scholarly journals Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Lu Zhang ◽  
Junbiao Zhang ◽  
Tao Xiong ◽  
Chiao Su
Keyword(s):  
Statistical Significance ◽  
Hybrid Approach ◽  
Futures Market ◽  
Futures Contracts ◽  
Support Vector ◽  
Futures Prices ◽  
Swarm Optimization ◽  
Modeling Process ◽  
Forecasting Performance ◽  
Daily Interval

This paper examines the interval forecasting of carbon futures prices in one of the most important carbon futures market. Specifically, the purpose of this study is to present a novel hybrid approach, which is composed of multioutput support vector regression (MSVR) and particle swarm optimization (PSO), in the task of forecasting the highest and lowest prices of carbon futures on the next trading day. Furthermore, we set out to investigate if considering some potential predictors, which have strong influence on carbon futures prices, in modeling process is useful for achieving better prediction performance. Aiming at testing its effectiveness, we benchmark the forecasting performance of our approach against four competitors. The daily interval prices of carbon futures contracts traded in the Intercontinental Futures Exchange from August 12, 2010, to November 13, 2014, are used as the experiment dataset. The statistical significance of the interval forecasts is examined. The proposed hybrid approach is found to demonstrate the higher forecasting performance relative to all other competitors. Our application offers practitioners a promising set of results with interval forecasting in carbon futures market.

scholarly journals Wheat Futures Prices Prediction in China: A Hybrid Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yunpeng Sun ◽  
Jin Guo ◽  
Shan Shan ◽  
Yousaf Ali Khan
Keyword(s):  
Artificial Intelligence ◽  
Statistical Significance ◽  
Arima Model ◽  
Hybrid Approach ◽  
Futures Market ◽  
Point Estimation ◽  
Futures Prices ◽  
Out Of Sample ◽  
Futures Price ◽  
Hidden Layer

Stocks markets play their financial roles of price shocks and hedging just when they are proficient. The imperative highlights of productive market are that one cannot make extraordinary profit from the stocks markets. This research investigates whether China wheat futures price can be predicted by employing artificial intelligence neural network. This would add to our knowledge whether wheat futures market is resourceful and would enable traders, sellers, and investors to improve cost-effective trading strategy. We utilize the traditional financial model to forecast the wheat futures price and acquire out of sample point estimates. We additionally assess the robustness of our outcomes by applying several alternative forecasting techniques such as artificial intelligence with one hidden layer and autoregressive integrated moving average (ARIMA) model. Furthermore, the statistical significance of our point estimation was further tested through the Mariano and Diebold test. Considering random walk forecast as the bench mark, we used a number of economic indicators, trader’s expectation towards futures prices, and lagged value of futures price of wheat in order to forecast the evaluation of wheat futures price. The computable significance of out of sample estimations recommends that our ANN with one hidden layer has the best anticipating presentation among all the models considered in this exploration and has the estimating power in foreseeing wheat futures returns. Furthermore, this investigation discovers that the futures price of wheat can be predicted, and the wheat futures market of China is not productive.

A tale of two coffees? Analysing interaction and futures market efficiency

2020 ◽  
Vol 37 (1) ◽  
pp. 89-109
Author(s):  
Mark J. Holmes ◽  
Jesús Otero
Keyword(s):  
Market Efficiency ◽  
Unit Root ◽  
Futures Market ◽  
Recursive Estimation ◽  
Futures Contracts ◽  
Futures Prices ◽  
Content Type ◽  
Spot Prices ◽  
Unit Root Testing ◽  
Price Differentials

Purpose The purpose of this paper is to assess the informational efficiency of Arabica (other milds) and Robusta coffee futures markets in terms of predicting future coffee spot prices. Design/methodology/approach Futures market efficiency is associated with the existence of a long-run equilibrium relationship between spot and future prices such that coffee futures prices are unbiased predictors of future spot prices. This study applies unit root testing to daily data for futures-spot price differentials. A range of maturities for futures contracts are considered, and the study also uses a recursive approach to consider time variation in futures market efficiency. Findings The other milds and Robusta futures prices tend to be unbiased predictors for their own respective spot prices. The paper further finds that other milds and Robusta futures prices are unbiased predictors of the respective Robusta and other milds spot prices. Recursive estimation suggests that the futures market efficiency associated with these cross cases has increased, though with no clear link to the implementation of the 2007 International Coffee Agreement. Originality/value The paper draws new insights into futures market efficiency by examining the two key types of coffee and analyses the potential interactions between them. Hitherto, no attention has been paid to futures contracts of the Robusta variety. The employment of unit root testing of spot futures coffee price differentials can be viewed as more stringent than an approach based on non-cointegration testing.

scholarly journals Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 6
Author(s):  
Marcin Fałdziński ◽  
Piotr Fiszeder ◽  
Witold Orzeszko
Keyword(s):  
Support Vector Regression ◽  
Absolute Error ◽  
Futures Contracts ◽  
Support Vector ◽  
Garch Models ◽  
Forecasting Performance ◽  
Asymmetric Garch ◽  
Heating Oil ◽  
Forecasting Volatility ◽  
Forecasting Errors

We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one of machine learning methods, which have gained attention and interest in recent years. We show that the accuracy of volatility forecasts depends substantially on the applied proxy of volatility. Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the GARCH models when the squared daily return is used as the proxy of volatility in an evaluation. Meanwhile, if we apply the Parkinson estimator which is a more accurate approximation of volatility, the results usually favor the GARCH models. Moreover, it is difficult to choose the best model among the GARCH models for all analyzed commodities, however, forecasts based on the asymmetric GARCH models are often the most accurate. While, in the class of the SVR models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases.

scholarly journals Analysis of Capital Flow in Commodity Futures Market Based on SVM

2018 ◽  
Vol 10 (8) ◽  
pp. 28
Author(s):  
Zi-ang Lin ◽  
Shaozhen Chen ◽  
Hongtao Liang ◽  
Hong Zhang
Keyword(s):  
Time Window ◽  
Principal Component ◽  
Futures Market ◽  
Capital Flow ◽  
Kernel Functions ◽  
Commodity Futures ◽  
Futures Contracts ◽  
Support Vector ◽  
Analysis Method

Commodity futures are futures contracts based on the physical commodities. Unlike commodity stocks, which must be “bought first and then sold”, commodity futures can also be “sold first and then bought”. Therefore, it is not possible to directly use the formula of capital flow in the stock market to characterize the capital flow in futures contracts. In this paper, the principal component analysis method is used to construct the principal component factors based on the K-line basic market data and one based on the K-line index data. Then the factors mentioned above are cross-validated using the Holdout verification form to generate the training set and test of the support vector machine. Then, this paper applies genetic algorithm to optimize the penalty parameters and kernel functions of SVM, and obtains the parameters with the highest accuracy of classification and prediction of capital flow. Finally, this paper uses the traversal algorithm to find the time window with the highest accuracy of the SVM classification to predict the capital flow. The research results of this paper show that the SVM-based classification of capital flow in commodity futures market is highly accurate.

scholarly journals Forecasting Performance Of Natural Gas Futures Market: An Assessment Of Recent Data

2011 ◽  
Vol 4 (11) ◽  
Author(s):  
Salah Abosedra ◽  
Khaled Elkhal ◽  
Faisal Al-Khateeb
Keyword(s):  
Natural Gas ◽  
Futures Market ◽  
Policy Implications ◽  
Energy Market ◽  
Futures Prices ◽  
Font Size ◽  
Spot Prices ◽  
Forecasting Performance ◽  
Futures Price ◽  
The U.S

<p class="MsoNormal" style="text-align: justify; margin: 0in 34.2pt 0pt 1in;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">Natural gas has assumed increasing importance in the global energy market. This study evaluates the forecasting performance of futures prices of natural gas in the large market of the U.S. at various time horizons. The results indicate that futures prices are unbiased predictors at the 1-, 6-, and 12- month horizons, but not at the 3- and 9- month horizons. The results further suggest that futures prices of natural gas, although biased at some intervals, significantly outperform na&iuml;ve forecasts in predicting future movements of spot prices. In addition, the information content of the 1-month ahead futures price proves especially useful as a forecasting device. Policy implications are also discussed.<span style="mso-bidi-font-style: italic; mso-bidi-font-weight: bold;"></span></span></span></p>

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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