The impact of virtual bidding on price volatility in New York's wholesale electricity market

Economics Letters 95 (2007) 66 – 72 www.elsevier.com/locate/econbase

The impact of virtual bidding on price volatility in New York's wholesale electricity market Lester Hadsell ⁎ School of Business, University at Albany, 1400 Washington Avenue, Albany, New York 12222, USA Received 2 January 2006; received in revised form 15 August 2006; accepted 6 September 2006 Available online 28 November 2006

Abstract This paper examines the impact that virtual bidding has had on price volatility in the New York State wholesale electricity market. Virtual bidding is found to be associated with reduced volatility in both the Day Ahead and Real Time markets. © 2006 Elsevier B.V. All rights reserved. Keywords: Electricity; Volatility; Virtual bidding JEL classification: Q4; G1; L94

1. Introduction Virtual bidding, in which customers place buy and sell orders that are settled financially instead of through physical delivery, began on the New York State Independent System Operator (NYISO) wholesale electricity market in November 2001. Designed to allow additional hedging flexibility as well as open the NYISO market to speculators, virtual bidding was intended to lead to convergence of Day Ahead and Real Time prices and to reduced price volatility. This paper investigates the effect that virtual bidding has had on price volatility in the Day Ahead and Real Time markets. The findings suggest that volatility has indeed been reduced. A growing body of literature is emerging that examines the general characteristics and extreme price volatility in electricity markets (Longstaff and Wang, 2004; Joskow and Kahn, 2002; Knittle and Roberts, ⁎ Tel.: +1 518 442 4933; fax: +1 518 442 3045. E-mail address: [email protected]. 0165-1765/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.econlet.2006.09.015

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2001). Hadsell et al. (2004) estimate conditional volatility in five U.S. markets and show that deregulated electricity markets exhibit levels and persistence of price volatility unparalleled in traditional commodity markets. The primary reasons for the observed price behavior include inelastic demand, non-storability of electricity, and congestion caused by transmission constraints (Borenstein, 2002). By opening markets to outsiders virtual bidding should reduce the persistence of deviations from expected returns, lessening the impacts of shocks. Virtual bids are price capped offers in the Day Ahead market to buy or sell a certain quantity (MWh) of electricity during a certain hour in a certain zone. Those offers are then reversed in the Real Time market. The difference between the Day Ahead and Real Time clearing prices represents the profit or loss on the transaction. 2. Data Each zone on NYISO is treated as a separate but connected market, with unique prices determined for each zone from the bids and offers submitted to NYISO combined with transmission constraints. Table 1 provides summary statistics for Day Ahead and Real Time prices for the period January 1, 2000 to June 16, 2004. The numbers in Table 1 reflect the daily average of 16 peak hours between 7 AM and 11 PM. These hours are similar in terms of load and prices. Regulators and participants are particularly keen on understanding the volatility aspects of peak hours as they make decisions in the short-term concerning generation and price, and in the long-term concerning investment in new generation and transmission capacity. In addition, futures contracts traded on NYMEX typically are based on peak hours. These are financially settled contracts based on 25 MW per hour for 16 peak hours for 19 to 23 days over the delivery month. Traders need detailed information on the properties of wholesale prices, including volatility, to price these contracts accurately. Table 1 NYISO summary statistics for average of 16 peak hours, January 1, 2000–June 16, 2004, dollars per MWh Capital Central Dunwoodie Genesee Hudson Valley Long Is. Millwood Mohawk North Day Ahead Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

NYC

West

Prices 51.22 44.10 52.65 50.33 43.71 51.43 633.63 372.96 579.70 17.10 14.62 2.25 24.13 17.26 23.99 11.31 5.02 9.50 239.25 82.25 179.41

43.16 42.48 378.25 14.21 17.29 5.26 88.01

51.88 50.47 562.90 17.97 23.30 9.66 181.81

61.52 58.00 468.42 27.92 25.27 4.56 52.92

51.83 50.41 572.65 5.94 23.63 9.59 181.43

45.23 45.05 386.02 10.29 17.97 4.93 80.71

43.74 60.75 41.45 43.79 58.58 40.35 369.14 577.92 375.27 −0.38 19.06 14.93 17.57 27.27 16.61 4.59 6.58 5.86 73.48 100.39 102.04

Real Time Prices Mean 49.32 40.54 51.85 Median 46.68 38.39 48.15 Maximum 479.94 267.86 400.20 Minimum 6.10 5.41 −6.16 Std. Dev. 25.66 19.20 27.98 Skewness 6.20 3.25 4.29 Kurtosis 80.15 27.32 34.21

40.68 38.65 269.29 5.33 19.50 3.11 25.23

49.09 46.04 392.68 5.97 24.97 4.86 46.70

64.22 58.94 446.62 6.39 31.80 3.41 24.13

51.10 47.32 394.18 − 0.30 27.74 4.27 33.57

41.56 39.74 266.25 −20.26 20.12 2.94 23.58

40.39 61.79 38.75 38.54 55.47 36.65 255.61 944.89 276.33 −35.03 6.22 4.85 19.82 42.53 18.58 2.78 7.87 3.66 21.74 126.57 33.65

1623 observations for each zone.

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Fig. 1. Day ahead (top) and real time (bottom) returns for capital zone.

To examine the effect of virtual bidding on NYISO electricity prices, we construct the return series Rit = ln(Pit / Pit−1) for each zone, where Pit is the DA (or RT) price for zone i in period t. Augmented Dickey–Fuller unit root tests indicate that both the DA and RT return series are stationary. Fig. 1 shows these returns for a representative zone (“Capital”) for the Day Ahead and Real Time markets. The standard deviations indicate annualized rates of volatility near 300% for DA and 600% for RT returns. These volatility levels, although not unusual for electricity markets, are considerably higher than volatility levels found for other commodities (Krapels, 2000). The figure shows that prices in the first year or two exhibited unusually high volatility compared to subsequent years (at least in the DA market). Some factors which may explain the apparent changes in volatility during this time include a learning curve for participants, entry and exit of generating capacity, and changes to market rules (discussed below in the concluding remarks). At the same, though, generators were prohibited from selling short and outsiders were unable to participate until virtual bidding became effective in late 2001. We investigate the possibility that the decline in volatility is associated with the introduction of virtual bidding. 3. Model and empirical results Because electricity markets often exhibit time-varying volatility, we use a GARCH model in our analysis. ARCH models have frequently been used in studies of prices in electricity, commodity, and

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Table 2 Virtual bidding and volatility o n NYISO, January 1, 2000–June 16, 2004 Day ahead returns

Capital

Central

Dunwoodie

Genesee

Hudson Valley

Long Island

Mohawk

Millwood

NYC

North

West

ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ

Coeff

S.E.

t-stat

p-value

0.0069 0.1431 0.8116 −0.0063 0.0023 0.1245 0.8338 −0.0017 0.0069 0.1287 0.8163 −0.0061 0.0023 0.1225 0.8369 −0.0017 0.0020 0.0965 0.9011 −0.0019 0.0153 0.4710 0.4826 −0.0119 0.0022 0.1294 0.8375 −0.0017 0.0050 0.1107 0.8616 −0.0048 0.0039 0.0840 0.8734 −0.0034 0.0034 0.2348 0.7658 −0.0029 −0.2424 0.1832 0.0714 −0.0209

0.0017 0.0266 0.0373 0.0016 0.0006 0.0227 0.0281 0.0005 0.0019 0.0306 0.0449 0.0017 0.0006 0.0222 0.0268 0.0005 0.0006 0.0222 0.0304 0.0005 0.0048 0.1619 0.1124 0.0041 0.0006 0.0226 0.0268 0.0005 0.0010 0.0255 0.0326 0.0010 0.0013 0.0293 0.0527 0.0011 0.0009 0.1080 0.0407 0.0008 0.0477 0.0352 0.0275 0.0116

4.09 5.38 21.75 −3.96 4.01 5.48 29.69 −3.83 3.68 4.21 18.19 −3.59 3.99 5.52 31.20 −3.74 3.26 4.34 29.61 −3.85 3.21 2.91 4.29 −2.92 3.85 5.74 31.21 −3.70 4.78 4.33 26.44 −4.91 2.91 2.87 16.59 −3.25 3.91 2.17 18.83 −3.74 −5.08 5.20 2.60 −1.81

0.0000 0.0000 0.0000 0.0001 0.0001 0.0000 0.0000 0.0001 0.0002 0.0000 0.0000 0.0003 0.0001 0.0000 0.0000 0.0002 0.0011 0.0000 0.0000 0.0001 0.0013 0.0036 0.0000 0.0035 0.0001 0.0000 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0037 0.0041 0.0000 0.0012 0.0001 0.0297 0.0000 0.0002 0.0000 0.0000 0.0094 0.0700

Rt− = μ + εt, εt|ϕt−1 ∼ N(0,σ2t ), σ2t = ω + αε2t−1 + βσ2t−1 + γVB.

α+β

Half-life(days)

0.9548

14.9777

0.9582

16.2394

0.9450

12.2512

0.9594

16.7161

0.9976

288.5850

0.9537

14.6066

0.9669

20.5969

0.9722

24.6004

0.9574

15.9266

1.0006

N/A

0.2547

0.5068

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Table 3 Virtual bidding and volatility on NYISO, January 1, 2000–June 16, 2004 Real time returns

Capital

Central

Dunwoodie

Genesee

Hudson Valley

Long Island

Mohawk

Millwood

NYC

North

West

ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ ω α β γ

Coeff

S.E.

t-stat

p-value

0.0751 0.3110 0.1149 − 0.0367 0.0671 0.2039 0.2089 − 0.0304 0.0219 0.1890 0.6730 − 0.0110 0.0677 0.1939 0.2209 − 0.0310 0.0796 0.3042 0.1335 − 0.0425 0.0033 0.0686 0.9091 − 0.0019 0.0643 0.2690 0.2716 − 0.0308 0.0849 0.3354 0.1462 − 0.0467 0.0253 0.1814 0.6968 − 0.0163 0.0732 0.3488 0.2289 − 0.0378 0.0728 0.1741 0.1708 − 0.0325

0.0141 0.0586 0.0775 0.0122 0.0133 0.0432 0.1290 0.0081 0.0073 0.0389 0.0738 0.0049 0.0140 0.0423 0.1333 0.0085 0.0126 0.0537 0.0842 0.0108 0.0019 0.0185 0.0256 0.0014 0.0123 0.0554 0.1031 0.0087 0.0127 0.0540 0.0693 0.0115 0.0078 0.0349 0.0666 0.0058 0.0136 0.0824 0.0911 0.0109 0.0155 0.0402 0.1470 0.0090

5.32 5.31 1.48 − 3.01 5.05 4.71 1.62 − 3.73 3.01 4.86 9.11 − 2.25 4.85 4.59 1.66 − 3.67 6.31 5.67 1.58 − 3.92 1.73 3.71 35.50 − 1.35 5.22 4.85 2.63 − 3.54 6.69 6.21 2.11 − 4.08 3.23 5.19 10.46 − 2.83 5.39 4.23 2.51 − 3.47 4.71 4.33 1.16 − 3.60

0.0000 0.0000 0.1381 0.0026 0.0000 0.0000 0.1054 0.0002 0.0026 0.0000 0.0000 0.0246 0.0000 0.0000 0.0974 0.0002 0.0000 0.0000 0.1130 0.0001 0.0843 0.0002 0.0000 0.1765 0.0000 0.0000 0.0084 0.0004 0.0000 0.0000 0.0350 0.0000 0.0012 0.0000 0.0000 0.0046 0.0000 0.0000 0.0120 0.0005 0.0000 0.0000 0.2453 0.0003

Rt− = μ + εt, εt|ϕt−1 ∼ N(0,σ2t ), σ2t = ω + αε2t−1 + βσ2t−1 + γVB.

α+β

Half-life(days)

0.4259

0.8120

0.4128

0.7833

0.8619

4.6655

0.4148

0.7876

0.4377

0.8390

0.9777

30.7252

0.5406

1.1268

0.4816

0.9486

0.8782

5.3352

0.5776

1.2630

0.3449

0.6512

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equity markets (Higgs and Worthington, 2005; Hadsell et al., 2004; Sadorsky, 2002; Poon and Granger, 2003). ARCH-LM and White heteroskedasticity tests on the return series confirm that the error terms are heteroskedastic. The GARCH model also allows us to measure the impact of virtual bidding on volatility: Rt ¼ A þ et

ð1Þ

et j/t−1 fNð0; r2t Þ

ð2Þ

r2t ¼ x þ ae2t−1 þ br2t−1 þ γVB

ð3Þ

The error term εt, conditioned on information available at time t − 1, is assumed to be normally distributed with mean zero and variance σt2 . The conditional variance σt2 , is specified as a function of the 2 (the ARCH term), and the previous mean ω, the news about volatility from the previous period εt−1 2 period's forecast variance σt−1 (the GARCH term). VB is a dummy variable which is 1 during the period virtual bidding was in effect, 0 otherwise. We are interested mainly in the coefficient on VB, indicating the effect of virtual bidding on volatility, and the level of volatility persistence. The point estimate of persistence, the time taken for volatility to move halfway back to its unconditional mean following a given deviation, is (α + β ). A value less than one implies a mean reverting conditional volatility process in which shocks are transitory in nature and the number of days it takes volatility to revert half-way back to its mean can be estimated as ln(1 / 2)/ln(α + β ). Table 2 shows that the coefficient on the virtual bidding dummy is statistically significant at the 1% level in all zones in the Day Ahead market (the standard errors are robust heteroskedastic consistent covariances). The Negative coefficient indicates that virtual bidding is associated with reduced volatility. Much the same also is true for the Real Time market, shown in Table 3. Overall, the effect of virtual bidding is greater in the RT market — the coefficient on the VB dummy in the RT market in eight of the eleven zones is between five and 20 times greater (in absolute value) than it is in the DA market. We also note that the level of persistence in the RT market is much lower than it is in the DA market. Comparing persistence pre- and post-virtual bidding, it appears that persistence declined slightly with the introduction of virtual bidding. 4. Conclusions To the extent that virtual bidding has reduced volatility it also has reduced uncertainty regarding generators' revenues and suppliers' costs, made it easier to price related derivatives, and likely has increased the overall attractiveness of the NYISO market for those with direct interests in physical electricity deliveries, as prices are more stable and hedging is available. As the NYISO is integrated into wider financial markets through innovations such as virtual bidding these markets may begin to behave more like other commodity markets. The extent of the integration of NYISO into the financial sector is illustrated by the number of NYISO customers that are traditional financial service companies (e.g., HSBC Bank, Credit Suisse First Boston, and Morgan Stanley Capital Group). Caution concerning the results of the impact of virtual bidding presented in this paper is warranted, given all that was happening in the first few years of this new market. First, traders were learning the

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characteristics of a new market — Borenstein et al. (2004) suggest that unusually high volatility can be explained by a learning curve for participants. Complicating this learning were shifts to supply caused by entry and exit of generation capacity and changes to the NYISO market software and rules. These changes included implementation of price-capped load bidding (implemented in 2001 before virtual trading); 1 changes to rules governing the treatment of operating reserves which became effective in June 2002;2 implementation in June 2003 of scarcity pricing provisions that set prices at $1000 during operating reserve shortages (Patton, 2004);3 implementation in June 2002 of mitigation procedures; and implementation of load pocket modeling for New York City in 2002 (which may have actually increased Real Time prices in that zone) (Babbel and Harvey, 2006). Each of these certainly may be a part of the story. Nonetheless, the evidence presented in this paper suggests that virtual bidding is associated with reduced price volatility in both the Day Ahead and Real Time markets. References Babbel, David F., Harvey, Scott M., 2006. Evaluation of NYISO virtual trading collateral multiple policy. http://www.nyiso.com/ public/webdocs/committees/bic_spwg_cptf/meeting_materials/2006-02-02/LECG_VT_Collateral_Policy_Report_1-31-06. pdf. Borenstein, Severin, 2002. The trouble with electricity markets: understanding California's restructuring disaster. Journal of Economic Perspectives 16 (1), 191–211. Borenstein, Severin, Bushnell, J., Knittel, C., Wolfram, C., 2004. Inefficiencies and Market Power in Financial Arbitrage: a Study of California's Electricity Markets. Center for the Study of Energy Markets, Working Paper, vol. 138. University of California Energy Institute. Hadsell, Lester, Marathe, Achla, Shawky, Hany A., 2004. Estimating the volatility of wholesale electricity spot prices in the US. Energy Journal 25 (4), 23–40. Higgs, Helen, Worthington, Andrew C., 2005. Systematic features of high-frequency volatility in Australian electricity markets: intraday patterns, information arrival and calendar effects. Energy Journal 26 (4), 23–41. Joskow, Paul, Kahn, Edward, 2002. A quantitative analysis of pricing behavior in California's wholesale electricity market during Summer 2000. Energy Journal 23 (4), 1–35. Knittle, C.R., Roberts, M.R., 2001. An Empirical Examination of Deregulated Electricity Prices. Working Paper PWP-087 University of California Energy Institute. Krapels, Edward N., 2000. Electricity Trading and Hedging. Risk Books, London. Longstaff, Francis A., Wang, Ashley W., 2004. Electricity forward prices: a high-frequency empirical analysis. Journal of Finance 59 (4), 1877–1900. Patton, David B., 2003. 2002 State of the Market Report New York Electricity Markets. http://www.nyiso.com/public/webdocs/ newsroom/current_issues/2002_annual_report.pdf. Patton, David B., 2004. 2003 State of the Market Report New York Electricity Markets. http://www.nyiso.com/public/webdocs/ newsroom/current_issues/2003_state_of_the_market_report_final_full_text.pdf. Patton, David B., 2005. 2004 State of the Market Report New York Electricity Markets. http://www.nyiso.com/public/webdocs/ documents/market_advisor_reports/2004_patton_final_report.pdf. Poon, Ser-Huang, Granger, Clive W.J., 2003. Forecasting volatility in financial markets: a review. Journal of Economic Literature 41 (2), 478–539. Sadorsky, Perry, 2002. Time-varying risk premiums in petroleum futures prices. Energy Economics 24 (6), 539–556.

1

NYISO Technical Bulletin 069 (May 11, 2001). See also Patton (2003). NYISO Technical Bulletin 093 (May 31, 2002) and Patton (2004, p. 13). 3 Due to the relatively mild weather in the summer of 2003 and increased imports from New England, there were no shortages in 2003. Hence, these pricing provisions were not triggered. The scarcity pricing provisions were replaced by reserve demand curves in February 2005 (after the period of study in this paper) (Patton, 2005). 2