Energy Pricing Techniques in the Electricity Market - PowerPoint PPT Presentation

About This Presentation

Energy Pricing Techniques in the Electricity Market


Energy Pricing Techniques in the Electricity Market Part 4 Application of Weather Derivatives Dr Harvey Stern, Bureau of Meteorology, Australia – PowerPoint PPT presentation

Number of Views:172
Avg rating:3.0/5.0
Slides: 61
Provided by: Victori174


Transcript and Presenter's Notes

Title: Energy Pricing Techniques in the Electricity Market

Energy Pricing Techniques in the Electricity
  • Part 4
  • Application of Weather Derivatives
  • Dr Harvey Stern,
  • Bureau of Meteorology, Australia

Outline of Presentation
  • Background.
  • An Historical Note.
  • Weather-related Risk.
  • The Growing Interest.
  • Asia-Pacific Region.
  • Some Statistics.
  • Weather Derivatives Explained.
  • Examples of Applications.
  • Concluding Remarks.

  • Weather risk is one of the biggest uncertainties
    facing business.
  • We get droughts, floods, fire, cyclones
    (hurricanes), snow ice.
  • Nevertheless, economic adversity is not
    restricted to disaster conditions.
  • A mild winter ruins a skiing season, dry weather
    reduces crop yields, rain shuts-down
    entertainment construction.

Background (cont.)
  • The increasing interest may be explained in terms
  • A desire to meet client needs.
  • A need to reduce the cost of capital.
  • Cross-fertilization between various fields.
  • Entry of new participants.
  • Growing responsibilities of Company Directors.
  • Source Prof. John Hewsons presentation to the
    Weather Risk Management Association

An Historical Note An Early Example
  • In 1992, the present author explored a
    methodology to assess the risk of climate
  • 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.

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

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

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)

Channels for Weather Risk Transfer
  • ART (Alternative Risk Transfer) is a generic
    phrase used to denote various non-traditional
    forms of re/insurance and techniques where risk
    is transferred to the capital markets.
  • Source http//

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
  • 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)

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
  • 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)

Why has ART grown?
  • Risk management moving up the agenda
  • A need to manage uninsurable liabilities
  • A need to protect against irregular income
  • Source Modern ART practice (Gerling Global
    Financial Products)

What is the future of ART?
  • The term Alternative Risk Transfer (ART) will
    soon be a misnomer. ART is fast becoming an
    essential risk management tool for primary
    insurers, reinsurers and non-insurance
  • Source Modern ART practice (Gerling Global
    Financial Products)

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.

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.

  • The reinsurance industry experienced several
    catastrophic events during the late 1980s early
  • 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

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
  • 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//

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
  • 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//

Weather Derivatives Explained Clewlow et al.
(2000) describe a derivative as "a financial
product that derives its value from other more
basic variables". These products include futures,
forwards, call options, put options, and swaps.
They describe weather derivatives as being
similar "to conventional financial derivatives,
the basic difference coming from the underlying
variables that determine the payoffs", such as
temperature, precipitation, wind, Heating Degree
Days (HDDs), and Cooling Degree Days (CDDs).
  • 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).
  • Direct modelling is chosen for the current
    exercise, the distribution of forecast errors
    being assumed to be normal.

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?

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
  • That is, when the current month's Southern
    Oscillation Index (SOI) is substantially
  • 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.

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.

Pricing Methodologies
  • Historical simulation.
  • Direct modeling of the underlying variables
  • Indirect modeling of the underlying variables
    distribution (via a Monte Carlo technique).

Payoff Chart for Decile 4 Put Option
Outcomes for Decile 4 Put Option
Evaluating the Decile 4 Put Option
  • 14.2 cases of Decile 1 yields (.142)x(4-1)x100
  • 13.2 cases of Decile 2 yields (.132)x(4-2)x100
  • 8.4 cases of Decile 3 yields (.084)x(4-3)x1008
  • The other 25 cases (Decile 4 or above) yield
  • leading to a total of 77.40, which is the price
    of our put option.

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

Another Example
  • Another 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.

Pay-off Chart for a CDDCall Option
Cooling Degree Days (1855-2000)(and climate
  • The chart shows frequency distribution of annual
    Cooling Degree Days at Melbourne using all data

Cooling Degree Days (1971-2000)
  • The chart shows frequency distribution of annual
    Cooling Degree Days at Melbourne using only
    recent data

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

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.

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

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
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.

Pay-off Chart 38 deg C Call Option
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

Historical Distribution of Outcomes
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.

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.

A Financial Guarantee The guarantee described is
that the forecast will be in error by no more
than 3C. The terms of the guarantee are that the
seller of the guarantee will pay the buyer
100.00 for each 0.1C greater than 3C that the
forecast is in error. It is the purpose of the
paper to develop an approach to pricing such a
financial guarantee, and to provide it as a
technique that is available on the web.
(after Stern Dawkins, 2003)
The Instrument The instrument is made up of a
combination of a call option and a put option
about the next day's maximum temperature at
Melbourne, the "strikes" being set respectively
3C above and below the forecast temperature.
The taker of this option combination receives
100 for each 0.1C that the observed temperature
is above or below the respective strikes. (after
Stern Dawkins, 2003)
Forecast Errors Dawkins and Stern (2003) show
that the magnitude of the forecast errors is
largely a function of season and synoptic
pattern. Dahni (2003) describes an automated
technique for "typing" synoptic patterns.
(after Stern Dawkins, 2003)
Forecast Errors as a Function of Season (after
Dawkins Stern, 2003)
Forecast Errors as a Function of Synoptic
Pattern (after Dawkins Stern, 2003)
The Approach Used
  • The approach used is as follows
  • The forecast verification data is stratified
    according to month, and also according to the
    nature of the prevailing atmospheric circulation
    - cyclonicity, direction and strength of the
    surface flow.
  • The distribution of the magnitude of forecast
    errors for each month (and also for each synoptic
    pattern type) is noted this distribution is
    adjusted in order to take into account a
    long-term downward trend in the magnitude of the
  • The distribution of forecast errors is assumed to
    be normal for each data subset, and a "fair
    value" price for the option combination for each
    month and each circulation type is then obtained.
  • (after Stern Dawkins, 2003)

  • Example
  • The example we shall use to illustrate the
    methodology is a forecast produced during the
    month of January, associated with a synoptic type
    flow possessing the following characteristics
  • weak strength
  • cyclonic (curvature)
  • from the north-north-west.
  • Over the 40-year period (1961-2000), occurrences
    of such a flow across SE Australia (over all
    months of the year) have been temperature
    forecasts with an RMS error of 2.70C.
  • (after Stern Dawkins, 2003)

Example (cont.) More recently (1991-2000), such a
flow has been accompanied by an RMS error of (a
much reduced) 2.26C. It is then assumed that the
forecast performance during the period 1991-2000
better represents what one might anticipate to be
the current level of performance, than does the
forecast performance over the 1961-2000
period. It is also assumed that the proportional
improvement in forecasting for each individual
month (January, February, March etc.) is the
same, that is, a proportional decrease in RMS
error of (2.26/2.70)(0.84) in the current
case. (after Stern Dawkins, 2003)
Example (cont.) The monthly RMS error calculated
over the 1961-2000 period for the current
synoptic type and the current month (3.32C in
this case) is then multiplied by the ratio (0.84)
in order to achieve an estimate of the likely RMS
error for the current forecast. So, the case of a
January cyclonic weak north-north-west synoptic
flow yields (0.84x3.32)2.79C for our estimated
RMS error. It is then assumed that the errors are
normally distributed and, utilising areas under
the standard normal curve, one calculates the
expected return on the guarantee to be 410. This
procedure is then repeated for all months and for
all synoptic patterns. (after Stern Dawkins,
  • The WEB Site
  • A web site is developed in order that
  • potential "customers" may readily obtain a price
    for the instrument and,
  • researchers may test its output.
  • This may be viewed and tested at
  • http//
  • (after Stern Dawkins, 2003)

A View of the WEB Site
Testing the Instruments Validity
It was considered that if, over a large number of
cases, writers of the option combination do not
make either a significant profit or a significant
loss, the validity of the "fair value" price
would be demonstrated. The instrument's validity
was then tested by calculating the "fair value"
price on independent cases taken for the entire
year of 2001. However, from an analysis of all
of the year-2001 cases, it was determined that
writers of the option combination would have
received 75,574 over the year, while paying out
only 23,800. (after Stern Dawkins, 2003)
Testing (cont.)
Nevertheless, this substantial profit (over 200
return) is not necessarily suggesting a possible
flaw in the valuation technique. On the
contrary, it may be explained in terms of the
spectacular improvement in the accuracy of
forecasts achieved during 2001 (see next
slide). One may show that had the forecasts been
of similar skill to those of previous years, the
payout would have been much closer to the monies
received. The profit achieved by the option
writers can, therefore, be explained in terms of
that increased skill. (after Stern Dawkins,
Sharp Improvement in Forecast Accuracy in 2001
(after Dawkins Stern, 2003)
  • Comments on the Financial Guarantee
  • A methodology to price a financial guarantee
    about the accuracy of a forecast has been
    described and demonstrated with "real" data.
  • It has been shown that had such a guarantee been
    applied to day-1 maximum temperature forecasts
    issued during 2001 for Melbourne, providers of
    the guarantee would have made a substantial
  • on account of the increased skill displayed by
    the forecasts.
  • (after Stern Dawkins, 2003)

Ensemble Forecasting(another approach to
measuring forecast uncertainty)
  • Another approach to obtaining a measure of
    forecast uncertainty, is to use ensemble weather
  • 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
  • Uncertainty associated with the forecasts may be
    derived by analysing the probability
    distributions of the outcomes.

Some Important Issues
  • Quality of weather and climate data.
  • Changes in the characteristics of observation
  • Security of data collection processes.
  • Privatisation of weather forecasting services.
  • Value of data.
  • Climate change.

Concluding Remarks
  • The sophistication of weather-related risk
    management products is growing.
  • In evaluating weather securities one needs to use
    historical weather data and forecast accuracy
    data, and also to take into account climate
  • Ensemble forecasting is a new approach to
    determining forecast uncertainty.
Write a Comment
User Comments (0)