مدلسازی قیمت و نوسانات روابط درونی در بازار هدف عمده فروشی برق استرالیا
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|3051||2009||9 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy Economics, Volume 31, Issue 5, September 2009, Pages 748–756
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 four markets 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 well-interconnected markets.
The Australian National Electricity Market (NEM) was established on 13 December 1998. It currently comprises four state-based [New South Wales (NSW), Victoria (VIC), Queensland (QLD) and South Australia (SA)] and one non-state-based [Snowy Mountains Hydroelectric Scheme (SNO)] regional markets operating as a nationally interconnected grid. Within this grid, the largest generation capacity is found in NSW, followed by QLD, VIC and SA, while electricity demand is highest in NSW, followed by VIC, QLD and SA. The NEM, encompasses privately and publicly owned generators, transmission and distribution network providers and traders (for details of the NEM's regulatory background, institutions and operations see Australian Competition and Consumer Commission, 2000, International Energy Agency, 2001 and National Electricity Market Management Company Limited, 2008a). However, each state's network was (and still is) characterized by a very small number of participants and sizeable differences in electricity prices were found. One of the objectives in establishing the NEM was to provide a nationally integrated and efficient electricity market. However, a defining characteristic of the NEM is the limitations of physical transfer capacity. QLD has two interconnectors that together can import and export to and from NSW, NSW can export to and from the SNO and VIC can import from the SNO and SA and export to the SNO and to SA. There is currently no direct connector between NSW and SA and QLD is only directly connected to NSW. As a result, the NEM itself is not yet strongly integrated. During periods of peak demand, the interconnectors become congested and the NEM separates into its regions, promoting price differences across markets and exacerbating reliability problems of regional utilities (Australian Competition and Consumer Commission, 2000, International Energy Agency, 2001 and National Electricity Market Management Company Limited, 2008a). While the appropriate regulatory and commercial mechanisms do exist for the creation of an efficient national market, and these are expected to have an impact on the price of electricity in each region, it is argued that the complete integration of the separate regional electricity markets has not yet been realised. In particular, the limitations of the interconnectors between the member states suggest that, for the most part, the regional spot markets are relatively isolated. This paper is motivated by the fact that the operations of the electricity market is similar to that of financial markets and modelling the dynamics of the conditional means which focus on the behaviour of the spot electricity prices and the conditional variance which assesses the risk management of these highly competitive markets. A fuller understanding of the dynamics of electricity pricing is likely to throw light on the efficiency of pricing and the impact of interconnection within the centralized markets which still are primarily composed of commercialized and corporatized public sector entities. A fuller understanding of the pricing relationships between these markets enables the benefits of interconnection to be assessed as a step towards the fuller integration of the regional electricity markets into a national electricity market. This provides policy inputs into both the construction of new interconnectors and the preparation of guidelines for the reform of existing market mechanisms. There are many studies that use various univariate generalized autoregressive conditional heteroskedasticity (GARCH) models to assess the dynamics within spot electricity markets. This is then extended to multivariate GARCH (MGARCH) models to capture volatility clustering between spot electricity prices. The univariate autoregressive conditional heteroskedasticity (ARCH) models [as introduced by Engle (1982)] and GARCH models [as proposed by Bollerslev (1986)] have already been widely employed in modelling the dynamics of spot electricity markets. Suitable surveys of GARCH modelling in the spot electricity markets may be found in Knittel and Roberts (2001), Solibakke (2002), Hadsell et al. (2004), Higgs and Worthington (2005) and Chan and Gray (2006). The only studies to date that have extended the univariate GARCH analyses to MGARCH applications as proposed by Bollerslev (1990) are De Vany and Walls (1999a), Bystrom (2003), Worthington et al. (2005) and Haldrup and Nielsen (2006). De Vany and Walls (1999a) use cointegration analysis between pairs of US regional electricity markets to assess market integration while Bystrom (2003) applies the constant correlation bivariate GARCH model to the short-term hedging of the Nordic spot electricity prices with electricity futures. Worthington et al. (2005) employ the multivariate GARCH (MGARCH) BEKK (Baba, Engle, Kraft and Kroner) model to capture the price and volatility spillovers among five spot electricity markets in Australia. The disadvantage of the MGARCH BEKK model is that the estimated coefficients for the variance–covariance matrix cannot be interpreted on an individual basis: “instead, the functions of the parameters which form the intercept terms and the coefficients of the lagged variance, covariance, and error terms that appear are of interest” (Kearney and Patton, 2000: 36). So far Worthington et al. (2005) produce the only study that utilizes the MGARCH model to assess the inter-relationships among five Australian spot electricity markets. Haldrup and Nielsen (2006) use a Markov regime switching model with long memory in each of the regime states to model the interdependence between pairs of electricity markets in the Nordic Pool regions. The aim of this research is to extend on the paper by Worthington et al. (2005) by employing a family of constant and dynamic conditional correlation MGARCH models to capture the effects of cross-correlation volatility spillovers between the four Australian spot electricity markets. This permits a greater understanding of pricing efficiency and cross-correlation volatility spillovers between these interconnected markets. If there is a lack of significant inter-relationships between regions then doubt may then be cast on the ability of the NEM to overcome the exercise of regional market power as its primary objective, and on its capacity to foster a nationally integrated and efficient electricity market. To the author's knowledge a detailed study of the applications of constant and dynamic correlation MGARCH models to assess the behaviour of the inter-relationships between more than two spot electricity markets has not been undertaken. It is within the context of previous limited empirical work that the present paper is conducted. Accordingly, the purpose of this paper is to investigate the price volatility and inter-relationships in four Australian regional electricity markets by employing three conditional correlation MGARCH models namely: the constant conditional correlation, Tse and Tsui's (2002) and Engle's (2002) dynamic conditional correlation MGARCH models. If there is a lack of significant inter-relationships between regional markets then doubt may then be cast on the ability of the NEM to foster a nationally integrated and efficient electricity market. The remainder of the paper is divided into four sections. The second section surveys the transmission and trading of electricity across the regional markets. The third section explains the data employed in the analysis and presents some brief summary statistics. The fourth section discusses the methodology employed. The results are dealt with in the fifth section. The paper ends with some brief concluding remarks in the final section.
نتیجه گیری انگلیسی
This study presents an analysis of inter-relationships of wholesale electricity prices and price volatility in the four Australian electricity markets of New South Wales, Queensland, South Australia and Victoria. The data consists of half-hourly prices for the period 1 January 1999 to 31 December 2007. Three different conditional correlation MGARCH models namely: the constant conditional correlation (CCC), Tse and Tsui's (2002) and Engle's (2002) DCC MGARCH models are estimated. The results indicate that the price and price volatility inter-relationships in the Australian wholesale electricity markets are best described by the Tse and Tsui (2002) DCC MGARCH specification. This model has the ability to capture the time-varying dynamics of the conditional correlations across pairs of electricity markets. The Student t specification is also included to accommodate the fat-tailed properties of the observed data. These findings make a significant contribution in estimating the volatility and the efficiency of the wholesale electricity markets by employing time-varying multivariate techniques that have not been previously explored in the Australian context. The assessment of these prices and volatility between regional markets allows for better understanding of the spot electricity dynamics by electricity producers, transmitters and retailers and the efficient distribution of energy on a national level. At the first stage, the univariate GARCH(1,1) models are used to identify the source and magnitude of the mean, innovation and volatility spillovers of each market. All four markets exhibit a significant own mean spillover. Only three of the markets exhibit a significant mean spillover from other lagged markets. This suggests, for the most part, that the lagged price information in one market cannot be used to forecast spot electricity prices in another market. Electricity prices on Saturday, Sunday and public holidays are lower than weekday prices. The results of the univariate GARCH(1,1) also show the presence of strong ARCH and GARCH effects with the exception of the QLD market. This indicates that for all regional markets volatility shocks are persistent over time. This persistence suggests that high (low) volatility of price changes is followed by high (low) volatility price changes; that is, like magnitudes of price changes cluster over time. This price clustering captures the non-normality and non-stability of Australian electricity spot prices. At the second stage, the conditional correlation volatility spillovers of the TTDCC model are positive and significant for all pairs of markets, indicating the presence of positive volatility effects between pairs of markets. The highest conditional correlations are evident between the well-connected markets namely: NSW and QLD; NSW and VIC; and SA and VIC. This indicates that the interconnectivity and/or geographic arbitrage between the separate regions in the NEM have fostered a nationally integrated and stable spot electricity market, thus indicating that the interconnected markets are informationally efficient. The lowest conditional correlation is evident between the not directly interconnected QLD and SA markets. As a general rule, the less direct the interconnection between regions, the lower the conditional correlations volatility spillover effects between these regions. This suggests that the main determinant of the interaction between regional electricity markets is geographical proximity and the number and size of the interconnectors. Accordingly, it may be unreasonable to expect that prices in electricity markets that are geographically isolated market will ever become fully integrated. Of course, the full nature of the interdependence and mean-reversion of the price and volatility inter-relationships between these separate markets could be due to seasonal factors or weather conditions. Spot prices are mean-reverting as weather is a dominant factor influencing the equilibrium price, through changes in demand. The cyclical nature of weather conditions tends to pull price back to its mean level. One future application would then include weather conditions in modelling the dynamics of the inter-relationships between electricity markets.