Finance Report: Weather Derivatives and Construction Projects

This report delves into the application of weather derivatives as a risk management tool within the context of construction projects. The study investigates the impact of weather derivatives, focusing on specific objectives such as determining the most impactful weather elements (rainfall and temperature), assessing the effectiveness of weather derivatives in mitigating unusual occurrences, and establishing the relationship between weather derivatives and construction project outcomes. The research employs the Construction Management theory, utilizing the Black-Scholes model and 'Burn Approach' analysis to evaluate weather derivative behavior. Regression analysis is applied to examine the relationship between weather derivatives and construction operations. The findings highlight the significant impact of rainfall and temperature on the construction industry and discuss the various instruments that contribute to project success. The report provides a comprehensive financial analysis of weather derivatives and their role in risk mitigation within the construction sector.

Running Head: FINANCE 1
Financial Mathematics
Institutional Affiliation
Tutor’s Name
Student’s Name
Date

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Abstract
The study explored the impacts Weather Derivatives as Risk Management Tool, a case of
Construction Projects. The study also contained the specific objectives. These included;
Determine the weather element with the strongest impact on the construction projects, find out
whether weather derivatives are effective in reduction of unusual occurrences to the construction
projects and to establish the relationship between weather derivative as a risk management and
the construction projects. The study was built on one theory and this was the Construction
Management theory. The study used Black-Scholes model and Analysis of ‘Burn Approach’ to
measure the weather derivative behavior. Also, the study used the regression analysis to examine
the relationship between weather derivatives as risk management tool and the construction
operation. The study concluded that rainfall and temperatures have got more impact on
construction industry. The study further discussed the contribution or the personal work obtained
by the researcher. Generally, not only the weather derivative tools but also there are various
instruments that yield the projects of constructions to move.

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Table of Contents
Abstract......................................................................................................................................................2
1. Introduction...........................................................................................................................................5
1.2 Description of the study...................................................................................................................15
1.3 Motivation of the Study...................................................................................................................16
1.4 Research Questions..........................................................................................................................16
1.5 Research Hypothesis........................................................................................................................17
1.6 Justification of study........................................................................................................................17
1.7 Contributions / Personal work.........................................................................................................17
2. Literature Review................................................................................................................................18
2.1 Conceptual Review..........................................................................................................................18
Weather options.....................................................................................................................................18
Weather swaps.......................................................................................................................................19
Weather measures..................................................................................................................................19
Heating Degree Days (HDD).................................................................................................................19
Cooling Degree Days (CDD).................................................................................................................19
Pricing of weather derivatives...............................................................................................................20
Hedging strategies.................................................................................................................................21
Weather Derivatives in the Construction Industry.................................................................................22
2.2 Related studies.................................................................................................................................23
2.3 Theoretical review...........................................................................................................................27
The Construction Management theory...................................................................................................27
3. Methodology.........................................................................................................................................29
3.1 Research Design..............................................................................................................................30
3.2 Weather Data...................................................................................................................................30
3.3 Evaluation of derivatives of weather and approaches of pricing......................................................33
CDD and HDD contrasts.......................................................................................................................35
Analysis of ‘Burn Approach’.................................................................................................................38
Construction Industry............................................................................................................................39
Derivatives of rainfall............................................................................................................................40
4. Empirical Analysis...............................................................................................................................40
4.1 Relationship between weather derivatives and construction Industry..............................................41
4.2 Distribution of Temperature............................................................................................................43

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4.3 Rainfall Variations...........................................................................................................................44
4.4 Regression analysis about the variables...........................................................................................46
5. Conclusions..........................................................................................................................................47
5.1 Determine the weather element with the strongest impact on the construction projects..................47
5.2 Effectiveness of Weather derivatives in reduction of unusual occurrences to the construction
projects..................................................................................................................................................48
5.3 Relationship between Weather Derivative as a risk management and the construction projects......49
5.4 Recommendations and Future works...............................................................................................50
References.............................................................................................................................................51
Appendices................................................................................................................................................65
List of Figures
Figure 2: Sample of the simulated rainfall at Sydney airport construction………….42
Figure 3: Weather Derivative Transactions ……………………………………………43
Figure 4: Relationship between weather derivative (temp) and construction
venture………………………………………………………………………………………45
Figure 5: Average Daily Temperature…………………………………………………….46
Figure 5: Annual Rainfall Correlation…………………………………………………….47
List of Tables
Table 1: Coefficients of Construction Projects performances……………………………49

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Weather Derivatives as Risk Management Tool, a case of Construction Projects
1. Introduction
Weather derivatives refer to the financial tools that organizations or private individuals
use in order to catalyze the process of risk management policies (Groningen, 2012). These risk
management strategies are used to minimize risks associated with weather conditions that are not
expected. In other words, the derivatives of weather are index-based tools that are used to
observe clearly the data about the weather especially at the weather stations (Agarwal et al,
2012). The observed data can be used to establish the way how payouts among companies can be
done. Among the indexes, total rainfall over the given specified time is one of the indexes used,
this can be used in the generation of hydro power (Holmes,2014). Also, this can be in areas with
minimum temperatures which reduce below zero in order to protect against companies from
agriculture department, construction, food and beverages among other departments from
damage. Taking the example of the parametric weather insurance, the provision of loss is nor
proved (Dorfleitner and Wimmer 2010). But however, with indemnity, the need of
demonstrating the loss encountered is nowhere to be involved. Therefore, insurance with
indemnity strategy for weather is the instrument which is rarely utilized. To settle issues with
weather derivatives, there should be the final value chosen about the index of weather to be used
in the stipulated time period (Admatiand Pfleiderer, 2009). Also, the contract settled in a given
period can be done only in few days (Huault&Rainelli-Weis, 2011).
At first, the weather derivative contract was done in July, 1996. This came about when
the Aquila Energy laid a dual-product hedge about the consolidated Edson (ConEd). The

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exchange duly involved the consolidated Edison’s purchase of hydroelectricity power (Adrian
and Shin, 2010). The hydroelectricity power was got from Aquila in August of 1996. With such
contract, the price for the electric power was reached to the agreement and also the clause for
weather was included in the contract (Jewson & Brix, 2010). The clause which was included in
the contract that Aquila should subscribe to the ConEd reduction in case the month of August
becomes more cool than expected (Adrian et al, 2013). The cooling Degree Days (CDDs) were
used in the measurements stipulated by the contract of ConEd to be determined especially in the
weather station at central park of New York city. In case the final Cooling Degree Days were
ranging from Zero (0) up to 100% which is below the resultant or expected 320. Thus the
company had received the power price without any discount (Manfredo& Richards, 2009). More
so, when the cooling level was in the range of 11-20% below the normal rate, Consolidated
Edison would have received discount which is estimated to the discount of $16,000. However,
several other levels of discounts were made from the normal one for greater departures
(Berrospide et al, 2010). Most important to note is that, the derivatives of weather began slowly
began in 1997 over the counter. When the market for the products increased (Dorfleitner and
Wimmer 2010), it was evidenced that even Chicago Mercantile Exchange (CME) introduced its
first ever exchange-traded future weather contrasts in 1999. The Chicago Mercantile Exchange
in the present situation has listed derivative contrasts about twenty-four (24) provinces in the
United States of America, eleven (11) European countries, six (6) in Canada, three (3) in
Australia and lastly Japan with three (3) (Chicago Mercantile Exchange, 2012). Also, most of the
various financial tools are used to track the degrees of cooling in days or heating levels
(Brunnermeier, 2009). However, other tools track the rainfall and the falling of snow especially
in 10 United States places. Also, there was an innovation initiated by the reinsurance company to

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provide contrasts. The CME index of Hurricane is the innovation which was created basing on
the formula got from speed of wind and the radius of the storms at the specific point of Landfall
in US (Brunnermeier and Pedersen, 2009).
In recent times, practices of risk management are on probabilities of greater dollar losses
for example measures such as value at risk (Sharif et al, 2015). However, these measures are
only a small portion of risk management. A full risk management system should be able to
address more two useful factors which are preferences and prices (Changnon and Changnon,
2010). There are therefore three p’s that make up the total risk management and these are; price,
probabilities and preferences (Chava and Purnanandam, 2011). For one to have better knowledge
on the relationship between prices, probabilities and preferences, there is need to put in mind the
fundamental principle of economics that is the law of supply and demand (Tita, 2015).
According to this law, a commodity’s price in the market and the total traded quantity are
determined by the interaction of both the supply and demand curves (Sen and Ray, 2013). In this
case, the demand curve shows the schedule of quantities required by customers at different prices
and the supply curve shows the schedule of quantities producers are predicting to sale at different
prices (Sharif et al, 2015).
A weather derivative is a financial instrument that companies or individuals use to protect
against risks that may cause losses by weather conditions (Dorfleitner and Wimmer 2010).
Usually there the seller and the buyer of weather derivatives get into a contract where the seller
of weather derivative takes the responsibility of bearing the risks expecting a payment of a
premium (Cheng et al, 2012). In case the contract expires before any loss from weather
conditions is realized, the seller gains profits. However, in cases of unexpected or hostile weather
conditions, the buyer of the derivative is free to ask for the amount agreed (Chincarini, 2011).

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Companies which base their businesses on weather conditions for example, hydro-
electric generation businesses, those operating on sporting events and other related businesses
are expected to use weather derivatives in managing risks (Duffie, 2010). Farmers too are
encouraged to apply weather derivatives in hedging against a bad harvest brought about by a lot
of rain or very little rain, severe sunshine, snow or destructive winds (Edmans, 2009). Weather
derivatives have an index that is used to measure different weather aspects, one index may be
used to measure rainfall of a given area within a specified time period (Garleanuand Pedersen,
2011). Another index can be used to measure temperature of an area over a specified period. One
of the climate index used to measure weather derivatives is the Heating degree days (HDD)
(Agnello et al, 2013). In the heat degree days’ contracts, the average temperature of each day
goes below the earlier decided reference point for a given time. The amount of the end time is
recorded and included in a cumulative count (Garleanu et al, 2009) The final results indicate
whether the seller receives the payment of pays to the buyer. Weather derivatives were
introduced in 1990s and they brought in an important role that lacked in the entire world. United
States as an economy is 20% affected by weather (Gennaioli et al, 2012). Weather affects
industries such as energy, construction, agriculture and travel that are considered very important
to the growth and development of an economy.
The un forecasted weather rarely results in price changes which usually cover the lost
revenue. Weather derivatives are thus used by various companies to hedge against weather which
may unfavorably affect the business.
Weather derivatives started trading in 1997 over-the-counter (OTC). They became
expensive with $ 8 billion after a very small period (Ahmad & Scott, 2015).The Chicago
Mercantile Exchange (CME) introduced weather derivative contracts for some cities of which

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most of them are from United States. Few hedge funds indicate weather derivatives as
investments Those investors interested in the weather derivatives are happy with the low
correlation with local markets.
Weather derivatives seem to be related but are different from insurance. Insurance is used
in protecting a business or company from weather catastrophes whose probabilities of happening
are limited such as hurricanes, earthquakes among others (Gopalan, 2009). On the other side,
weather derivatives are used to cover events like excessive rain and too much sunshine whose
occurrences are highly determined (Ahmad et al, 2013). Also, the truth stands that insurance
cannot prevent a decrease on demand brought about by little wetter summer than average mean,
however, weather derivatives can easily be used to solve that issue (Gromband Vayanos, 2010).
Since both weather derivatives and insurance are applied under different conditions, a company
may easily pick interest in buying both of them. Another difference between the two is that
buyers of weather derivatives do not need to display a loss as the contract is based on index. But
for one to acquire insurance, the damage must be visible (He and Krishnamurhty, 2013).
On a global note, weather is seen as the most thing which affects several economies. This
is because weather have got statistical or significant influence on the revenue, costs or both of
the companies (Yang et al, 2009). It is observed that 4/5 of the global economy is seen to be
directly or indirectly attributed to weather (He and Krishnamurthy, 2012). The degree of
exposure and sensitive to weather can be elaborate by the level of sales, costs of production
especially to the elements of meteorology such as sunshine, rainfall, temperature, snowfall, wind
and many others. It is evident that, when the weather situations or elements cause the volatility
of the outcome in the specified company that is to say construction, food among others, such
sector is termed to be sensitive to weather (Berrospide et al, 2010). Some studies show that the

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sensitive of weather changes between the economic areas and the sectors of geography
(Worthington, 2009). However, among all the sectors, the sectors of economics are to larger
extent sensitive to weather.
Furthermore, basing on the several impacts of weather, such weather impacts can be
categorized into two that is; catastrophic and non-catastrophic weather situations (Alquist&
Gervais, 2013). For the case of catastrophic weather; deals with the phenomena characterized
with the low chances of occurrences that are likely to bring about malicious financial damages
(Levitt and List, 2011). These catastrophes may include the following; floods, hurricanes,
tornadoes and so on. Also, for the case of non-catastrophic weather, this involves the deviations
which are minor from the weather conditions that are normal. The examples of non-catastrophe
include; warmer winters not like use ones, and unusual rains than the normal summers
(Worthington, 2009). Besides, the major difference between no-catastrophic weather and
catastrophic weather is that non-catastrophes affect the performance of the company but does not
affect the property and the lives of the people (Alvesson& Sandberg,2013). In addition,
unpredicted flow of cash because of the deviations in seasons on the average basis that is normal
weather can well explain the definition of non-catastrophic risks of weather (MDA Federal,
2013). Weather is categorized as the source of risk simply because if fundamentally affect the
amount of production as well as the amount of demand for the specified good or product which
does not reflect the price of the product being sold (Yu et al, 2010). In most cases, weather is
among the risks which are volumetric than the risk of price.
The reduction in consumption of heating energy especially in the unusual winters which
are extremely cold are considered to be among the examples of unfavorable weather that impacts
on the demand of the product (Poulter, 2011). Moreover, weather is seen as the significant factor

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in determining the quality and quantity of the output and the prices in different sectors such as
agriculture, power generation that are generated from the sources which are renewed. these
renewable resources include the following; sun, water and wind, outcome of recreation, tourism
with other outdoor activities, local municipalities’ budgets to remove snow costs, retail sales
among others (Amato et al, 2015). The risks attributed from the side of weather are highly
localized geographically. This means that weather changes significantly even when the distances
of small spatial are considered.
In this study, weather derivatives show the likely promising solutions. Although weather
products especially those that are not traded on exchange formally exit in terms of rainfall,
temperature, humidity and snowfall, though they are common (Yu et al, 2010). However, it is
believed that such formal exchanges are mostly used to the company’s risk managers. On the
important note, weather derivatives give vivid knowledge to the companies or firms on the way
of managing volumetric risks that are emanated from adverse events of weather of deviations in
seasons from the norm of long climatic condition (Yang et al, 2011). In relation to the
conventional hedging of price or forward contracting, the derivatives of weather are likely to
provide the revenue risk management potential that is proved to be more attractive to various
kinds of firms. The reports by Turvey indicates that about 4000 transactions of weather
derivative occurred in 2000 which was approximately worth $8 billion. Given the number of
firms which are able to use the weather derivatives to higher advantage, this therefore increases
its value (Poulter, 2011). By attaining such kind of the liquidity, it thence requires clear general
evaluation and understanding of how such instruments work (Andrews, 2013).
In addition, each and every weather contract has got five major essential elements. These
elements include the following; the index of underlying weather, the time to which the index

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accumulates in a season or a month, the station of weather which reports the maximum and
minimum daily temperatures, the value of the dollar that is attached to the value of index and the
fifth one is the value of reference the underlying index (Tuttle, 2013). Moreover, weather
derivatives are unforeseen securities that gain value especially when temperature is greater or
less than the value of benchmark that is typical at degrees Fahrenheit of 65 at a given reference
location (Mitchell and Pulvino, 2011). For each and every day, temperature averages are greater
than the benchmark that provides the cooling degree day as that of the value of cumulative
cooling degree day index (Yang et al, 2011). On the other hand, each and everyday sums up to
the heating degree day (HDD)index. When the agreed period of the contract expires, the holder
of the contract receives the payment if the amount accumulated about the underlying index
reduces below the level of strike (Allen, 2014).
Furthermore, the payment amount is likely to be equal to the number of cooling degree
day which is above the level of strike that is multiplied various dollar values for each unit of
index (National Weather Service. 2010a). Besides, the purchasers of the weather derivatives are
therefore compensated for the amount of money that releases the losses of the real business that
occurs as the result of the emergence of patterns of weather (Tuttle, 2013). For instance, the
amusement arena which offers to buy the CDD that puts the payouts in case there is unusual
string of cold days. The value of the pay that has accumulated for a long can be in position of
offsetting the revenue that is lost from the customers especially those who stayed from the
adverse period of weather (Angrist, 2015). On the other hand, when the intervening period is
involved with the unusual hot times in order for the cooling degree day (CDD) to rise above the
level of strike (National Weather Service, 2010b). Therefore, the expiry date puts the amusement
park owner to be worthless out of the premium to be paid in order to initiate the company