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Energy trading scenario 2016

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Financial Algorithms presents the energy trading scenario for the year 2016. In this presentation, after examining various fundamental factors in energy sector, FA forecasts the crude oil price, gasoline & natural gas price levels for the year 2016; in case of mean volatility levels and high volatility levels, both. FA also focuses on how to model price levels and volatility surfaces in low volatility and high volatility scenarios under forward & forward-forward models using various energy contracts and spreads i.e. crack spread. Various greek sensitivities including second order & third order greeks, which can be helpful in projecting the price & volatility levels, are also described. At the end, correlation factors, fundamental & technical both, are discussed. These correlation factors are exogenous in price forecasting, and new emerging trends which can affect the energy trading in a long run also been discussed.

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Energy trading scenario 2016

  1. 1. Rakesh Sharma, Executive Director & Head of Financial Engineering Team, Financial Algorithms Energy Trading Scenario 2016 Slump in Crude Oil Prices – Modelling Prices & Volatilities March, 2016
  2. 2. Financial Algorithms™ 2Energy Trading Scenario 2016 Topics 1. Oil Prices – examining fundamentals as uncertainty continues 2. Modelling Oil Price, Volatility from Market Instruments 3. Correlation monitoring in energy derivatives
  3. 3. Financial Algorithms™ 3Energy Trading Scenario 2016 1 Oil Prices – examining fundamentals as uncertainty continues
  4. 4. Financial Algorithms™ 4Energy Trading Scenario 2016 World Oil Outlook 2015 – a subtext » The OPEC published its World Oil Outlook 2015 (WOO) in late December 2015, which struck a much more pessimistic note on the state of oil markets. On the one hand, OPEC does not see oil prices returning to triple-digit territory within the next 25 years, a strikingly bearish conclusion. » The group expects oil prices to rise by an average of about $5 per year over the course of this decade, only reaching $80 per barrel in 2020. From there, it sees oil prices rising slowly, hitting $95 per barrel in 2040. » Although this estimate carries an error, barring price modeling which involves an array of variables, and modifications in certain assumptions – such as GDP projections or the pace of population growth – this can lead to dramatically different conclusions. So the estimates should be taken only as a reference case rather than a serious attempt at predicting crude prices in 25 years. » In estimates, the world will consume an extra 6.1 million barrels of oil per day between now and 2020. But demand growth slows thereafter: 3.5 mb/d between 2020 and 2025, 3.3 mb/d for 2025 to 2030; 3 mb/d for 2030 to 2035; and finally, 2.5 mb/d for 2035 to 2040. The reasons for this are multiple: slowing economic growth, declining population rates, and crucially, efficiency and climate change efforts to slow consumption. » In fact, since 2014 WOO, OPEC lowered its 2040 oil demand projection by 1.3 mb/d because it sees much more serious climate mitigation policies coming down the pike than it did last year. Such outcomes yet to be seen but we are seeing some shifts in oil production levels (next slide) OPEC released World Oil Outlook 2015 http://oilprice.com/Energy/Crude-Oil/10-Trillion-Investment-Needed-To-Avoid-Massive-Oil-Price-Spike-Says-OPEC.html
  5. 5. Financial Algorithms™ 5Energy Trading Scenario 2016 Crude Oil Production in OPEC region over the last few years 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Jan 2013 Jan 2014 Jan 2015 Jan 2016 MillionBarrelsperday Estimated Historical Unplanned OPEC Crude Oil Production Outages million barrels per day Indonesia Saudi Arabia Kuwait Iraq Nigeria Libya Iran Source: Short-Term Energy Outlook, March 2016.
  6. 6. Financial Algorithms™ 6Energy Trading Scenario 2016 Crude Oil Production in Non-OPEC region over the last few years 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Jan 2013 Jan 2014 Jan 2015 Jan 2016 MillionBarrelsperday Estimated Historical Unplanned Non-OPEC Liquid Fuels Production Outages million barrels per day Other United States Mexico Canada Sudan / S. Sudan Colombia Brazil North Sea Yemen China Syria Source: Short-Term Energy Outlook, March 2016.
  7. 7. Financial Algorithms™ 7Energy Trading Scenario 2016 What is next after Shell gas discovery : Energy security scenario across regions affecting oil price levels » There was a gap between business as-usual supply and business-as-usual demand of around 400 EJ/a – the size of the entire oil & natural gas industry in 2000. Though ,this has been reversed by various factors over the last few years. » As in focus on national energy security, immediate pressures drive decision makers, especially the need to secure energy supply in the near future for themselves and their allies. National government attention naturally falls on the supply-side levers readily to hand, including the negotiation of bilateral agreements and incentives for local resource development. Growth in coal and biofuels becomes particularly significant. 0.00 1.00 2.00 3.00 World Oil Supply World Oil Demand Million barrels per day World Oil Demand-Supply growth for first 3 quarters of 2015 Source: By OPEC Secretariat » Despite increasing rhetoric, action to address climate change and encourage energy efficiency is pushed into the future, leading to largely sequential attention to supply, demand and climate stresses. » Clean energy sources such as nuclear energy rapidly gaining support from energy thirsty economies. » Demand-side policy is not pursued meaningfully until supply limitations are acute. Likewise, environmental policy is not seriously addressed until major climate events stimulate political responses. » Customized tool development such as DECC 2050 for major economies is gaining popularity; but it has its limitations.
  8. 8. Financial Algorithms™ 8Energy Trading Scenario 2016 Oil Prices : OPEC investments in US economy, a driving factor A big risk is that the Saudi Kingdom is selling some of its treasury holdings, believed to be among the largest in the world, to raise needed dollars. As a matter of policy, the US Treasury has never disclosed the holdings of Saudi Arabia, long a key ally in the volatile Middle East, and instead groups it with 14 other mostly OPEC nations including Kuwait, the United Arab Emirates and Nigeria. Source : http://ticdata.treasury.gov/Publish/mfh.txt China,, 1237.9 Japan, 1123.5 Carib, 350.5 Oil Exporters, 293 Brazil, 255.7 Ireland, 252.2 Switzerland, 237.4 United Kingdom, 223.2 Hong Kong, 201.6 Luxembourg, 200.1 Other, 862.3 US Treasury Holdings by Top 10 Countries - Jan 2016 in billion dollars» Earlier OPEC nations were plowing cash into U.S. Treasuries at a more than 50 percent faster rate than all other foreign investors, during the time when crude oil was trading above $100 a barrel. » Higher prices boosted their currency reserves. While booking super profits, OPEC countries parked this profit in US treasuries.
  9. 9. Financial Algorithms™ 9Energy Trading Scenario 2016 2 Modelling Oil Prices, Volatility from Market Instruments
  10. 10. Financial Algorithms™ 10Energy Trading Scenario 2016 WTI : Probabilities projecting price levels Using realized volatility (historical) – probabilities were calculated to project price levels for the range of WTI contracts. These probabilities imply that WTI prices may trade between USD 30-45 in most likely scenario for the entire year; though with limited upside chances. 0% 10% 20% 30% 40% 50% Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Contract month Probability of WTI spot price falling below certain levels Price < $25 Price < $30 Price < $35 0% 10% 20% 30% 40% 50% Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Contract month Probability of WTI spot price exceeding certain levels Price > $55 Price > $50 Price > $45 Source: EIA Short-Term Energy Outlook, March 2016, and CME Group (http://www.cmegroup.com)
  11. 11. Financial Algorithms™ 11Energy Trading Scenario 2016 Zomma (DGammaDVol) Sensitivity on WTI Futures using Black’s Forward Model WTI Price as of 27th March 2016 : $ 37.87 & OVI Index level : 52 week low 29; current 47.18, 52 week high : 109. Zomma is a useful sensitivity to monitor when maintaining a gamma-hedged portfolio as Zomma helps the trader to anticipate changes to the effectiveness of the hedge as volatility changes. 37.50 35.55 33.60 31.65 29.70 27.75 25.80 23.85 21.90 19.95 18.00 -0.0050 -0.0040 -0.0030 -0.0020 -0.0010 0.0000 0.0010 0.0020 0.0030 0.10 0.36 0.63 0.89 WTI price Zommalevels Time to maturity Deep in the money Call - DGammaDvol for the year 2016 62.50 59.25 56.00 52.75 49.50 46.25 43.00 39.75 36.50 33.25 30.00 -0.0030 -0.0025 -0.0020 -0.0015 -0.0010 -0.0005 0.0000 0.0005 0.0010 0.0015 0.10 0.36 0.63 0.89 WTI price ZommaLevels Time to maturity Deep in the money Put - DGammaDvol for the year 2016
  12. 12. Financial Algorithms™ 12Energy Trading Scenario 2016 High volatility sensitivity : Zomma & Vanna (DDeltaDvol) for Call using forward-forward model OVI Level : 109 : Vanna is also a very useful sensitivity to monitor when maintaining a delta- or vega- hedged portfolio as vanna will help the trader to anticipate changes to the effectiveness of a delta-hedge as volatility changes or the effectiveness of a vega-hedge against change in the underlying spot price. Here suggesting WTI price to hover between USD 30-45 zone for the year. 45.00 42.00 39.00 36.00 33.00 30.00 27.00 24.00 21.00 18.00 15.00 -0.0004 -0.0003 -0.0002 -0.0001 0.0000 0.0001 0.0002 0.0003 0.0004 0.10 0.36 0.63 0.89 WTI price ZommaLevelsinhighvolatilityscenario Time to maturity Deep in the money Call - DGammaDvol for the year 2016 45.00 42.00 39.00 36.00 33.00 30.00 27.00 24.00 21.00 18.00 15.00-0.0020 -0.0010 0.0000 0.0010 0.0020 0.0030 0.0040 0.0050 0.10 0.36 0.63 0.89 WTI price VannaLevelsinhighvolatilityscenario Time to maturity Deep in the money Call - DDeltaDvol for the year 2016
  13. 13. Financial Algorithms™ 13Energy Trading Scenario 2016 Gasoline Prices in US : RBOB in long term mean zone » As shown in the figures below, the spread of gasoline and diesel is almost flat over the last few years with respect to crude oil prices. » Except for January 2015, the volatility levels were in a normal zone, suggesting mean reversion factor acting up and maintaining the price levels at $1.9 – 2.3 for gas & $2-2.5 for diesel. Forecast 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 Jan 2012 Jan 2013 Jan 2014 Jan 2015 Jan 2016 Jan 2017 U.S. Gasoline and Crude Oil Prices dollars per gallon Price difference Retail regular gasoline Crude oil Source: Short-Term Energy Outlook, March 2016. Crude oil price is composite refiner acquisition cost. Retail prices include state and federal taxes. Forecast 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 Jan 2012 Jan 2013 Jan 2014 Jan 2015 Jan 2016 Jan 2017 U.S. Diesel Fuel and Crude Oil Prices dollars per gallon Price difference Retail diesel fuel Crude oil Source: Short-Term Energy Outlook, March 2016. Crude oil price is composite refiner acquisition cost. Retail prices include state and federal taxes.
  14. 14. Financial Algorithms™ 14Energy Trading Scenario 2016 Speed Greek Sensitivity : mean level vs. high level vols for calls & puts using forward-forward model 2.25 2.10 1.95 1.80 1.65 1.50 1.35 1.20 1.05 0.90 0.75 -1.0000 -0.5000 0.0000 0.5000 1.0000 1.5000 2.0000 0.10 0.36 0.63 0.89 Gasoline price Time to maturity Speed sensitivity at high level vols for Gasoline 2.25 2.10 1.95 1.80 1.65 1.50 1.35 1.20 1.05 0.90 0.75 -15.0000 -10.0000 -5.0000 0.0000 5.0000 10.0000 15.0000 0.10 0.36 0.63 0.89 Gasoline price Time to maturity Speed sensitivity at mean level vols for Gasoline 3.75 3.50 3.25 3.00 2.75 2.50 2.25 2.00 1.75 1.50 1.25 -4.0000 -3.0000 -2.0000 -1.0000 0.0000 1.0000 2.0000 3.0000 4.0000 5.0000 0.10 0.36 0.63 0.89 Diesel price Time to maturity Speed sensitivity at mean level vols for Diesel 3.75 3.50 3.25 3.00 2.75 2.50 2.25 2.00 1.75 1.50 1.25-0.4000 -0.3000 -0.2000 -0.1000 0.0000 0.1000 0.2000 0.3000 0.4000 0.5000 0.6000 0.7000 0.10 0.36 0.63 0.89 Diesel price Time to maturity Speed sensitivity at high level vols for Diesel
  15. 15. Financial Algorithms™ 15Energy Trading Scenario 2016 NatGas Henry Hub : Probabilities projecting price levels » In a highly correlated market, Natural Gas exhibiting range bound price levels. In a most likely scenario, NatGas may move between US$ 3.00 to US$ 3.50, with a very limited upside price levels for the entire years. » During spring and summer time, seasonality holding down Henry Hub spot prices below US$ 2.00 but supporting levels pushing up prices in a US$ 2.00-3.00 range. 0% 10% 20% 30% 40% 50% Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Contract month Probability of Henry Hub spot price exceeding certain levels Price > $4.00 Price > $3.50 Price > $3.00 0% 10% 20% 30% 40% 50% Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Contract month Probability of Henry Hub spot price falling below certain levels Price < $1.25 Price < $1.50 Price < $1.75 Source: EIA Short-Term Energy Outlook, March 2016, and CME Group (http://www.cmegroup.com)
  16. 16. Financial Algorithms™ 16Energy Trading Scenario 2016 Vanna & price adjusted Gamma sensitivity using forward model for Henry Hub underlying » Although, price levels suggesting US$ 2.00-3.50 range for NatGas, Vanna sensitivity suggesting high volatility levels may stop rally in the NatGas & price may remain range bound i.e. US$ 1.50 – 2.75 for the entire year. » Price adjusted Gamma exhibiting skewness towards positive side suggesting price to hover between US$ 2.00 – 3.25 in a most likely scenario. 3.75 3.38 3.00 2.63 2.25 1.88 1.50 -0.0080 -0.0060 -0.0040 -0.0020 0.0000 0.0020 0.0040 0.0060 0.0080 0.0100 0.0120 0.10 0.36 0.63 0.89 Henry Hub price Time to maturity Henry Hub ITM Call - Vanna (DDeltaDvol) for the year 2016 3.75 3.38 3.00 2.63 2.25 1.88 1.500.0000 0.0050 0.0100 0.0150 0.0200 0.0250 0.0300 0.0350 0.0400 0.0450 0.10 0.36 0.63 0.89 Henry Hub price Time to maturity Henry Hub ITM Gamma-P Call for the year 2016
  17. 17. Financial Algorithms™ 17Energy Trading Scenario 2016 3 Correlation monitoring in energy derivatives
  18. 18. Financial Algorithms™ 18Energy Trading Scenario 2016 Correlation factors : Eagle’s eye can slice good profits OPEC & Non-OPEC Oil Production & Trading Price Levels, Spreads i.e. Crack / RBOB/Diesel etc. Currency Reserves Investments overseas US treasuries & Bonds i.e. 10Y/30Y Investments in other regions Impact on g-local economies Price Levels determining riskiness of asset classes across regions Economic growth & Core inflationary levels OPEC Production forecasting using internal models World GDP growth & energy demand per capita projections
  19. 19. Financial Algorithms™ 19Energy Trading Scenario 2016 Climate Change Status Planetary Boundaries Status Climate Change (atmospheric CO2 concentration and change in radiative forcing) Boundary Exceeded Rate of Biodiversity Loss Boundary Exceeded Nitrogen Cycle -part of a boundary with the Phosphorus Cycle Cycle Boundary Exceeded Phosphorus Cycle -part of a boundary with the Nitrogen Cycle Cycle Approaching Limit Ocean acidification Approaching Limit Global fresh water use Approaching Limit Change in land use Approaching Limit Stratospheric ozone depletion Not exceeded Atmospheric aerosol loading Not yet quantified Chemical pollution Not yet quantified Source : Shell Scenarios 2050 signals sign posts Research published by the Stockholm Resilience Centre in early 2009 proposes a framework based on ‘biophysical environmental 2 subsystems’. The Nine Planetary Boundaries collectively define a safe operating space for humanity where social and economic development does not create lasting and catastrophic environmental change. Political response to these metrics will affect the energy market and shift in preferences of energy products.
  20. 20. Financial Algorithms™ 20Energy Trading Scenario 2016 Geo-political & Behavioral factors – next generation variables » The global economic crisis has coincided with a shift in geopolitical and economic power from west to east. This decisive shift is transforming the global economic and political system. Middle east is turbulent, US & Russian grappling with Eurasian political scenario. » The world is facing a period of uncertain global politics. Strategic fault lines are emerging. Rising powers are increasingly and confidently asserting what they see as their national interests. – Key drivers going forward : G20 governance | The China-US relationship | Sharing the burdens of adjustment | New policy paradigm » Behavioral economics has enhanced our ability to understand how consumers make choices. It has helped governments find ways to reduce energy demand without losing votes. It has helped businesses develop more innovative and profitable ways to serve consumers. Hydro-gen engines may be the next big thing in utility driven energy markets suggesting shifting trends as the economy of scale introduces the cost effectiveness in car manufacturing with such engines. » The environment and climate change were overshadowed by concerns about economic security as the financial crisis deepened in the last decade. Events such as the Gulf of Mexico oil spill, while hardening public attitude towards energy providers, did little to change the energy consumption habits of consumers. » A new communications boom is also creating marked shifts in consumer behavior. While connectivity accelerates the spread of information, it can also deepen uncertainty. Research shows that the structure of the network connections people use can strengthen or weaken the spread of behavioral trends in unpredictable ways.
  21. 21. Financial Algorithms™ 21Energy Trading Scenario 2016 Financial Algorithms » Financial Algorithms offers wide variety of services including derivatives pricing, model validation, valuation of exotic and structured products. » Advisory services includes advisory in trading derivatives & structured products, hedging of various exposures in Interest Rates, Foreign Exchange, Commodities and Equities. » Quant trading services includes algo-trade (technical) and quant strategies across asset classes. » For Banks’ ALM, we offer advisory in behavior modeling of Loans and Deposits including prepayments, delinquencies, deposit run-off. Besides, we also offer detailed models for usage of facilities etc. for matched maturity of assets and liabilities. » To know more about Financial Algorithms service offerings, please visit : www.financialalgorithms.co » Or email at contact@financialalgorithms.co
  22. 22. Financial Algorithms™ 22Energy Trading Scenario 2016 Rakesh Sharma Executive Director & Head of Financial Engineering Team Financial Algorithms 18, Scheme No. 59 (II), Western Ring Road Indore – 452001, India Email : contact@financialalgorithms.co www.financialalgorithms.com Copyrights reserved by Financial Algorithms

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