Modeling Spot Price Dependence in Australian Electricity Markets with Applications to Risk Management

Ignatieva, Katja; Trück, Stefan

doi:10.2139/ssrn.1991452

Cited by 15 publications

(36 citation statements)

References 68 publications

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“…The lower tail dependence was estimated non-parametrically, while the dependence specification was estimated using the canonical maximum likelihood method [43]. Ignatieva and Truck [44] analyzed the dependence structure in the electricity markets using different copula models and found the Student t copula provided better dependence structure modeling accuracy and VaR estimation accuracy [44]. Boubaker and Sghaier [45] identifies the long memory feature in the financial returns dependence structure captured using the copula model.…”

Section: Copula Model In the Energy Marketsmentioning

confidence: 99%

Forecasting Energy Value at Risk Using Multiscale Dependence Based Methodology

Zha

Chen

et al. 2016

Entropy

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Abstract:In this paper, we propose a multiscale dependence-based methodology to analyze the dependence structure and to estimate the downside portfolio risk measures in the energy markets. More specifically, under this methodology, we formulate a new bivariate Empirical Mode Decomposition (EMD) copula based approach to analyze and model the multiscale dependence structure in the energy markets. The proposed model constructs the Copula-based dependence structure formulation in the Bivariate Empirical Mode Decomposition (BEMD)-based multiscale domain. Results from the empirical studies using the typical Australian electricity daily prices show that there exists a multiscale dependence structure between different regional markets across different scales. The proposed model taking into account the multiscale dependence structure demonstrates statistically significantly-improved performance in terms of accuracy and reliability measures.

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Section: Copula Model In the Energy Marketsmentioning

confidence: 99%

Forecasting Energy Value at Risk Using Multiscale Dependence Based Methodology

Zha

Chen

et al. 2016

Entropy

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“…As they note (at p. 384) " if spare import capacity is (not) available, [price] spikes should be smaller (larger) in size as generation capacity from the nearby region can (cannot) be transmitted to meet the local demand." Prior research on electricity price linkages and information transmission in the NEM has identified the existence of spillovers of volatility (or second moment) risk (e.g., Apergis et al, 2017a;Han et al, 2017;Ignatieva and Truck, 2016;Higgs, 2009;Worthington et al, 2005;Higgs and Worthington, 2005). However, focusing on volatility alone ignores that the distribution of electricity prices is skewed and heavily tailed and what this implies for risks due to transmission of extreme events in terms of their magnitude (via skewness spillover) and the likelihood of their occurrence (via kurtosis spillover).…”

Section: Introductionmentioning

confidence: 99%

“…The benefit of focusing on higher moment channels, and not just volatility risk, is that higher moment risks contain more predictive information about underlying network constraints and future evolution of electricity market prices, given that regional energy markets exhibit significant tail dependence and asymmetries (Ignatieva and Truck, 2016). For instance, the fatter the tail, the greater the probability of obtaining price changes that are extreme.…”

Section: Introductionmentioning

confidence: 99%

Interconnectedness in the Australian National Electricity Market: A Higher‐Moment Analysis*

2020

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We examine the risk transmission mechanisms in the interconnected Australian National Electricity Market (NEM). We illustrate that the transmission of extreme events in terms of their magnitude (via skewness) and the likelihood of their occurrence (via kurtosis) should be considered when promoting NEM interconnectedness. Our empirical findings suggest that interconnectedness costs can be limited by providing sufficient transmission capacities as it can expand generation capacity. Our results suggest that a one percent increase in NEM generation capacity can decrease the transmission of these risks by between 0.9 percent and 1.7 percent, depending on the moment of the electricity return distribution.

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“…Using futures prices instead of spot prices does not only allow us to take a forward-looking approach, but is also less sensitive to short-term events in electricity spot markets, such as extreme price spikes (Weron, 2006). This is of particular importance for the Australian NEM, where regional markets have been characterized as being among the most volatile and spikeprone in the world (Janczura et al, 2013;Clements et al, 2015;Ignatieva and Trück, 2016). Using futures instead of spot electricity prices, we also do not require detailed information on the actual fuel mix for the generation of electricity at each point in time to determine carbon pass-through rates.…”

Section: Introductionmentioning

confidence: 99%

Carbon Pricing, Forward Risk Premiums and Pass-Through Rates in Australian Electricity Futures Markets

2016

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We investigate the impacts of the carbon tax (effective July 2012 to July 2014) on wholesale electricity prices in the Australian National Electricity Market (NEM). Analyzing spot and futures contracts in four major regional markets, we first compute ex-ante forward risk premiums in the pre-tax period, then use them to derive market-implied carbon premiums and pass-through rates in the carbon tax and post-tax periods. We find that carbon premiums and pass-through rates became increasingly higher, once the Clean Energy Bill had been introduced and subsequently passed in 2011. We also find strong evidence for a quick reaction of the extracted carbon premiums to changes in opinion polls for the Australian federal election in 2013 and the decision to repeal the tax. During periods where market participants could be relatively certain that the tax would be effective, we find expected carbon pass-through rates between 65% and 140%, which seem to be inversely related to emission intensities.

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Modeling Spot Price Dependence in Australian Electricity Markets with Applications to Risk Management

Cited by 15 publications

References 68 publications

Forecasting Energy Value at Risk Using Multiscale Dependence Based Methodology

Forecasting Energy Value at Risk Using Multiscale Dependence Based Methodology

Interconnectedness in the Australian National Electricity Market: A Higher‐Moment Analysis*

Carbon Pricing, Forward Risk Premiums and Pass-Through Rates in Australian Electricity Futures Markets

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