Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.
Course outline       Financial Time Series Analysis                                                                       ...
British rainfall extremes                                             •    British Rainfall (Symons, G. J.,1885) lists all...
Electricity load and temperature                              Electricity and temperature                                 ...
Derivative pricing                                      Energy utility case study                                         ...
Appetite for weather derivatives                                       Index-based insurance (IBI)    •  “I think it’s a p...
Drought protection in                                                                             IBI challenges          ...
Nächste SlideShare
Wird geladen in …5

Oxford University - Jan 2010 - UBS Global Warming Index - Weather Derivatives - ilija Murisic

930 Aufrufe

Veröffentlicht am

Oxford University - Jan 2010 - UBS Global Warming Index - Weather Derivatives - ilija Murisic

Keywords: auction, CDD, CME, derivatives, futures, global warming, HDD, ilija murisic, Index, UBS, UBS Global Warming Index, Weather

Veröffentlicht in: Wirtschaft & Finanzen
  • Als Erste(r) kommentieren

  • Gehören Sie zu den Ersten, denen das gefällt!

Oxford University - Jan 2010 - UBS Global Warming Index - Weather Derivatives - ilija Murisic

  1. 1. Course outline Financial Time Series Analysis •  1. Data analysis, probability, correlations, visualisation techniques •  2. Time series analysis, random walk, autoregression, moving average Patrick McSharry •  3. Technical analysis, trend following, mean reversion patrick@mcsharry.net www.mcsharry.net •  4. Nonlinear time series analysis •  5. Nonlinear modelling, regime switching, neural networks •  6. Parameter estimation, model selection, forecast evaluation Trinity Term 2010 •  7. Volatility forecasting, GARCH, leverage effect Dartington House Seminar Room 1 Mathematical Institute •  8. Risk analysis, value at risk, quantile regression University of Oxford •  9. Energy consumption, demand forecasting •  10. Ensemble prediction, wind power generation •  11. Weather derivatives, index-based insurance •  12. Quantitative trading strategies, algorithmic trading Lecture 11 Copyright © 2010 Patrick McSharry Overview Carbon emissions •  Historical weather observations •  Future projections •  Weather indices •  Weather derivatives •  Index-based insurance Source: www.globalwarmingart.com Reconstructed temperature Global warmingSource: www.globalwarmingart.com Source: www.globalwarmingart.com 1
  2. 2. British rainfall extremes •  British Rainfall (Symons, G. J.,1885) lists all observed 24 hour rainfall depths which exceeded 2.5 inches (63.5mm). See Rodda et al. (2008)Source: Wikipedia Change in Extreme rainfall trend mean annual temperature by the end of this centurySource: Little et al. (2008) Source: http://peseta.jrc.ec.europa.eu/ Change in Weather risk mean annual precipitation •  Fluctuations in the weather affect the revenues of many sectors (agriculture, energy, retail, by the end of transportation and construction) this century •  Weather risk tends to influence demand rather than price and adjustments of the latter are rarely adequate to compensate for lost revenues •  Insurance offers cover for low-probability extreme events but it does not provide compensation for the reduced demand that may result from slight variations in the weather such as the temperatureSource: http://peseta.jrc.ec.europa.eu being warmer or colder than expected. 2
  3. 3. Electricity load and temperature Electricity and temperature •  Electricity demand displays a non-linear U-shaped dependence on temperature •  Demand increases both for decreasing and increasing temperatures •  This results from the use of electric heating appliances in winter and air conditioners in summer •  Pardo et al. (2002) analysed the dependence of electricity load on a population weighted •  There is a minimum around 18oC where the temperature index in Spain demand is inelastic to temperature changes Summer and winter regimes Heating and cooling degree-days •  The nonlinearity in the response function •  Heating degree-day (HDD) and cooling degree- prompted a separation of the effect into day (CDD) are indices designed to reflect the summer and winter regimes demand for energy needed to heat or cool: •  Typically 18oC (or 65 oF) is used as a threshold variable to switch between summer and winter regimes •  HDD measures the intensity and duration of cold •  Within each regime, the response is in the winter approximately linear which facilitates traditional linear time series analysis •  CDD measures the intensity and duration of heat in the summer Weather derivatives Weather and revenues •  Weather derivatives are financial instruments that •  By creating a weather index that can be linked to the can form a risk management strategy to mitigate revenues of a particular organisation, it is then possible risk associated with adverse or unexpected to trade the weather in order to mitigate the risk of weather conditions adverse weather conditions •  Unlike other derivatives the underlying asset •  Advantages of decreased earnings volatility are efficient (temperature, rainfall, wind, frost or snow) has no use of equity, improving the company value to direct value stakeholders and availability of lower debt costs and higher advance rates •  Weather derivatives, in contrast to insurance, provide protection against low-risk high-probability •  The Chicago Mercantile Exchange (CME) introduced events by treating the weather as a tradable exchange-traded weather derivatives in 1999 and now commodity, similar to a stock price or interest rate provides standardised contracts for 18 US, nine European, six Canadian and two Japanese citieswww.cme.com/trading/prd/weather/ 3
  4. 4. Derivative pricing Energy utility case study •  If an energy utility believes that November may •  Approaches employed for pricing weather be hotter than usual, possibly leading to reduced derivatives vary greatly, reflecting different revenues, it could take out a HDD swap assumptions made about the variability of future •  Suppose the reference was 18oC and the actual weather conditions over the duration of the average temperature on each day was 16oC, the contract utility would receive 60 (30 x 2) times the agreed sum of money for each degree-day •  The disparity results from the availability of a range of atmospheric and statistical models •  Alternatively if the average temperature on each day was 19oC, the utility would have to pay 30 •  Further complications arise from the inclusion of (30 x 1) the agreed sum of money for each the effect of El Niño in the model. degree-day Restaurant case study Agriculture •  In May 2002, Element Re announced a new weather derivative transaction with The Rock •  Farmers are exposed to financial risk arising from Garden, a London restaurant. the variability in crop yield that occurs due to •  The contract was designed to protect the different weather conditions. restaurant against financial loss from colder- •  Crop yield and price are weather-dependent. In than-normal weather from March 1-June 30. the case of corn, both temperature and precipitation provide significant explanatory •  The contract will pay out if the maximum daily variables. temperature is less than a pre-agreed level for that month for more than a specified number of •  Drought in particular presents a substantial risk. days (8.5oC in March; 11oC in April; 14.5oC in Many farmers depend upon pre-financing against their future revenue streams for seed, fertilizer, and May; 18oC in June). other agro chemical products. Global warming Weather Risk Management index •  A survey carried out for the Weather Risk Management Association by PricewaterhouseCoopers in 2006 •  In April 2007, UBS launched a Global Warming index concluded that: (UBS-GWI) allowing businesses most affected by the •  The total value of trades in the 2005/6 survey reached uncertainty of climate change to hedge their profits $45.2 billion, compared to $9.7 billion in the 2004/5 against it in a simple and transparent fashion survey •  The index is based on weather derivative contracts for •  The CME experienced significant increases in both the winter and summer traded on the Chicago Mercantile number of trades (increasing by a factor of 4) and the Exchange (CME) value of those trades (increasing by a factor of 8) •  The index is currently based on 15 US cities, including •  HDD remains most common type of trade New York, Chicago, Atlanta and Las Vegas •  If you think Mr Gore is an alarmist sensationalist, you would sell the Index.www.wrma.org www.ubs.com/globalwarming 4
  5. 5. Appetite for weather derivatives Index-based insurance (IBI) •  “I think it’s a perfect market. You can’t spook it, you can’t •  IBI is an innovative means of managing weather risk by manipulate it. You can’t make people think it’s going to assessing risk in terms of expected loss of income by be 110 degrees in London next week,” he says. “And of affected people whose livelihoods depend on agriculture/ course, weather is absolutely uncorrelated [to other farming that is highly susceptible to weather. asset classes].” - Peter Brewer, Cumulus Weather Fund •  Traditional insurance products are based on an individuals circumstances and losses. •  No contract is too small. We’ve sold a weather derivative contract for a dollar” - David Friedberg, Weatherbill •  In developing countries or remote rural areas, traditional insurance services may not be available because of high •  Weatherbill’s clients include car wash companies, hair administration costs. salons and golf courses. “The other day one farmer rang •  IBI is a more appropriate as it is based on a fixed trigger me up and said his sows wouldn’t make a move to mate mechanism not directly related to any individual farm. if the temperature went above 95 degrees Fahrenheit. This could be calculated on average crop yields, area He wanted to hedge against that.” average livestock mortality rates, cumulative rainfall or even data from satellite imagery.Source: FT, 20 June, 2007 Implementing IBI Advantages of IBI •  Index-based insurance can be effectively delivered •  Where traditional insurance products through a wide range of products. arent available (too costly or impractical) •  Government or international aid agencies can purchase famine insurance based on a weather index tied to the •  Where moral hazard is high likelihood of droughts. •  A means of reducing risk exposure for •  Individual producers can purchase insurance privately from insurance providers, local banks, NGOs or some vulnerable agricultural producers, giving partnership of these. them confidence to invest in inputs and strategies that will potentially give them •  Insurance clauses can be folded into loan agreements whereby indexes triggered by adverse events relax the higher returns in other years. terms of the loan or pay it off. Livestock insurance in How IBI works Mongolia •  Crop- and area-specific estimates are •  In Mongolia, livestock husbandry accounts for 87% of agricultural GDP and supports at least half the population. aggregated and mapped to income via price •  The country is prone to extreme climatic events that can cause high estimates, and then converted into a livelihood rates of livestock mortality. loss index. •  The insurance sector is immature and under capitalised. •  Livestock insurance is a key element of risk mitigation but the conventional approach, based on individual herder losses, has been •  If weather data gathered throughout the contract ineffective in Mongolian conditions. It has proved unpopular with period indicates that rainfall was significantly both insurers and herders with a high cost of verification and moral below historic averages the insurer pays out. hazard. •  The government has now introduced an indexed mortality product which embraces risks exposure of the herders •  No moral hazard, no complication of counting •  Risks are covered by the private insurance sector in Mongolia dead cows. •  A disaster recovery program is to be provided by Government (maybe in years ahead by international reinsurers). 5
  6. 6. Drought protection in IBI challenges Ethiopia •  Lack of accurate climate models in•  In 2006 the Ethiopian Government, in partnership with developing countries the World food Program insured its farmers against rainfall failure. •  Limited availability of reliable and objective•  In return for a premium of US$930,000, the insurance data. company would have paid out anything up to $7.1 million in the case of severe drought. •  Lack of weather stations in remote areas.•  This money would have been given to the Ethiopian Government on the condition that it would be used for •  Models that make index-based insurance emergency relief and recovery. contracts attractive to both buyers and•  This means that the World food Program can rely on the insurance money to pay for their relief work if there is a sellers. climatic emergency, rather than having to depend on donations, which can entail long delays. 6