Syllabus_BIM_Commodity_Markets-TD.pdf

UNIVERSITY OF PARIS DAUPHINEMASTER 268

BIM

SYLLABUS OF THE COURSE: COMMODITY MARKETS

Professor:

This course will be taught by Professors Sophie MERITET and Julien CHEVALLIER. The course materials are in English.

Overview:

This economic seminar aims at studying various commodity markets (agricultural products, industrial metals, precious metals, energy commodities). From a financial economics perspective, the course provides a detailed analysis of each type of commodity, in relation with the global macro-economic environment, and by discussing the long-term fundamental value present in a basket of commodities and financial assets (equities, bonds, FX). The course also contains a special focus on energy markets (oil, natural gas, electricity, coal and renewable energies), with different approaches necessary to understand the world energy scene and its dynamics in the European Union. To understand the current European energy situation, with its constraints, its history and its possible evolutions in the long term, a comparison will be made with the other energy consumption and production areas in the world. The objective in these lectures is a better understanding of the fundamentals of energy economics: (1) how energy (oil, natural gas, coal and electricity) is supplied, distributed and used, (2) the economic, financial and environmental consequences of such patterns and (3) the role that public policy could play.

Course Objectives:

The course contains the main insights about the evolution of commodity prices over the long run. During the more recent period, the course also discusses the extent to which the financial crisis has been impacting the price path of commodities. In addition, the course presents the fundamentals of energy economics with the world current energy situation and its outlook. What can be said about energy consumption, energy resources and the energy mix of the future, taking in account the economic development of emerging countries, financial constraints, different energy policies and the questions related to global warming ?

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http://www.master-bim.fr/index.php

The seminar will be divided into 6 sessions, as detailed in the course schedule. The lectures present the basic economic features related to the various commodities. A special attention is given to physical energy markets, price mechanisms, the organization of the energy industry and the European situation (energy policy, security of supply, deregulation, etc.).

Course mechanics:

As Lectures are an introduction on the commodity markets and their economic fundamentals, it will be a lecture form with some country cases. Class participation is encouraged. Slides will be posted on the Dauphine Intranet website before the course and on the lecturer’s personal webpage.

Readings (optional):

- International Energy Agency (2010), World Energy Outlook 2010, Global Energy trends.

- Schofield, NC. 2007. Commodity Derivatives: Markets and Applications. Editions: Wiley Finance (315 pages). ISBN: 978-0-470-01910-8.

- Geman, H. 2005. Commodities and Commodity Derivatives: Modeling and Pricing for Agriculturals, Metals and Energy. Editions: Wiley Finance (396 pages). ISBN: 0-470-01218-8.

- Vogelvang, B.2005. Econometrics: Theory and Applications with Eviews. Editions: Pearson Education (363 pages). ISBN: 978-0-273-68374-2.

- Handbook of Sustainable Energy, Edward Elgar, 2011.- CHEVALIER JM (2009), The new energy challenges : economics, geopolitics

and climate, Palgrave- CHEVALIER JM (2004), Les Grandes batailles de l’énergie. Gallimard Folio

2004.- JOSKOW P. (2007), “Lessons learned from electricity market liberalization”, December

8, 2007, MIT www.econ-www.mit.edu/files/2093- EUROPEAN COMMISSION COMPETITION DG, (2006), “Green Paper : European

Strategy for Sustainable, Competitive and Secure Energy”, Bruxelles, http://ec.europa.eu/energy/green-paper-energy/doc/2006_03_08_gp_document_en.pdf

More references are posted on the intranet

Grading

The numerical grade distribution will dictate the final grade, according to the faculty’s recommended grade distribution. Class participation: Active class participation – this is what makes classes lively and instructive. Come on time and prepared. Class participation is based on quality of comments, not quantity.Exam policy: In the exam, students will not be allowed to bring any. Unexcused absences from exams or failure to submit cases will result in zero grades in the calculation of numerical averages. Exams are collected at the end of examination periods.

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http://ec.europa.eu/energy/green-paper-energy/doc/2006_03_08_gp_document_en.pdf

http://www.econ-www.mit.edu/files/2093

The mode of assessment is a final exam with multiple choice questions, short open questions and probably a longer question.

Academic integrity

All work turned in for this course must be your own work, or that of your own group. Working as part of a group implies that you are an active participant and fully contributed to the output produced by that group. When you use the web, please state your sources.

Course Schedule

Session 1Introduction on the energy markets and world energy situation.Oil markets: economic fundamentals, oil curse, peak oil, peak demand….+ the country cases of Norway, UK and Brazil.

Session 2Natural gas markets : economic fundamentals, Dutch disease, LNG, unconventional gas, gas bubble, long term contracts ….+ the country cases of Russia and US.

Session 3Electricity markets and renewable energies: economic fundamentals, nuclear electricity, CO2 price, renewable energies, reorganisation of industries… + the country cases of Denmark, France and Spain.

Session 4Focus on carbon markets and emissions trading: market mechanisms and price development in the EU ETS and the Kyoto Protocol’s Clean Development Mechanism.

Session 5Agricultural products: commodity by commodity analysis, link with the macro-economic environment and fundamental value.

Session 6Industrial metals and precious metals: commodity by commodity analysis, link with the macro-economic environment and fundamental value.

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Commodities Markets

MSc Investment Banking and Markets

Université Paris Dauphine

Julien Chevallier

LECTURE #4: CO2 MARKETS

1.1 Background

1.1.1 Review of current climate policies

1.1.2 How is a tradable permit different?

1.1.3 Market design issues

1.2 Key Design Issues of the EU ETS

1.2.1 Scope

1.2.2 Allocation

1.2.3 Calendar

1.2.4 Transactions

1.2.5 Penalties

1.2.6 Banking provisions

1.3 CO2 Price Fundamentals

1.3.1 Explanatory variables (1/3): EC announcements

1.3.2 Explanatory variables (2/3): Energy prices and Abatement options

1.3.3 Explanatory variables (3/3): temperatures variables

1.3.4 Capturing the effects of economic activity

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1.4 The Clean Development Mechanism

1.4.1 CERs contracts and Price Development

1.4.2 CERs Price Drivers

1.4.3 Arbitrage Strategies: the EUA-CER Spread

1.4.4 Idiosyncratic risks

1.4.5 Common risk factors between EUAs and CERs

Reading List

• Chevallier, J. 2011. The European Carbon Market (2005-2007): Banking, Pricing and Risk- Hedging Strategies' in Handbook of Sustainable Energy, Chapter 19, 395-414, edited by Galarraga, I., Gonzalez-Eguino, M., Markandya, A. Editions: Edward Elgar (624 pages). ISBN: 978-1-84980-115-7.

• Chevallier, J. 2011. The Clean Development Mechanism: A Stepping Stone Towards World Carbon Markets?' in Handbook of Sustainable Energy, Chapter 20, 415-440, edited by Galarraga, I., Gonzalez-Eguino, M., Markandya, A. Editions: Edward Elgar (624 pages). ISBN: 978-1-84980-115-7.

Commodities Markets



Julien Chevallier

LECTURE # 5 : AGRICULTURAL MARKETS

1 Measuring the impact of economic news on agricultural markets

1.1 Database of news

1.2 News analysis

1.3 Estimation results

1.4 Some conclusions

2 Stylized behavior of agricultural markets depending on the economic cycle

2.1 The Markov Switching model – Hamilton (1989)

2.2 Dataset

2.3 Country by country analysis

2.4 Business Cycle to Agricultural Markets Relation

2.5 Reminder on the Sharpe Ratio


3 The nature of the US business cycle

3.1 Dataset

3.2 Number of regimes

3.3 Estimated probabilities

3.4 Leading score

3.5 Agricultural performances across business cycle phases

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Reading List

• Schofield, NC. 2007. Commodity Derivatives: Markets and Applications. Editions: Wiley Finance (315 pages). ISBN: 978-0-470-01910-8.

• Geman, H. 2005. Commodities and Commodity Derivatives: Modeling and Pricing for Agriculturals, Metals and Energy. Editions: Wiley Finance (396 pages). ISBN: 0-470-01218-8.

• Vogelvang, B.2005. Econometrics: Theory and Applications with Eviews. Editions: Pearson Education (363 pages). ISBN: 978-0-273-68374-2.

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Commodities Markets



Julien Chevallier

LECTURE #6: PRECIOUS AND INDUSTRIAL METALS

1 Cross-commodity linkages

1.1 Database of precious and industrial metals

1.2 Equilibrium relationships

1.3 Notion of cointegration


2 Relationships with traditional asset markets

2.1 Equities and Bonds

2.2 Exchange rates


3 The fundamental value of precious and industrial metals

3.1 Relationship with the macroeconomy

3.2 Relationship with inflation


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Reading List

• Schofield, NC. 2007. Commodity Derivatives: Markets and Applications. Editions: Wiley Finance (315 pages). ISBN: 978-0-470-01910-8.

• Geman, H. 2005. Commodities and Commodity Derivatives: Modeling and Pricing for Agriculturals, Metals and Energy. Editions: Wiley Finance (396 pages). ISBN: 0-470-01218-8.

• Vogelvang, B.2005. Econometrics: Theory and Applications with Eviews. Editions: Pearson Education (363 pages). ISBN: 978-0-273-68374-2.

TD #4: CO2 Markets

Julien Chevallier∗

Commodities Markets, M.Sc. Investment Banking and Markets

March 28, 2012

Instructions:

• Comment each estimation Table, as detailed in the Lecture.

• Write down the equation of the estimated model, if it helps you.

• Focus on the main intuitions for the statistically significant variables.

• Send your TD #4 to your instructor by email at the end of the session.

∗University Paris Dauphine. Email: [email protected]: sites.google.com/site/jpchevallier

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Full Period(1) (2) (3)

Pt(-1) 0.2118***(0.0755)

0.2129***(0.0754)

0.1979***(0.0747)

Pt(-2) -0.0989*(0.0583)

-0.0980*(0.0579)

-0.1123*(0.0588)

Constant 0.0013(0.0026)

0.0006(0.0023)

0.0005(0.0024)

Break 1 -0.0175***(0.0057)

-0.0165***(0.0054)

-0.0125***(0.0049)

Break 2 - - -

Brent - - -

NaturalGas

0.0732**(0.0305)

0.0730***(0.0300)

0.0736**(0.0306)

Coal -0.0978**(0.0470)

-0.0999**(-0.0471)

-0.1018**(0.0471)

Switch - - -

Electricity 0.0082***(0.0027)

0.0083***(0.0027)

0.0079***(0.0027)

CleanDark

-0.0527***(0.0148)

-0.0525***(0.0146)

-0.0526***(0.0148)

CleanSpark

0.0396**(0.0169)

0.0394**(0.0167)

0.0398**(0.0170)

Tempext5 -0.0097(0.0068)

Tempext95 0.0056(0.0663)

Win06 -

Win07 -0.0074**(0.0035)

R-squ. 0.3560 0.3543 0.3694Adj. R-squ.

0.3407 0.3417 0.3558

F-Stat 0.0000 0.0000 0.0000D.W. 1.8944 1.8892 1.8900LM test 0.1277 0.1155 0.1472White test 0.0002 0.0001 0.0000AIC -3.2974 -3.3030 -3.3226SC -3.1920 -3.2153 -3.2260Procedure NW OLS NW OLS NW OLS

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Variable EUAt,i

(1) (2)constant 0.0001

(0.0009)0.0001(0.0009)

brentt 0.0019***(0.0003)

coalt -0.0012***(0.0003)

gast 0.0003***(0.0001)

switch 0.0005***(0.0002)

momentumt -0.0095***(0.0013)

-0.0100***(0.0012)

NAP phase II -0.0102*(0.0056)

-0.1094**(0.0055)

AdjustedR2 0.1046 0.0767Log-Likelihood 1262.635 1250.022ARCH LM Test 0.6833 0.70002Q(20) Statistic 26.553 24.999AIC -4.7811 -4.7405SIC -4.6997 -4.6755N 529 529

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Variable CERt,i

(3) (4) (5)constant 0.0001

(0.0007)0.0001(0.0008)

0.0007(0.0013)

brentt 0.0013***(0.0002)

0.0005*(0.0003)

coalt(1) -0.0011***(0.003)

-0.0017***(0.0003)

gast 0.0002**(0.0001)

0.0002*(0.0001)

momentumt -0.0019(0.0012)

-0.0028**(0.0012)

linking 0.0199*(0.0123)

CDM pipelinet 0.0005(0.0013)

AdjustedR2 0.0883 0.0707 0.0463Log-Likelihood 1309.432 1302.025 660.743ARCH LM Test 0.8447 0.9342 0.7560Q(20) Statistic 25.555 24.531 20.724AIC -4.9730 -4.9428 -4.5827SIC -4.8997 -4.8859 -4.4542N 529 529 286

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Variable Spreadt,i(6) (7)

constant -0.0145(0.0111)

0.0080(0.0134)

momentumt 0.0945***(0.0121)

0.0997***(0.0151)

linking -0.2610***(0.0733)

EUA price levelt 0.9562***(0.0322)

0.6726***(0.0469)

∆ vol EUAt -0.0007**(0.0004)

-0.0001***(0.0001)

∆ vol CERt -0.0013**(0.0007)

V IXt -0.3364***(0.1213)

crisis 0.4299***(0.0711)

CDM EB meeting 0.0901***(0.0541)

threshold CER -0.0764***(0.0134)

CDM pipelinet 0.0161***(0.0013)

open int EUAt -0.0001***(0.0001)

AdjustedR2 0.5800 0.5658Log-Likelihood 141.5910 79.0179ARCH LM Test 0.5540 0.5278Q(20) Statistic 36.765 26.371AIC -0.5023 -0.4699SIC -0.4209 -0.3016N 529 286

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TD #5: Agricultural Markets



June 1, 2012

1 News analysis

1.1 GSCI agricultural sub-index

Table 1: News analysis for the GSCI agricultural sub-index

GSCI Agri.

Intercept 2.46 (1.61)AR -0.01 (-0.27)

Non Farm Payroll 0.14 (1.56)ISM 0.08 (0.84)

Jobless Claims 0.05 (0.52)US CPI MoM 0.18** (2)

US Retail Sales 0.09 (0.79)Fed Target Rate -0.23* (-1.75)

US GDP 0.15 (0.91)ZEW Economic Sentiment -0.31** (-2.76)

IFO Expectations -0.06 (-0.5)EMU CPI Flash Estimate -0.36** (-3.2)

EMU GDP 0.05 (0.26)France Business Confidence 0.19* (1.69)

ECB Refinancing Rate 0.2* (1.88)China CPI YoY -0.08 (-0.74)

China Industrial Production 0.03 (0.24)China PMI -0.19 (-1.36)


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Question #1: Comment the results obtained for the Goldman Sachs Commodity sub-Index for agri-cultural products in Table 1.

1.2 NBER Recessions/Expansion Phases

Table 2: News analysis for GSCI agricultural sub-index withNBER expansion / recession periods

GSCI Agri.

Intercept 2.99* (1.94)AR -0.01 (-0.39)

Recession Non Farm Payroll 0.45* (1.88)ISM 0 (0.01)

Jobless Claims 0.07 (0.43)US CPI MoM 0.37** (2.18)

US Retail Sales 0.15 (1.06)Fed Target Rate -0.4** (-2.36)

US GDP 0.53 (1.08)ZEW Economic Sentiment -0.7** (-2.96)

IFO Expectations -0.73** (-3.35)EMU CPI Flash Estimate -0.8** (-3.55)

EMU GDP 0.24 (0.39)France Business Confidence 0.4** (2.12)

ECB Refinancing Rate 0.29** (2.14)China CPI YoY -0.16 (-0.43)

China Industrial Production -0.37 (-1.4)China PMI -0.22 (-1.46)

Expansion Non Farm Payroll 0.09 (0.91)ISM 0.11 (1.05)

Jobless Claims 0.04 (0.4)US CPI MoM 0.11 (1.05)

US Retail Sales -0.01 (-0.04)Fed Target Rate -0.06 (-0.28)

US GDP 0.1 (0.56)ZEW Economic Sentiment -0.21 (-1.6)

IFO Expectations 0.25* (1.65)EMU CPI Flash Estimate -0.2 (-1.51)

EMU GDP 0.03 (0.15)France Business Confidence 0.1 (0.75)

ECB Refinancing Rate 0.03 (0.19)China CPI YoY -0.07 (-0.65)

China Industrial Production 0.09 (0.84)China PMI 0.36 (0.88)

Question #2: Comment the results obtained when splitting the sample accordingly to the US reces-sion and expansion periods – as dated by the NBER – in Table 2.

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1.3 Graph analysis

Figure 1: Percentage of news with a statistical impact over different sectors of the commodity market

Figure 2: Rolling sensitivity index of news with a statistical impact over different sectors of thecommodity market

Question #3: Comment the following graphs presenting the evolution of the percentage of marketmovers for the GSCI Agricultural sub-index, and its evolution over time in Figure 1 and Figure 2.

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2 Measuring the business cycle

Figure 3: GSCI agricultural sub-indicex evolution over 1983-2011 along with NBER recession peri-ods and the time series of the Fed’s target rate

Question #4: Comment the following graph that charts the GSCI agricultural sub-index, along withNBER recession phases and the Fed’s target rate.

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3 Commodity performances depending on the nature of each economicregime

Table 3: Investment performances in agricultural commodities

Loadings Agri.Static -0.11

Strong exp. Reg. 1 0.12Slowdown Reg. 2 -0.75

Strong Crisis Reg. 3 -0.07Stalling Reg. 4 0.02

Medium Expans. Reg. 5 0.06

PnL Cmdty Static Cmdty DynamicAvg. Return 5.85% 9.97%

Volatility 10.01% 10.95%Sharpe Ratio 0.58 0.91

1985 1990 1995 2000 2005 2010

200

400

600

800

1000

1200

Commodity

Single regime5 Regimes

Question #5: Comment Table 3 and Graph 3 which display the Markowitz gains from investing intoa pure commodity portfolio.

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TD #6: Precious and Industrial Metals



June 13, 2012

1 Cross-commodity linkages

1.1 Gold↔Crude Oil

Question #1: Theoretically, which economic reasons can you think of concerning the Gold-CrudeOil linkage?

Table 1: Johansen Cointegration Test Results for Gold-WTI

1993-2011 Max. Eigen. 10% 5% 1%r ≤ 1 5.97 10.49 12.25 16.26r = 0 8.92 16.85 18.96 23.65

1993-2000 Max. Eigen. 10% 5% 1%r ≤ 1 2.35 10.49 12.25 16.26r = 0 9.22 16.85 18.96 23.65

2000-2011 Max. Eigen. 10% 5% 1%r ≤ 1 5.74 10.49 12.25 16.26r = 0 16.88 16.85 18.96 23.65

Question #2: Comment the results obtained in Table 1. Can you identify any cointegrating rela-tionship between Gold and WTI? If yes, during which period / sub-period? what is the number ofcointegrating relationships? at what statistical levels?

Question #3: Comment the results obtained in Table 2. Can you confirm thatthe VECM has beenwell estimated? If yes, which commodity is the driving forcetowards the long term equilibrium?Comment in terms of size of the coefficients, statistical significance and relative importance.


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Table 2: VECM results for the2000-2011 sub period for Gold-WTI

Err. Correction TermGold 1WTI -0.201Trend -0.001

VECM ∆Gold ∆WTIECT -0.019 0.019

(t.stat) (-2.98) (1.64)Intercept 0.098 -0.097(t.stat) (3.01) (-1.63)∆Gold -0.013 0.089(t.stat) (-0.46) (1.71)∆WTI -0.035 -0.063(t.stat) (-2.19) (-2.2)

Figure 1: Cointegration relationship between Gold and WTI

2006 2008 2010 2012

−0.

2−

0.1

0.0

0.1

0.2

Cointegration relationship

Question #4: Comment the results obtained in Figure 1. What can you conclude with respect to thestability of the cointegrating relationship between the 2 variables?

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Figure 2: Fundamental value for Gold

2006 2008 2010 2012

6.0

6.5

7.0

7.5

Fund. vs. Market value : Gold

Fund. valueMarket value

2006 2008 2010 2012

−0.

04−

0.02

0.00

0.02

0.04

0.06

%

% of departure from FV

Figure 3: Fundamental value for WTI

2006 2008 2010 2012

3.6

3.8

4.0

4.2

4.4

4.6

4.8

5.0

Fund. vs. Market value : WTI

Fund. valueMarket value

2006 2008 2010 2012

−0.

15−

0.10

−0.

050.

000.

050.

100.

15

%

% of departure from FV

Question #5: Comment the results obtained in Figures 2 and 3. What can youconclude with respectto the deviation of each commodity with respect to its fundamental value (in %) along the cointegrat-ing relationship?

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Table 3: Lütkepohl et al. (2004) Cointegration Test Resultswith Structural Break for Gold and WTI

1993-2011 Max. Eigen. 10% 5% 1%r ≤ 1 4.50 5.42 6.79 10.04r = 0 27.35 13.78 15.83 19.85

Table 4: VECM Results with Structural Break (1993-2011) forGold and WTI

Err. Correction TermGold 1WTI -1.60279548755915

VECM ∆Gold ∆WTIECT 0.001 0.009

(t.stat) (0.48) (4.8)∆Gold -0.023 -0.02(t.stat) (-1.22) (-0.58)∆WTI -0.016 -0.019(t.stat) (-1.49) (-0.98)

Question #6: Comment the results obtained in Tables 3 and 4. Do these results confirm the previousfindings?

Figure 4: Cointegration relationship with structural break between Gold and WTI

1995 2000 2005 2010

−1.

0−

0.5

0.0

0.5

1.0

1.5


Question #7: Comment the results obtained in Figure 4. How is it different from Figure 1? Whencan you detect the presence of a structural break (approximately)?

Question #8: What is your conclusion concerning the link between Gold and WTI?

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Commodity Markets

Examen Partiel

Universite Paris Dauphine

Master 2 Banque d’Investissement et de Marche

Sophie Meritet, Julien Chevallier

Lundi 25 juin 2012

Duree: 2h

Aucun document ni calculatrice autorises

Repondre sur copie separee

Question de cours (2 points)

Question #1 (1 point)

• Definissez les notions de Clean Dark Spread, Clean Spark Spread et Prix Switch.


• En quoi ces spreads sont-ils utiles a la determination du prix du CO2?

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Exercice I (4 points)

Question #3 (2 points)

Table 1: Impact of economic news on GSCI sub-indices of commodities according to the NBERRecessions/Expansion Phases

GSCI Agri. GSCI Energy GSCI Ind. Metals GSCI Prec. MetalsIntercept 2.99* (1.94) -1.78 (-1.16) -4.67** (-3.03) 1.29 (0.84)

AR -0.01 (-0.39) 0.04 (1.19) 0.02 (1.17) 0.04** (2.2)REC Non Farm Payroll 0.45* (1.88) 0.8** (2.03) 0.76** (2.75) 0.07 (0.33)

ISM 0 (0.01) -0.22 (-0.65) -0.02 (-0.09) -0.41** (-2.19)Jobless Claims 0.07 (0.43) -0.58** (-2.3) -0.42** (-2.39) -0.01 (-0.04)US CPI MoM 0.37** (2.18) 0 (0.02) 0.02 (0.08) -0.22 (-1.41)US Retail Sales 0.15 (1.06) -0.41* (-1.75) -0.02 (-0.15) -0.1 (-0.78)Fed Target Rate -0.4** (-2.36) -0.01 (-0.03) 0.1 (0.52) 0.02 (0.1)

US GDP 0.53 (1.08) 1.16 (1.45) 1.1* (1.95) -0.05 (-0.11)ZEW Economic Sent. -0.7** (-2.96) -1.23** (-3.19) -0.85** (-3.16) 0.12 (0.58)

IFO Expectations -0.73** (-3.35) -0.88** (-2.46) -0.82** (-3.28) -0.36* (-1.81)EMU CPI Flash Estim. -0.8** (-3.55) -0.73** (-1.96) -0.14 (-0.54) -0.36* (-1.78)

EMU GDP 0.24 (0.39) 0.71 (0.71) -1.05 (-1.5) -0.48 (-0.88)France Business Conf. 0.4** (2.12) 0.81** (2.64) 0.41* (1.89) 0.53** (3.11)ECB Refinancing Rate 0.29** (2.14) 0.48** (2.2) 0.54** (3.53) -0.05 (-0.4)

China CPI YoY -0.16 (-0.43) -0.08 (-0.13) 0.17 (0.4) -0.28 (-0.84)China Industrial Prod. -0.37 (-1.4) -0.64 (-1.48) -0.04 (-0.14) 0.04 (0.19)

China PMI -0.22 (-1.46) 0.32 (1.28) 0.29* (1.67) -0.59** (-4.26)EXP Non Farm Payroll 0.09 (0.91) 0 (0.01) 0.09 (0.81) -0.2** (-2.29)

ISM 0.11 (1.05) 0.18 (1.11) 0.28** (2.38) 0.07 (0.77)Jobless Claims 0.04 (0.4) -0.09 (-0.48) -0.16 (-1.25) -0.05 (-0.49)US CPI MoM 0.11 (1.05) -0.05 (-0.29) -0.1 (-0.8) 0.13 (1.37)US Retail Sales -0.01 (-0.04) -0.01 (-0.05) 0.03 (0.15) -0.09 (-0.61)Fed Target Rate -0.06 (-0.28) 0.18 (0.51) 0 (0.01) 0 (0.01)

US GDP 0.1 (0.56) 0.1 (0.34) 0.12 (0.58) -0.02 (-0.12)ZEW Economic Sent. -0.21 (-1.6) 0.09 (0.44) 0 (-0.02) 0.07 (0.6)

IFO Expectations 0.25* (1.65) 0.36 (1.43) 0.09 (0.53) 0.28** (2.01)EMU CPI Flash Estim. -0.2 (-1.51) -0.04 (-0.17) -0.13 (-0.86) 0.03 (0.27)

EMU GDP 0.03 (0.15) 0.04 (0.12) -0.33 (-1.47) -0.04 (-0.22)France Business Conf. 0.1 (0.75) -0.1 (-0.47) 0.11 (0.68) -0.07 (-0.61)ECB Refinancing Rate 0.03 (0.19) -0.03 (-0.12) 0.03 (0.16) -0.14 (-0.91)

China CPI YoY -0.07 (-0.65) 0.06 (0.33) 0.05 (0.35) -0.09 (-0.83)China Industrial Prod. 0.09 (0.84) -0.05 (-0.26) -0.08 (-0.64) 0.05 (0.48)

China PMI 0.36 (0.88) -0.33 (-0.5) 0.35 (0.75) 0.34 (0.92)

Note: Expansion and Recession periods are taken from the NBER Business Cycle Dating Committee. T-stats are

between brackets. Bold figures indicate statistically significant figures at a 10% risk level.

• Commentez le Tableau 1. Degagez les principales conclusions economiques en fonction du type desous-indice GSCI et du type de news.

2


Table 2: Performance analysis - Balanced Portfolio with commodities

Loadings Agri. Energy Ind. Metals Prec. Metals S&P 500 US BondsStatic -0.10 0.05 0.19 0.14 0.17 0.98

Strong exp. Reg. 1 0.05 0.08 0.29 0.04 0.42 0.08Slowdown Reg. 2 -0.28 0.04 -0.08 0.03 -0.18 1.37

Strong Crisis Reg. 3 -0.13 0.00 0.07 0.03 -0.38 0.51Stalling Reg. 4 0.02 0.15 0.25 0.24 0.12 0.46

Medium Expans. Reg. 5 -0.07 0.04 -0.06 0.09 0.22 0.98

Eq.-Bds-Comdty Static Eq.-Bds-Comdty DynamicAvg. Return 13.71% 17.02%Volatility 10.03% 10.46%

Sharpe Ratio 1.37 1.63

Note: PnL stands for Profits and Losses, Eq.-Bds-Comdty Static for the structural view of investing strategy inequities/bonds/commodities, and Eq.-Bds-Comdty Dynamic for the regime-switching view of investing strategy inequities/bonds/commodities.

Figure 1: Equity-Bonds-Commodity Allocation

1985 1990 1995 2000 2005 2010

010

0020

0030

0040

0050

0060

0070

00

Equity − Bonds − Commodity allocation

Single regime5 Regimes

• Commentez le Tableau 2. Rappelez l’interet d’investir dans les marches de matieres premieres en fonctiondu cyle des affaires.

• Commentez la Figure 1. Concluez sur les gains obtenus avec un portefeuille diversifie d’actions, obligationset matieres premieres.

3

Exercice II (4 points)


• Quelle(s) raison(s) economique(s) pouvez-vous enoncer concernant l’existence d’un lien entre marchesagricoles (soja) et energie (petrole)?


Table 3: Johansen Cointegration Test Results between Soybean and WTI

1993-2011 Max. Eigen. 10% 5% 1%r ≤ 1 4.96 10.49 12.25 16.26r = 0 8.71 16.85 18.96 23.65

1993-2000 Max. Eigen. 10% 5% 1%r ≤ 1 3.72 10.49 12.25 16.26r = 0 7.88 16.85 18.96 23.65

2000-2011 Max. Eigen. 10% 5% 1%r ≤ 1 5.13 10.49 12.25 16.26r = 0 8.74 16.85 18.96 23.65

• Commentez le Tableau 3. Que pouvez-vous en conclure concernant l’existence d’une relation de cointegrationentre les series de prix du soja et du WTI?

4


Table 4: Lutkepohl et al. (2004) Cointegration Test Results with Structural Break for Soybean and WTI

1993-2011 Max. Eigen. 10% 5% 1%r ≤ 1 6.50 5.42 6.79 10.04r = 0 23.69 13.78 15.83 19.85

Table 5: VECM Results with Structural Break (1993-2011) for Soybean and WTI

Err. Correction TermWTI 1

Soybean -0.609

VECM ∆WTI ∆SoybeanECT -0.011 -0.001(t.stat) (-4.16) (-0.67)∆WTI -0.048 -0.055(t.stat) (-2.52) (-3.99)

∆Soybean 0.069 0.03(t.stat) (2.59) (1.55)

• Commentez les Tableaux 4 et 5. En quoi vos resultats sont-ils differents de la question precedente?

5


Figure 2: Cointegration relationship for the 1993-2011 full period between Soybean and WTI

1995 2000 2005 2010

−1.

5−

1.0

−0.

50.

00.

5


• Commentez la Figure 2. Detaillez la stabilite de la relation de cointegration ainsi que la date du break.

6

Commodity Markets

Examen Partiel




Lundi 22 avril 2013

Duree: 2h



Question de cours (2,5 points)

• Decrivez precisement ce que mesurent les news suivantes: ‘ISM’, ‘IFO Expectations’ et ‘China PMI’.

• Quelle(s) raison(s) economique(s) pouvez-vous trouver pour expliquer l’existence de relations de long-terme entre deux (ou plus) matieres premieres?

1

Exercice I (2,5 points)

Soit l’equation suivante:

diffcert = α0 + β1diffeuat + β2diffbrentt + β3diffgast + β4diffcoalt + εt (1)

avec diffcert le rendement du prix mondial du carbone (Certified Emissions Reduction), diffeuat le rende-ment du prix europeen du carbone (European Union Allowance), diffbrentt le rendement du prix du petrole,diffgast le rendement du prix du gaz, et diffcoalt le rendement du prix du charbon. α0 represente la constante.βj (j = 1, . . . , 4) sont des coefficients. εt capture le terme d’erreur.

Figure 1: Regression OLS: les determinants du prix CER

• Commentez la Figure 1 en fonction du signe et de la significativite statistique de chaque parametre.Commentez egalement les tests de validation du modele.

2

Figure 2: Fonction d’autocorrelation: Residus du modele OLS sur CER

0 2 4 6 8 10 12 14 16 18 20−0.2

0

0.2

0.4

0.6

0.8

Lag

Sam

ple

Aut

ocor

rela

tion

Residuals Sample Autocorrelation Function (ACF)

Figure 3: QQ Plot: Residus du modele OLS sur CER

−4 −3 −2 −1 0 1 2 3 4−0.25

−0.2

−0.15

−0.1

−0.05

0

0.05

0.1

Standard Normal Quantiles

Qua

ntile

s of

Inpu

t Sam

ple

QQ Plot of Sample Data versus Standard Normal

• Commentez les Figures 2 et 3.

• Degagez les principales conclusions concernant les fondamentaux du prix mondial du carbone CER.Que manque-t-il a cette analyse selon vous?

3

Exercice II (2,5 points)

Soit le modele AR(1)-GARCH(1-1) suivant:

diffzinct = φ0 + φ1diffzinct−1 + βfomct + εt (2)

σ2t = ω + αε2t−1 + θσ2

t−1 (3)

avec diffzinct le rendement du prix du zinc, diffzinct−1 le processus auto-regressif d’ordre 1, fomc la newsU.S. relative aux meetings du Federal Open Market Committee, φ0 la constante de l’equation de la moyenne,φ1 et β des parametres, et εt le terme d’erreur. La deuxieme equation represente la specification GARCH(1,1)pour la prise en compte de la variance non-constante.

Figure 4: Modele GARCH: Impact des news sur les metaux

• Commentez chaque parametre estime dans la Figure 4, ainsi que les tests de diagnostic. Pouvez-vousidentifier une influence de la variable de politique monetaire U.S. sur le rendement de la matiere premiere?

4

Figure 5: Innovations et Sigmas: Estimations tirees du modele GARCH

0 50 100 150 200 250 300 350 400−600

−400

−200

0

200

400

600

800Innovations

Inno

vatio

n

0 50 100 150 200 250 300 350 4000

50

100

150

200

250

300

350

400Conditional Standard Deviations

Sta

ndar

d D

evia

tion

Figure 6: Fonction d’autocorrelation: Residus du modele GARCH sur zinc

0 2 4 6 8 10 12 14 16 18 20−0.2

0

0.2

0.4

0.6

0.8

Lag

Sam

ple

Aut

ocor

rela

tion

Innovations: Sample Autocorrelation Function (ACF)

• Commentez les Figures 5 et 6.

• Concluez sur la validite du modele AR(1)-GARCH(1,1) estime.

5

Exercice III (2,5 points)

Soit la Figure 7 des series de prix brutes de l’huile de soja (SoybeanOil) et de l’huile de palme (PalmOil):

Figure 7: Graphique des series brutes: SoybeanOil et PalmOil

0 50 100 150 200 250 300 350 4000

500

1000

1500

Soybean OilPalm Oil

• D’apres vous, ces series peuvent-elles etre candidates a une analyse de cointegration? Justifiez votrereponse.

6

Figure 8: Tests de cointegration de Johansen: SoybeanOil et PalmOil

• Commentez la Figure 8. Que pouvez-vous en conclure concernant l’existence d’une relation de cointegrationentre les series SoybeanOil et PalmOil? Mentionnez clairement le rang r de la cointegration obtenu.

7

Figure 9: Modele a correction d’erreurs: SoybeanOil et PalmOil

• Commentez les resultats du modele a correction d’erreurs (ECM) dans la Figure 9. Pouvez-vous identifierune variable moteur dans l’ajustement vers l’equilibre de long-terme?

8

Figure 10: Relation de Cointegration: SoybeanOil et PalmOil

0 50 100 150 200 250 300 350 400−7000

−6000

−5000

−4000

−3000

−2000

−1000Cointegration Relationship between Soybean Oil and Palm Oil

• Commentez la Figure 10. Pouvez-vous ecrire l’equation qui a permis d’aboutir a ce graphique? Detaillezla stabilite de la relation de cointegration. Pouvez-vous valider ce modele?

9

Commodity Markets

Examen Partiel




12 mai 2014

Duree: 2h



1

1 Exercice I (10 points)

Dans l’article intitule ‘Do FOMC minutes matter to markets? An intraday analysis of FOMC minutes releaseson individual equity volatility and returns’ (Review of Financial Economics), Jubinski et Tomljanovich (2013)etudient l’impact des news sur la rentabilite et la volatilite des cours boursiers CRSP entre 2006 et 2007. Lesnews etudiees concernent le Board of Governors of the Federal Reserve Open Market Committee (FOMC).

La specification econometrique est la suivante:

Rt = µ+

2∑j=1

(ρj Rt−j) + η0 REL+

1∑t=1

2(ηi REL Dt) + εt (1)

avec µ la constante, ρ1 et ρ2 des processus AR(1) et AR(2), REL une variable dummy egale a 1 lors de lanews et 0 sinon, Dt une variable dummy capturant 12 intervalles de temps encadrant de 30 minutes avant/apresl’annonce de la news a 02:00 PM precises, et εt le terme d’erreur.


Commentez les resultats de la Figure 1, dans laquelle se trouvent les resultats d’estimation pour les 12 intervallesde temps.

Identifier precisement les variables estimees, leur significativite statistique, ainsi que leur interpretationeconomique.


Commentez les resultats de la Figure 2, qui represente la volatilite des coefficients estimee a partir d’un modeleGARCH.

Preciser ce que vous observez, ainsi que vos principales conclusions.

2

Figure 1: Jubinski et Tomljianovich (2013): Resultats de l’impact des news FOMC

3

Figure 2: Jubinski et Tomljianovich (2013): Graphs des coefficients estimes

4

Exercice II (10 points)

Dans l’article intitule ‘Is gold the best hedge and a safe haven under changing stock market volatility’ (Reviewof Financial Economics), Hood et Malik (2013) etudient les proprietes des metaux precieux (or, argent, platine)en tant que valeur refuge (compare aux marches actions) sur la periode Novembre 1995-Novembre 2010.

Le modele econometrique utilise est le suivant:

Rasset,t = a+ btRstock,t + εt (2)

bt = c0 + c1D(Rstockq10 + c2D(Rstockq5) + c3D(Rstockq1) (3)

ht = ω + αε2t−1 + βht−1 (4)

avec Rasset,t la rentabilites des actifs de metaux precieux (or, argent, platine) ou de marches action (VIX),εt le terme d’erreur, D(. . .) une variable dummy capturant les forts declins des marches boursiers valant 1 au-dessus d’un certain seuil et 0 sinon, q10 [q5] {q1} les 0.10 [0.05] {0.01} quantiles de la distribution des rendementsdu marche boursier. L’equation (4) represente un GARCH(1,1).

Si les coefficients de l’equation (3) sont statistiquement significatifs et negatifs, l’actif considere est un refugede valeur.


Commentez les resultats de la Figure 3, dans laquelle se trouvent les resultats d’estimation pour les refuges devaleur.

Identifier precisement les variables estimees, leur significativite statistique, ainsi que leur interpretationeconomique.


Commentez les resultats de la Figure 4, qui represente les couples de risque/rendement en ajoutant soit de l’or,soit du VIX dans un portefeuille compose de S&P500.

Preciser ce que vous observez, ainsi que vos principales conclusions.

5

Figure 3: Hood et Malik (2013): Resultats de la strategie d’estimation des valeurs refuges

6

Figure 4: Hood et Malik (2013): Graphs des portefeuilles diversifies

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