Latest Developments in Weather Risk Management presentation to

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Latest Developments inWeather Risk
Managementpresentation to Risk Finance ,
22-24 March, 2004The Finance and Treasury
Association

• Dr Harvey Stern,
• Shoni Dawkins Robin Hicks
• Bureau of Meteorology, Melbourne

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Important WEB Sites

• http//www.bom.gov.au
• http//www.artemis.bm/artemis.htm
• http//www.wrma.org

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Outline of Presentation

• Introduction
• The foundation of the weather market.
• The growing diversification of weather risk
  products and their interest.
• Sources of meteorological data, their quality
  control and application.
• Managing weather risk using daily weather
  forecasts and seasonal outlooks.

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Outline of Presentation

• Introduction

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The Noah Rule

• Predicting rain doesnt count
• Building arks does.
• Warren Buffett,
• Australian Financial Review,11 March 2002.

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Weather-linked Securities

• Weather-linked securities have prices which are
  linked to the historical weather in a region.
• They provide returns related to weather observed
  in the region subsequent to their purchase.
• They therefore may be used to help firms hedge
  against weather related risk.
• They also may be used to help speculators
  monetise their view of likely weather patterns.

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Some Recent News

• The next few slides illustrate some recent news.

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Outline of Presentation

• The foundation of the weather market

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Foundation of the Weather Market

• The foundation of todays financial weather
  contracts is in the US power market
• For the weather-sensitive end-user, not to hedge
  is to gamble on the weather.
• Robert S. Dischell

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Outline of Presentation

• The growing diversification of weather risk
  products and their interest

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WRMA 2002 Survey Results.The Growing Interest.

• 3,937 contracts transacted in last 12 months (up
  43 compared to previous year).
• Notional value of over 4.3 billion dollars (up
  72).
• Market dominated by US (2,712 contracts), but
  growth in the past year is especially so in
  Europe and Asia.
• Australian market accounts for 15 contracts worth
  over 25 million (6 contracts worth over 2
  million, previously).
• Source Weather Risk Management Association
  Annual Survey (2002)

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WRMA 2002 Survey Results. The Diversification.

• Another significant development is the
  diversification of the types of contracts that
  were transacted.
• Temperature-related protection (for heat and
  cold) continues to be the most prevalent, making
  up over 82 percent of all contracts (92 last
  year)
• Rain-related contracts account for 6.9 (1.6
  last year), snow for 2.2 (0.6 last year) and
  wind for 0.4 (0.3 last year).
• Source Weather Risk Management Association
  Annual Survey (2002)

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Views prior to the release of the WRMA 2003
Survey Results

• Most market participants are predicting an
  increase in total notional volumes
• The general malaise that has clouded the weather
  risk market in the past year may be on the wane
• we will see a sizeable decrease in volumes as
  Enron, Aquila have left the market
• The effect of market departures was clearly felt
  but big players more than compensated for the
  loss, providing liquidity and execution of
  service
• weather forecasting improvements could pose a
  threat to market development
• Energy Power Risk Management
• May2003

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WRMA 2003 Survey Results (a)

• A near tripling of contracts transacted (11,756
  contracts compared with 3937 previously)
• Notional value of contracts fell slightly
  (US4.2b compared with US4.3b previously)
• Indicates a surge in smaller contracts, and a
  broader spectrum of users
• Total business generated over the past 6 years
  US15.8b

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WRMA 2003 Survey Results (b)

• North American market 2217 contracts compared
  with 2712 previously (20 decline)
• European market 1480 contracts compared with 765
  previously (90 increase)
• Asian market 815 contracts compared with 445
  previously (85 increase)

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WRMA 2003 Survey Results (c)

• Diversification Increasing
• Temperature related contracts 85 compared with
  90 previously
• Rain related contracts 8.6 compared with 6.9
  previously
• Wind-related contracts 1.6 compared with 0.3
  previously
• Snow related contracts 2.1 compared with 2.2
  previously

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The Asia-Pacific Region

• Interest in weather risk management has grown in
  the Asia-Pacific Region (covering electricity,
  gas, agriculture). Countries involved include
• Japan
• Korea and,
• Australia/New Zealand.
• Source Weather Risk Management Association.

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Australian Developments

• For many years, the power industry has received
  detailed weather forecasts from the Bureau.
• Now, Australia has joined the global trend
  towards an increased focus on the management of
  weather-related risk.
• The first instance of an (Australian) weather
  derivative trade occurred about three years ago.
• A number of businesses have now moved into the
  trading of weather risk products, almost all
  over the counter.
• Partnerships are emerging between merchant banks
  and weather forecasting companies.

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Securitisation

• The reinsurance industry experienced several
  catastrophic events during the late 1980s early
  1990s.
• The ensuing industry restructuring saw the
  creation of new risk-management tools.
• These tools included securitisation of insurance
  risks (including weather-related risks).
• Weather securitisation may be defined as the
  conversion of the abstract concept of weather
  risk into packages of securities.
• These may be sold as income-yielding structured
  products.

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Catastrophe Bonds

• A catastrophe (cat) bond is an exchange of
  principal for periodic coupon payments wherein
  the payment of the coupon and/or the return of
  the principal of the bond is linked to the
  occurrence of a specified catastrophic event.
• The coupon is given to the investor upfront, who
  posts the notional amount of the bond in an
  account.
• If there is an event, investors may lose a
  portion of (or their entire) principal.
• If there is no event, investors preserve their
  principal and earn the coupon.
• Source Canter Cole at http//www.cnare.com

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Catastrophe Swaps

• A catastrophe (cat) swap is an alternative
  structure, but returns are still linked to the
  occurrence of an event.
• However, with swaps, there is no exchange of
  principal.
• The coupon is still given to the investor
  upfront, but the structure enables investors to
  invest the notional amount of the bond in a
  manner of his own choosing.
• Source Canter Cole at http//www.cnare.com

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Weather Derivatives

• Weather derivatives are similar to conventional
  financial derivatives.
• The basic difference lies in the underlying
  variables that determine the pay-offs.
• These underlying variables include temperature,
  precipitation, wind, and heating ( cooling)
  degree days.

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Derivative or Insurance?

• A Derivative
• -has ongoing economic value,
• -is treated like any other commodity,
• -is accounted for daily,
• -may therefore affect a companys credit
  rating.
• An Insurance Contract
• -is not regarded as having economic value,
• -therefore does not affect a companys
  credit rating.

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A Weather-linked Option

• An example of a weather linked option is the
  Cooling Degree Day (CDD) Call Option.
• Total CDDs is defined as the accumulated number
  of degrees the daily mean temperature is above a
  base figure.
• This is a measure of the requirement for cooling.
• If accumulated CDDs exceed the strike, the
  seller pays the buyer a certain amount for each
  CDD above the strike.

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Specifying the CDD Call Option

• Strike 400 CDDs.
• Notional 100 per CDD (gt 400 CDDs).
• If, at expiry, the accumulated CDDs gt 400, the
  seller of the option pays the buyer 100 for each
  CDD gt 400.

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Pay-off Chart for the CDDCall Option
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An Historical Note An Early Example

• In 1992, the present author explored a
  methodology to assess the risk of climate
  change.
• Option pricing theory was used to value
  instruments that might apply to temperature
  fluctuations and long-term trends.
• The methodology provided a tool to cost the risk
  faced (both risk on a global scale, and risk on a
  company specific scale).
• Such securities could be used to help firms hedge
  against risk related to climate change.

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Carbon Disclosure Project (2003)

• "Investors failing to take account of climate
  change and carbon finance issues in the asset
  allocation and equity valuations may be exposed
  to significant risks which, if left unattended,
  will have serious investment repercussions over
  the course of time."

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Cooling Degree Days (1855-2000)(and climate
change)

• Frequency distribution of annual Cooling Degree
  Days at Melbourne using all data

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Cooling Degree Days (1971-2000) (and climate
change)

• Frequency distribution of annual Cooling Degree
  Days at Melbourne using only recent data

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Outline of Presentation

• Sources of meteorological data, their quality
  control and application

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Types of Data Available

• Rainfall daily, monthly, seasonal, analyses,
• Temperature hourly, maximum and minimum, dew
  point, monthly averages and extremes
• Wind speed, hourly , maximum wind gust, wind run

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Sources of Observations

• Bureau Staffed Sites
• Fully trained observers
• Equipment maintenance

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Bureau Stations

• Some in remote locations
• Some located at major airports

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Automated Weather Stations
Currently 513 sites
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Features of an AutomaticWeather Station

• In general, compared to human observers
• AWS are more consistent in their measurement
• AWS provide data at a significantly greater
  frequency
• AWS provide data in all weather, day and night,
  365 days per year
• AWS can be installed in sparsely populated areas
• AWS are significantly cheaper than human
  observers

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Features of an AutomaticWeather Station (cont.)

• However, AWS suffer a number of disadvantages.
  These are
• Some elements are difficult to automate (e.g.
  cloud cover)
• AWS require a large capital investment
• AWS are less flexible than human observers

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Automatic Weather Stations (cont.)

• Consistency between sites
• Bureau Specification 2013, based on WMO
  guidelines
• Different sensors because some sites are designed
  around specific users / programs
• Aviation, agriculture, climate, marine
• Inspection routine to ensure calibration,
  preventative maintenance, software upgrades

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Automated Weather Stations (cont.)

• Sites are fenced to
• minimise obstructions,
• reduce
• vandalism, interference from animals
• Rural locations generally representative of local
  area

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Cooperative Observers

• Currently about 300 sites
• Historically main source of surface observations
• Lighthouses
• Post Offices
• Generally up to 7 observations per day
• Replacement with AWS, or concurrent for cloud,
  visibility observations

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Rainfall only observations

• Some 20000 sites historically, about 6000 sites
  currently open
• Majority send monthly returns key sites daily
• Daily 9am observations

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Pluviograph

• Sites often owned by water authorities
• Gives indication of timing of heavy rain
• Data generally not available for long period
  after an event
• 1000 sites with data, 300 Bureau sites currently
  open

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Things that can go wrong

• Instrumentation problems
• Unattended sites
• equipment problems
• Vandalism
• Communication problems remote areas
• Power cuts, spikes
• Calibration of instruments, time accuracy

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Effects of changes in instrumentation
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Sensor characteristics

• Resolution - the smallest change the device can
  detect (this is not the same as the accuracy of
  the device).
• Repeatability - the ability of the sensor to
  measure a parameter more than once and produce
  the same result in identical circumstances.
• Response time - normally defined as the time the
  sensor takes to measure 63 of the change.
• Drift - the stability of the sensor's calibration
  with time.
• Hysteresis - the ability of the sensor to produce
  the same measurement whether the phenomenon is
  increasing or decreasing.
• Linearity - the deviation of the sensor from
  ideal straight line behaviour.

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Observing Practices

• Observers receive training in standard practices
• Scheduling of manual observations often affected
  by availability of observer, or access to site
• Change in use of Daylight Savings Time

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How representative is the site?

• Site might be located in valley or on hilltop
• Surrounding vegetation might not be typical of
  general area
• Many sites become surrounded by buildings over
  time - urbanisation

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Urbanisation
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Distance of site from area of interest

• Rainfall totals can vary significantly over short
  distances because of terrain or thunderstorms
• Minimum temperatures drop sharply as one travels
  inland from the coast, particularly in winter
• Frost hollows, funneling of winds

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Changes in site location

• Moves to less urban airport sites
• Reference Climate Stations
• Min 30 years of continuous record with minimal
  inhomogenieties
• Minimally affected by urban effects
• Site changes forced by change in observer

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Bureau sources of data

• SILO
• Climate Data Services
• SSU
• Regional Offices

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Useful tools in Silo

• Point patched data
• To estimate missing historical data
• Uses neighbouring sites
• Data Drill
• Uses gridded data no original observations
• Resolution of 0.05 degrees (about 5km)

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Timeliness

• Data available on SILO and Bureau web site in
  close to real time
• Subject to more errors, gaps etc
• Data available after quality control processes
  have been applied

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Future trends

• More automated observation sites
• Automated data quality control procedures to
  enable more checks to be performed
• More data and at higher frequencies
• Increased use of remotely sensed data for
  estimations in data sparse regions

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Future trends in data
Solar radiation data traditional network versus
satellite derived estimates
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Outline of Presentation

• Managing weather risk using daily weather
  forecasts and seasonal outlooks

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Should Companies Worry?

• In the good years, companies make big profits.
• In the bad years, companies make losses.
• - Doesnt it all balance out?
• - No. it doesnt.
• Companies whose earnings fluctuate wildly receive
  unsympathetic hearings from banks and potential
  investors.

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Weather-related Industry Risk

• "Shares in Harvey Norman fell almost 4 per
  cent yesterday as a cool summer and a warm start
  to winter cut into sales growth at the furniture
  and electrical retailer's outlets Investors
  were expecting better and marked the shares down
  3.8 per cent to a low of 3.55
• Sales at Harvey Norman were hit on two
  fronts. Firstly, air conditioning sales were
  weak because of the cool summer, and a warmer
  than usual start to winter had dampened demand
  for heating appliances.
• Source The Australian of 18 April, 2002

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Weather-related Agricultural Risk

• The Australian sugar industry is facing its
  fifth difficult year in a row with a drought
  dashing hopes of an improved crop in Queensland,
  where 95 of Australia's sugar is grown...
• Whilst dry weather during the May-December
  harvest period is ideal for cane, wet weather
  during this time causes the mature cane to
  produce more shoots and leaves, reducing its
  overall sugar content.
• (Australian Financial Review of 8 May, 2002)

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The Road toWeather Risk Management.

• The era of (mostly) categorical forecasts.
• The rapid increase in the application of
  probability forecasts.
• The provision of forecast verification (i.e.
  accuracy) data.
• The era of the guaranteed forecast, with user
  communities being compensated for an inaccurate
  prediction.
• The purchase of stakes in the industry (by
  multi-national companies).

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• Pricing Derivatives
• There are three approaches that may be applied to
  the pricing of derivatives.
• These are
• Historical simulation (applying "burn analysis")
  
• Direct modelling of the underlying variables
  distribution (assuming, for example, that the
  variable's distribution is normal) and,
• Indirect modelling of the underlying variables
  distribution (via a Monte Carlo technique).

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Returning to the Cane Grower

• Suppose that our cane grower has experienced an
  extended period of drought.
• Suppose that if rain doesn't fall next month, a
  substantial financial loss will be suffered.
• How might our cane grower protect against
  exceptionally dry weather during the coming month?

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One Approach

• One approach could be to purchase a Monthly
  Rainfall Decile 4 Put Option.
• Assume that our cane grower decides only to take
  this action when there is already a risk of a dry
  month.
• That is, when the current month's Southern
  Oscillation Index (SOI) is substantially
  negative.
• So, the example is applied only to the cases when
  the current month's Southern Oscillation Index
  (SOI) is in the lowest 5 of possible values,
  that is, below -16.4.

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Specifying the Decile 4 Put Option

• Strike Decile 4.
• Notional 100 per Decile (lt Decile 4).
• If, at expiry, the Decile is lt Decile 4, the
  seller of the option pays the buyer 100 for each
  Decile lt Decile 4.

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Payoff Chart for Decile 4 Put Option
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Outcomes for Decile 4 Put Option
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Evaluating the Decile 4 Put Option

• 14.2 cases of Decile 1 yields (.142)x(4-1)x100
  42.60
• 13.2 cases of Decile 2 yields (.132)x(4-2)x100
  26.40
• 8.4 cases of Decile 3 yields (.084)x(4-3)x1008
  .40
• The other 25 cases (Decile 4 or above) yield
  nothing.
• leading to a total of 77.40, which is the price
  of our put option.

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Weather Climate Forecasts

• Daily weather forecasts may be used to manage
  short-term risk (e.g. pouring concrete).
• Seasonal climate forecasts may be used to manage
  risk associated with long-term activities (e.g.
  sowing crops).
• Forecasts are based on a combination of solutions
  to the equations of physics, and some
  statistical techniques.
• With the focus upon managing risk, the forecasts
  are increasingly being couched in probabilistic
  terms.

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An Illustration of theImpact of Forecasts

• When very high temperatures are forecast, there
  may be a rise in electricity prices.
• The electricity retailer then needs to purchase
  electricity (albeit at a high price).
• This is because, if the forecast proves to be
  correct, prices may spike to extremely high
  (almost unaffordable) levels.

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Impact of Forecast Accuracy

• If the forecast proves to be an over-estimate,
  however, prices will fall back.
• For this reason, it is important to take into
  account forecast accuracy data in determining the
  risk.

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Forecast Accuracy Data The Australian Bureau of
Meteorology's Melbourne office possesses data
about the accuracy of its temperature forecasts
stretching back over 40 years. Customers
receiving weather forecasts have, recently,
become increasingly interested in the quality of
the service provided. This reflects an overall
trend in business towards implementing risk
management strategies. These strategies include
managing weather related risk. Indeed, the US
Company Aquila developed a web site that presents
several illustrations of the concept http//www.g
uaranteedweather.com
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Using Forecast Accuracy Data

• Suppose we define a 38 deg C call option
  (assuming a temperature of at least 38 deg C has
  been forecast).
• Location Melbourne.
• Strike 38 deg C.
• Notional 100 per deg C (above 38 deg C).
• If, at expiry (tomorrow), the maximum temperature
  is greater than 38 deg C, the seller of the
  option pays the buyer 100 for each 1 deg C above
  38 deg C.

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Pay-off Chart 38 deg C Call Option
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Determining the Price of the38 deg C Call Option

• Between 1960 and 2000, there were 114 forecasts
  of at least 38 deg C.
• The historical distribution of the outcomes are
  examined.

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Historical Distribution of Outcomes
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Evaluating the 38 deg C Call Option (Part 1)

• 1 case of 44 deg C yields (44-38)x1x100600
• 2 cases of 43 deg C yields (43-38)x2x1001000
• 6 cases of 42 deg C yields (42-38)x6x1002400
• 13 cases of 41 deg C yields (41-38)x13x1003900
• 15 cases of 40 deg C yields (40-38)x15x1003000
• 16 cases of 39 deg C yields (39-38)x16x1001600
• cont.

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Evaluating the 38 deg C Call Option (Part 2)

• The other 61 cases, associated with a temperature
  of 38 deg C or below, yield nothing.
• So, the total is 12500.
• This represents an average contribution of 110
  per case, which is the price of our option.

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Finally Ensemble Forecasting

• Another approach to obtaining a measure of
  forecast uncertainty, is to use ensemble weather
  forecasts.
• The past decade has seen the implementation of
  these operational ensemble weather forecasts.
• Ensemble weather forecasts are derived by
  imposing a range of perturbations on the initial
  analysis.
• Uncertainty associated with the forecasts may be
  derived by analysing the probability
  distributions of the outcomes.
• A parallel approach is to run different models
  with the same initial analysis
• Spot the differences on the next slide

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