Price volatility of gas and electricity by month: Day-ahead contracts (GB)

Chart

Source: ICIS data & Ofgem calculations.

Information correct as of: July 2019

This chart shows the monthly average of volatility for GB gas, electricity baseload and electricity peakload prices based on Day-ahead contracts.

Policy Areas:

  • Electricity - wholesale markets
  • Gas - wholesale markets

Data Table

Price volatility of gas and electricity by month: Day-ahead contracts (GB)
Month beginningElectricity (baseload)Electricity (peakload)Gas
01/01/200399%643%332%
01/02/2003102%635%313%
01/03/200356%232%158%
01/04/200338%135%128%
01/05/200340%177%141%
01/06/200347%216%192%
01/07/2003116%531%111%
01/08/2003159%687%141%
01/09/200384%424%210%
01/10/200356%323%194%
01/11/200360%334%135%
01/12/200377%484%125%
01/01/200490%407%256%
01/02/200494%452%444%
01/03/200439%127%113%
01/04/200428%118%105%
01/05/200430%162%115%
01/06/200442%228%67%
01/07/200435%169%58%
01/08/200418%105%70%
01/09/200418%125%100%
01/10/200443%144%126%
01/11/200456%238%133%
01/12/200442%211%119%
01/01/200546%231%131%
01/02/200537%167%126%
01/03/200581%319%416%
01/04/200532%153%160%
01/05/200517%72%93%
01/06/200526%124%135%
01/07/200546%279%126%
01/08/200517%106%92%
01/09/200529%146%89%
01/10/200533%176%103%
01/11/200550%194%226%
01/12/200579%338%294%
01/01/200670%282%209%
01/02/200659%205%205%
01/03/2006114%436%389%
01/04/200681%331%291%
01/05/200623%94%121%
01/06/200629%145%219%
01/07/200665%302%182%
01/08/200690%444%90%
01/09/200638%190%105%
01/10/200648%227%567%
01/11/200672%297%217%
01/12/200687%328%183%
01/01/200763%294%154%
01/02/200741%194%120%
01/03/200730%154%140%
01/04/200733%167%86%
01/05/200742%219%106%
01/06/200753%290%84%
01/07/200740%181%107%
01/08/200733%129%95%
01/09/200744%172%127%
01/10/200775%248%122%
01/11/200792%413%113%
01/12/200777%363%91%
01/01/200861%266%92%
01/02/200820%102%50%
01/03/200815%70%49%
01/04/200831%156%41%
01/05/200824%111%78%
01/06/200843%234%48%
01/07/200843%203%75%
01/08/200833%160%212%
01/09/200834%149%146%
01/10/200841%156%105%
01/11/200848%212%144%
01/12/200860%277%98%
01/01/200960%275%110%
01/02/200935%151%100%
01/03/200923%98%87%
01/04/200916%66%96%
01/05/200928%102%71%
01/06/200931%109%82%
01/07/200931%112%68%
01/08/200918%65%81%
01/09/200928%111%203%
01/10/200921%92%266%
01/11/200918%93%106%
01/12/200922%89%66%
01/01/201031%127%141%
01/02/201020%50%99%
01/03/201010%42%60%
01/04/201013%51%75%
01/05/201020%77%113%
01/06/201016%60%73%
01/07/201014%59%55%
01/08/201011%52%49%
01/09/20108%34%72%
01/10/201013%47%83%
01/11/201014%56%42%
01/12/201051%239%49%
01/01/201123%112%40%
01/02/20118%30%38%
01/03/201112%37%31%
01/04/201113%40%53%
01/05/20116%26%55%
01/06/20116%26%22%
01/07/20116%31%22%
01/08/20118%32%24%
01/09/201112%48%66%
01/10/201113%65%86%
01/11/201114%51%48%
01/12/20119%44%34%
01/01/201212%43%36%
01/02/201225%108%144%
01/03/201215%70%70%
01/04/201214%50%49%
01/05/201211%42%29%
01/06/201211%54%29%
01/07/201210%39%28%
01/08/20128%34%29%
01/09/201210%34%30%
01/10/201212%43%25%
01/11/201221%87%24%
01/12/201220%70%20%
01/01/201320%80%32%
01/02/201320%79%32%
01/03/201337%125%87%
01/04/201333%119%105%
01/05/201314%42%25%
01/06/201313%44%35%
01/07/201313%45%46%
01/08/201311%29%18%
01/09/201313%35%15%
01/10/201314%51%21%
01/11/201320%75%33%
01/12/201321%73%20%
01/01/201421%77%17%
01/02/201416%60%19%
01/03/201419%53%35%
01/04/201415%47%33%
01/05/201412%41%30%
01/06/201410%35%42%
01/07/201412%53%43%
01/08/201412%53%47%
01/09/201418%74%51%
01/10/201423%89%41%
01/11/201442%187%50%
01/12/201426%108%26%
01/01/201528%90%36%
01/02/201524%97%39%
01/03/201518%74%38%
01/04/201517%51%27%
01/05/201517%48%28%
01/06/201517%50%28%
01/07/201514%47%22%
01/08/201512%37%31%
01/09/201511%41%28%
01/10/201511%43%24%
01/11/201517%64%36%
01/12/201523%71%34%
01/01/201629%115%39%
01/02/201630%149%39%
01/03/201633%170%25%
01/04/201628%121%27%
01/05/201624%117%58%
01/06/201621%96%39%
01/07/201624%104%32%
01/08/201626%126%43%
01/09/2016151%495%131%
01/10/2016182%663%87%
01/11/2016141%638%43%
01/12/201685%425%41%
01/01/201730%167%54%
01/02/201718%89%52%
01/03/201717%79%30%
01/04/201717%69%25%
01/05/201723%126%35%
01/06/201742%189%79%
01/07/201782%371%77%
01/08/201732%130%42%
01/09/201722%100%30%
01/10/201726%94%64%
01/11/201719%94%28%
01/12/201721%82%52%
01/01/201821%81%42%
01/02/201816%76%47%
01/03/201867%248%478%
01/04/201829%128%83%
01/05/201818%95%33%
01/06/201817%66%44%
01/07/201817%71%36%
01/08/201810%54%24%
01/09/201813%63%33%
01/10/201818%58%50%
01/11/201819%81%49%
01/12/201829%134%43%
01/01/201923%104%54%
01/02/201927%115%34%
01/03/201913%54%29%

More information

Price volatility of gas and electricity (Day-ahead contracts): At-a-glance summary

The volatility of day-ahead gas and power prices has been generally decreasing in recent years. Volatility for both gas and power remained low over the course of 2015 and the first half of 2016, but increased during winter 2016/17.

A sharp increase in the average of volatility for GB gas occured in March 2018, with record high gas prices due to both high demand and supply issues: gas demand reached the highest level in seven years and there was a series of unplanned outages across GB supply infrastructure that increased the gas price further. Since May 2018 volatility for both gas and electricity has remained low and stable, hovering between 24% to 50% for gas, and 10% and 29% for electricity.

Relevance and further information

Price volatility is an important market indicator for a range of reasons. For example:

  • Companies with capacity that can flexibly increase or decrease their supply of gas or electricity can profit from high volatility by increasing output when prices are high and decreasing output when prices are low. Such companies include gas storage facilities or conventional coal and gas-fired power stations.
  • High levels of volatility can challenge smaller companies where they are less able to access credit and collateral.

Methodology

  • The monthly calculation takes the logarithmical differences of daily average prices across two consecutive trading days. These are used to calculate the relative standard deviation on a rolling monthly basis (21 trading days).
  • To show the data in annual terms, the value obtained is multiplied by the square root of the total number of trading days in a year (252 trading days).
  • Volatility values are usually expressed as a percentage, so the annual value is finally multiplied by 100.
  • All volatility values for a given month are then averaged together to get a single monthly data point.
Date correct
July 2019
Policy areas