Improving forecasting accuracy of crude oil price using decomposition ensemble model with reconstruction of IMFs based on ARIMA model

The accuracy of crude oil price forecasting is more important especially for economic development and considered as the lifeblood of the industry. Hence, in this paper, a decomposition-ensemble model with the reconstruction of intrinsic mode functions (IMFs) is proposed for forecasting the crude oil prices based on the well-known autoregressive moving average (ARIMA) model. Essentially, the reconstruction of IMFs enhances the forecasting accuracy of the existing decomposition ensemble models. The proposed methodology works in four steps: decomposition of the complex data into several IMFs using EEMD, reconstruction of IMFs based on order of ARIMA model, prediction of every reconstructed IMF, and finally ensemble the prediction of every IMF for the final output. A case study was carried out using two crude oil prices time series (i.e. Brent and West Texas Intermediate (WTI)). The empirical results exhibited that the reconstruction of IMFs based on order of ARIMA model was adequate and provided the best forecast. In order to check the correctness, robustness and generalizability, simulations were carried out

A data-driven based decomposition?integration method for remanufacturing cost prediction of end-of-life products

Remanufacturing cost prediction is conducive to visually judging the remanufacturability of end-of-life (EOL) products from economic perspective. However, due to the randomness, non-linearity of remanufacturing cost and the lack of sufficient data samples. The general method for predicting the remanufacturing cost of EOL products is very low precision. To this end, a data-driven based decomposition–integration method is proposed to predict remanufacturing cost of EOL products. The approach is based on historical remanufacturing cost data to build a model for prediction. First of all, the remanufacturing cost of individual EOL product is arranged as a time series in reprocessing order. The Improved Local Mean Decomposition (ILMD) is employed to decompose remanufacturing cost time series data into several components with smooth, periodic fluctuation and use this as input. BP neural network based on Particle Swarm Optimization (PSO-BP) algorithm is utilized to predict the cost of each component. Finally, the predicted components are added to obtain the final prediction result. To illustrate and verify the feasibility of the proposed method, the remanufacturing cost of DH220 excavator is applied as the sample data, and empirical results show that the proposed model is statistically superior to other benchmark models owing to its high prediction accuracy and less computation time. And proposed method can be utilized as an effective tool to analyze and predict remanufacturing cost of EOL products

Forecasting methods in energy planning models

Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs

Study of the Influence of Oil Prices on Stock Markets’ Indices and Macroeconomic Factors in OPEC Countries and Top Economies and the Prediction of Future Oil Prices

Oil as one of the main fossil fuel energy sources, its price changes and fluctuation has the ability to influence the local economy or even the world economy. Especially for the oil-exporting countries, like OPEC countries, they have big influence on the oil prices. Whilst the proof of oil prices themselves have been examined, the influence of the oil prices on the relationship between different indices and between macroeconomic is not clear and the usage of Holt-Winter model on the oil price prediction has not been proofed. The aim of this thesis was to determine influence two oil prices (WTI and Brent crude oil prices) on the relationship between top economies in the world (Japan, the UK and the US). To achieve this the simple regression model, the VAR and the VECM model was including to examine the relationship of oil prices with indices and macroeconomic factors. The cointegration tests were used first to test whether they are stationary or non-stationary. Then, the VAR and the VECM model were employed to examine the short-run and long-run relationship between them. In addition, the Holt-Winter model was applied to test its predictability by estimate the oil prices. This thesis was the first to investigate the influence of oil prices on the relationship between different indices between OPEC countries and top economies’ stock indices. The key findings were that the oil prices changes’ conditions have influence on the relationship between different indices but limited. Secondly, by using the Holt-Winter model indicates that the oil market is inefficient where the prediction period had large difference between real period data. Thirdly, this thesis concluded that the oil prices and macroeconomic variables had causality relationship. These indicate that it is necessary to consider the influence of oil prices when analyse the world economy

Quantitative Methods for Economics and Finance

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice

Decarbonization cost of Bangladesh's energy sector: Influence of corruption

As a rapidly developing lower-middle income country, Bangladesh has been maintaining a steady growth of +5% in the gross domestic product (GDP) annually since 2004, eventually reaching 7.1% in 2016. The country is targeting to become uppermiddle- income and developed by 2021 and 2041 respectively, which translates to an annual GDP growth rate of 7.58% during this period. The bulk of this growth is expected to come from the manufacturing sector, the significant shift towards which started at the turn of this century. Energy intensity of manufacturing-based growth is higher, the evidence of which can be seen in the 3.17 times increase in national energy consumption between 2001 and 2014. Also, Bangladesh aims to achieve 100% electrification rate by 2021 against an annual population growth rate of 1.08%. With the increasing per capita income, there is now a growing middle class fuelling the growth in demand for convenient forms of energy. Considering the above drivers, the Bangladesh 2050 Pathways Model suggested 35 times higher energy demand than that of 2010 by 2050. The government and private sector have started a substantial amount of investments in the energy sector to meet the signi ficant future demand. Approximately US$104 billion would be invested in the power sector of Bangladesh for establishing 33 GW installed capacity by 2030, the majority of which would be financed by national and international loans. However, Bangladesh is one of the most corrupted country in the world which may influence the energy planning development. The current policies of Bangladesh power sector paved the future direction towards predominantly coal-based energy mix which would augment the greenhouse gas (GHG) emissions five times (117.5 MtCO2e) in 2030 than that of 2010. By increasing GHG emissions, the country would undermine the worldwide effort of keeping global temperature rise in 21st century below 2°C, as per the Paris agreement and COP21. V The objective of this research was to develop a framework to explore the cost of decarbonizing the Bangladesh's energy sector by 2050. For the study, six emissions scenarios business as usual (BAU), current policy (CPS), high-carbon (HCS), medium-carbon (MCS), low-carbon (LCS) and zero-carbon scenarios (ZCS), and three economic conditions high, average and low costwere considered. The combination of emissions and economic scenarios rendered 18 different emissionseconomic scenarios for the research. The results showed that Bangladesh would emit 343 MtCO2e by 2050 without any emissions reduction strategies under HCS. However, Bangladesh can reduce 23 of HCS by adopting decarbonization strategies such as energy mix change towards renewable and nuclear. On the optimistic side, the emissions can be reduced 73 by 2050 under ZCS than that of HCS. The study demonstrated that a zero carbon future is not yet feasible for Bangladesh by 2050 because the operational fossil fuel based plants would be operational. Therefore, the GHG emissions are going to rise even if Bangladesh adopts renewables and nuclear dominating energy mix. However, it will be possible to keep the GHG emissions approximately 2 tCO2e/capita threshold if the country adopts LCS. On the other hand, only MCS and LCS can meet the projected energy demand by 2050. The energy sector can meet the projected demand under ZCS only if the electricity consumption is reduced 26 2050. In terms total cost, the MCS was found to be 3.9 LCS by 2050. LCS would have a higher cost than that of MCS up to 2030, due to the high capital cost of renewable technologies. The total cost under LCS would start to be lower than of MCS after 2035 for the fossil fuel cost. Accumulated fuel cost would reach$250 billion in 2050 under HCS, which can be reduced 23% under ZCS. The cost of decarbonization would be 3.6, 3.4 and 3.2 times under average cost of MCS, LCS, and ZCS, than that of HCS. As the energy sector of Bangladesh is under rapid development, the accumulated capital would be comparatively high by 2050. However, fuel cost can be significantly reduced under LCS and ZCS which would also ensure lower emissions. The study suggested that energy mix change, technological maturity, corruption and demand reduction can influence the cost of decarbonization. However, the most significant influencer for the decarbonization of Bangladeshi energy sector would be the corruption. Results showed that if Bangladesh can minimize the effect of corruption on the energy sector, it can reduce the cost of decarbonization 45-77% by 2050 under MCS, LCS, and ZCS