WEATHER DERIVATIVES – CONCEPT, CHALLENGES, AND FEASIBILITY

By  Ishan Kekre & Girish C

Introduction

A weather derivative is a tool for managing weather risk. It is a financial contract that allows a firm to hedge itself against unexpected and adverse weather. A weather derivative contract or WD derives its value from future weather conditions. Contrary to stereotypical weather insurance, the payout of this kind of derivative is based on a parametric weather index. For instance, the index could be centimeters or millimeters of rainfall. The index could also be a cumulative frequency distribution of temperatures across many locations. The underlying of WD could also be related to snowfall or hurricanes.

Origin of Weather Derivatives

The weather derivative market as compared to other financial instruments is relatively young. The first transaction in the WD market dates back to 1997. The sector developed due to the severe repercussions of El Niño. These events were forecasted correctly by the meteorological community. Firms that had their revenues linked to weather realized the importance of protecting themselves against seasonal weather risks. Many companies who were in the business of dealing with financial futures and options saw WDs as attractive tools to hedge weather risks.

The insurance sector achieved substantial financial consolidation. As a result, there was significant capital to hedge weather risks. Insurance firms started writing options with payoffs linked to weather events. This, in turn, elevated the liquidity for the development of a WD market. Thus, the WD market evolved over the years into a strong over-the-counter market.

In September 1999, the Chicago Mercantile Exchange (CME) introduced exchange-traded and temperature-related weather options and futures. This was done primarily for two reasons:

  • To increase the market size of WDs.
  • To eliminate the counter-party credit risk involved in OTC weather contracts.

These contracts provided avenues for investors to manage their weather risks using standardized contracts at the best available prices.

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Figure 1: Value of the Contracts trades on CME (in $ billions)

Hedging Weather Risk: Concept of HDDs & CDDs

A company going in for a weather deal has a plethora of choices. Energy companies mostly opt for Heating Degree Day option for dealing with winter temperature risk or Cooling Degree Day option for dealing with summer temperature risk.

Calculation of HDD and CDD Indices

  • HDD Index: It measures the cold waves during winter months. HDD index is calculated by subtracting the mean of the daily high and low temperatures from 65°F (in the US) or 18° Higher the value of the index, colder the day. Payout is calculated as below:
  1. 0, (when actual temperature is greater than the base temperature)
  2. Base temperature – Actual temperature, (when actual temperature is less than the base temperature)
  • CDD Index: It measures the warmth during summer months. CDD index is calculated by subtracting 18°C from the mean of daily high and low-temperature Higher the value of the index, warmer the day. The payout is calculated as below:
  1. 0, (when actual temperature is less than the base temperature)
  2. Actual temperature – Base temperature (when actual temperature is greater than the base temperature)

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Since there cannot be a heating degree and cooling degree both on the same day, the obtained HDD and CDD values are accumulated over months or seasons. On the basis of these monthly cumulative HDDs or CDDs weather options are written. HDD and CDD values are never negative.

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Figure 2: Values of CDDs over Jun-Oct

An amalgam of both indices is required to cover the extremes of winter and summer. A firm that expects a very cold day would sell off HDDs. If winter actually turns out to be warmer, it can again buy HDDs at a lower price earning profits. Thus, the profit earned is the difference between the selling and purchasing price of the HDDs. This profit will offset the loss due to lower revenues in case of unexpected weather. Similarly, a firm can buy CDDs expecting the night to be cold. If this weather condition prevails, then the company makes profits. If it does not happen then the firm can exercise a call option and gain on later transactions which will again offset the loss in revenues.

Valuation of HDD and CDD Indices

For example, on a winter day, the maximum and minimum temperatures were 40°F and 26°F respectively. Hence, the mean temperature would be 33°F. Therefore, HDD would be 32°F (65°F-33°F). Similarly, on a summer day, the maximum and minimum temperatures were 90°F and 80°F respectively. Hence, the mean temperature would be 85°F. Therefore, CDD would be 20°F (85°F-65°F).

Assuming the accumulation of degree days, for an entire month of June the daily mean temperature was a 60°F for the first week. For the entire week, a total of 35 HDDs (5 HDD x 7) will accumulate.

During the second week of the month, it turns out to be warmer with daily mean temperatures of 71°F. For the entire week, a total of 42 CDDs (6 CDD x 7) will accumulate.

Assuming, that HDD or CDD index is valued at $20. Hence, for an HDD index of 35, the value of the HDD futures will be $700 (35 HDDs X $20). Similarly, for a CDD index of 42, the value of the CDD futures will be $840 (42 CDDs X $20).

Scenario of Weather Derivatives in India

India ranks as the second largest producer of farm output globally. With over 50% of its rural workforce and a GVA (gross value added to GDP) of 17.4% in 2014, agriculture plays a significant role in the Indian economy.  Weather phenomenon like shortage of rainfall can lead to huge losses to farmers. This calls for a risk management tool for the farmers to shield them from losses occurring due to seasonal weather changes.

Traditionally, India had crop insurance which indemnified farmers against the shortfall in crop yield. This required the insurance companies to perform a field analysis of any claims of yield loss. Huge premiums along with the cumbersome process of collecting proof of damage made crop insurance less attractive.

Weather Based Crop Insurance Scheme (WBCIS) was first introduced to the Indian farmers in 2003. It aims to indemnify farmers for anticipated losses due to adverse changes in weather parameters like rainfall, temperature, frost, et al. WBCIS uses weather-based parameters as a proxy for crop yields and the payout is done based on these parameters. It is the first forage for India’s agriculture insurance to area approach rather than an individual approach. Reference Weather Stations (RWS) have been set up and weather parameters are monitored in these areas on a daily basis. Any deviation of actual weather from the trigger weather leads to a proportionate insurance pay-out automatically.

WBCIS is mainly targeted towards the agriculture sector with differing premiums for different crops, weather derivatives are a generalized instrument to hedge against the impact of adverse weather conditions.

In 2016, SEBI had decided to allow trading of weather derivatives in the financial market. This road is blockaded by a plethora of challenges. Some of the major challenges are listed below:

  1. Pricing of Weather Derivatives: The immediate challenge in front of the SEBI panel is to tackle the issues of pricing of such derivatives. Unlike other financial derivatives, weather derivatives don’t have a definite pricing model. The primary reason for this is that a monetary value cannot be assigned to the underlying. As the underlying could be rainfall or the sunshine, it cannot be bought or sold. Pricing relies heavily on the accuracy of data collected to calculate the indices. Thus, forecasting becomes important. It forms the backbone of WDs. Therefore, an understanding of the environmental factors and statistical weather data becomes important. WDs have a longer maturity as compared to the forecasting data which results in problems associated with curve fitting (normal or lognormal). Thus, option pricing models like the Black-Scholes, 1973 are not applicable in this context. Implementation of a simple option pricing model is more relevant and is shown below:
  • Simple Option Pricing: In this model, a probability distribution is made to fit a set of monthly HDDs or CDDs and is then combined with the payout of the option.The value of E depends on the strike price, the probability distribution of CDDs, and the number of dollars per CDD.Expected Payout (of a CDD option) is given by:

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  • Deciding the Pricing Index:  The parameter (rainfall or temperature) that the index captures should be accurately measurable and should have a strong correlation with the production variable (crop yield in case of agriculture). In India, rainfall can be used as a parameter for a weather index since it is the main determinant of crop yield.

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Figure 3: Payout Scheme for a Put Option (Underlying is Rainfall)

In a simple put option, the cost of the derivative is the option price for the producer. A striking point is decided which is the minimum rainfall threshold below which the owner of the put option earns an indemnity.

A rainfall index Rt for a given year can be determined by calculating the weighted average effective rainfall.

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Where, rit is effective rainfall in period i for year t

ri* is actual rainfall in period i

CAPi is the additional rainfall not contributing to increased crop yield

Payout is triggered when the index price Rt, falls below a threshold level of rainfall denoted by T. T is generally decided by taking weather forecasts into consideration.

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2. Infrastructure Issues: Another major challenge is related to infrastructure. The spread of WDs in India would be largely determined by the availability and reliability of weather data. Computerized mini-weather kiosks at the village level, categorized as public goods, will be required to be setup. These kiosks can provide real-time weather data. Indian Government can divert the funds from subsidies and crop insurance schemes towards providing weather kiosk infrastructure and involving private organizations to develop innovative WDs. The exchanges, brokers, and consumers are all significant parts of the infrastructure required for trading in WD.

3.Basis Risk: It is a risk that occurs in a hedging strategy in which offsetting investments doesn’t experience price alterations in completely opposite directions from one other. This negative correlation between the two investments creates the opportunity for excess gains or losses in a hedging strategy, thus adding risk to the position. Hence, for WDs, it arises when weather in one city is not exactly equal to weather used to value the futures or options. In the WDs market, basis risk is huge and agricultural risk managers must be aware of this risk, being inherent in these markets. Basis can be traded in OTC market after writing a contract on the difference in the CDDs or HDDs between two cities.

4. Willingness to Pay: Willingness-to-pay for WDs is calculated as approximately 8.8% of the maximum payout of the WD contract. This metric is derived from a framework that provides an estimate of the ‘willingness to pay’ for hedging the weather uncertainties. The concept involved is that of expected utility. For a farmer growing a crop, his expected utility can be expressed as:

   E(U) = E(S) – C – R

Where, E (U) is the expected utility,

E(S) is the expected sale price,

C is the cost of inputs and R is a risk premium.

The utility is maximized with respect to planned production.

The understanding of the subtle traits of WDs, adequate training and knowledge needs to be imparted to market participants like farmers, consumers and financial institutions, etc.

Role of Regulations

Forward Market Commission (FMC) and SEBI have to work closely to permit derivative trading on underlying assets like weather indices, etc. In the current scenario, SEBI regulates derivative and cash market on exchanges of all financial securities (i.e. stocks and bonds) while FMC regulates commodity futures and forwards. The priority is to enforce strong regulations to facilitate the introduction of weather derivatives. The Forward Contract (Regulation) Act, encompasses forward trading (derivative) in tangible goods. The ambit of the act needs to be widened in order to allow trading on intangibles like weather, electricity, freight etc.

Following are the key points on the basis of which SEBI will deliberate before introducing WDs in India:

  1. Investor Protection: Below are the four approaches to ensure investor protection:
  • The trading rules should ensure that trading is conducted in a fair and transparent manner.
  • Safeguarding clients’ money is critical. Securities deposited by clients with the trading members should be kept in a separate clients’ account.
  • It should be ensured that trading by dealers on their own account is totally segregated from that for clients.
  • Market integrity: The trading system should ensure that the market’s integrity is safeguarded by minimizing the possibility of defaults.
  1. Quality of markets: Quality of markets goes beyond market integrity and aims at enhancing important market qualities, such as cost-efficiency, price-continuity, and price-discovery. This is a much broader objective than market integrity.
  2. Innovation: As the concept of WDs is relatively new, enforcing regulations should be done with utmost care. This will ensure that innovation which is the beacon of economic growth doesn’t get stifled.

Conclusion

India is one of the most successful developing countries in term of a vibrant market for exchange traded derivatives. In developed countries, WDs are being used by sectors other than agriculture to hedge systemic risk caused by minor weather fluctuations. In India, the government is in continuous pursuit to insulate farmers from financial shock occurring due to crop failures. Hence, the feasibility of introducing weather derivatives is relevant. By investing in weather stations with sophisticated equipment, large coverage across the country, efficient index structuring and creating awareness among the farmers the success of weather derivatives can be assured. The Forward Contract (Regulation) Act needs to be amended which can enable SEBI to introduce and coordinate the new and innovative hedging products like weather derivatives. These financial instruments can cater to the risk appetite of farmers to hedge risk.

Bibliography

  1. Anu Jajoo (2011). Weather Derivative – A tool for weather risk hedging. Retrieved January 14, 2017, from:http://www.jimkanpur.ac.in/Finanace_club/WEATHER%20DERIVATIVES.pdf
  1. Geoffrey Considine (2000). Introduction to Weather Derivatives. Retrieved January 15, 2017, from: http://www.agroinsurance.com/files/weather%20derivatives.pdf
  2. Seth (2008). Prospects and Challenges of Weather Derivatives in India. Retrieved January 15, 2017, from: http://shodhganga.inflibnet.ac.in/bitstream/10603/53950/2/02_abstract.pdf
  3. Sean Campbell (2000). Weather Forecasting for Weather Derivatives. Retrieved January 16, 2017, from: http://fic.wharton.upenn.edu/fic/papers/02/0242.pdf
  4. Anjali Choksi (2012). Emergence of Weather Derivatives – Feasibility in Indian Context. Retrieved January 16, 2017, from: http://www.zenithresearch.org.in/images/stories/pdf/2012/May/ZIJBEMR/11_ZIBEMR_VOL2_ISSUE5_MAY2012.pdf
  5. Shrimi Choudhary (2016, April 4). SEBI plans trading in weather derivatives. Retrieved January 16, 2017, from: http://www.business-standard.com/article/markets/sebi-plans-trading-in-weather-derivatives-116040400740_1.html
  6. Stephen Gandel (2014, January 04). How Wall Street got snowed on weather derivatives. Retrieved January 16, 2017, from: http://fortune.com/2014/01/03/how-wall-street-got-snowed-on-weather-derivatives/
  7. Speedwell Weather (2013). A Quick Guide to Weather Derivatives. Retrieved January 16, 2017, from: http://www.speedwellweather.com/PDF/Consultancy/A%20Quick%20Guide%20to%20Weather%20Derivatives.pdf
  8. Agriculture Insurance Company of India Ltd. Weather Based Crop Insurance Scheme. Retrieved January 27, 2017, from: http://www.aicofindia.com/AICEng/General_Documents/Product_Profiles/WBCIS_FAQ.pdf
  9. Jayeeta Paul (2013, January). A study on the feasibility of weather derivatives in India. Retrieved January 31, 2017, from: https://www.worldwidejournals.com/paripex/file.php?val=January_2013_1358519394_979e8_06.pdf

aviary_1489079561732About the author: 

Ishan Kekre is currently a student of PGDM batch 2016-18. His areas of interests are finance and economics. He has 3 years of experience in the field of Business Intelligence and Performance Management. He holds an NCFM certification in financial markets. He is an ethical hacker and has basic knowledge of cyber security as well. He is also trained in Kumite and Nunchucks. You can contact him at ishan.18@tapmi.edu.in

Girish C

About the author:

Girish C. is currently a student of PGDM batch 2016-18. His areas of interests are financial markets and corporate finance. You can contact him at girish.18@tapmi.edu.in

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