Forecasting Long‐Run Coal Price in China: A Shifting Trend Time‐Series Approach

Dong, Baomin; Li, Xuefeng; Lin, Boqiang

doi:10.1111/j.1467-9361.2010.00567.x

Cited by 15 publications

(3 citation statements)

References 17 publications

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“…The basic characteristics of the coal market are the price of the commodity and its quality (Li et al 2014). Furthermore, an important role is played by demand for energy and prices of other fuels (LaRose 2014; Dong et al 2010). Of course, when the price of substitute goods (other energy sources) falls, the demand for a given commodity based on the past price will also drop, and the price will have to adjust.…”

Section: Coalmentioning

confidence: 99%

Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

Drachal,

Pawłowski

2024

IJFS

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This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities’ prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approach to symbolic regression, based on genetic programming, was also used as a benchmark tool. Secondly, various other econometric methods dealing with variable uncertainty were estimated including Bayesian Model Averaging, Dynamic Model Averaging, LASSO, ridge, elastic net, and least-angle regressions, etc. Therefore, this study reports a concise and uniform comparison of an application of several popular econometric models to forecasting the prices of numerous commodities. Robustness checks and statistical tests were performed to strengthen the obtained conclusions. Monthly data beginning from January 1988 and ending in August 2021 were analysed.

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Section: Coalmentioning

confidence: 99%

Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

Drachal,

Pawłowski

2024

IJFS

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“…The commonly used stochastic models for commodity price modelling are ordinary Brownian motion, geometric Brownian motion, and mean reversion models, like the Ornstein-Uhlenbeck (OU) process (known as the Vasicek model for interest rate) or its extension for non-constant coefficients. We refer the readers to the papers [25][26][27][28][29][30][31], where different Gaussian stochastic processes are applied for the commodity price modelling. However, some of the authors argue that the Gaussian-based models are inappropriate for commodity price description as they do not take into account the possible large observations apparent in market data [30].…”

Section: Introductionmentioning

confidence: 99%

Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling

Bielak¹,

Grzesiek²,

Janczura³

et al. 2021

Preprint

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Mining companies to properly manage their operations and be ready to make business decisions, are required to analyze potential scenarios for main market risk factors. The most important risk factors for KGHM, one of the biggest companies active in the metals and mining industry, are the price of copper (Cu), traded in US dollars, and the Polish zloty (PLN) exchange rate (USDPLN). The main scope of the paper is to understand the mid-and long-term dynamics of these two risk factors. For a mining company it might help to properly evaluate potential downside market risk and optimise hedging instruments. From the market risk management perspective, it is also important to analyze the dynamics of these two factors combined with the price of copper in Polish zloty (Cu in PLN), which jointly drive the revenues, cash flows, and financial results of the company. Based on the relation between analyzed risk factors and distribution analysis, we propose to use two-dimensional vector autoregressive (VAR) model with the α−stable distribution. The non-homogeneity of the data is reflected in two identified regimes: first -corresponding to the 2008 crisis and second -to the stable market situation. As a natural implication of the model fitted to market assets, we derive the dynamics of the copper price in PLN, which is not a traded asset but is crucial for the KGHM company risk exposure. A comparative study is performed to demonstrate the effect of including dependencies of the assets and the implications of the regime change. Since for various international companies, risk factors are given rather in the national than the market currency, the approach is universal and can be used in different market contexts, like mining or oil companies, but also other commodities involved in the global trading system.

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“…Instead, these authors analyze the causes of energy consumption in the case of Hong Kong with the principal components approach. 5,6 Finally, other researchers have focused on forecasting the demand of energy consumption such as Crompton and Wu (2005), Adam and Shachmurove (2008), Wang et al, (2009), Dong et al, (2010) and Zhu et al, (2011). 7 Thus, in this paper we complement existing literature on the seasonality of China's energy by investigating the seasonal patterns of coal and electricity production of each individual Chinese province.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Patterns of Energy in China

Herrerías¹,

Girardin²

2013

The Energy Journal

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ACL-1International audienceThis paper provides evidence on the relevance of modeling adequately the seasonal character of coal and electricity production across Chinese regions. Unlike other work, this paper relaxes the assumption of deterministic seasonality, allowing for time and regional variation in this economy. More specifically, we analyze and distinguish the type of seasonality around the year that prevails in the case of coal and electricity production of each individual Chinese province. Our results indicate that for the majority of the provinces seasonality is stochastic in both types of energy considered. Our findings provide new evidence of a Lunar New-Year effect in February and Summer as well as Winter effects in coal and electricity production. However, in terms of seasonal patterns and their evolution over time, there are significant differences between the Northern Southern regions. Besides for each type of energy, regional clusters matter for the appropriate design of energy-development policy

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Forecasting Long‐Run Coal Price in China: A Shifting Trend Time‐Series Approach

Cited by 15 publications

References 17 publications

Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling

Seasonal Patterns of Energy in China

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