weather to buy or sell. extreme weather impact on corn futures

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Master Thesis

Weather to Buy or Sell.

Extreme Weather Impact on Corn Futures Market

Eugene Filimon

Department of Management, Technology, and Economics (D-MTEC)

Chair of Entrepreneurial Risks (ER)

Supervisor: Prof. Dr. Didier Sornette

January 2011


Declaration

I hereby declare that this thesis was performed and written on my own and that references and resources used within this work have been explicitly indicated.

I am aware that making a false declaration may have serious consequences.

____________________ ______________________ (Place and date) (Signature)


Acknowledgements

In the first place, I would like to thank my supervisor Prof. Didier Sornette for support. His passion

for interdisciplinary work is very inspiring. And his paradigm “more is different” that shows how to

bridge different disciplines is among key learning outcomes for me.

I also would like to thank Zurich-based weather risk management start-up where I worked during

this thesis and personally his founder Mark. While this company turned out to be very serious

competitor for my time (getting most of it and leaving very little for academic research) it was

interesting to see inside workings of weather derivatives business. And it was obviously must-to-do

thing to cover living costs in Zurich…

I’m also very much grateful to all the people who developed R and its very useful packages (and in

particular to authors of quantmod, zoo, RMetrics, ggplot2 and PerformanceAnalytics).

Many thanks to my parents and friends for their support and trust in me!


Contents

Introduction ....................................................................................................................................................... 6 Chapter 1. Commodity Investment.................................................................................................................... 8 1.1 Introduction into Commodities................................................................................................................ 8

1.2 Commodity Investment ........................................................................................................................... 9

1.3 Demand/Supply Analysis........................................................................................................................ 11

1.3.1 Supply.............................................................................................................................................. 11

1.3.1.1 Current State of Supply ............................................................................................................ 11

Production........................................................................................................................................ 11

Inventories ....................................................................................................................................... 16

Infrastructure ................................................................................................................................... 17

1.3.1.2 Projected Changes in Supply .................................................................................................... 18

Production........................................................................................................................................ 18

Inventories ....................................................................................................................................... 20

Infrastructure ................................................................................................................................... 20

1.3.2 Demand........................................................................................................................................... 21

1.3.2.1 Current State of Demand ......................................................................................................... 21

Bona fide Demand............................................................................................................................ 21

Speculative Demand ........................................................................................................................ 25

1.3.2.2 Projected Changes in Demand ................................................................................................. 29

1.4 Risks in Agriculture................................................................................................................................. 30

1.5 Summary of the Chapter........................................................................................................................ 30

Chapter 2. Weather Risk in Agriculture ........................................................................................................... 31 2.1 Weather Dependence of Agriculture ..................................................................................................... 31

2.1.1 Climate Change, Weather Variability and Extremes ....................................................................... 32

2.1.2 Geographical Concentration of Production .................................................................................... 35

2.2 Observation and Prediction of Weather Shocks .................................................................................... 37

2.2.1 Weather Monitoring ....................................................................................................................... 37

2.2.2 Weather Forecasting....................................................................................................................... 38

2.2.3 Speed of Access............................................................................................................................... 38

Chapter 3. Weather to Buy or Sell. Quantitative Analysis ............................................................................... 41


3.1 Introduction ........................................................................................................................................... 41

3.2 Data........................................................................................................................................................ 42

3.2.1 Financial Data.................................................................................................................................. 42

3.2.2 Weather Data.................................................................................................................................. 46

3.3 Empirical Results .................................................................................................................................... 47

3.4 Summary of the Chapter........................................................................................................................ 61

4. Summary and conclusion ............................................................................................................................. 63 A. Appendix...................................................................................................................................................... 64 A.1 Summary of the Weather-‐based Trading Strategy ................................................................................ 64

A.2 General Assessment Framework ........................................................................................................... 65

A.3 Screenshots of the Developed Application............................................................................................ 67

A.4 Information Schema of the Developed Application .............................................................................. 69

Bibliography ..................................................................................................................................................... 70


Introduction

“Population, when unchecked, increases in a geometrical ratio. Subsistence increases only in an arithmetical ratio. A slight acquaintance with numbers will show the immensity of the first power in comparison of the second.”

An Essay on the Principle of Population

Thomas Robert Malthus, 1798

Although a Malthusian catastrophe is not at hand, recent spike in commodities prices, and in

particular of agricultural ones, indicates their shortage. In this situation excess demand puts upward

pressure on the market price until it reaches higher equilibrium price (with lower demand and higher

supply).

This work sets the goal of investigating both long-term and short-term opportunities in agriculture

commodities investment. The work starts with brief introduction into futures markets followed by

supply-demand analysis. While the factors identified and approach itself is applicable in general to

most of the agriculture products, we focus on corn because it’s major source of food, animal feed

and ethanol (average daily traded volume and open interest in 2010 were about 2.6 times larger than

same parameters of futures contract for wheat). The first part is concluded with review of

speculative developments. We don’t reiterate details of food crisis but identify indicators that might

serve as alarm of loosening in the relationship between prices and supply and demand conditions.

The remainder of this work is organized as follows. Section 2 discusses the link between climatic

conditions and level of supply. We present evidence to support that short term trading strategy

driven by weather induced supply shocks will benefit from increasing weather variability and

geographic concentration of production. We also review improvements in weather analysis and

forecasting (e.g. extended coverage, more accurate prediction and faster access) and their value from

the perspective of participants in commodities markets.

Last part is dealing with quantitative analysis of corn futures prices, in particular their response to

extreme weather conditions. We apply statistical methods such as copula and measures of extreme

dependence to develop result describing dependence structure between market returns and

maximum temperatures. We also investigate timing of market response to check for developments

similar to high frequency trading in equities market. As surface weather observations are the

fundamental data used, we ask the question if getting access to real time observations and covering

additional locations can give competitive advantage.

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During working on this thesis, we have learned a lot. Amassed collection of links about weather risk and closely related topics (insurance/reinsurance, climate change, meteorology, environmental protection, carbon finance. agriculture, food security and energy) is made freely available at http://wxrisk.wikia.com/


Chapter 1. Commodity Investment

1.1 Introduction into Commodities According to Oxford English Dictionary commodity is “a useful or valuable thing”. More precise

definition taken from financial glossary (CFA Institute) is “commodities are articles of commerce—

such as agricultural goods, metals, and petroleum; tangible assets that are typically relatively

homogeneous in nature”.

While commodities are produced by many different producers; the items from each different

producer are considered equivalent. On a commodity exchanges the underlying standard stated in

the contract defines the commodity, not any quality related to specific producer's product. On the

basis of production method and usage they are usually divided into several groups – agriculture (or

soft) commodities (goods that are grown), hard commodities (goods that are extracted through

mining) and energy commodities (include electricity, gas, coal and oil).

The focus of this work is on agriculture commodities (excluding livestock and their products). The

list of major ones traded on commodity exchanges is provided in Table 1 and exchange information

– in Table 2. Bold highlighting in Table 1 is used to mark 20 most actively traded contracts in 2009

(Futures Industry Association, 2010).

Commodity Main Exchanges GRAINS group Barley ASX (AU), ICE (CA), LIFFE(FR), NCDEX (IN) Corn BM&F (BR), CBOT (US), DCE (CN), KEX (JP), LIFFE(FR), MGEX

(US), NCDEX (IN), ROFEX (AR), SAFEX (SA), TGE (JP) Oats CBOT (US) Rice CBOT (US), NCDEX (IN), ZCE (CN) Wheat ASX (AU), CBOT (US), KCBT (US), LIFFE(FR), LIFFE(UK), MGEX

(US), NCDEX (IN), ROFEX (AR), SAFEX (SA), TurkDEX (TR), ZCE (CN)

OILSEEDS group Canola ASX (AU), ICE (CA) Rapeseed LIFFE(FR) Soybeans BM&F (BR), CBOT (US), DCE (CN), KEX (JP), MGEX (US), NCDEX

(IN), ROFEX (AR), SAFEX (SA), TGE (JP) SOFTS group Cocoa ICE (US), LIFFE(UK), NYMEX (US) Coffee BM&F (BR), ICE (US), LIFFE(UK), NCDEX (IN), NYMEX (US), TGE

(JP) Cotton BM&F (BR), ICE (US), NCDEX (IN), NYMEX (US), TurkDEX (TR)),

ZCE (CN) Sugar BM&F (BR), ICE (US), LIFFE(UK), NCDEX (IN), NYMEX (US), TGE

(JP), ZCE (CN) Table 1. List of main agriculture commodities (name, list of exchanges trading commodity futures, country codes according to ISO

3166). Source: author’s research

Chapter 1. Commodity Investment Error! Unknown switch argument.

Code Exchange(s) Name ASX Sydney Futures Exchange of Australian Stock Exchange group BM&F Bolsa de Mercadorias & Futuros exchange of BM&FBovespa group CBOT Chicago Board of Trade of Chicago Mercantile Exchange (CME) group DCE Dalian Commodity Exchange ICE ICE Futures exchanges of IntercontinentalExchange group (includes former New

York Board of Trade (NYBOT), the Coffee, Sugar and Cocoa Exchange (CSCE) and Winnipeg Commodities Exchange (WCE))

KEX Kansai Commodities Exchange KCBT Kansas City Board of Trade LIFFE exchanges of NYSE Euronext group MGEX Minneapolis Grain Exchange NCDEX National Commodity & Derivatives Exchange NYMEX New York Mercantile exchange of Chicago Mercantile Exchange (CME) group ROFEX Rosario Futures Exchange SAFEX South African Futures Exchange TGE Tokyo Grain Exchange TurkDEX Turkish Derivatives Exchange ZCE Zhengzhou Commodity Exchange

Table 2. List of commodity futures exchanges. Source: author’s research

Extensive list of exchanges is important for several reasons. First of all it allows us to search for

arbitrage opportunities between different markets. Secondly, it’s likely that some of the smaller

exchanges are targeting local markets (and even have “source-of-origin” restrictions) making

impacts of weather-related supply shocks more proliferated.

1.2 Commodity Investment A fundamental rule of portfolio construction is to divide investments between assets classes that

have low correlation with each other and such process is called asset allocation. There are three

“traditional” asset classes - stocks, bonds, and cash.

Little or no correlation with other asset classes is one of the requirements to consider instrument as

separate asset class. Number of papers (Gorton & Rouwenhorst, 2005), (Idzorek, 2006), (Mongars

& Dombrat, 2006) supports the claim that commodities constitute their own asset class. In (Gorton

& Rouwenhorst, 2005) Professors Gary Gorton and Geert Rouwenhorst demonstrated that an

investment in a diversified commodity index over a 45-year period would have resulted in positive

returns with negative1 correlation to stocks and bonds. It has important implication for asset

allocation as by including commodities in their portfolios investors can achieve diversification

benefits.

1 The fact is questioned recently as since 2008 correlation between the S&P 500 and the S&P GSCI commodity index has risen to nearly 0.8. In response, authors gave interview to Financial Times emphasizing “over a long period” view.


For number of reasons – ranging from legal2 (e.g. position limits) to market conditions (e.g. stock

market and housing prices booms3) - they were largely ignored by the general investment

community. The last bull market in commodities was back in 1970s. The situation changed in 2000s

when commodities markets attracted interest again as evidenced by Figure 1 below.

Figure 1. Number of futures contracts traded, year-‐to-‐year change, %. Source: Futures Industry Association statistics

In addition to academic research, strong support for commodities investment was given by number

of prominent investors including American Jim Rogers4. In 2004 he wrote in his “Hot Commodities:

How Anyone Can Invest Profitably in the World's Best Market” book that “supply and demand is

terribly out of balance for nearly all commodities right now. I believe that investing in commodities

will represent an enormous opportunity for the next decade or so.”

Given the availability of exchange-traded futures contracts, this evidence led to dramatic surge in

commodities investment. Impact of this “financialization” of commodity futures markets was

enormous, resulting in large spike in commodity prices and so called “food crisis of 2007-2008”.

We will present summary of research on this issue in the upcoming part. We need to examine it to

correctly understand price drivers and situation of supply and demand. In the part below we also

derive broad perspectives on possible future developments and summarize unique characteristics

that differentiate commodities and, in particular, soft ones from other investments. Main goal

remains the same - assess weather impact on agriculture commodities’ prices.

2 Onion futures’ trading was banned in the US with effect from 1959. (Jacks, 2005) analyzed prices prior and after ban and, contrary to popular belief , concluded that “futures markets were associated with, and most likely caused, lower commodity price volatility”. 3 3500% rise of NASDAQ Composite index in period of 1980 – 2000 is an example of conditions in equity markets 4 known for co-‐founding Quantum Fund with George Soros, achieving 4200% return over 10 years

0.00

10.00

20.00

30.00

40.00

50.00

60.00

2002 2003 2004 2005 2006 2007 2008 2009

%

Number of futures contracts Year-‐toyear change, %

Agricultural

Energy

Total


1.3 Demand/Supply Analysis Economic law of supply and demand is the main basis for the fundamental analysis of commodities

prices. For the supply side we should account for the inventories - amount that is carried over from

previous year(s) of production – and production - amount that is being grown during the current

year. Demand is represented by the amount of commodity that is consumed at a given price level.

We focus on corn which is major source of food, animal feed and ethanol (corn, soybeans, wheat

and rice together provide 60% of human food supply (Matson, Parton, Power,, & Swift, 1997)).

Importance of the analysis should be viewed not only from academic but practical perspective. This

work is done at the Swiss-based company that is planning to start its agricultural fund (and join in

this trend number of other investment managers (Kelleher, 2010)) therefore practical knowledge of

supply-demand analysis and familiarity with major data sources has significant importance.

Overall situation is summarized in the Table 3 below.

Supply Side Demand Side Decreasing Supply Increasing Supply Decreasing Demand Increasing Demand Rising energy costs Advances in

biotechnology Unaffordability of food in developing countries

Population growth and urbanization

Rising fertilizers costs Improvements in infrastructure

Reduction in food waste

Economic growth

Land constraints and degradation

Increase in area as response to prices

Increasing meat consumption

Water constraints Expansion in biofuels Stricter environmental protection policies

Dollar devaluation

Adverse weather events Large foreign exchange reserves

Table 3. Factors contributing to higher food commodity prices. Source: author’s summary

1.3.1 Supply

1.3.1.1 Current State of Supply Looking at the supply in more details we can identify number of factors potentially affecting it –

production levels, inventories and infrastructure.

Production Corn production has increased by 50% in the last two decades reaching record 826 million metric

tons in 2010. Major producers are United States, China, European Union, Brazil and Mexico, with

US accounting for 40% of total output (Figures 2 and 3).


Figure 2. Top 10 corn producers, 2010/11. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

Figure 3. Changes in corn production during last 20 years. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

To predict total crop output for a given year we need to estimate product of area and yield.

Area is the amount of land used for growing particular crop in given year. In agriculture statistics

this data is typically reported by two measurements – area planted (or sown) and area harvested. The

area harvested is always smaller or equal to area planted.

Main decision makers are producers (farmers) as in market economy they usually make a choice

about land use. Their decisions depend on price expectations (for all range of crops potentially

suitable for growing), habit persistence/inertia and input costs (e.g. energy and fertilizers). Higher

40%

20%

7%

6% 3%

3%

2% 2%

1% 1%

15%

Corn Produchon, % top 10 countries in 2010/11 USA

China

EU-‐27

Brazil

Mexico

Argenhna

India

South Africa

Ukraine

Canada

Other

0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000

1000 M

T

Corn Produchon, 1000 MT top 10 countries

USA China EU-‐27 Brazil Mexico Other USA China EU-‐27 Brazil


prices for commodities will lead to an increase in allocated land as it is more profitable to produce

commodities when their prices are high.

Areas allocated for corn and their temporal changes in major producing countries are presented in

the Figures 4 and 5. Major positive changes over the last 20 years are in US and China. China has

expanded its corn harvested area by 33% in the last decade reaching US in absolute numbers.

Figure 4. Corn area harvested. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

Figure 5. Changes in corn area harvested. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

Weather during planting period and expected climatic conditions of growing season also have strong

impact on crop choice. In US and Canada wet weather in spring usually delay sowing and results in

farmers decision to leave land unplanted or switch to crops that mature faster (e.g. from corn to

21%

19%

8% 5%

5% 5% 3%

2%

2%

2%

28%

Corn Area Harvested, % top 10 countries in 2010/11 USA

China

Brazil

India

EU-‐27

Mexico

Nigeria

Indonesia

Tanzania

South Africa

Other

China USA India

Nigeria Tanzania

Other Indonesia Mexico EU-‐27

South Africa Brazil

-‐2000.0 0.0 2000.0 4000.0 6000.0 8000.0 10000.0

Changes in Corn Area, 1000 HA top 10 countries, 1991/92 -‐2010/2011


soybeans). This impact was observed in 2010 in Canada where many fields were left under water,

preventing seeding or affecting the development of those crops already planted (Olson, 2010).

Unfavorable climatic conditions during the harvest (e.g. rains making ground too wet to allow

machinery) can delay it, force a harvest in bad weather (impacting yields and quality) or severely

damage the crops (e.g. fungal diseases due to excessive rainfall or impact of early frost).

Acreage response for corn is very fast as demonstrated below in Figure 6. Farmers had grown about

32% more corn in '07 than in '06 as planting corn was more profitable than planting soybeans.

Higher price for corn was driven by expectations of high demand by bioethanol production industry

as big production increase plan - 35 billion gallons by 2017 - was announced by George Bush in

January 2007. It’s worth to note that number of agriculture commodities need substantially longer

periods of time to adjust land use. While change is fast for single-year crops, similar process for

multi-year plants such as fruit trees can take many years (Hausman, 2009).

Figure 6. Changes in area harvested for 3 major crops, US. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

Yield is the measure of the crop output per unit area of land under cultivation. Agro technology

factors that influence plant productivity include its variety, fertilizer use and crop rotation practices.

Advances in agro technology led to significant yield growth but it has slowed since the 1990s. It can

be indicator that easy gains through adoption of “green revolution” inputs have already been

realized. The slowdown can be attributed to decrease in R&D spending and slow acceptance of new

biotechnology products due to regulatory barriers and consumer backlash. More detailed analysis

can be found in (Jaggard, Qi, & Ober, 2010).

15,000

20,000

25,000

30,000

35,000

40,000

1000 HA

Major Crops Area Harvested, 1000 HA United States

Corn

Soybean

Wheat


Over the last 50 years corn average yields significantly increased in United States, Canada,

Argentina and China (5.3 times), however productivity in India, South Africa and Mexico didn’t

improve with the same rate, staying below average world yields (Figure 7).

Figure 7. Corn average yields in major producing countries since 1960. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

Weather and natural disasters (e.g. floods and hails) have significant impact on yields that will be

demonstrated in separate chapter. Its influence on plant growth is not limited to direct effects but has

also implications for development of weeds, diseases and pests. As an example increase in rainfall

leads to an increase of atmospheric humidity which combined with higher temperatures, could favor

the development of fungal diseases.

Number of commercial companies developed products aiming to monitor weather’s impact, actual

and forecasted, on vegetation health (Planalytics).

-‐1

1

3

5

7

9

11

1960/1961 1970/1971 1980/1981 1990/1991 2000/2001 2010/2011

MT/HA

Corn Average Yields major producers and world average

Argenhna

Brazil

Canada

China

India

Mexico

South Africa

United States


Figure 8. Commercial product of Planalytics, provider of business weather intelligence, combines hourly weather forecast data with pest prediction models to identify areas of the country where crops are vulnerable to insects, weeds and/or disease

pathogens.

Inventories Amount of commodities in storage impacts their supply. Larger amounts exert downward pressure

on market prices.

Figure 9. World corn total consumption and stocks-‐to-‐use ratio. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

10.00

15.00

20.00

25.00

30.00

35.00

450,000

550,000

650,000

750,000

850,000

1000 M

T

Corn Use, 1000 MT and Stocks/Use raho, % total world

Use

Stocks/Use


Figure 10. United States corn total consumption and stocks-‐to-‐use ratio. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

While corn use is rising globally and in US specifically, stocks as a percentage of total use are

decreasing rapidly. In this year in US ending stocks are the lowest since 2003/04 with stocks-to-use

ratio the lowest since 1995/96 (Figures 9 and 10).

Shelf life and storage costs of agriculture commodities vary significantly (soybeans are more

perishable than cereals). That’s why most of the trade in several commodities is done in form of

semi-processed derivatives like soybean meal or dry milk.

Infrastructure Commodities in general and agriculture ones in particular are physical goods that require necessary

infrastructure. It ranges from machinery and storage facilities to processing plants and transportation

networks. Supply chain from farmer to consumer is considerably longer than comparable supply

chain for industrial goods (Westlake, 2005).

As the result any potential bottleneck can have negative effect on supply. In 2010 India decided to

export about 590,000 tons of sugar which was imported but stayed at the port due to a shortage of

railway wagons (Bloomberg, 2010). Another example from this year is record 135 ships backlog at

Brazil’s ports (accounts for 54 percent of the world’s sugar exports) due to rain-caused shipment

disruptions (Bloomberg, 2010).

Such challenges are unlikely in the top exporter – United States (Figure 11). However Brazil,

Argentina and Ukraine may experience such constraints especially if production continues to

increase (their combined share of exports grew from close to zero to 30% over the last decades)

(Figure 12). This small number of major exporters also makes corn prices highly vulnerable to a

weather disruption in any of these countries.

0 5 10 15 20 25 30

0

100,000

200,000

300,000

400,000

1000 M

T

Corn Use, 1000 MT and Stocks/Use raho, % United States

Use

Stocks/Use


Figure 11. Corn exports, country shares. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

Figure 12. Corn exports, temporal developments. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

1.3.1.2 Projected Changes in Supply

Production Number of developments can affect production of agriculture commodities. Major themes are

land/water constraints, speed of technology change/adoption, input costs and climate change impact.

Cultivated land area is likely to decrease in countries with strong economic growth and increasing

population and urbanization. It will be accompanied by land use change from agriculture to industry,

infrastructure and residential. Likely soil deterioration (and, as results, yields) is another negative

outcome associated with intensive agriculture, especially in India and rest of Asia (Mythili).

58% 15%

8%

6%

3%

2%

2%

1% 1% 1%

3%

Corn Exports, % top 10 countries in 2010/11 USA

Argenhna

Brazil

Ukraine

South Africa

Serbia

India

Paraguay

Thailand

EU-‐27

Other

0

20,000

40,000

60,000

80,000

100,000

120,000

1000 M

T

Corn Exports, 1000 MT top 10 countries

Other

Thailand

Paraguay

India

Serbia

South Africa

Ukraine

Brazil

Argenhna

USA


At the same time number of countries in Latin America, Eastern Europe and Africa can contribute to

the pool of arable land. In fact, EU has overproduction of food and attempts to withdraw some land

from agriculture. European Union has agricultural subsidies system called Common Agricultural

Policy. CAP-mandated demand is higher than demand in the free market which leads to the EU

purchasing of surplus output at guaranteed price and storing it in large quantities before selling to

developing nations. This is negatively affecting well-being of farmers in developing countries

(Armin, 2010). In US about 8 percent of the cropland in the country is not cultivated as it is rented

to government under the Conservation Reserve program. Owners of the land receive annual

payments totaling $1.8 billion for ca. 14.6 million hectares (compare to US corn harvested area of

32.8 million hectares).

Advances in agro technology (such as development of drought resistant varieties) can also permit

use of currently unproductive territories. Irrigation can be viable option for many territories in

Africa where current issue is often lack of investment and not the absence of water resources.

Growth in cultivated land may be negatively affected by government and international policies on

the issues of biodiversity, forest protection (e.g. deforestation agreement between US and Brazil,

signed in 2010) or water access rights (market for access entitlements in Australia (Parker & Speed,

2010)). Competition for fresh water is worth of particular attention because water scarcity and water

pollution are the top environmental concerns and their impact is already evident while stresses on

water supply will only continue to grow (GlobeScan, 2010). Further introduction of new policies is

very likely given that the last IPCC report concludes that agriculture accounts for 54% of methane

emissions, roughly 80% of nitrous oxide emissions, and virtually all carbon dioxide emissions tied

to land use (IPCC, 2007) .

As mentioned above speed of productivity improvements will depend on R&D spending and

regulators, producers and consumers acceptance of genetically modified food. In fact public sector

spending dropped significantly and most of R&D activities are within private sector dominated by

“big six”, which are BASF, Bayer, Syngenta, Dupont, Dow and Monsanto (Piesse & Thirtle, 2010).

Cost of inputs such as agricultural machinery, chemicals, fertilizers, seeds and energy commodities

is unlikely to decrease. Among recent developments - patent protected seeds as agri-biotech

companies wish to ensure a profitable return on their investment; increasing prices and

concentration in fertilizer industry.


Climate change is attracting much attention so we devoted it separate part of this study. Main

conclusions are that global warming is changing regional climates and weather patterns and

contributing to increase in natural disasters and weather variability.

In attempt to combine these impacts on supply side together we used data from Risk Map published

annually by Aon Risk Services (mostly focusing on political conditions, natural disasters, water

insecurity and global warming) (Aon, 2010). Interestingly, corn is in the top of the table meaning

higher risks than average.

Commodity Sourcing Countries Most at Risk Other Primary Producers# Cocoa Ivory Coast, Nigeria, Indonesia Ghana Corn China Brazil, USA Rice Myanmar, Bangladesh, Thailand, Indonesia,

China, Vietnam, India -

Sugar Cane Pakistan, Thailand, China, India, Mexico Brazil Coffee Ethiopia, Colombia, Uganda, Indonesia, Vietnam,

India, Mexico, Guatemala Brazil

Sorghum Sudan, Ethiopia, Nigeria, China, India, Mexico USA Wheat Pakistan, Russia, China, India France, USA Sugar Beet Russia, Ukraine, Turkey, China France, Germany, Poland, USA Barley Iran, Russia, Ukraine, Turkey, China Spain, France, Canada, Germany

# more than 5% of global production

Table 4. Agriculture commodity supply risk. Source: (Aon, 2010)

Inventories After two decades of low and stable food prices, government and private sector reduced stocks in

favor of “just-in-time” inventory management. Due to the recent developments these decisions are

likely to be reversed.

Smoothing impact on prices may increase as number of countries and regional blocks establishes

strategic reserves. They replenish them while prices are low and in general benefit from increased

bargaining power. Establishment of regional food security mechanism was recently discussed at the

first food security forum of the Asia-Pacific Economic Cooperation.

Infrastructure The lack of infrastructure in many developing countries and poor harvesting/growing techniques are

likely to remain. Improvements in Argentina and Brazil (with significant growth of soybean market

share) demonstrated that at least 4 factors need to be aligned in time – changes in agricultural

technology, public investment, entrepreneurial approach and supportive government policy.


Not only roads, railroads and port facilities are needed. Supply of agriculture products can be

increased by addressing issue of food waste (reaching 30-40% of total produced amount). It was

shown by (Parfitt, Barthel, & Macnaughton, 2010) that in the developing world major share of food

is lost due to limited post-harvest storage and processing infrastructure, technologies and associated

managerial skills.

1.3.2 Demand Number of trends has impacted demand side. Some of them are relatively recent developments that

happened within last decade (e.g. expanding biofuel production) while others are long-term changes

such as growth in average income.

1.3.2.1 Current State of Demand Demand for agriculture products can be spitted into two categories: bona fide demand and

speculative demand which we discuss separately below.

Bona fide Demand As shown in Figure 13 Japan is the largest importer of corn in the world with rather stable pattern of

demand. Mexico, South Korea, Taiwan, Egypt and Colombia are other major corn importers.

Figure 131. Corn imports by country, 2010/11. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

19%

10%

9%

6%

3%

5%

4% 4%

3% 3%

34%

Corn Imports, % top 10 countries in 2010/11 Japan

Mexico

Korea, South

Egypt

EU-‐27

Taiwan

Colombia

Iran

Malaysia

Algeria

Other


Figure 14. Corn imports by country, temporal developments. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

Figure 15. Corn Imports by world regions, temporal developments. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

Number of developments affects demand for agriculture products and corn specifically. We can

divide these factors into three groups: population growth, income growth, and expansion in biofuel.

Contrary to all taken measures (e.g. China's one child policy) human population is still growing with

corresponding increase in food demand. While the trend is toward slower growth number of people

is still increasing by about 75 million (1.1 percent) per year. Population growth has outpaced growth

in agricultural output on the global scale (Trostle, 2008).

Not only the absolute number of people is increasing but also their quality of life is improving. Most

rapid economic growth is also happening in the developing countries. Average real GDP growth

0

20,000

40,000

60,000

80,000

100,000

120,000

1000 M

T

Corn Imports, 1000 MT top 10 countries

Other

Algeria

Malaysia

Iran

Colombia

Taiwan

EU-‐27

Egypt

Korea, South

Mexico

Japan

0 20,000 40,000 60,000 80,000 100,000 120,000

1000 M

T

Corn Imports, 1000 MT regions of world

South America

Oceania

North America

Middle East

FSU

Europe

Central America

Asia

Africa


rates in China and India (about 40% of the world’s population) were within 6 to 9% range over the

last decades. It allowed their population to increase consumption of food.

Increase in average incomes not only led to higher demand for staple food but also for meat and

dairy products. Particular shift is in China where rising proportion of middle class and changing

tastes lead to increased consumption of meat products – from 3.6 kg per person in 1961 to over 54 in

recent years. This diet diversification resulted in the substantial rise of demand for grain and animal

feeds (see Figure 16) because feed-to-meat conversion rates are ranging between 2.6 for chicken to

7.0 for beef. This means 2-7 times more grain is required to produce the same amount

of calories through livestock as through direct grain consumption. In fact this trend toward higher

meat consumption clearly demonstrates world inefficiency as there are still around 800 million

people on the planet who suffer from hunger or malnutrition.

Figure 16. Change in total domestic consumption of corn. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

-‐10000 10000 30000 50000 70000

USA Other China Brazil

Mexico India EU-‐27

Canada S. Africa

Egypt Japan

Change in Total Consumphon, 1000 MT average in 2008-‐2010 vs. average in 1999-‐2001


Figure 17. Corn consumption in China. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

Biofuels are also competing for their share of produced agro commodities. Their production has

expanded rapidly in recent years with major capacity concentrated in United States and Brazil.

While Brazil is using primarily sugarcane the United States grows corn for ethanol production.

Amount of corn processed into biofuels has increased 7.5 times since 2003 reaching the point in

2010 when it’s more than 2 times larger than total US corn exports (Figure 18).

There number of factors influencing this growth. Renewable Fuels Standard (RFS) law, rise in fossil

fuel prices, subsidies for ethanol production and energy security motives are among them (Westcott,

2010). As corn will continue to be the primary source of ethanol in the near future, it will likely be

diverted from exports.

0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000

56,500 79,000 94,000 104,000

Total Consumphon, 1000 MT China

Feed and Residual Food, seed, and industrial


Figure 18. Corn consumption in US. Source: Food and Agriculture Organization statistics and U.S. Department of Agriculture Production, Supply and Distribution database

Another type of biofuels is biodiesel. European Union has taken leading role and mandated that

biodiesel accounts for 10% share of transportation fuel by 2020. In EU, Russia and Ukraine

rapeseed is primary feedstock for production while Brazil and Argentina are using soybean oil.

Such macroeconomic developments as devaluation of the U.S. dollar, increasing forex reserves of

developing countries and growth in assets under management of sovereign wealth funds also have

impact of increased demand. One reason is that most active commodities contracts that set industry

benchmark prices are valued in U.S. dollars and therefore they become more affordable when dollar

is weak. Availability of excess funds (sovereign investment vehicles are considered to hold more

than $10 trillion (Maslakovic, 2010)) is another important factor which we discuss in next part.

Speculative Demand We don’t set the goal of reiterating here all the details about developments in agriculture prices

during 2008 which is commonly known as “food crisis”. They can be found in numerous academic

papers5, investigations (e.g. (United States Senate, 2009), U.S. Department of Agriculture (Trostle,

2008)), studies conducted by intergovernmental organizations (e.g. reports by OECD (Irwin &

Sanders, 2010) and United Nations Conference on Trade and Development (UNCTAD, 2009)) and

newspaper publications (Financial Times), (Kaufman, 2010) just to name a few.

5 Among the ones read by author are (Irwin, 2008), (Caballero, Farhi, & Gourinchas, 2008), (Liu & Tang, 2010)

0

100,000

200,000

300,000

400,000

1991/1992 1996/1997 2001/2002 2006/2007

Total Consumphon, 1000 MT United States

Food, seed, and industrial less ethanol Feed and Residual Exports Ethanol


Overall situation has developed as the following. Change in commodity markets prices is evident

since 2007 and in particular since beginning of financial crisis in Aug 2007. Commodity prices in

both futures and spot markets experienced dramatic rise between January 2007 and June 2008 and

then sharp fall in 2008 (Figure 19). Commodity index holdings rose from $13bn in 2003 to $317bn

in 2008.

Figure 19. Thomson Reuters/Jefferies CRB commodity price index. Source: Thomson Reuters and/or Jefferies Financial Products

Prices of agriculture products or soft commodities have increased significantly too. Over the course

of just three years, the IMF agriculture index more than doubled, peaking in June 2008. Thereafter,

international agricultural commodity prices collapsed as a consequence of the global financial crisis

and by the end of 2008 the index had fallen back to approximately 150 percent of its average 1998–

2000 level (Figures 20 and 21).

Prices of non-traded commodities increased also as consumers tried to substitute expensive traded

commodities. For example durum wheat and edible beans don’t have futures markets; however their

prices were 308% and 78% higher in April 2008 compared to January 2006.

0 50

100 150 200 250 300 350 400 450 500

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01/03/10

points

Thomson Reuters/Jefferies CRB commodity price index


Figure 20. World price indices of selected cereals, Jan-‐06=100. Source: FAO

Figure 21. World price indices of selected commodities, 2005=100. Source: BIS

We would like to mention that there is ongoing debate (see for example (Irwin & Sanders, 2010) for

overview of arguments) about causality in futures markets with one side claiming that money flows

from investors (particular attention is paid to index funds) leads to boom in commodity prices while

alternative hypothesis is that traders increase long positions after prices increase (behave as trend-

0

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1980M01

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points

World price indices of selected commodihes

Corn

Rice

Soybean

Wheat


followers). Prevailing view seems to be that flow of investment funds increased while the markets

themselves were not big enough to withstand it without price rise. UN report states that “various

studies find that financial investors have accelerated and amplified price movements at least for

some commodities and some periods of time” (UN, 2009).

Without taking any side of debate we can identify number of data sources that in aggregate may be

useful to monitor situation and serve as alarm of loosening in the relationship between prices and

supply and demand conditions. Among them is value of long-only commodity index trader positions

(Commodity Index Trader (CIT) report from the Commodity Futures Trading Commission), number

of outstanding and volume of futures contracts (data from futures exchanges, Futures Industry

Association and Bank for International Settlements on both exchange traded and OTC contracts),

excess liquidity (AuM of sovereign investment vehicles and hedge funds, interest and forex rates).

Another development that needs to be monitored is growth in commodity exchange-traded funds

(ETFs) due to their accessibility for retail investors and claimed “simplicity/low fees” image (see

point 3 below about role of less sophisticated investors in bubbles). It’s reported based on data from

Barclays Capital that commodity ETFs have reached $121.5bn (10.6% increase) at the end of the

second quarter of 2010 compared to $111bn in assets tracking commodity indices.

We need to remember that while typical commodities markets refer to the futures markets where

most of investors are not taking actual delivery of the physical commodity, there are also attempts of

physical control of supply such as Armajaro failed market bet (Rohrlich, 2010) with purchase of 7%

of world cocoa, launch by ETF Securities of exchange-traded gold and silver funds backed by

bullion, deal between Deutsche Bank and Czarnikow for physical sugar trade, and plans of Credit

Suisse and Glencore for exchange-traded fund backed by aluminum supplies (Financial Times,

2009). Such investments directly affect supply/demand balance and therefore the prices. It’s

assumed that these actions are to avoid proposed “position limits” in futures markets. This is the

case when regulation may cause the result it is trying to prevent.

We conclude this section with the abridged story of financial bubbles and crashes from (Sornette,

2003) as we see some similarities.

1. The bubble starts smoothly with increasing demand for some commodity

2. The attraction to investments with good potential gains then leads to increasing investments,

possibly with leverage. This leads to price appreciation.


3. This in turn attracts less sophisticated investors and, in addition, leveraging is further developed.

Demand is rising faster than the rate at which real money is put in the market.

4. Behavior of the market becomes weakly coupled with real wealth production.

5. At high prices number of new investors decreases. Market enters phase of nervousness, until a

point when the instability is revealed and the market collapses.

1.3.2.2 Projected Changes in Demand There are not many projections that lead to decrease in agriculture commodities demand, especially

for corn.

Growing prices can make food unaffordable in developing countries, especially in the ones that

depend on the imports. For example as a consequence of food crisis number of hungry people

worldwide reached historic high in 2009 with 1020 million people undernourished worldwide (FAO,

2009). It led to social unrest in more than 20 of countries with riots taking place in Mexico, Thailand

and Egypt among others.

Figure 22. Number of undernourished people in the world, 1969–71 to 2010. Source: (FAO, 2010)

Reduction in demand is theoretically possible by reducing food waste at consumer level which

according to estimates is as high as 30% in developed countries (Parfitt, Barthel, & Macnaughton,

2010). However, change of habits is not trivial task and success not easily predictable.


1.4 Risks in Agriculture As presented in previous part agriculture production can be impacted by number of factors causing

wide swings in commodity prices. This page serves as summary of these risks.

Production risk

• weather; • natural disasters; • weeds, diseases and pests

Price or market risk

• expected prices of commodities; • cost of inputs (e.g. energy, fertilizers)

Financial risk

• access to financing (e.g. credit availability and conditions); • interest rates and inflation

Institutional risk

• financial support from government (e.g. subsidies, loans, tax incentives); • consumer protection / environmental regulation; • government price intervention (e.g. price guarantees); • free trade restrictions (e.g. import/export tariffs or bans)

Operational risk

• access to skilled labor force; • human health / relationships;

Geopolitical risk

• war, terrorism, and political violence

1.5 Summary of the Chapter As evidenced by analysis above, higher prices for corn (and other major agriculture commodities -

wheat, corn and soybeans), especially over the long term, are very likely. Meeting demand will

require capacity expansion, mostly through higher-cost sources. Potential of price growth is stronger

for corn and soybeans as they are used in livestock feed and biofuel production.

Similar reasoning and prognosis are drivers behind establishment of new investment vehicles with

focus on agricultural sector including recently launched BlackRock’s World Agriculture and First

State’s Global Agribusiness funds (Kelleher, 2010).


Chapter 2. Weather Risk in Agriculture In the previous chapter we discussed number of price-impacting developments in modern

agriculture markets. They are usually considered long-term trends as they are extending over one or

two planting and harvesting cycles. Fundamental analysis is one of the main tools to spot them.

Another strategy is to focus on short-term ‘‘high-frequency’’, seasonal cycles which we will discuss

in this chapter. Agriculture market is characterized by large changes in prices because production

technology is subject to natural interference, be that weather, disease, or pests.

In the following parts we will assess potential of investment strategy that exploits these natural (with

particular focus on weather) shock situations. Performance of proposed strategy will generally

depend on availability of weather induced supply shocks and investor skills of observing/predicting

them.

2.1 Weather Dependence of Agriculture This section is intended to demonstrate overall link between climatic conditions and level of supply,

while consecutive ones will focus on two beneficial for the strategy factors – increasing variability

of weather and limited diversification due to geographical concentration of production.

There are many studies on the relationship between crop yields and weather. For example,

(Tannuram, Irwin, & and Good, 2008) built regression models of the relationship between

technology, monthly rainfall, monthly temperatures, and U.S. corn yields; (Schlenkera & Robertsb,

2008) also applied regression but used fine-scale weather dataset that incorporated the whole

distribution of temperatures within each day and across all days in the growing season; (Carew,

2009) and (Chen, McCarl, & Schimmelpfennig, 2004) employed stochastic Just-Pope production

function to examine the relationship between weather conditions, other inputs and yields; (Awan &

Noor, 2006) used clustering for predicting oil-palm yields; and (Bokusheva, 2010) measured

dependence structure between yield and weather variables using copula.

This area has attracted particular attention in recent years as many researchers are attempting to

assess climate change impact on agriculture, just to name a few - (Schlenkera & Robertsb, 2008),

(Gallego, Conte, Dittmann, Stroblmair, & Bielza, 2007), (Kucharik & Serbin, 2008). However, there

are fewer studies on price impacts and this is likely because it extends beyond typical area of interest

of food and agriculture sciences. Among the studies examining weather impact on prices are (Roll,

Chapter 2. Weather Risk in Agriculture Error! Unknown switch argument.

1984) discussing orange futures market6; (Aker, 2010) investigating extreme rainfall and grain

markets in West Africa; (Holt & Inoue, 2005) analyzing relationship between climate anomalies and

world primary commodity prices.

The general conclusion of listed above studies is that weather is the leading factor that influences the

short-term development in agriculture. Weather patterns in crop-producing areas affect crop yields

and as result cause supply shocks. Due to the short-run inelasticity of supply and demand for

agricultural products, there are only two variables that can adjust to equilibrate supply and demand.

It can be change in inventory and/or a change in price.

When reserves are adequate, stockpiles decrease with relatively small price changes. However, if

there are not sufficient inventories, only the price can respond, hence it will move up sharply (see

(Geyser & Cutts, 2007) for discussion of corn price volatility at South African futures exchange).

For example, in 2007-08, U.S. inventories dropped to an all-time low of 8.3 million tons of wheat.

Low supplies of the grain due to bad weather led to global food crisis and riots in number of

developing countries .In comparison, during 2010 year wheat production was also affected by

unfavorable natural conditions - excessive rains in Canada, severe drought in Russia, Ukraine and

parts of European Union, pest outbreak in Australia. Prices increased significantly, nonetheless

below the levels of 2008 due to much higher reserves.

2.1.1 Climate Change, Weather Variability and Extremes Beyond the weather, agriculture markets are affected by many factors ranging from economic

growth to foreign exchange rates. As only large fluctuations in farm production will result in

significant price impact we need to focus on weather extremes such as floods, droughts, storms and

extreme temperatures. From definition they don’t occur every year, however it’s unlikely to limit

application of the strategy.

First of all, overall agricultural production is spread geographically and includes many countries on

different continents. Presence of major agriculture suppliers in both hemispheres (e.g. US, EU and

China are in Northern hemisphere, while Brazil, Argentina and Australia are in Southern

hemisphere) provides investment opportunities throughout the entire year.

6 Recent presentation of Professor Colin Carter at Weather Risk Management Association concluded that it’s no longer the case that most FCOJ winter price variation occurs when there is a possibility of freeze. Reasons cited are better freeze protection technology and Increase in storage and imports (Carter, 2010).


Second, but perhaps more important factor is the increasing variability of weather. This year

presented number of severe weather-related events – flooding in large areas of Asia, heat wave of

rare intensity and duration in Russia and some parts of Europe, excessive rainfall in Canada, severe

droughts in sub-Saharan Africa, mudslides in China. Occurrence of these events itself doesn’t

confirm or reject climate change, however their intensity and frequency match projections of IPCC

report. In fact World Meteorological Organization issued press statement concerning these

developments under the title “Unprecedented sequence of extreme weather events” (WMO, 2010).

According to (IPCC, 2007), FAQ 10.1 and 3.3 “the type, frequency and intensity of extreme events

are expected to change as Earth’s climate changes, and these changes could occur even with

relatively small mean climate changes. Changes in some types of extreme events have already been

observed, for example, increases in the frequency and intensity of heat waves and heavy

precipitation events. Since 1950, the number of heat waves has increased and widespread increases

have occurred in the numbers of warm nights. The extent of regions affected by droughts has also

increased as precipitation over land has marginally decreased while evaporation has increased due to

warmer conditions” (see also Table 5 below).

Phenomenon Change Region Period Confidence Section Low-temperature days/nights and frost days

Decrease, more so for nights than days

Over 70% of global land area

1951–2003 (last 150 years for Europe and China)

Very likely 3.8.2.1

High-temperature days/nights

Increase, more so for nights than days

Over 70% of global land area

1951–2003 Very likely 3.8.2.1

Cold spells/snaps (episodes of several days)

Insufficient studies, but daily temperature changes imply a decrease

Warm spells (heat waves) (episodes of several days)

Increase: implicit evidence from changes of daily temperatures

Global 1951–2003 Likely FAQ 3.3

Cool seasons/ warm seasons (seasonal averages)

Some new evidence for changes in inter-seasonal variability

Central Europe 1961–2004 Likely 3.8.2.1

Heavy precipitation events (that occur every year)

Increase, generally beyond that expected from changes in the mean (disproportionate)

Many mid-latitude regions (even where reduction in total precipitation)

1951–2003 Likely 3.8.2.2

Rare precipitation events (with return periods > ~10 yr)

Increase Only a few regions have sufficient data for reliable trends (e.g., UK and USA)

Various since 1893

Likely (consistent with changes inferred for more robust

3.8.2.2


statistics) Drought (season/year)

Increase in total area affected

Many land regions of the world

Since 1970s Likely 3.3.4 and FAQ 3.3

Tropical cyclones Trends towards longer lifetimes and greater storm intensity, but no trend in frequency

Tropics Since 1970s Likely; more confidence in frequency and intensity

3.8.3 and FAQ 3.3

Extreme extratropical storms

Net increase in frequency/intensity and poleward shift in track

NH land Since about 1950 Likely 3.8.4, 3.5, and FAQ 3.3

Small-scale severe weather phenomena

Insufficient studies for assessment

Table 5. Change in extremes for phenomena over the specified region and period, with the level of confidence and section where

the phenomenon is discussed in detail. Source: (IPCC, 2007)

Additional analysis of impact on agriculture is available at (US Environmental Protection Agency)

website. IPCC is also planning to release in 2011 special report on “Managing the Risks of Extreme

Events and Disasters to Advance Climate Change Adaptation” with separate part devoted to

agriculture.

We would like to stress that not warming (see Figure 23 below) or cooling trend has importance but

variability. It was noted correctly in (Thompson, 1975) that “when weather variables deviate greatly

from normal that yields are lowest” (interestingly that paper discussed cooling trend in the world's

climate).

Figure 23. Jan -‐ Dec global mean temperature over land and ocean. Source: (National Climatic Data Center, 2010)


2.1.2 Geographical Concentration of Production While in general agriculture production is spread across the world, specific crops exhibit significant

level of spatial concentration. Production statistics are usually reported on a geopolitical – often

national – basis and therefore within-country concentration may escape attention.

Specialization and concentration both at farm and geographical level are not modern trend. This

phenomenon was known for many years with papers discussing it back in 1893 (Hyde, 1893) and

noting that “certain localities are given up almost entirely to the cultivation of particular product”.

The maps below show levels of concentration for 4 major crops (corn, rice, wheat, and soybeans).

Figure 24. Harvested area of each crop as the proportion of each grid cell. Source: (Monfreda, Ramankutty, & Foley, 2008)

Figure 25. Crop yields in tons per hectare per harvest. Source: (Monfreda, Ramankutty, & Foley, 2008)

The extent is even more visible from the following maps produced by (USDA):


Figure 26. Corn. Numbers indicate average annual contribution of each state as percentage of the national production (2000 to 2004). Source: (USDA)

Figure 27. Soybeans. Numbers indicate average annual contribution of each state as percentage of the national production (2000 to 2004). Source: (USDA)

Figure 28. Spring wheat. Numbers indicate average annual contribution of each state as percentage of the national production (2000 to 2004). Source: (USDA)


Several exchange traded agriculture commodities have even higher spatial concentration. For

example just two countries in West Africa (Ivory Coast and Ghana) produce ca. 60% of world's

cocoa beans, while around 70% of oranges used for juice production in US are grown in a few

counties of Florida.

Market competitiveness which in turn might depend on soil, climate or infrastructure conditions of

the region is driving force behind such concentration. However, it subjects production of this

particular crop to higher exposure to weather risk. Even major commodities like corn are at

significant risk. Small number of its major exporters makes corn prices highly vulnerable to a

weather disruption in any of these countries.

2.2 Observation and Prediction of Weather Shocks It is recognized fact that competitive advantage in agriculture can be achieved through better

decision-making subject to producer’s access to improved weather analysis and forecasting (Sonka,

Lamb, Hollinger, & Mjelde, 1986). In general, business-related uses of climate and weather

information are growing in the past 15 years (Changnon & Changnon, 2009) with examples

including weather derivatives (hedging weather risk) and weather-risk models (assessing potential

losses). However extent to which the market participants in commodities markets systematically

utilize weather information remains largely unclear. Beneficial improvements can be categorized as

following: improved coverage, more accurate prediction and faster access.

2.2.1 Weather Monitoring Weather monitoring network is an important issue in many developing countries, especially in

Africa. According to World Meteorological Organization calculations Africa needs 10,000 weather

stations while now it has less than 200 weather stations that meet WMO standards. With total

number of 744 weather stations situation creates major weather “data gap”.

Strengthening weather observation in Africa is well known need. In 2009 “Weather Info for All”

initiative (WMO, 2009) was started as public-private partnership aiming to deploy automatic

weather stations (AWSs) at cellular network sites across Africa. Current status of project is unclear

as one of the founding members of the initiative, Global Humanitarian Forum, was closed due to

lack of funding. It’s reported that only the pilot phase with 19 new stations was completed.

WMO together with African Union are also attempting to tackle the problem. In 2010 conference of

Ministers Responsible for Meteorology in Africa was organized for the first time. There were made


number of high-level declarations however practical steps are not known and any major

development is unlikely in short term.

Number of commercial companies, especially involved in cocoa beans trade (see part about geo

concentration of production) is addressing issue by installing their privately owned weather stations.

For example, founder of major commodity trader, Armajaro, admitted in the interview (Opalesque

TV) that the company not only utilize data from several thousands of existing public weather

stations but also has its own weather network to help anticipate yields of commodities around the

world. He called this competitive “data advantage”.

Such “data advantage” was already used back in 1980s by another cocoa trader Commodities

Corporation7 which used privately collected rainfall and humidity data to “evaluate the maturing

crops in the plantations of Ghana and Ivory Coast before publication of government figures, which

were often inaccurate anyway” (Fortune, 1981).

2.2.2 Weather Forecasting Accuracy of weather forecasting is another important source of competitive advantage. Most

modern weather approaches are based on mathematical models of the atmosphere and use current

weather conditions as input to predict the weather. The issue is extremely complex with the latest

models using petabyte-scale datasets and running on petaflop (1 million billion calculations in a

second) supercomputers. Therefore, developing new forecasting models is clearly out of scope of

this work. Let’s just notice while this area is still dominated by government agencies and academic

institutions there are few private companies building their weather forecasting systems. For

example, WeatherBill claims it has built “the world's first real-time 2-year forward weather

simulation system” (WeatherBill, 2009) while Weather Trends International recently launched

consumer-friendly 360 days weather forecasting website (Weather Trends International, 2010).

2.2.3 Speed of Access Before discussing issues strictly related to weather data access let’s look at modern financial

markets that are becoming increasingly electronic.

Computers or, more precisely, computer algorithms decide to initiate orders without human

interaction. They usually do it well before the human traders can process (often even receive)

7 Company was founded by MIT PhD holder, acquired in 1997 by Goldman Sachs. Firm is credited for launching the careers of many notable hedge fund investors.


information. This phenomenon is known as High-Frequency Trading and recently attracted

significant attention, especially after market crash of May 6, 2010 (Nanex, 2010).

This computer automated trading is highly dependent on ultra-low latency (electronic market data is

collected, and orders are created, routed and executed in sub-millisecond times). As every

microsecond counts companies co-locate their trading platforms next to exchanges matching

engines. This speed is giving rise to the arguments of unfair advantage.

Returning back to commodities markets the question is whether similar developments are happening

or will likely to happen in agriculture commodities markets. Surface weather observations are the

fundamental data used; therefore analogous development will be getting access to real time

observations and covering additional locations with private measurement equipment. It’s more than

feasible development as in fact number of meteorological services (at least in developed countries)

already collect weather data in fully automated manner with near real time frequency.

For example, MeteoSwiss provides layer for Google Earth, free mapping software, that displays

charts of minimum and maximum temperatures, depth of snow, wind speed and direction,

precipitation, relative humidity, air pressure and sunshine duration with 10 minutes frequency

(MeteoSwiss). In US there are several automatic weather monitoring networks including the

Automated Weather Observing System (AWOS), Automated Surface Observing System (ASOS),

and Automated Weather Sensor System (AWSS).

Figure 29. Example of real-‐time weather data from MeteoSwiss. Source: Author’s screenshot


Except traditional surface weather stations real-time weather data can be gathered using remote

sensing technologies such as meteorological satellites or unmanned aerial vehicles (UAV). Low-

cost, wireless sensor network could be interesting alternative to traditional weather stations (at least

for the applied purposes described in this work) (Weber, 2009).

Will the market structure morph to one where those who have access to extended weather data and

better forecasts also have all of the advantages? We will investigate this quantitatively in the next

chapter.


Chapter 3. Weather to Buy or Sell. Quantitative Analysis

3.1 Introduction In this chapter we will investigate potential of using weather data for trading corn futures.

Corn, a major source of food for both humans and animals, is grown in more countries than any other crop. United States provides some of the best growing conditions for corn in the world, making the country the world's top producer. As we discussed in “Weather Dependence of Agriculture” part, variability of weather leads to inter-annual crop productivity changes. When weather-dependent, volatile supply is met by a stable or growing demand it results in changes of commodity prices. In case of corn productivity is heavily dependent on the outcome of weather in the U.S. Midwest. Heat-waves in July and August impair corn pollination prospects. We analyze this impact on prices of corn futures contracts throughout this chapter.

First of all we note that both quality and quantity of crop is impacted by adverse weather; however only amount of crop produced is truly varying as contract specification sets minimum quality standard. Products not meeting it are diverted for other, non-primary uses. In case of corn it’s production of silage instead of grain. Usually it accounts for about two percent of harvested area but in years with unfavorable weather for grain yields, more plants are harvested as silage.

We identified that for trading strategy development there is a choice between focusing on overall period (planting, growing and harvesting times) and capitalizing on the outcomes of single extreme weather events. We will briefly discuss their respective advantages and disadvantages below.

The first approach aims to monitor crop development over the whole season. Uncertainty of future outcomes is highest in the beginning (pre-planting and planting times) and steadily decreasing over the risk period with ultimate resolution of concerns when all crops are harvested and put into storage. As it was previously presented, weather may impact crop during whole risk period (see 1.3.1.1 and 2.1). However, crop development is also influenced by many other parameters; it is nonlinear process with path dependency.

It is worth to note that this approach goes along with crop yield modeling. Two main classes of models are statistical and simulation (describing actual physiological mechanisms as functions of environment - weather and soil properties). Relatively simple ones like regression models don’t outperform average market information processing abilities while accurate simulation quickly becomes demanding in terms of data (e.g. solar radiation, soil moisture, and amount of fertilizers).

Changes in agro technology (e.g. new heat resistant genetically modified corn) are also impacting precision. In addition yields are only one of the factors driving prices in global macroeconomic environment. To conclude we think it might be feasible direction for single commodity trading houses like mentioned above Armajaro, however in our case it was discarded in favor of second approach with more attractive risk-return profile, simplicity and diversification potential.

Chapter 3. Weather to Buy or Sell. Quantitative Analysis Error! Unknown switch argument.

The second idea is to capitalize on the way market participants observe and interpret weather conditions. When extreme weather conditions are believed to affect crop prospects, traders drive prices in some (yet presumed unknown) direction. This response can be foundation of our weather-based trading strategy. There are two fundamental assumptions that need to be valid to make this approach feasible.

The first one is that market participants behave in consistent way in response to similar extreme weather events. Our hypothesis is that when characteristics of harmful weather (such as temperature thresholds, commonly known by traders) are exceeded, it becomes main driver of prices eclipsing other impacting factors. This assumption can be statistically verified by measuring asymptotic dependence between weather index and market returns.

The second assumption is trickier as it questions informational efficiency of commodity markets. In particular its semi-strong-form efficiency which states that prices adjust to publicly available new information very rapidly and in an unbiased fashion. While weather is obviously public knowledge, one of the following - improved coverage, more accurate prediction and faster access – can still be source of competitive advantage. To analyze this assumption we will look at changes in asymptotic dependence between market returns and different lags of weather index.

Before we proceed to measuring asymptotic dependence we briefly describe and analyze in the next part necessary data elements.

3.2 Data

3.2.1 Financial Data Corn futures are traded on at least ten major exchanges as corn is grown all around the world. As we saw in “Geographical Concentration of Production” part, corn has one of the lowest geographical concentrations among 4 major crops (corn, rice, wheat, and soybeans).

However, corn futures contract (ticker C, see specification below) traded at CME Group exchange (historically known as Chicago Board of Trade) is considered benchmark. It’s used not only for exchange cleared contracts but servers as reference price for majority of over-the-counter (OTC) transactions therefore having significant impact on world trade in corn. CME’s settlement prices are also licensed by regional exchanges (e.g. South Africa's JSE Limited) to create cash-settled corn futures contracts traded in local currencies.

Contract Size 5,000 bushels (~ 127 Metric Tons) Deliverable Grade #2 Yellow at contract Price, #1 Yellow at a 1.5 cent/bushel premium #3 Yellow at a 1.5

cent/bushel discount Pricing Unit Cents per bushel Tick Size (minimum fluctuation)

1/4 of one cent per bushel ($12.50 per contract)

Contract Months/Symbols

March (H), May (K), July (N), September (U) & December (Z)

Trading Hours CME Globex (Electronic

6:00 pm - 7:15 am and 9:30 am - 1:15 pm central time, Sunday - Friday Central Time


Platform) Open Outcry (Trading Floor)

9:30 am - 1:15 pm Monday - Friday Central Time

Daily Price Limit $0.30 per bushel expandable to $0.45 and then to $0.70 when the market closes at limit bid or limit offer. There shall be no price limits on the current month contract on or after the second business day preceding the first day of the delivery month.

Settlement Procedure

Physical Delivery

Last Trade Date The business day prior to the 15th calendar day of the contract month. Last Delivery Date Second business day following the last trading day of the delivery month.

Table 6. Corn futures contract specifications. Source: CME Group

CME traded corn futures contract is one of the deepest and most liquid markets in agriculture commodities. Both average daily traded volume and open interest in 2010 were about 2.6 times larger than that of benchmark futures contract for wheat. Among all commodities, corn has the second largest volume of trades after crude oil. Recent NYSE Liffe exchange group numbers demonstrated 61 percent growth of volume traded in 2010.

Corn has significant weights in major commodity price indices. In 3 out of 5 major indices it has higher share than wheat, while in all cases except Dow Jones-UBS Commodity index its weight is larger or equal to share of copper, important industrial metal, or gold, traditionally popular among investors precious metal.

Agriculture Metals Energy Corn Wheat Soybean Rice Copper Gold Crude Oil

Deutsche Bank Liquid Commodity Index

5.625 5.625 5.625 - 4.67 8.00 12.375

Dow Jones-UBS Commodity Index

7.72 5.99 8.06 - 7.78 10.41 13.12

Rogers International Commodity Index

4.75 7.00 3.35 0.50 4.00 3.00 21.00

S&P GSCI # 3.95 5.03 2.36 - 3.73 3.08 35.02 Thomson Reuters / Jefferies CRB Index

6.00 1.00 6.00 - 6.00 6.00 23.00

# formerly the Goldman Sachs Commodity Index Table 7. Components dollar weights of commodity price indices, in percentages. Source: author’s compilation from respective

companies publications

There are five contracts per year with delivery in March, May, July, September and December for the next four years. However, similar to conclusions of (Roll, 1984), we observed that open interest is concentrated in near-maturity contracts. Given that price data from thin markets in fourth and longer maturities will not improve evaluation of weather induced shocks it was discarded. Among the near maturity contracts the one with delivery in December was chosen as it prices current year’s crop. It is in fact the most actively traded during the key risk period of July – August. Using this contract also frees us from explaining price behavior for contracts near its expiration date (substantial price volatility) such as July and September ones. Such focus on December contract is widely accepted practice, for example Deutsche Bank is using December to roll underlying grain contracts of its index.


Figure 30. Traded volume of corn futures contracts (C) in 2010. December 2010 delivery contract is represented by green color. Source: Datastream market data

Prices of corn were historically volatile. Large changes in mid-1980s are due to farm crisis (about 235000 farms failed during that period). Another significant agricultural depression in the late 1990s is also visible on the chart. Excluding big spike in 2008 real, inflation-adjusted prices of corn has trended downwards. Average real price of corn for 3 last decades were: $4.88 per bushel in 1980s, $3.30 in 1990s and $2.31 between 2000 and 2006. One of the explanations for this trend is the U.S. farm policy promoting overproduction of commodities.

Figure 31. Nominal corn futures prices, Chicago Board of Trade. Traded volume is shown at the bottom of chart. Source: Datastream market data, plotted with quantmod R package

0

100000

200000

300000

400000

500000

600000 C 0310 C 0510 C 0710 C 0910 C 1210 C 0311 C 0511 C 0711 C 0911 C 1211 C 0312 C 0512 C 0712 C 0912 C 1212 C 0713 C 1213 C 0714


Chart of ten day annualized realized price volatility of CBOT corn prices (we follow Chicago Board of Trade definition of volatility8 as a measurement of the change in price over a given period of time) for years 1980-2010 is presented in Figure 32 below.

Figure 32. 10 Day annualized price volatility of CBOT corn price

Several spikes in volatility may lead to questions about data quality, however as identified from raw data they correspond to genuine price spikes happening around the beginning of July in 1986, 1988 and 1996. Major drought happened in the Midwest in 1988 (yield dropped by 30% compared to 1987) and year 1996 is known as “grain shock of 1996” (Stevens, 1999). Most rapid changes are localized in July as at this time futures contracts are rolled. It was popular in 1990s to hedge production risks with the use of futures that preceded harvest (known as rolling hedge technique). It’s in its basic form is the bet on that July futures contract declines by more than the deferred contract and reward the hedger by the amount of this extra decline. Possibility of losses due to July futures moving sharply higher relative to the deferred contracts was known from similar developments in 1970s but was largely ignored as discussed in (Stevens, 1999).

8 Alternative approaches include measuring volatility as absolute percentage change in the price levels, moving average of the standard deviation of the growth rate of the nominal price, variance of the price around its trend and ARCH/GARCH approach


Figure 33. Box and whisker plot of daily returns, years 2000-‐2010

3.2.2 Weather Data Most of the corn produced in the United States (40% of the world production) is grown in the Corn Belt. The Corn Belt includes the states of Michigan, Minnesota, South Dakota, Wisconsin, Ohio, Illinois, Indiana, Iowa, Missouri, Kansas, and Nebraska.

There is extensive coverage by weather stations with data available from National Climatic Data Center. Most of them belong to automatic weather monitoring network and report hourly temperature and precipitation among many other measurements. This data also undergoes quality checks and usually has long histories dating back to the beginning of twentieth century.

General conditions of access are very unobstructive - electronic data, well formatted, documented and made freely available to many categories of users including U.S. businesses. It is worth to note that in many countries the situation is significantly different – even in many European countries access to weather data is very difficult (e.g. Italy where Air Force is the recognized National Meteorological Service or Ukraine where 30 years of weather data per station cost $6,500 (IFAD, 2010)).


Other sources of information are available such as the weekly USDA Weather Bulletin or private companies’ publications. Major weather-induced losses in US are listed in “Billion dollar U.S. weather disasters” database (NCDC).

3.3 Empirical Results For corn it was demonstrated (Schlenkera & Roberts, 2009) that yields increase with temperatures up to 29°C, but that temperatures above this threshold are very harmful, with slope of the decline above the optimum is significantly steeper than the incline below it. (Tannuram, Irwin, & and Good, 2008) concluded that yields were particularly affected by the magnitude of temperatures during July and August and to the lesser extent by magnitude of precipitation during June and July. The effect of temperatures during May and June appeared to be minimal. Most productive years are the ones with cooler-than-usual temperatures during August and abundant rainfall during July.

Figure 34. Nonlinear relation between temperature and yields. Graph at the top display changes in log yield if the crop is exposed for one day to a particular 1° C temperature interval. Histograms at the bottom of each frame display the average temperature

exposure. Source: (Schlenkera & Roberts, 2009)

First of all we analyze (log) returns and weather measurements. Looking at the Figures 35 and 36 we can make logical conclusion that threshold excesses in temperature series are not independent. One hot day is likely to be followed by another hot day.


Figure 35. Daily maximum temperatures for weeks 25-‐36 of years 2000-‐2009 Source: NCDC

Figure 36. Autocorrelation of daily maximum temperatures for weeks 25-‐36 of years 2000-‐2009

However, daily log returns don’t exhibit similarity between observations, even the ones with low lags.

Figure 37. Autocorrelation of daily returns of corn futures contract


We decided to summarize both of the variables on weekly9 basis for the past 10 years. It turned out to be the most useful time horizon – such averaging over time removes daily noise but doesn’t obscure weather extremes. We limit analysis to the risk period starting from the last week of June and ending at the first week of September (10 weeks). Therefore, analyzed dataset contains 130 observations10 of weekly returns, weekly sum of daily maximum temperatures and number of days per week with maximum temperature exceeding threshold.

Choice of weather stations can be rather arbitrary as there is significant spatial correlation of maximum temperatures across different stations in the central part of Corn Belt. We have chosen ones with the least amount of missing values that are located in rural areas (to minimize urbanization impact).

Name Latitude Longitude Altitude WMO MN - ROCHESTER - ROCHESTER INTERNATIONAL ARPT

43.904 -92.492 402 72644

NE - OMAHA - EPPLEY AIRFIELD ARPT 41.31 -95.899 299 72550 IA - SPENCER - SPENCER MUNICIPAL ARPT 43.164 95.202 408 0

Table 8. List of weather stations

Scatter plots of weather measurements vs. weekly returns reveal pattern that might signalize market reaction to extremely hot weather. As the futures price is assumed to be informationally efficient, reflecting traders’ knowledge of next week weather, we plot returns against both current weather conditions and lagged ones. Vertical lines on the plots mark the threshold of 31°C average daily temperature.

9 Week numbers calculated according to ISO 8601 which defines the week as always starting with Monday. The first week is the week which contains the first Thursday of the calendar year.

10 For non-‐US customers, weather data is provided on paid basis. Data for 1997 year was the earliest available within the company for this region.


Figure 38. Scatter plots of weather measurements during previous week (weekly sum of daily maximum temperatures) vs. weekly returns.

Figure 39. Scatter plots of weather measurements during current week (weekly sum of daily maximum temperatures) vs. weekly returns.


Figure 40. Scatter plots of weather measurements during next week (weekly sum of daily maximum temperatures) vs. weekly returns.

Figures 38 - 40 are scatter plots of weekly returns plotted versus corresponding weekly sum of daily maximum temperatures. The difference between 3 figures is in how weeks are paired – 1st figure shows returns versus weather during previous week, 2nd one - weather during current week and finally the 3rd one concerns the weather during future week. Vertical lines (on Figures 38 – 40 it corresponds to 31°C daily maximum temperature, averaged over week) divide observations in two subsamples.

Ignoring visual cues (we assume that it has occurred by chance unless we demonstrate that result is statistically significant) we perform Wilcoxon rank sum test (equivalent to the Mann-Whitney test). It is non-parametric statistical hypothesis test for assessing whether two independent samples of observations have equally large values.

Our null hypothesis is that the distributions of returns to the right of threshold and distributions of returns to the left of threshold differ by zero location shift and the alternative is that they differ by some other location shift. For the three described above returns – weather pairs and nine different dividing thresholds (ranging from 25°C to 33°C) we estimated location shift and 0.95 confidence interval. Results are presented below in the Figures 41 – 43.


Figure 41. Location shift with confidence interval for previous week case.

Figure 42. Location shift with confidence interval for current week case.

Figure 43. Location shift with confidence interval for previous week case.

-‐15

-‐10

-‐5

0

5

10

25 26 27 28 29 30 31 32 33

Locagon

shih

Daily maximum temperature

Previous Week

Locahon shim

Boundary of confidence interval

-‐15

-‐10

-‐5

0

5

10

25 26 27 28 29 30 31 32 33

Locagon

shih


Current Week

Locahon shim


-‐10

-‐5

0

5

10

15

25 26 27 28 29 30 31 32 33 Locagon

shih


Future Week

Locahon shim



Other suitable tests are Cramer-von Mises and Kolmogorov-Smirnov two sample tests (test whether two independent samples were drawn from the same population). In certain cases, the Cramer-von Mises test is more powerful than the Kolmogorov-Smirnov test, but it is less widely used than the latter. Null hypothesis is that the two samples come from the same distribution. We present results in Figure 44 and Table 9 below.

Figure 44. Cramer-‐von Mises test results

Past week Current week Future week Daily max temperature score p-value score p-value score p-value

25 0.0515 0.0123 0.0791 0.0123 0.2414 0.0123

26 0.0786 0.0133 0.1310 0.0133 0.0294 0.0133

27 0.1197 0.0136 0.2204 0.0136 0.0316 0.0136

28 0.1647 0.0137 0.2409 0.0137 0.0769 0.0137

29 0.2506 0.0137 0.0985 0.0137 0.0926 0.0137

30 0.2962 0.0136 0.2392 0.0136 0.3221 0.0136

31 0.1286 0.0132 0.3222 0.0132 1.0672 0.0130

32 0.0926 0.0123 0.3266 0.0123 0.6380 0.0118 Table 9. Cramer-‐von Mises test results

One conclusion that can be made from these results is that mean of returns is different at different weather conditions. In the presence of warmer-than-normal temperatures during future week weekly returns tend to exhibit positive skewness.

Before proceeding further we calculate values for traditional measures of dependence between two random variables X and Y – Pearson's linear correlation coefficient, Kendall’s Tau and Spearman’s Rho. These three measures of dependence are all in the range [-1, 1] with 0 indicating independent random variables. As all of them are functions of whole distributions (calculated on the full set of values which means we look also at situations when average weather conditions don’t influence market) we don’t find any signs of dependence (especially if examined at 95% confidence level). And they are known to be poor measure of dependence anyway (Malevergne & Sornette, 2006).

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

25 26 27 28 29 30 31 32

Score


Cramer-‐von Mises test score

Past week

Current week

Future week


Weekly Returns Index Pearson's coefficient Spearman’s Rho Spearman’s Rho

Number of days above threshold

0.149 0.0727 0.0983 N

ext

Wee

k Cumulative maximum temperature

0.0463 0.0251 0.0464


0.0382 0.0279 0.0535

Cur

rent

W

eek

Cumulative maximum temperature

0.0765 0.0230 0.0302


-0.0564 -0.0065 -0.0032

Prev

ious

W

eek

Cumulative maximum temperature

-0.0523 -0.0032 -0.0053

Table 10. Values for different measures of dependence between weather measurements and weekly returns

We need to consider measures of dependence defined for large and extreme events. There are both conditional (e.g. correlation coefficient conditional over a given threshold) and unconditional (e.g. coefficient of tail dependence). Idea of calculating correlation between two variables conditioned on signed exceedance of one or both variables may look promising but conditional correlation coefficients are known to suffer from theoretical and empirical deficiencies and consequently are of weak statistical value (Malevergne & Sornette, 2006).

We turn our attention to copulas as they provide a way to model joint distributions with flexibility both in choice of marginal distributions and the dependence structure. Copula is basically a function linking marginal variables into a multivariate distribution. It can be derived both from known distribution and constructed from given marginal distributions and copula. We refer reader to thorough presentation on the topic of copula and dependence in (Malevergne & Sornette, 2006).

Advantage of copula is that it can be applied to a pair of marginal distributions (estimated either parametrically or through nonparametric techniques like kernel density estimation). On other side this flexibility demonstrates shortcoming of copula – there are an infinite number of copula function. Performance of different copulas can be compared but there is no known procedure of choosing the “optimal” one (Kole, Verbeek, & Koedijk, 2007).

Idea to use copula is based on decomposition of joint density into product of marginal densities and copula density (compare to decomposition of covariance into product of standard deviations and correlation for elliptical distributions). To proceed we can consider full ML method (estimate marginal parameters and copula parameters in one stage), two step procedure known as inference for margins (IFM) (estimate parameters for the marginal distributions, than the parameters of the copula), semi-parametric route (empirical cumulative distribution functions for the margins) or fully non-parametric estimation (Würtz, 2010). Literature suggests that two-step procedure can be faster but at the cost of lower efficiency and a higher bias.

We obviously include normal distribution among possible candidates for margins because the distribution of an average tends to be normal, even when the distribution from which the average is


computed is non-normal (according to Central Limit Theorem). We also can’t reject null hypothesis that distribution is normal based on normality tests (see results in Table 10). The same tests allowed us to reject hypothesis regarding the normal distributional form for daily values. In fact we can easily demonstrate good fit of generalized extreme value (GEV) distribution for extreme daily maximum temperatures.

Empirical evidence shows that distributions of weekly cumulative maximum temperatures and weekly returns are platykurtichave (negative excess kurtosis) and both exhibit slight negative skewness, therefore we select second candidate distribution for margins – gamma for weekly cumulative maximum temperature and skew-t (extension of the Student’s t family) for weekly returns (Q-Q plots are shown in the Figure 45 and 46). Estimated parameters of these marginal distributions are presented in Table 11.

Name of Test Weekly cumulative maximum temperature

Weekly returns

Shapiro-Wilk test 0.756 0.9717 Jarque-Bera test 0.6497 0.8222 Anderson-Darling test 0.8863 0.9948 Cramer-Von Mises test 0.8316 0.9907 Lilliefors test 0.8716 0.9020 Pearson’s chi-square test 0.3974 0.9903

Table 11. p-‐values for different tests of normality.


Figure 45. QQ plots for weekly returns.


Figure 46. QQ plots for weekly cumulative maximum temperatures.

Weekly returns Weekly cumulative maximum temperatures Normal Skew-t (df = 10) Normal Gamma mean: -0.778 (0.435) location: -2.237 mean: 198.007 (1.446) shape: 145.319 (18.138) sd: 4.925 (0.308) scale: 4.737 sd: 16.361 (1.023) rate: 0.734 (0.092) shape: 0.383

Table 12. Estimated parameters of marginal distributions.

After fitting marginal distributions we need to apply maximum likelihood estimation to copula parameters. We select copulas from two popular families – elliptical and Archimedean copulas. For elliptical copulas (normal and t), the standardized dispersion matrix, or correlation matrix, determines the dependence structure. Commonly used dispersion structures are AR(1), exchangeable, Toeplitz and unstructured, however in bivariate case these copulas have single parameter ρ, the linear correlation coefficient. Among Archimedean copulas we consider Clayton copula (greater dependence in the negative tail), Frank copula (symmetric) and Gumbel copula (greater dependence in the positive tail).


Results of maximum likelihood estimation of copula parameters are presented in Table 12. Three rows in the table correspond to three joint distributions of weekly cumulative maximum temperatures and weekly returns. The top row is the case of joint distribution of next week cumulative maximum temperatures and current week returns, while 2nd and 3rd ones correspond to joint distributions of current week returns and current and past weeks’ temperatures respectively.

As higher log-likelihood indicates a better fit to the data, we conclude that Gumbel copula is outperforming other considered ones in case of joint distribution of next week cumulative maximum temperatures and current week returns, and Clayton - in case of current and past weeks’ temperatures. We remember that choice of copula a priori defines whether or not tail dependence can be modeled; therefore we compared different alternatives for parameterization with the goal of making grounded choice between them. We plotted these three best fitting copulas in Figures 47 – 49 below.

Maximized log-likelihood values Copula

Normal t (df 10) Clayton Frank Gumbel Next Week 0.805 1.247 0.058 0.576 2.539 Current Week 0.003 -0.882 1.421 0.007 0.161 Previous Week 0.007 0.111 0.372 0.004 0.072

Table 13. Joint distribution of weekly cumulative maximum temperatures and weekly returns. Maximized loglikelihood values for different copulas (with normal margins, IFM method).

Conclusions of previous paragraph were made based on the results of IFM estimates with normal margins. While estimates of parameters were sometimes noticeably different when we proceeded with full ML method, used empirical CDF of each marginal distribution or selected alternative form of margins, choice of the best fitting copula (among discussed alternatives) stayed the same.


Figure 47. Gumbel copula, joint distribution of next week cumulative maximum temperatures and current week returns.

Figure 48. Clayton copula, joint distribution of current week cumulative maximum temperatures and current week returns.


Figure 49. Clayton copula, joint distribution of previous week cumulative maximum temperatures and current week returns.

We turn our attention to dependence measures for these copulas - Kendall’s tau, Spearman’s rho, and tail dependence index. Results are presented in Table 13 which is organized in the manner similar to Table 12. For each dependence measure there are three rows in the table that correspond to three different joint distributions of weekly cumulative maximum temperatures and weekly returns.

Kendall’s tau Copula

Normal t (df 10) Clayton Frank Gumbel Next Week 0.071 0.066 -0.018 0.063 0.102 Current Week 0 0.003 -0.092 0.006 0.028 Previous Week -0.007 -0.002 -0.042 0.005 0.017

Spearman’s rho Copula

Normal t (df 10) Clayton Frank Gumbel Next Week 0.107 0.099 -0.028 0.095 0.151 Current Week 0 0.004 -0.138 0.010 0.038 Previous Week -0.010 -0.002 -0.063 0.008 0.023

Lower tail dependence index Copula

Normal t (df 10) Clayton Frank Gumbel Next Week 0 0.012 0 0 0

Current Week 0 0.007 0 0 0

Previous Week 0 0.007 0 0 0


Upper tail dependence index Copula Normal t (df 10) Clayton Frank Gumbel Next Week 0 0.012 0 0 0.137 Current Week 0 0.007 0 0 0.037 Previous Week 0 0.007 0 0 0.023

Table 14. Joint distribution of weekly cumulative maximum temperatures and weekly returns. Kendall’s tau, Spearman’s rho, and tail dependence index for different copulas (with normal margins, IFM method).

For the first two measures (Kendall’s tau, Spearman’s rho) we have values around zero and it can be explained because they are functions of whole distributions. It’s more important the asymptotic property – tail dependence index. We turn out attention to Gumbel copula as it allows for upper tail dependence.

Using expressions of the coefficients of upper and lower tail dependence for Archimedean copulas (Malevergne & Sornette, 2006) and estimation results for copula parameter theta, we calculate the following 0.95 confidence intervals.

Confidence intervals Gumbel Copula Theta, est. Theta

variance, est. Confidence interval for upper tail

dependence index, est. Next Week 1.116 0.005 0.128-0.149 Current Week 1.029 0.003 0.031-0.046 Previous Week 1.018 0.003 0.017-0.031

Table 14. Confidence intervals (95% confidence level) for upper tail dependence index, Gumbel copula

Nonzero value for next week case implies absence of asymptotic independence. Same measures for current and past week situations are close zero which means asymptotic independence (but not necessarily independence). We note that these results are in line with observations from Figures 38-40.

We also calculated conditional Spearman’s rho as described in (Malevergne & Sornette, 2006). Its unconditional value was around 0.1, while for thresholds large than quantile 0.6 it ranged between 0.3 and 0.45 sharply dropping to almost zero after quantile 0.9. The drop seems to be connected to the scarcity of data at these levels. Overall, measures and their behavior seem to be highly influenced by the choice of tool used to probe dependence.

3.4 Summary of the Chapter In this chapter we modeled distribution of individual variables and dependence structure between them for weekly maximum temperatures and weekly returns. Using copula method we identified presence of asymptotic dependence in upper tail of joint distribution of current week returns and next week cumulative maximum temperatures (and asymptotic independence in cases of past and current week temperatures). It quantitatively confirms reasonable belief that, at least in larger markets and regions with developed weather observation networks, competitive advantage in commodities trading can be achieved primarily with knowledge of future developments and not of


current weather conditions. However trading based on real time weather data might still work in other cases, e.g. when growing region has insufficient infrastructure.

It’s important to note that in case of forecasts prediction horizon needs to be relatively short (10-15 days can be sufficient). We also checked if market behavior reflects future heat-wave earlier than demonstrated above case of one week before, however results suggested asymptotic independence.

As with any attempt to analyze complex subject, we can identify number of potential changes to make it more accurate. One potential improvement is spatial interpolation of surface weather observations. Instead of using point measurements we may look at grid data over the growing region.

Data from remote sensing seems to be very promising input into model to assess extent of crop damage. Analyzing high-frequency tick data11 may also help us to reveal some hidden behavior trends that relevant for this strategy. It might be promising to evaluate if not only speed but also precision and extended coverage can provide advantage. The point is to capitalize on situations when market under- or overestimate damage from weather event. Such situations will ultimately be corrected during harvest.

We can also look more into direction of extreme value analysis to model severe weather events, their return levels and temporal dependence structure.

11 Interestingly, Benoit Mandelbrot suggested the financial markets might have fractal properties when he examined cotton and corn prices.


4. Summary and conclusion This work (performed during the internship at the weather risk management company) evaluates

investment opportunities in agriculture commodities. To large extent the work was exploratory with

goal to find particular niche in the commodities investment universe. We decided to focus on

fundamental approach with particular emphasis on price effects of weather induced supply shocks.

The aim was to utilize existing company’s expertise and infrastructure as competitive advantage.

The first part is devoted to supply/balance analysis of the market. This is rather traditional approach

however we attempted to be broader and more interdisciplinary than in typical case to discover more

subtle trends than, for example, growth in population. It was major learning experience about

macroeconomic developments of the world spanning multitude of sciences and topics. As evidenced

by sections of author’s created wiki (available at wxrisk.wikia.com) it ranged from agriculture and

microinsurance to renewable energy and weather risk management. Going beyond analyst reports

and news stories to look at original data was important part of the work. Another conclusion is that

valuation of relatively unsophisticated things such as corn immediately demonstrated us complexity,

interconnectedness and interrelatedness of real world.

I have to note that in fact our perspective was closer to “global macro” strategy with focus on risk

side of trading disregarding its sources – heat waves in growing regions due to climate change or

termination of government subsidies for bioethanol. For the future work I envision looking at

modeling commodity prices more mathematically and quantitatively.

In the second part, we focused on supply shocks caused by weather extremes. Instead of forecasting

level of supply we analyzed direct market reaction to adverse weather which gives this method

wider applicability to all weather sensitive markets. Demonstrated presence of asymptotic

dependence in upper tail of joint distribution of current week returns and next week cumulative

maximum temperatures leads us to the conclusion that competitive advantage can be achieved with

short-term (about 10-15 days) forecasts of (extreme) weather.

In the course of this work we came across other markets beyond agriculture that might be of interest

for further research. One such example is salmon market in Norway where harsh weather

immediately impacts fishing operations. Another example is energy markets which were always

weather sensitive on the demand side (and to lesser extent on supply, e.g. low rainfall cuts hydro

power generation), however with growing share of photovoltaic and wind power generation weather

will also have strong impact on supply side.


A. Appendix

A.1 Summary of the Weather-based Trading Strategy This is simplified conceptual demonstration of the idea in the form of flow chart. It was presented to

the board of the company at the beginning of the project.

As presented in the Figure 50, trading strategy development starts with identifying key growing

regions (and representative basket of weather stations), key growing stages (risk periods) and

corresponding tradable commodity (choice of exchange and maturity). Next step is to select weather

index best explaining variations in the yield and define the rule transforming these index values into

the trading signals. After back testing, the strategy is executed during risk periods as following –

system is continuously measuring weather index, tracking its deviation from average weather and

performing trades based on the generated trading signal (and, occasionally, trader’s discretionary

input).

Figure 50. Weather-‐based trading strategy

A. Appendix Error! Unknown switch argument.

A.2 General Assessment Framework This is more detailed, text-based version of development process of weather-based trading strategy.

It is not limited to commodities markets but is applicable to any weather sensitive sector of economy

(e.g. energy, agriculture, transportation, construction, travel, retail). In fact, (Larsen, Lawson, Lazo,

& Waldman, 2008) estimated that US annual output varies by more than 3 percent due to changes in

the weather ($260 billion in 2000 dollars).

Framework

• identify weather sensitive industry (company) / commodity o with minimum geographical diversification o focus on locally traded companies with the least number of independent business lines

• gather necessary information for weather sensitive industry (company) / commodity o weather data sources o exchange, ticker, corresponding market index (to stay market neutral if bets are on

individual company / commodity)

• assess supply/demand characteristics o composition of supply/demand (weights, e.g. for energy – hydro, wind, solar) o historical development of composition, its trend o elasticity of supply/demand o storage levels and their impact (can be indirect reserves, e.g. water reservoir for future

electricity production)

• understand weather impact o weather index with the highest dependence (volume vs. weather index) o risk period (potentially multiple ones, e.g. growing and harvest period for wheat) o regions and representing weather stations (weights)

• monitor weather (for relevant risk period) o deviation from average scenario o portion of risk period observed (more observed – less uncertainty)

• produce trading signal o raw signal – solely based on deviation of weather index o fine-‐tuned signal

account also for weather forecasts (predicted values and historically observed precision of forecasts)

analysts forecasts for financial instrument


• decide whether trade or not trade (discretionary) o impact on existing investment portfolio o expected time till market price reflect extreme weather (quarterly return released,

commodity harvested) o corresponding exit strategy (timing, stop losses, etc.)


A.3 Screenshots of the Developed Application Among the duties of author during the internship was development of corresponding tools (many of

them were used in core business line of the company – trading of weather derivatives). Below are

presented screenshots of the application used for weather-based trading strategy development.

It was designed using Model–View–Controller (MVC) software architecture with model managing

the behavior and data of the application (developed in SQL Server, see A.4 for details), view

rendering the model into a form suitable for interaction, i.e. user interface (created with Python and

Qt framework, see Figures 51-55 below) while controller layer implemented necessary business

logic (using R programming language).

Figure 51. Securities. Price data on commodities and indices. Figure 52. Regions. Groupings of weather stations.

Figure 53. Baskets. Regions and weather indices combined. Figure 54. Strategies. Trading strategy definition (rules, intervals).


Figure 55. Back-‐testing. Calculation of daily signals and P&L.


A.4 Information Schema of the Developed Application Figure 56 presents information schema of the application created for weather-based trading strategy

development.

It was designed to accommodate needs of the framework presented in A.2 with main subparts being

instrument (info on exchange listed tradable financial instrument), supply/demand (represented by

Supply, SubSupply, SubSupplyHistory, SubSupplyDev, Demand, SubDemand, SubDemandHistory,

SubDemandDev entities) and weather data (WeatherStation, WeatherData, WeatherElement,

WeatherIndex entities).

Figure 56. Information Schema.


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