Latest Developments in Weather Risk Management presentation to “Risk Finance”, 22-24 March, 2004 The Finance and Treasury Association Dr Harvey Stern,

Latest Developments inLatest Developments inWeather Risk ManagementWeather Risk Management

presentation to presentation to “Risk Finance”“Risk Finance” , 22-24 March, 2004 , 22-24 March, 2004

The Finance and Treasury AssociationThe Finance and Treasury Association

Latest Developments inLatest Developments inWeather Risk ManagementWeather Risk Management

presentation to presentation to “Risk Finance”“Risk Finance” , 22-24 March, 2004 , 22-24 March, 2004

The Finance and Treasury AssociationThe Finance and Treasury Association

Dr Harvey Stern,Dr Harvey Stern,

Shoni Dawkins & Robin HicksShoni Dawkins & Robin Hicks

Bureau of Meteorology, MelbourneBureau of Meteorology, Melbourne

Dr Harvey Stern,Dr Harvey Stern,

Shoni Dawkins & Robin HicksShoni Dawkins & Robin Hicks

Bureau of Meteorology, MelbourneBureau of Meteorology, Melbourne

Important WEB SitesImportant WEB SitesImportant WEB SitesImportant WEB Sites

• http://www.bom.gov.au

• http://www.artemis.bm/artemis.htm

• http://www.wrma.org

http://www.bom.gov.au/

http://www.artemis.bm/artemis.htm

Outline of PresentationOutline of PresentationOutline of PresentationOutline 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.

• 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.


• Introduction…• Introduction…

The Noah RuleThe Noah RuleThe Noah RuleThe Noah Rule

“Predicting rain doesn’t count;

Building arks does”.

Warren Buffett,Australian Financial Review,11 March 2002.

“Predicting rain doesn’t count;

Building arks does”.

Warren Buffett,Australian Financial Review,11 March 2002.

Weather-linked SecuritiesWeather-linked SecuritiesWeather-linked SecuritiesWeather-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.

• 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.

Some Recent NewsSome Recent NewsSome Recent NewsSome Recent News

• The next few slides illustrate some recent news.• The next few slides illustrate some recent news.


• The foundation of the weather market…• The foundation of the weather market…

Foundation of the Weather MarketFoundation of the Weather MarketFoundation of the Weather MarketFoundation of the Weather Market

“The foundation of today’s 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

“The foundation of today’s 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


• The growing diversification of weather risk products and their interest…

• The growing diversification of weather risk products and their interest…

WRMA 2002 Survey Results.WRMA 2002 Survey Results.The Growing Interest.The Growing Interest.

WRMA 2002 Survey Results.WRMA 2002 Survey Results.The Growing Interest.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)

• 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).


WRMA 2002 Survey Results. WRMA 2002 Survey Results. The Diversification.The Diversification.

WRMA 2002 Survey Results. WRMA 2002 Survey Results. The Diversification.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).


• 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).


Views prior to the release of the Views prior to the release of the WRMA 2003 Survey ResultsWRMA 2003 Survey Results

Views prior to the release of the Views prior to the release of the WRMA 2003 Survey ResultsWRMA 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 ManagementMay2003

“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 ManagementMay2003

WRMA 2003 Survey Results (a)WRMA 2003 Survey Results (a)WRMA 2003 Survey Results (a)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

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

WRMA 2003 Survey Results (b)WRMA 2003 Survey Results (b)WRMA 2003 Survey Results (b)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)

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)

WRMA 2003 Survey Results (c)WRMA 2003 Survey Results (c)WRMA 2003 Survey Results (c)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

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

The Asia-Pacific RegionThe Asia-Pacific RegionThe Asia-Pacific RegionThe 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.

• 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.

Australian Developments Australian Developments Australian Developments 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.

• 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.

SecuritisationSecuritisationSecuritisationSecuritisation

• 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.

• 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.

Catastrophe BondsCatastrophe BondsCatastrophe BondsCatastrophe 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

• 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.


Catastrophe SwapsCatastrophe SwapsCatastrophe SwapsCatastrophe 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.


• 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.


Weather DerivativesWeather DerivativesWeather DerivativesWeather 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.

• 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.

Derivative or Insurance?Derivative or Insurance?Derivative or Insurance?Derivative or Insurance?

• A Derivative:

-has ongoing economic value,

-is treated like any other commodity,

-is accounted for daily, &

-may therefore affect a company’s credit rating.• An Insurance Contract:

-is not regarded as having economic value, &

-therefore does not affect a company’s credit rating.

• A Derivative:

-has ongoing economic value,

-is treated like any other commodity,

-is accounted for daily, &

-may therefore affect a company’s credit rating.• An Insurance Contract:

-is not regarded as having economic value, &

-therefore does not affect a company’s credit rating.

A Weather-linked OptionA Weather-linked OptionA Weather-linked OptionA 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”.

• 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”.

Specifying the CDD Call OptionSpecifying the CDD Call OptionSpecifying the CDD Call OptionSpecifying the CDD Call Option

• Strike: 400 CDDs.

• Notional: $100 per CDD (> 400 CDDs).

• If, at expiry, the accumulated CDDs > 400, the seller of the option pays the buyer $100 for each CDD > 400.

• Strike: 400 CDDs.

• Notional: $100 per CDD (> 400 CDDs).

• If, at expiry, the accumulated CDDs > 400, the seller of the option pays the buyer $100 for each CDD > 400.

Pay-off Chart for the CDDPay-off Chart for the CDDCall OptionCall Option

Pay-off Chart for the CDDPay-off Chart for the CDDCall OptionCall Option

An Historical Note: An Historical Note: An Early ExampleAn Early Example

An Historical Note: An Historical Note: An Early ExampleAn 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.

• 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.

Carbon Disclosure Project (2003)Carbon Disclosure Project (2003)Carbon Disclosure Project (2003)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."

• "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."

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

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

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

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

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

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

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

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


• Sources of meteorological data, their quality control and application…

• Sources of meteorological data, their quality control and application…

Types of Data AvailableTypes of Data AvailableTypes of Data AvailableTypes 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

• Rainfall – daily, monthly, seasonal, analyses,

• Temperature – hourly, maximum and minimum, dew point, monthly averages and extremes

• Wind speed, hourly , maximum wind gust, wind run

Sources of ObservationsSources of ObservationsSources of ObservationsSources of Observations

• Bureau Staffed Sites– Fully trained

observers– Equipment

maintenance

• Bureau Staffed Sites– Fully trained

observers– Equipment

maintenance

Bureau StationsBureau StationsBureau StationsBureau Stations

• Some in remote locations

• Some located at major airports

• Some in remote locations

• Some located at major airports

Automated Weather Automated Weather StationsStations

Automated Weather Automated Weather StationsStations

Currently 513 sites

Features of an AutomaticWeather Station

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

• 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

Features of an AutomaticWeather Station (cont.)

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

• 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

Automatic Weather Stations (cont.)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

• 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

Automated Weather Stations (cont.)

Automated Weather Stations (cont.)

• Sites are fenced to – minimise

obstructions, – reduce

• vandalism, interference from animals

• Rural locations generally representative of local area

• Sites are fenced to – minimise

obstructions, – reduce

• vandalism, interference from animals

• Rural locations generally representative of local area

Cooperative ObserversCooperative ObserversCooperative ObserversCooperative 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

• 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

Rainfall only observationsRainfall only observationsRainfall only observationsRainfall only observations

• Some 20000 sites historically, about 6000 sites currently open

• Majority send monthly returns – key sites daily

• Daily 9am observations

• Some 20000 sites historically, about 6000 sites currently open

• Majority send monthly returns – key sites daily

• Daily 9am observations

PluviographPluviographPluviographPluviograph

• 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

• 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

Things that can go wrongThings that can go wrongThings that can go wrongThings that can go wrong

• Instrumentation problems– Unattended sites

• equipment problems• Vandalism

– Communication problems – remote areas– Power cuts, spikes– Calibration of instruments, time accuracy

• Instrumentation problems– Unattended sites

• equipment problems• Vandalism

– Communication problems – remote areas– Power cuts, spikes– Calibration of instruments, time accuracy

Effects of changes in Effects of changes in instrumentationinstrumentation

Effects of changes in Effects of changes in instrumentationinstrumentation

Sensor characteristicsSensor characteristicsSensor characteristicsSensor 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.

• 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.

Observing PracticesObserving PracticesObserving PracticesObserving 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

• 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

How representative is How representative is the site?the site?

How representative is How representative is the site?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

• 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

UrbanisationUrbanisationUrbanisationUrbanisation

Distance of site from Distance of site from area of interestarea of interest

Distance of site from Distance of site from area of interestarea 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

• 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

Changes in site locationChanges 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

• 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

Bureau sources of dataBureau sources of dataBureau sources of dataBureau sources of data

• SILO • Climate Data

Services• SSU• Regional

Offices

• SILO • Climate Data

Services• SSU• Regional

Offices

http://www1.ho.bom.gov.au/silo/SILO2/




Useful tools in SiloUseful tools in SiloUseful tools in SiloUseful 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)

• 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)

TimelinessTimelinessTimelinessTimeliness

• 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

• 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

Future trendsFuture trendsFuture trendsFuture 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

• 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

Future trends in dataFuture trends in dataFuture trends in dataFuture trends in data

Solar radiation data – traditional network versus satellite derived estimates


• Managing weather risk using daily weather forecasts and seasonal outlooks…

• Managing weather risk using daily weather forecasts and seasonal outlooks…

Should Companies Worry? Should Companies Worry? Should Companies Worry? Should Companies Worry?

• In the good years, companies make big profits.• In the bad years, companies make losses. - Doesn’t it all balance out?- No. it doesn’t.• Companies whose earnings fluctuate wildly receive

unsympathetic hearings from banks and potential investors.

• In the good years, companies make big profits.• In the bad years, companies make losses. - Doesn’t it all balance out?- No. it doesn’t.• Companies whose earnings fluctuate wildly receive

unsympathetic hearings from banks and potential investors.

Weather-related Industry RiskWeather-related Industry RiskWeather-related Industry RiskWeather-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

"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 RiskWeather-related Agricultural RiskWeather-related Agricultural RiskWeather-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)

“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)

The Road toThe Road toWeather Risk Management. Weather Risk Management.

The Road toThe Road toWeather Risk Management. Weather 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).

• 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).

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 variable’s distribution (assuming, for example, that the variable's distribution is normal); and,

•Indirect modelling of the underlying variable’s distribution (via a Monte Carlo technique).

Returning to the Cane GrowerReturning to the Cane GrowerReturning to the Cane GrowerReturning 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?

• 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 ApproachOne ApproachOne ApproachOne 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.

• 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.

Specifying the Decile 4 Put OptionSpecifying the Decile 4 Put OptionSpecifying the Decile 4 Put OptionSpecifying the Decile 4 Put Option

• Strike: Decile 4. • Notional: $100 per Decile (< Decile 4).• If, at expiry, the Decile is < Decile 4, the seller of

the option pays the buyer $100 for each Decile < Decile 4.

• Strike: Decile 4. • Notional: $100 per Decile (< Decile 4).• If, at expiry, the Decile is < Decile 4, the seller of

the option pays the buyer $100 for each Decile < Decile 4.

Payoff Chart for Decile 4 Put OptionPayoff Chart for Decile 4 Put OptionPayoff Chart for Decile 4 Put OptionPayoff Chart for Decile 4 Put Option

Outcomes for Decile 4 Put OptionOutcomes for Decile 4 Put OptionOutcomes for Decile 4 Put OptionOutcomes for Decile 4 Put Option

Evaluating the Decile 4 Put OptionEvaluating the Decile 4 Put OptionEvaluating the Decile 4 Put OptionEvaluating 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)x100=$8.40• 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.

• 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)x100=$8.40• 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.

Weather & Climate ForecastsWeather & Climate ForecastsWeather & Climate ForecastsWeather & 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.

• 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.

An Illustration of theAn Illustration of theImpact of Forecasts Impact of Forecasts An Illustration of theAn Illustration of theImpact of Forecasts Impact 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.

• 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 Impact of Forecast Accuracy Impact of Forecast Accuracy 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.

• 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.

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.guaranteedweather.com

http://www.guaranteedweather.com/



Using Forecast Accuracy DataUsing Forecast Accuracy DataUsing Forecast Accuracy DataUsing 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.

• 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 OptionPay-off Chart: 38 deg C Call OptionPay-off Chart: 38 deg C Call OptionPay-off Chart: 38 deg C Call Option

Determining the Price of theDetermining the Price of the38 deg C Call Option38 deg C Call Option

Determining the Price of theDetermining the Price of the38 deg C Call Option38 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.

• Between 1960 and 2000, there were 114 forecasts of at least 38 deg C.

• The historical distribution of the outcomes are examined.

Historical Distribution of OutcomesHistorical Distribution of OutcomesHistorical Distribution of OutcomesHistorical Distribution of Outcomes

Evaluating the 38 deg C Evaluating the 38 deg C Call Option (Part 1)Call Option (Part 1)


• 1 case of 44 deg C yields $(44-38)x1x100=$600• 2 cases of 43 deg C yields $(43-38)x2x100=$1000• 6 cases of 42 deg C yields $(42-38)x6x100=$2400• 13 cases of 41 deg C yields $(41-38)x13x100=$3900• 15 cases of 40 deg C yields $(40-38)x15x100=$3000• 16 cases of 39 deg C yields $(39-38)x16x100=$1600

cont….

• 1 case of 44 deg C yields $(44-38)x1x100=$600• 2 cases of 43 deg C yields $(43-38)x2x100=$1000• 6 cases of 42 deg C yields $(42-38)x6x100=$2400• 13 cases of 41 deg C yields $(41-38)x13x100=$3900• 15 cases of 40 deg C yields $(40-38)x15x100=$3000• 16 cases of 39 deg C yields $(39-38)x16x100=$1600

cont….



• 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.

• 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.

Finally … Ensemble ForecastingFinally … Ensemble ForecastingFinally … Ensemble ForecastingFinally … 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 …

• 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 …

Documents

Latest Developments in Weather Risk Management presentation to “Risk Finance”, 22-24 March, 2004 The Finance and Treasury Association Dr Harvey Stern,