Modelling interregional links in electricity price spikes

Clements, Adam; Herrera, Rodrigo; Hurn, Stan

doi:10.1016/j.eneco.2015.07.014

Cited by 59 publications

(39 citation statements)

References 47 publications

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“…Reference [1] interpret the time series of price spikes as a discrete-time point process and they forecast spikes for the Australian Market. Reference [2] additionally investigate price spikes across connected regions also in the Australian Market. Reference [3] survey the influence of exogenous variables (such as load, weather, capacity constraints) on the occurrence and intensity of price spikes in Australian Market.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Other authors suggest using a fixed threshold value for the whole time series under consideration (e.g. [13]; [1]; [2]). As our sample includes the daily values from 2012 to 2016, so including different market conditions, not correctly selected by a fixed threshold, similarly to [14] and [15] we computed the spike threshold as the price average plus three times its volatility recorded during the previous year.…”

Section: Prices Spikes In the Italian Power Marketmentioning

confidence: 99%

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Price Spikes in the Italian Electricity Market: An Economic Analysis

Zedda¹,

Masala²

2019

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Since their liberalization, the electricity markets are characterized by a rather high volatility, which includes price spikes as one of the stylized and specific characteristics of this market. In this paper, we investigate the factors and mechanisms determining these spikes. Based on the Italian electricity market data, we perform a specific analysis of the auctions mechanisms and of the hourly bid and offer of electricity characteristics, for determining how and why do price spikes occur.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Prices Spikes In the Italian Power Marketmentioning

confidence: 99%

Price Spikes in the Italian Electricity Market: An Economic Analysis

Zedda¹,

Masala²

2019

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“…A new avenue for forecasting VaR was opened up when the point-process approach to POT models was released in [ 4 ]. This methodology was later extended in several publications [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. The benefit of this model is that it does not require prefiltering returns using GARCH-family estimates while at the same time it can capture the clustering effects of extreme losses and maintain the merits of the extreme value theory.…”

Section: Introductionmentioning

confidence: 99%

“…The point-process POT model approximates the time-varying conditional probability of an extreme loss over a given day with the help of a conditional intensity function that characterizes the arrival rate of such extreme events. The intensity function can either be formulated in the spirit of the self-exciting Hawkes process [ 4 , 5 , 10 , 11 , 12 ] (which is extensively used in geophysics and seismology), in the form of the observation-driven autoregressive conditional intensity (ACI) model [ 13 ], or using the autoregressive conditional duration (ACD) models [ 6 , 7 , 8 ] (the last two methodologies were very popular in the area of market microstructure and the modeling of financial ultra-high-frequency data [ 15 , 16 , 17 ]). In all cases, the timing of extreme losses depends on the timing of extreme losses observed in the past.…”

Section: Introductionmentioning

confidence: 99%

Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial Markets

Bień-Barkowska

2020

Entropy

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Forecasting market risk lies at the core of modern empirical finance. We propose a new self-exciting probability peaks-over-threshold (SEP-POT) model for forecasting the extreme loss probability and the value at risk. The model draws from the point-process approach to the POT methodology but is built under a discrete-time framework. Thus, time is treated as an integer value and the days of extreme loss could occur upon a sequence of indivisible time units. The SEP-POT model can capture the self-exciting nature of extreme event arrival, and hence, the strong clustering of large drops in financial prices. The triggering effect of recent events on the probability of extreme losses is specified using a discrete weighting function based on the at-zero-truncated Negative Binomial (NegBin) distribution. The serial correlation in the magnitudes of extreme losses is also taken into consideration using the generalized Pareto distribution enriched with the time-varying scale parameter. In this way, recent events affect the size of extreme losses more than distant events. The accuracy of SEP-POT value at risk (VaR) forecasts is backtested on seven stock indexes and three currency pairs and is compared with existing well-recognized methods. The results remain in favor of our model, showing that it constitutes a real alternative for forecasting extreme quantiles of financial returns.

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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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Modelling interregional links in electricity price spikes

Cited by 59 publications

References 47 publications

Price Spikes in the Italian Electricity Market: An Economic Analysis

Price Spikes in the Italian Electricity Market: An Economic Analysis

Looking at Extremes without Going to Extremes: A New Self-Exciting Probability Model for Extreme Losses in Financial Markets

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

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