A higher-order Markov chain-modulated model for electricity spot-price dynamics

Xiong, Heng; Mamon, Rogemar

doi:10.1016/j.apenergy.2018.09.039

Cited by 19 publications

(9 citation statements)

References 44 publications

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“…Thus, a particular research's natural direction is to extend the MSM approach to a hidden Markov model (HMM) approach with an online parameter estimation scheme (Tenyakov et al, 2016a(Tenyakov et al, , 2016bGu et al, 2020;Xiong and Mamon, 2018). Then, a further extension with the embedding of a higher-order HMM could be formulated; see, for example, Xi and Mamon (2011) or Xiong and Mamon (2019). The HMM setting assumes that the exact state of the system is unknown and evolves along with the movement of the observation process.…”

Section: Discussionmentioning

confidence: 99%

Modelling exchange-driven fish price dynamics

Xiang

Jones

Mamon

et al. 2021

JM2

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Purpose This paper aims to put forward and compare two accessible approaches to model and forecast spot prices in the fishing industry. The first modelling approach is a Markov-switching model (MSM) in which a Markov chain captures different economic regimes and a stochastic convenience yield is embedded in the spot price. The second approach is based on a multi-factor model (MFM) featuring three correlated stochastic factors. Design/methodology/approach The two proposed approaches are analysed in terms of parameter-estimation accuracy, information criteria and prediction performance. For MSM’s calibration, the quasi-log-likelihood method was applied directly while for the MFM’s parameter estimation, this paper designs an enhanced multi-variate maximum likelihood method with the aid of moments matching. The numerical experiments make use of both simulated and actual data compiled by the Fish Pool ASA. Data on both the Fish Pool’s forwards and Norwegian T-bill yields were additionally used in the MFM’s implementation. Findings Using simulated data sets, the MSM estimation gives more accurate results than the MFM estimation in terms of the norm in $l^2$ between the “true” and “computed” parameter estimates and significantly lower standard errors. With actual data sets used to evaluate the forecast values, both approaches have similar performances based on the error analysis. Under some metrics balancing goodness of fit and model complexity, the MFM outperforms the MSM. Originality/value With the aid of simulated and observed data sets examined in this paper, insights are gained concerning the appropriateness, as well as the benefits and weaknesses of the two proposed approaches. The modelling and estimation methodologies serve as prelude to reliable frameworks that will support the pricing and risk management of derivative contracts on fish price evolution, which creates price risk transfer mechanisms from the fisheries/aquaculture sector to the financial industry.

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Section: Discussionmentioning

confidence: 99%

Modelling exchange-driven fish price dynamics

Xiang

Jones

Mamon

et al. 2021

JM2

Self Cite

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“…etc., which can lower both the information required for implementation and the on-board computing burden [37], [38]. To improve the prediction accuracy of the MC, some papers use the second-order or even higher-order Markov algorithm [39], [40], but the calculation amount of the algorithms will double or many times. Some papers propose online selflearning MC, such as adding forgetting factors and weight coefficients [41], [42], or combining MC with other advanced algorithms such as Monte Carlo Method [43].…”

Section: A Literature Reviewmentioning

confidence: 99%

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

et al. 2021

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This paper introduces an energy management strategy design method for a plug-in hybrid electric vehicle based on the data-driven approach and online signal analysis. It includes two parts, mode division strategy design, and power distribution strategy design. Using the random forest in data mining technology to analyze optimization results of dynamic programming can quickly extract key information and establish optimal and understandable mode division strategy with high precision and stability directly. Besides, integrating the classic "Engine optimal operating curve control" strategy with wavelet transform and Markov prediction, which not only enhances the adaptability of the strategy to different driving conditions but also improves the fuel economy by reducing the impact of transient power on the engine operation. At the same time, to improve the prediction accuracy of the algorithm without increasing the computational complexity, this paper adds a prediction result correction function to the first-order Markov prediction model to reduce the impact of slow update of the probability matrix on prediction accuracy. The simulation results show the average prediction error of the improved Markov prediction model is reduced by 5.3% and the new energy management strategy designed reduces fuel consumption by 8.28% at the cost of a small increase in electricity consumption.INDEX TERMS Plug-in hybrid electric vehicle, dynamic programming, random forest, Markov chain, wavelet transform.

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“…Given the above assumptions, total electricity purchasing costs C(T) for the period between 0 and T, can be written as given Equation (13).…”

Section: Portfolio Risk Managementmentioning

confidence: 99%

“…In a more recent study, Ref. [13] uses a modelling approach where they integrate the deterministic seasonal components with regime-switching feature based stochastic factors through higher order hidden Markov models. Statistical time series methods are used to model the future price through a mathematical function of the previous prices.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Electricity Spot Price Modelling and Risk Management Applications

Çanakoğlu¹,

Adıyeke²

2020

Energies

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In dealing with sharp changes in electricity prices, contract planning is considered as a vital risk management tool for stakeholders in deregulated power markets. In this paper, dynamics of spot prices in Turkish electricity market are analyzed, and predictive performance of several models are compared, i.e., time series models and regime-switching models. Different models for derivative pricing are proposed, and alternative portfolio optimization problems using mean-variance optimization and conditional value at risk (CVaR) are solved. Expected payoff and risk structure for different hedging strategies for a hypothetical electricity company with a given demand are analyzed. Experimental studies show that regime-switching models are able to capture electricity characteristics better than their standard counterparts. In addition, evaluations with various risk management models demonstrate that those models are highly competent in providing an effective risk control practice for electricity markets.

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A higher-order Markov chain-modulated model for electricity spot-price dynamics

Cited by 19 publications

References 44 publications

Modelling exchange-driven fish price dynamics

Modelling exchange-driven fish price dynamics

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

Comparison of Electricity Spot Price Modelling and Risk Management Applications

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