FRE 501 2013Lab 8 (Oct 28th)

1. Reflections on 6 weeks of Trading2. Applying Theory to Real-life

Mission Statement

Understand how commodity futures markets work, Formulate and refine trading and hedging strategies,

Learn and practice risk management,In preparation for future professional roles

Trading Game RankingsFirst Name Last Name Average Gain/Loss per

unit riskRank by Average Gain per unit risk % profits Rank by

profits Sharpe Rank by Sharpe

Haoye Ji 32.1% 1 59.5% 1 5.14 1Ling Mao 27.7% 2 17.8% 5 3.32 2Gianmarco Vasquez 14.2% 3 29.9% 2 1.62 6Yimeng Zheng 9.0% 4 -7.4% 16 -0.91 13Airlie Trescowthick 7.1% 5 9.8% 8 1.4 7Febiana Tedja 6.0% 6 15.0% 6 2.12 4Behzad Memarzadeh 6.0% 7 26.6% 3 2.12 3RUIJI WU 4.9% 8 24.7% 4 1.99 5Alejandro Barrero 3.6% 9 0.3% 9 -0.47 10Yiwen Chen 1.7% 10 -2.0% 11 -0.8 11Benet Copeland 1.6% 11 11.8% 7 0.63 8Harmony Bjarnason 0.9% 12 -5.1% 14 -1.47 15De Lun Li -2.2% 13 -8.1% 17 -1.63 16Dong Pan -3.1% 14 -18.8% 21 -0.88 12juan fercovic -4.3% 15 -27.7% 23 -2.16 17Ariel Kagan -4.6% 16 -13.5% 19 -4.12 22Tekuni Nakuja -4.6% 17 -1.6% 10 -0.04 9yifei peng -4.8% 18 -11.6% 18 -2.27 18Weilin Li -4.9% 19 -5.8% 15 -1.17 14Brendan McCaffery -6.8% 20 -19.1% 22 -3.73 21suey jiang -8.4% 21 -14.3% 20 -2.97 19Laura Uguccioni -9.1% 22 -4.5% 13 -3.12 20zejun wang -9.6% 23 -49.2% 25 -4.22 23Sang Sheng -10.2% 24 -4.0% 12 -4.27 24Alex LI -22.7% 25 -29.3% 24 -5.68 25

Relationship between Ranks

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Profit Rank

Rank: Average Gain per unit risk

Ranks: Avg Gain per unit risk vs Sharpe

0 5 10 15 20 250

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Sharpe Ratio Rank

Rank: Average Gain per unit risk

-60% -40% -20% 0% 20% 40% 60% 80%

-30%

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0%

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Profit %

Average Gain per unit risk

Quadrant of skill vs risk appetite

Would rather invest money with those who can allocate capital efficiently – demonstrated proficiency at generating good returns per unit of risk taken

It is easier

to teach / encourage a superior investor with excellent judgment to increase/decrease the size of their positions to get a desired

return

Than it is to

teach a risk-loving person to become a superior investor

Who would you invest with?

Applying Theory to Real-Life

Broadly, You have covered

Concepts• Prices over space• Prices over time• Prices over form

Models – 2 basic categories• Simple models

(two factor) • Complex models

(multi-factor )

Simple ModelsYou use your two factor models (other factors are exogenous) to illustrate/solve simple what-if and dynamic problems: • 3-panel trade/storage diagramx

(only 2 countries or 2 periods)• Convenience yield diagram• Single-commodity blending

Complex ModelsWe have to use the complex models for multi-factor problems (excel models maximizing NAW)• multi-country• multi-period• multi-commodity blending

Most real-world problems that deserve attention are pretty much complex. Outcomes of complex situations are often non-linear and jerky – complex models are important for understanding these dynamics.

A quick comment about Econometrics:Scientists use data analysis techniques to calibrate parameters for their modelsEconomists started using the same techniques to find parameters to model their data – the common ‘kitchen sink’ approach.Naïve assumptions based on historical data can lead to very bad policy

Real World Applications: Spatial

Remember 5 country spatial model for tomatoes

Do you think Spain’s ban on Argentinian Soy Methyl Ester (Apr 2012) had any effect on the market price of SME or soybeans?

Hardly – global traders simply shuffled trade around. Argentinian SME went to Italy and Germany instead. Brazil and US SME went to Spain.

1. Does the application of an export tax on Crude Palm Oil in Indonesia make the domestic price higher or lower?2. Does it make the export price higher than the world market price?

1. Lowers it by the amount of the tax2. Indonesian exporters are price takers, domestic

producers bear the full burden of the tax

Real World Applications: Spatial

Introducing the element of time and hence inter-temporal costs such as storage and costs of capital – help you to understand why and how spatial equilibriums can be violated (e.g. commodity finance importing in China)

And why futures curves can swing between contango and backwardation

Real World Applications: Temporal

The real cost of storage is extremely high for many food commodities, especially in tropical climates, where fruit rots quickly – products must often be processed within 24 hours

Tropical: Sugarcane; Palm OilTemperate: Milk

Significant market power is in the hands of the processors, the primary producers always get squeezed

Real World Applications: Temporal

Examples covered in lecture are primarily blending of one commodity to optimize prices according to contract standards

The basic concept is one of mixing ABC in different proportions to get specified qualities of DEF

Why is blending important?This concept can be broadly applied to many other

areas of business and policy

Real World Applications: Blending

• Cereals are routinely blended by companies– Special-K, Honey-Oat, Captain Crunch

• Animal/Livestock Feed• Consumer Vegetable Oil (Supermarket retail)• Biodiesel

All multi-factor blending optimization problems Blending is basically your concept of demand

substitution in action.

Real World Applications: Blending

Vegetable Oil Cloud Points: (start solidifying into fat)– Rapeseed Oil : -3.9 °C (most expensive)– Soybean Oil : -3.9 °C– Refined Palm Oil : 8-10 °C (cheapest)

Blenders/Bottlers optimize their blends depending on market prices and market requirements. North Asia (N. china, Korea, Japan) gets close to 100% soybean or rapeseed oil. South Asia gets close to 100% palm oil. Places in the middle get a blend.

All are labeled and sold as 100% vegetable oil, which is true.

Real World Applications: Blending

Real World Applications: Blending

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

NOV

DEC

-200.0

0.0

200.0

400.0

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1000.0

-25.0

-5.0

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35.0

55.0

75.0

95.0

115.0

RME (-14) SME (-4) PME(15) Min Temperature(000s) mt

Seasonal Biodiesel Demand in Europe: 2010

Real World Applications: BlendingHoney: Manuka- NZ and AUS produce 1700 tonnes Manuka honey/year, but 10,000 tonnes are sold each year (a lot in China). Part blending part fraud

Chinese Honey:When US, Canada, EU banned Chinese honey because of antibiotic use (Chloramphenicol), exports from Australia tripled the next year (much more than they actually produce)

Where do you think it came from?

Pollination of pear trees by hand in China: All insects dead from pesticide

Coffee Roasts are also blends - Alejandro will discuss next week

Tea Blends – Bee will discuss later

Real World Applications: Blending