"Serving the energy market" - Articles - Price modeling

Cointegration between gas and power spot prices

no@spam.com (Cyriel de Jong) — Mon, 23 Aug 2010 10:44:56 CEST

Keywords: power prices, cointegration, regime switching, spot prices
Published in: The Journal of Energy Markets
Publication year: 2009, Volume 2, Number 3
Co-author 1: Stefan Schneider, EON Energy Trading

In this paper we show how cointegration can be applied to capture the joint dynamics of multiple energy spot prices. As an exemplary system we study the gas spot markets TTF, Zeebrugge and NBP, and additionally the power spot market APX, since these markets are strongly connected in terms of physical transportation and generation of power from gas. We develop a cointegrating multi-market model framework which is able to plausibly connect different single market spot price models. This is achieved by considering the mean-reverting spot-forward price spreads instead of spot prices only. Our analysis shows that the gas prices are strongly cointegrated, with a specific connection pattern of the markets, whereas cointegration of gas and power prices is on long-term forward price levels only.

Solving stochastic complementarity problems in energy market modeling using

no@spam.com (Steven Gabriel) — Tue, 11 Aug 2009 16:22:32 CEST

Published in: European Journal of Operational Research

Publication year: 2008
Co-Author 1: Jifang Zhuang

Co-Author 2: Ruud Egging

In this paper, we analyze market equilibrium models with random aspects that lead to stochastic complementarity problems. While the models presented depict energy markets, the results are believed to be applicable to more general stochastic complementarity problems. The contribution is the development of new heuristic, scenario reduction approaches that iteratively work towards solving the full, extensive form, stochastic market model. The methods are tested on three representative models and supporting numerical results are provided as well as derived mathematical bounds.

Electricity prices and fuel costs: Long-run relations and short-run dynamics

no@spam.com (Hassan Mohammadi) — Tue, 11 Aug 2009 16:04:48 CEST

Published in: Energy Economics

Publication year: 2009

The paper examines the long-run relation and short-run dynamics between electricity prices and three fossil fuel prices – coal, natural gas and crude oil – using annual data for the U.S. for 1960–2007. The results suggest (1) a stable long-run relation between real prices for electricity and coal (2) Bi-directional long-run causality between coal and electricity prices. (3) Insignificant long-run relations between electricity and crude oil and/or natural gas prices. And (4) no evidence of asymmetries in the adjustment of electricity prices to deviations from equilibrium. A number of implications are addressed.

Computing the market price of volatility risk in the energy commodity markets

no@spam.com (James Doran) — Tue, 11 Aug 2009 15:59:44 CEST

Published in: Journal of Banking & Finance

Publication year: 2008
Co-Author 1: Ehud I. Ronn

In this paper, we demonstrate the need for a negative market price of volatility risk to recover the difference between Black–Scholes [Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637–654]/Black [Black, F., 1976. Studies of stock price volatility changes. In: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, pp. 177–181] implied volatility and realized-term volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative market price of volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical volatility in both equity and

commodity-energy markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a 10 year period, we estimate the volatility risk premium and demonstrate that the premium is negative and significant for

all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher volatility risk premiums. Computing such a negative market price of volatility risk highlights the importance of volatility risk in understanding priced volatility in these financial markets.

A supply and demand based volatility model for energy prices

no@spam.com (Takashi Kanamura) — Fri, 07 Aug 2009 17:29:59 CEST

This paper proposes a new volatility model for energy prices using the supply–demand relationship, which we call a supply and demand based volatility model. We show that the supply curve shape in the model determines the characteristics of the volatility in energy prices. It is found that the inverse Box–Cox transformation supply curve reflecting energy markets causes the inverse leverage effect, i.e., positive

correlation between energy prices and volatility.
The model is also used to show that an existing (G)ARCH-M model has the foundations on the supply–demand relationship. Additionally, we conduct the empirical studies analyzing the volatility in the U.S. natural gas prices.

Modeling price and volatility inter- relationships in the Australian wholesale spot electricity markets

no@spam.com (Helen Higgs) — Fri, 07 Aug 2009 14:52:15 CEST

Keywords: wholesale spot electricity market, multivariate GARCH
Published in: Energy Economics
Publication year: 2009

This paper examines the inter-relationships of wholesale spot electricity prices among the four regional electricity markets in the Australian National Electricity Market (NEM): namely, New South Wales, Queensland, South Australia and Victoria using the constant conditional correlation and Tse and Tsui's (Tse, Y.K., Tsui, A.K.C., 2002. A multivariate generalised autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics 20 (3), 351–362.) and Engle's (Engle, R.,2002. Dynamic conditional correlation: a sample class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics 20 (3), 339–350.) dynamic conditional

correlation multivariate GARCH models. Tse and Tsui's (Tse, Y.K., Tsui, A.K.C., 2002. A multivariate generalised autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics 20 (3), 351–362.) dynamic conditional correlation multivariate GARCH model which takes

account of the Student t specification produces the best results. At the univariate GARCH(1,1) level, the mean equations indicate the presence of positive own mean spillovers in all fourmarkets and little evidence of mean spillovers from the other lagged markets. In the dynamic conditional correlation equation, the highest

conditional correlations are evident between the well-connected markets indicating the presence of strong interdependence between these markets with weaker interdependence between the not so wellinterconnected markets.

Stochastic price modeling of high volatility, mean-reverting, spike-prone commodities: The Australian wholesale spot electricity market.

no@spam.com (Helen Higgs) — Fri, 07 Aug 2009 14:34:34 CEST

It is commonly known that wholesale spot electricity markets exhibit

high price volatility, strong mean-reversion and frequent extreme

price spikes. This paper employs a basic stochastic model, a mean-reverting

model and a regime-switching model to capture these

features in the Australian national electricity market (NEM),

comprising the interconnected markets of New South Wales,

Queensland, South Australia and Victoria. Daily spot prices from 1

January 1999 to 31 December 2004 are employed. The results show

that the regime-switching model outperforms the basic stochastic and

mean-reverting models. Electricity prices are also found to exhibit

stronger mean-reversion after a price spike than in the normal period,

and price volatility is more than fourteen times higher in spike periods

than in normal periods. The probability of a spike on any given day

ranges between 5.16% in NSW and 9.44% in Victoria.

Towards a European market of electricity: Spot and derivatives trading

no@spam.com (Helyette Geman) — Tue, 19 Feb 2008 13:35:18 CET

Keywords: energy markets, electricity spikes, power options
Published in:
Publication year: 2002

Deregulation of electricity markets is spreading worldwide at a high speed : it has been completed for some years in Scandinavia and the United Kingdom, is well under way in the United States and being embraced in most continental Western Europe outside France. Germany and the Netherlands are quite deregulated, followed by Spain. Italy is establishing power trading in a competitive environment. This represents a multi-billion spot market that is developing very quickly. And the same pattern of evolution as in the financial markets is being observed, with the growth of a variety of derivative instruments such as forward and Futures contracts swaps, plain-vanilla and exotic options.
The main problem associated with the pricing of those derivatives is that the fundamental financial models were established for stocks and bonds and do not capture the unique features of electricity, in particular the non-storability (except for hydroelectricity), the seasonality and spikes of prices, the difficulties of transportation, (existence of high voltage lines, constraints at the hubs imposed by the Kirchoff laws), not to mention the necessity for the European Community to define clear rules for cross-border electricity transmission.
The goal of this paper is to discuss the main features of electricity spot prices and investigate the pricing issues attached to power options.

Strategic Behavior in Spot Markets for Electricity when Load is Stochastic

no@spam.com (Tim Mount) — Mon, 28 Jan 2008 16:52:09 CET

Keywords: spot power modeling "regime switches" "market power"
Published in:
Publication year: 2000

In the first part of the paper, daily price data for the past three summer seasons in the PJM wholesale market are used to estimate a stochastic regime switching model. These data show that the average price in 1999, when market-based offers were allowed, was twice as high as it was in the previous two seasons when offers had to be cost-based. The primary cause was that the price spikes in 1999 were much higher than they were in 1997-98, but not more frequent. The second part of the paper derives an optimum set of offers for individual suppliers endowed with different levels of market power. A supplier controlling generation equivalent to 20% of the expected load in the market is shown to submit offers that are up to 80% higher than the true cost. Nevertheless, these offers are still much lower than the offers that set the high prices in the PJM market. The explanation is that suppliers with sufficient market power are indifferent to whether or not marginal units are dispatched, and they can set high offers on these units without forfeiting expected profits.
The author wishes to thank Yumei Ning for research assistance in estimating the price models in Section 2, and Jonell Blakeley and Dan Chapman for help preparing the manuscript. All remaining errors are the responsibility of the author.

Spot price simulation and volatility analysis in the future Iberian electricity market

no@spam.com (Jorge Sousa) — Mon, 28 Jan 2008 14:59:54 CET

Keywords: Price volatility, Risk management, Conjectural variations, Iberian Electricity Market, GAMS simulation
Published in:
Publication year:
Co-author 1: Joao Lagarto
Co-author 2: Rui Pestana

Price volatility is a major issue in liberalized electricity markets as far as risk management is concerned. In this paper we evaluate the impact of the Portuguese and Spanish electricity markets integration on the day-ahead market price volatility. For that purpose we develop an adaptive conjectural variations model which is implemented in GAMS language. We estimate the degree of competition in the Spanish pool using an iterative secant method applied to a conjectural variations oligopoly model. Using the estimate obtained, we simulate the Portuguese and Spanish markets in autarky and the integrated market, known as the Iberian Electricity Market (IBELM). We present conclusions on the impact of the IBELM on prices and volatilities from the Portuguese and Spanish markets point of view.