Regime-Switching Model on Hourly Electricity Spot Price Dynamics

Gyamerah, Samuel Asante; Ngare, Philip

doi:10.4236/jmf.2018.81008

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Therefore, statistical inference with regard to the likelihood of occurrence of each of the regimes at any time should be drawn. MRS has been used effectively in modelling the behaviour of the stock market and spot price of electricity [22][23][24][25]. Chevallier and Goutte [26] used sixteen international stock markets to compare the performance of regime-switching Lévy models.…”

Section: Markov Regime-switching (Mrs) Modelmentioning

confidence: 99%

Regime-Switching Temperature Dynamics Model for Weather Derivatives

Gyamerah¹,

Ngare

Ikpe

2018

International Journal of Stochastic Analysis

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Weather is a key production factor in agricultural crop production and at the same time the most significant and least controllable source of peril in agriculture. These effects of weather on agricultural crop production have triggered a widespread support for weather derivatives as a means of mitigating the risk associated with climate change on agriculture. However, these products are faced with basis risk as a result of poor design and modelling of the underlying weather variable (temperature). In order to circumvent these problems, a novel time-varying mean-reversion Lévy regime-switching model is used to model the dynamics of the deseasonalized temperature dynamics. Using plots and test statistics, it is observed that the residuals of the deseasonalized temperature data are not normally distributed. To model the nonnormality in the residuals, we propose using the hyperbolic distribution to capture the semiheavy tails and skewness in the empirical distributions of the residuals for the shifted regime. The proposed regime-switching model has a mean-reverting heteroskedastic process in the base regime and a Lévy process in the shifted regime. By using the Expectation-Maximization algorithm, the parameters of the proposed model are estimated. The proposed model is flexible as it modelled the deseasonalized temperature data accurately. However, most farmers in Africa have rarely heard about this effective hedging tool. WD, if introduced in Africa, will be more viable, reliable, and efficient to the agricultural industry and can hedge against the increasing weather changes that affect agriculture since it is devoid of factors like loss adjustments, moral hazards, adverse selections, high premiums, and complex information requirements. To avoid

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Section: Markov Regime-switching (Mrs) Modelmentioning

confidence: 99%

Regime-Switching Temperature Dynamics Model for Weather Derivatives

Gyamerah¹,

Ngare

Ikpe

2018

International Journal of Stochastic Analysis

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Identifying electricity price jumps based on GARCH-ARJI Model

Chen,

Wu,

Chen

et al. 2024

Proceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial I

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Markov Regime Switching Analysis for COVID-19 Outbreak Situations and their Dynamic Linkages of German Market

Kangrong¹,

Tokinaga²

2023

Adv. sci. technol. eng. syst. j.

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German stock index(DAX) Markov Regime Switching Analysis (MRSA) GARCH(Generalised Autoregressive heteroscedasticity) Stock returns Growth rates of the disease Volatility This paper deals with the analysis of the dynamic linkage, co-movement between COVID-19 outbreak situations and German stock market. Firstly, Markov Regime Switching Analysis(MRSA) is proposed and employed to investigate the situations in the pandemic, as to catch the dynamics of how the daily number of the newly-infected changes, and also to assess the impact of the pandemic situations on German Market. Secondly, we compute the log growth rates of the weekly new cases and the log-returns of weekly DAX index, then fit the GARCH models to both of them to acquire their volatilities. We then employ the MRSA model once more to expose the dynamic linkages and co-movement between these two volatilities series. Through our empirical analyses, we find that GARCH models can capture the dynamics of stock returns and the growth rates. On the other hand, the MRSA models work well to identify the dynamics between different regimes with different states in dealing with the volatilities obtained from the estimated GARCH models. Our proposed econometric methods are highly practical, it indicates the possibility of replicating the results obtained in this study to assess the impact of other epidemics and negative factors on economic activities. Knowing what may happen during a pandemic, more effective measures and actions can be taken to protect people while dealing with another pandemic in the future.

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Regime-Switching Model on Hourly Electricity Spot Price Dynamics

Cited by 4 publications

References 11 publications

Regime-Switching Temperature Dynamics Model for Weather Derivatives

Regime-Switching Temperature Dynamics Model for Weather Derivatives

Identifying electricity price jumps based on GARCH-ARJI Model

Markov Regime Switching Analysis for COVID-19 Outbreak Situations and their Dynamic Linkages of German Market

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