Pricing of hourly exercisable electricity swing options using different price processes

Hirsch, G.

doi:10.21314/jem.2009.018

Cited by 18 publications

(13 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Independent spike (IS) MRS models, where the base regime is modeled by an AR(1) process and the spike regime by an independent normal (log-normal or Pareto) random variable, were introduced by Huisman and de Jong (2003) and Weron et al (2004a). In the following years various MRS models have enjoyed extensive use due to their relative parsimony and the ability to capture the unique characteristics of electricity spot prices (Bierbrauer et al, 2007;De Jong, 2006;Erlwein et al, 2010;Haldrup et al, 2010;Higgs and Worthington, 2008;Hirsch, 2009;Huisman, 2009;Weron, 2010, 2012;Kanamura andŌhashi, 2008;Karakatsani and Bunn, 2008;Keles et al, 2012;Kholodnyi, 2005;Mari, 2008;Mount et al, 2006;Weron, 2009).…”

Section: Stochastic Models For the Deseasonalized Spot Pricementioning

confidence: 99%

“…While it is clear that price spikes should be captured by an adequate stochastic model, like mean reverting jump-diffusion (Bierbrauer et al, 2007;Borovkova and Permana, 2006;Cartea and Figueroa, 2005;Clewlow and Strickland, 2000;Geman and Roncoroni, 2006;Jabłońska et al, 2011;Nomikos and Soldatos, 2010;Seifert and Uhrig-Homburg, 2007;Weron, 2008) or a regimeswitching model (Becker et al, 2007;De Jong, 2006;Higgs and Worthington, 2008;Hirsch, 2009;Huisman and Mahieu, 2003;Weron, 2010, 2012;Keles et al, 2012;Mari, 2008;Mount et al, 2006;Weron, 2009), the literature does not agree on whether these observations have to be included or excluded in the estimation of the seasonal pattern.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

et al. 2012

View full text Add to dashboard Cite

An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations, known as electricity price spikes. Improved robustness of the model can be achieved by (a) filtering the data with some reasonable procedure for outlier detection, and then (b) using estimation and testing procedures on the filtered data. In this paper we examine the effects of different treatment of extreme observations on model estimation and on determining the number of spikes (outliers). In particular we compare results for the estimation of the seasonal and stochastic components of electricity spot prices using either the original or filtered data. We find significant evidence for a superior estimation of both the seasonal short-term and long-term components when the data have been treated carefully for outliers. Overall, our findings point out the substantial impact the treatment of extreme observations may have on these issues and, therefore, also on the pricing of electricity derivatives like futures and option contracts. An added value of our study is the ranking of different filtering techniques used in the energy economics literature, suggesting which methods could be and which should not be used for spike identification.

show abstract

Section: Stochastic Models For the Deseasonalized Spot Pricementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

et al. 2012

View full text Add to dashboard Cite

show abstract

“…The definitions of the individual regimes can be arbitrarily chosen depending on the modeling needs. Again for the sake of clarity, in this paper we focus only on two commonly used in the energy economics literature specifications of MRS models (Ethier and Mount 1998;De Jong 2006;Hirsch 2009;Huisman and de Jong 2003;Janczura and Weron 2010;Mari 2008). The first one (denoted by I or type I) assumes that the process X t is driven by two independent regimes: (1) a mean-reverting AR(1) process:…”

Section: Model Definitionmentioning

confidence: 99%

Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices

Janczura

Weron

2012

AStA Adv Stat Anal

View full text Add to dashboard Cite

This paper complements a recently published study (Janczura and Weron in AStA-Adv Stat Anal 96(3):385-407, 2012) on efficient estimation of Markov regime-switching models. Here, we propose a new goodness-of-fit testing scheme for the marginal distribution of such models. We consider models with an observable (like threshold autoregressions) as well as a latent state process (like Markov regimeswitching). The test is based on the Kolmogorov-Smirnov supremum-distance statistic and the concept of the weighted empirical distribution function. The motivation for this research comes from a recent stream of literature in energy economics concerning electricity spot price models. While the existence of distinct regimes in such data is generally unquestionable (due to the supply stack structure), the actual goodness-offit of the models requires statistical validation. We illustrate the proposed scheme by testing whether commonly used Markov regime-switching models fit deseasonalized electricity prices from the NEPOOL (US) day-ahead market.

show abstract

“…In particular, if the real data shows that the price remained high for some time after a jump, then regimeswitching models are better to depict this phenomenon [6]. For example, different types of regime-switching models were selected in [6][7][8][9][10]. Besides, various kinds of jump-diffusion models were applied in [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Valuation of Swing Options under a Regime‐Switching Mean‐Reverting Model

Shao

Xiang

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this paper, we study the valuation of swing options on electricity in a model where the underlying spot price is set to be the product of a deterministic seasonal pattern and Ornstein-Uhlenbeck process with Markov-modulated parameters. Under this setting, the difficulties of pricing swing options come from the various constraints embedded in contracts, e.g., the total number of rights constraint, the refraction time constraint, the local volume constraint, and the global volume constraint. Here we propose a framework for the valuation of the swing option on the condition that all the above constraints are nontrivial. To be specific, we formulate the pricing problem as an optimal stochastic control problem, which can be solved by the trinomial forest dynamic programming approach. Besides, empirical analysis is carried out on the model. We collect historical data in Nord Pool electricity market, extract the seasonal pattern, calibrate the Ornstein-Uhlenbeck process parameters in each regime, and also get market price of risk. Finally, on the basis of calibration results, a specific numerical example concerning all typical constraints is presented to demonstrate the valuation procedure.

show abstract

Pricing of hourly exercisable electricity swing options using different price processes

Cited by 18 publications

References 0 publications

Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices

Valuation of Swing Options under a Regime‐Switching Mean‐Reverting Model

Contact Info

Product

Resources

About