Regression tree model versus Markov regime switching: a comparison for electricity spot price modelling and forecasting

Samitas, Aristeidis; Armenatzoglou, Aggelos

doi:10.1007/s12351-014-0149-6

Cited by 12 publications

(7 citation statements)

References 35 publications

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“…To mention a few, we refer to Lucia and Schwartz (2002) and Cartea and Figueroa (2005) who propose models that account for the special features of electricity spot prices. Also, Huisman (2008), Samitas and Armenatzoglou (2014) and Weron and Misiorek (2008) use regime switching models to account for spiky behaviour and mean reversion. In these articles, the authors demonstrate the dependencies between markets and show how energy commodity prices volatility and correlation of returns are of great concern to oil, natural gas and electricity market participants.…”

Section: Literature Reviewmentioning

confidence: 99%

Volatility Spillovers in Electricity Markets: Evidence From the United States

Pantos

Polyzos

Armenatzoglou

et al. 2019

IJEEP

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This paper examines the degree of market integration, as observed by measuring volatility spillovers, in selected wholesale electricity spot markets from United States. We choose markets located at interconnected and non-interconnected areas. We use a Multivariate GARCH framework, which allows us to model time varying correlations and to conclude whether the markets show evidence of interdependency. We estimate the variance-covariance and correlation structure, in order to observe the evolution of interactions among markets, accounting for asymmetric effects. We find evidence of significant correlations between interconnected markets, which are mainly due to electricity transmission, since the observed correlations are above 0.5, but our results show that the desired level of integration has not been accomplished yet. Nevertheless, full integration is not an objective target, unless new technologies offer a boost towards that direction. Our results suggest that we should move towards a more integrated market, through legislation reforms and investment in infrastructure, which could increase competition and could lead to capital savings through lower electricity prices. The unique selection of the markets under examination and the 4-variate BEKK model for electricity markets are special characteristics of this paper.

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Section: Literature Reviewmentioning

confidence: 99%

Volatility Spillovers in Electricity Markets: Evidence From the United States

Pantos

Polyzos

Armenatzoglou

et al. 2019

IJEEP

Self Cite

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“…The requirement for minimal assumptions regarding the underlying processes and the excellent forecasting performance for both classification and regression tasks made the ML methods attractive for many scientific areas. Samitas and Armenatzoglou (2014) examine the ability of the Regression Trees (RTs) and a 2-Regime MSR to capture the extreme behavior of the electricity prices in New England's spot market. The empirical findings show that the RTs outweighs MSR models in most cases.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Price Spikes in Electricity Markets

Papadimitriou

Gogas

2021

REA

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Electricity markets are considered to be the most volatile amongst commodity markets. The non-storability of electricity and the need for instantaneous balancing of demand and supply can often cause extreme short-lived fluctuations in electricity prices. These fluctuations are termed price spikes. In this paper, we employ a multiclass Support Vector Machine (SVM) model to forecast the occurrence of price spikes in the German intraday electricity market. As price spikes, we define the prices that lie above the 95th quantile estimated by fitting a Generalized Pareto distribution in the innovation distribution of an AR-EGARCH model. The generalization ability of the model is tested in an out-of-the-sample dataset consisting of 4080 hours. Furthermore, we compare the performance of our best SVM model against Neural Networks (NNs) and Gradient Boosted Machines (GBMs).

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“…This model involves multiple structures (equations) that can characterize the time series behaviors in different regimes. Samitas and Armenatzoglou (2014) think that the regime-switching models offer the possibility to divide the time series into different regimes with different underlying processes parameters to be estimated. By permitting switching between these structures, the model is able to capture more complex dynamic patterns.…”

Section: Research Methods and Proceduresmentioning

confidence: 99%

“…Liu and Chyi (2006) used a nonlinear two-state (contraction period and expansion period) Markov switching model to explore the use of semiconductor sales growth rates and international trade data to capture the switch timing of the semiconductor industry cycle, and forecasted that from 1990 to 2003 the semiconductor industry would be in a sales contraction period. Samitas and Armenatzoglou (2014) model electricity spot prices using a Markov regime-switching model and compare the result to that of regression tree model.…”

Section: Literature Researchmentioning

confidence: 99%

A study on the rationality of expensing of Taiwan employee stock bonuses

Lee

Liu

2016

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Purpose In order to follow the international trend of increasing transparency in financial statements, Taiwan began to implement regulations on expensing employee bonuses in 2008, a process that involves the use of specific dates as the basis for issuing the bonuses but which may also have the drawback of resulting in some degree of unfairness. The purpose of this paper is to study and solve the above problem. Design/methodology/approach The present study used Markov regime-switching models to obtain durations of different states, thereby obtaining average stock durations for use as the basis to calculate the number of shares to be distributed. Findings Empirical results show that replacing share prices on specific dates with those of average durations when issuing employee bonuses could better reflect employee-deserved real wages while keeping a company’s managers and management teams from being motivated to manage real earnings and manipulate share prices. When stock prices are higher, companies will tend to issue cash rather than stock bonuses, and vice versa when the prices are lower. Originality/value This study proposes a different point of view with regards to the basis for the allotted number of shares for employee bonuses under current laws and regulations, and suggests using the concept of average stock prices in place of the single-price concept implemented under the current system in order to avoid incentives to manipulate by people, so as to fairly express the true state of the enterprise.

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Regression tree model versus Markov regime switching: a comparison for electricity spot price modelling and forecasting

Cited by 12 publications

References 35 publications

Volatility Spillovers in Electricity Markets: Evidence From the United States

Volatility Spillovers in Electricity Markets: Evidence From the United States

Forecasting Price Spikes in Electricity Markets

A study on the rationality of expensing of Taiwan employee stock bonuses

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