to housing prices. However, extreme volatility of housing stock returns may impair the forecasting quality.

Practical implications

The endurance index of housing investor sentiment is easy to construct and use for forecasting housing stock returns. The demonstrated predictability of the index on housing stock returns in this study can have broad implications on housing-related business practices through providing an effective forecasting tool to investors and analysts of housing stocks, as well as housing policy-makers.

Originality/value

Despite different investor sentiment proxies suggested in the previous studies, few of them can effectively predict stock returns, due to some embedded limitations. Many increases and decreases inn prices cancel out each other during the trading day, as many unreliable sentiments cancel out each other. This dynamic process reveals not only investor sentiment but also resilience or endurance of sentiment. It is only long-lasting resilient sentiment that can be built in the closing price. It means that the only feasible way to use investor sentiment contained in stock prices to forecast future stock prices is to detach resilient investor sentiment from stock prices and construct an index of endurance of investor sentiment.

Details

Journal of Financial Economic Policy, vol. 7 no. 2
Type: Research Article
ISSN: 1757-6385

Keywords

To view the access options for this content please click here
Article
Publication date: 1 January 1986

ROLAND HERRMANN

Price stabilization in international commodity markets is a main element of the North‐South dialogue. Within the Integrated Programme on Commodities (IPC) of UNCTAD, it is…

Abstract

Price stabilization in international commodity markets is a main element of the North‐South dialogue. Within the Integrated Programme on Commodities (IPC) of UNCTAD, it is intended to create buffer stocks for 10 core commodities: sugar, natural rubber, cocoa, coffee, tea, cotton, jute, hard fibres, copper, and tin. Several theoretical studies justify these plans by stressing the positive effects of a functioning buffer stock scheme on different economic goals. It is argued that price stabilization will, “potentially at least, improve aggregate welfare” (Turnovsky, 1978, p. 143) and that risk benefits in the case of risk‐averse producers “will be far more important” (Bigman, 1982, p. 1984; on the concept, see Newbery/Stiglitz, 1981, pp. 267 et seq.) than the transfer benefits, if income uncertainty is reduced by the stabilization policy. Other positive effects of buffer stocks are stressed with respect to food security (Bignan, 1982, pp. 129 et seq.) and, except for the case of supply‐induced fluctuations and a price elastic import demand, with respect to the stability of export earnings (Nguyen, 1980, pp. 343 et seq.). The export earnings stabilizing effect as well as a mostly earnings‐raising effect is confirmed for several core commodities by simulation analyses (Behrman/Ramangkura, 1978, p. 166) and by dynamic optimization (Lee/Blandford, 1980, p. 385). Moreover, stable export earnings of less developed countries (LDCs) are expected to induce higher growth rates of GNP than unstable ones (Lim, 1976, pp. 311 et seq.).

Details

Studies in Economics and Finance, vol. 10 no. 1
Type: Research Article
ISSN: 1086-7376

To view the access options for this content please click here
Article
Publication date: 5 November 2018

Marc Gürtler and Thomas Paulsen

Study conditions of empirical publications on time series modeling and forecasting of electricity prices vary widely, making it difficult to generalize results. The key…

Abstract

Purpose

Study conditions of empirical publications on time series modeling and forecasting of electricity prices vary widely, making it difficult to generalize results. The key purpose of the present study is to offer a comparison of different model types and modeling conditions regarding their forecasting performance.

Design/methodology/approach

The authors analyze the forecasting performance of AR (autoregressive), MA (moving average), ARMA (autoregressive moving average) and GARCH (generalized autoregressive moving average) models with and without the explanatory variables, that is, power consumption and power generation from wind and solar. Additionally, the authors vary the detailed model specifications (choice of lag-terms) and transformations (using differenced time series or log-prices) of data and, thereby, obtain individual results from various perspectives. All analyses are conducted on rolling calibrating and testing time horizons between 2010 and 2014 on the German/Austrian electricity spot market.

Findings

The main result is that the best forecasts are generated by ARMAX models after spike preprocessing and differencing the data.

Originality/value

The present study extends the existing literature on electricity price forecasting by conducting a comprehensive analysis of the forecasting performance of different time series models under varying market conditions. The results of this study, in general, support the decision-making of electricity spot price modelers or forecasting tools regarding the choice of data transformation, segmentation and the specific model selection.

Details

International Journal of Energy Sector Management, vol. 12 no. 4
Type: Research Article
ISSN: 1750-6220

Keywords

1 – 10 of over 27000