Commodity Modeling and Pricing E-Book

Peter V. Schaeffer

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Finance Editions
• Sprache: Englisch

Seitenzahl: 469

Veröffentlichungsjahr: 2008

Beschreibung

Commodity Modeling and Pricing provides extensions and applications of state-of-the-art methods for analyzing resource commodity behavior. Drawing from the seminal work of Professor Walter Labys on the development of econometric methods for forecasting commodity prices, this collection of essays features expert contributors ranging from practitioners in private industry, public sector, and nongovernmental organizations to scholars in higher education–all of whom were Labys's former students or collaborators. Filled with in-depth insights and expert advice, Commodity Modeling and Pricing contains the information you need to excel in this demanding environment.

Leseprobe

Table of Contents

Title Page

Copyright Page

Dedication

Preface

Acknowledgements

PART One - Dynamics of Commodity Price Behavior

CHAPTER 1 - Indirect Inference and Long Memory A New Truncated-Series ...

INTRODUCTION

ALMOST SURE CONSISTENCY OF THE NLS ESTIMATOR FROM OUR TRUNCATED MODEL

IDENTIFICATION AND ESTIMATION OF THE TRUNCATED LONG MEMORY PROCESS

INFORMAL PROOF OF THE CONVERGENCE OF THE ABOVE ESTIMATION PROCEDURE

APPLICATIONS

CONCLUSIONS

APPENDIX: PROOF OF CONSISTENCY OF THE ESTIMATOR D*

REFERENCES

CHAPTER 2 - Procyclicality of Primary Commodity Prices A Stylized Fact?

INTRODUCTION

COMMODITY PRICES AND BUSINESS CYCLES

EMPIRICAL PROCEDURE

EMPIRICAL EVIDENCE

CONCLUSIONS

REFERENCES

CHAPTER 3 - Nonlinear Features of Comovements between Commodity Prices and Inflation

INTRODUCTION

BACKGROUND

HRISTU-VARSAKELIS AND KYRTSOU NONLINEAR GRANGER CAUSALITY TEST (2006)

DATA DESCRIPTION AND EMPIRICAL RESULTS

CONCLUSIONS

APPENDIX: PRICE SERIES USED

REFERENCES

CHAPTER 4 - The Oil Price and the Dollar Reconsidered

INTRODUCTION

PREVIOUS RESEARCH

EVALUATING THE COINTEGRATING RELATIONSHIP

EVALUATING THE CAUSAL RELATIONSHIP

CONCLUSIONS

REFERENCES

PART Two - Inventory Dynamics and Price Behavior

CHAPTER 5 - Time-Varying Ratios of Primary and Scrap Metal Prices Importance ...

INTRODUCTION

PRIMARY AND SCRAP PRICE BEHAVIOR

PRIMARY-TO-SCRAP PRICE RATIOS

MODELING PRICE RATIOS

EMPIRICAL RESULTS

CONCLUSIONS

REFERENCES

CHAPTER 6 - Metal Prices and the Supply of Storage

INTRODUCTION

INVENTORY BEHAVIOR AND PRICES

THEORY OF STORAGE

EMPIRICAL STORAGE CURVE

COINTEGRATION AND CAUSALITY

CONCLUSIONS

REFERENCES

CHAPTER 7 - Testing for Temporal Asymmetry in the Metal Price-Stock Relationship

INTRODUCTION

STOCKS AND MARKET EQUILIBRIUM

ECONOMETRIC METHODOLOGY

EMPIRICAL TESTS FOR THRESHOLD COINTEGRATION

EMPIRICAL TESTS FOR CAUSALITY AND IMPLICATIONS

CONCLUSIONS

APPENDIX: THREE ASYMPTOTICALLY EQUIVALENT TESTS FOR GRANGER-CAUSALITY

REFERENCES

CHAPTER 8 - Do Fluctuations in Wine Stocks Affect Wine Prices?

INTRODUCTION

BACKGROUND

INVENTORY AND PRICE BEHAVIOR

TESTING FOR TRENDS

COINTEGRATION BETWEEN STOCKS AND PRICES

VECTOR AUTOREGRESSION AND IMPULSE RESULTS

CONCLUSIONS

REFERENCES

WINE DATA REFERENCES

PART Three - Dynamics of Resource Markets

CHAPTER 9 - Dynamic Quadratic Programming in Process Control

INTRODUCTION

PROCESS CONTROL MODELS COMPARED

CONCLUSIONS

REFERENCES

CHAPTER 10 - Pollution Taxes and Price Control in the U.S. Coal Market A Rent ...

INTRODUCTION

SMITH’S RENT-MINIMIZATION SPATIAL EQUILIBRIUM MODEL

SIMULATED RESULTS OF THE U.S. COAL MARKET

CONCLUSIONS

REFERENCES

CHAPTER 11 - A Forecasting Simulation of Coal in Indonesia’s Energy Future

INTRODUCTION

CHRONOLOGY OF COAL DEVELOPMENT

TECHNO-ECONOMIC EVALUATION OF COAL’S POTENTIAL

MACROECONOMIC IMPACTS OF FUEL DEVELOPMENT

CONCLUSIONS

REFERENCES

CHAPTER 12 - Structural Decomposition Analysis of Changes in Material Demand in ...

INTRODUCTION

REVIEW OF THE LITERATURE

CONCEPTUAL FRAMEWORK

ESTIMATING EQUATIONS

APPLICATION

CONCLUSIONS

REFERENCES

PART Four - Environmental Resource Dynamics

CHAPTER 13 - Linking Trade and the Environment in China

INTRODUCTION

TRADE AND THE ENVIRONMENT IN CHINA

MODEL SPECIFICATION AND ESTIMATION

DATA AND VARIABLES

MODEL SIMULATION

RESULTS AND DISCUSSIONS

IMPACTS OF AN INCREASE IN TRADE VALUES

CONCLUSIONS

REFERENCES

CHAPTER 14 - Critical Needs in China’s Water Resources

INTRODUCTION

CHINA FACES A SEVERE WATER CRISIS

TECHNICAL SOLUTIONS ARE AVAILABLE AND ECONOMICALLY FEASIBLE

CREATING A WATER MARKET FOR THE LONG TERM

STRENGTHENING POLLUTION MANAGEMENT

CONCLUSIONS

REFERENCES

CHAPTER 15 - Public Input in Rural Land Preservation

INTRODUCTION

STANDARD STRENGTH OF PREFERENCE MODEL

MODEL ALLOWING FOR PREFERENCE ASYMMETRIES

DATA

EMPIRICAL RESULTS

CONCLUSIONS

REFERENCES

CHAPTER 16 - African Women in Mining Partnerships

INTRODUCTION

WOMEN’S INVOLVEMENT IN MINING

FACTORING IN WOMEN INTO GLOBAL PARTNERSHIPS

TRUE FOCUS REMAINS SUSTAINABLE DEVELOPMENT

CONCLUSIONS

REFERENCES

EPILOGUE

List of Contributors

Index

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Copyright © 2008 by Peter V. Schaeffer. All rights reserved.

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Library of Congress Cataloging-in-Publication Data:

Schaeffer, Peter V. Commodity modeling and pricing : methods for analyzing resource market behavior / Peter V. Schaeffer. p. cm. - (Wiley finance series) Includes index.

eISBN : 978-0-470-44743-7

1. Commodity exchanges-Mathematical models. 2. Primary commodities-Prices. 3. Prices-Mathematical models. I. Title. HG6046.S353 2008 332.64’4-dc22 2008022839

In MemoriamDaniel J. Gijsbers and Thomas F. Torries

Preface

Resource commodity markets are extremely important to agricultural producers, processors, consumers, foresters, and the wood processing industry and in the mineral and energy industries. They play a central role in economic development, international trade, and global economic and political stability. Globalization and the spectacular growth and industrial development of China, India, and other Southeast Asian countries have significantly added to total resource commodity demands and caused price increases. Additional pressures on prices have come from an increased use of agricultural commodities, particularly corn and sugar, for ethanol production, a recent development that has had significant impacts on food prices. In general, the closer integration of resource markets has been accompanied by growing economic and financial instability.

Resource-producing countries need export revenues: Brazil, from Amazon timber; Chile, from copper; Iraq, from crude oil; South Africa, from diamonds; and Argentina and the United States, from wheat. Resource-consuming countries need imports for industry: China, India, and Japan for raw materials and energy, the United States for crude oil. Because of cycles in consumption and production, these markets face high price instability. More than 60 commodity futures markets exist to ameliorate this problem. World commodity markets are again under scrutiny, and the economic analysis and modeling of these markets is as important as ever before.

This collection of chapters reflects the influence of Professor Walter C. Labys on the development of econometric methods for forecasting commodity prices. The contributors are former students and collaborators, ranging from practitioners in private industry, public sector and nongovernmental organizations, to scholars in higher education. They are from Australia, China, France, Indonesia, the Ivory Coast, Luxembourg, Tunisia, and the United States. Some of them came together in Morgantown, West Virginia, on the occasion of Professor Labys’s retirement from West Virginia University for a symposium showcasing the current state of the art in commodity price modeling and forecasting, while the others joined them later to produce this volume.

During his career, which spanned over 40 years, Professor Labys published 15 books, 150 research articles, and gave 130 invited lectures. His many honors and recognitions include being appointed the first Gunnar Myrdal Scholar by the United Nations in Geneva, receiving a Master Knighthood in the Brotherhood of the Vine in California, being named a Benedum Distinguished Scholar at West Virginia University, and garnering the William H. Miernyk Award for Career Scholarly Achievement by the Regional Research Institute at West Virginia University.

Professor Labys grew up in southwest Pennsylvania. He received his undergraduate education at Carnegie Tech, now Carnegie Mellon University, where he studied engineering and also took courses in painting and sculpture. He then earned a master’s degree in Economics at Harvard University. While attending a seminar presentation there, he met Professor Clive W.J. Granger, who would later become the 2003 Nobel laureate in economics. When Professor Granger moved to the University of Nottingham, Professor Labys followed him to complete a Ph.D. in Economics under his direction.

Shortly after receiving his Ph.D., he started work as a consultant for the Commodities Division at the World Bank, where a chance encounter with Alfred Maizels led to an invitation to join the United Nations Conference on Trade and Development in Geneva as a commodities specialist. At the time, the United Nations in Geneva was a virtual whirlwind of economic activity and research, and Professor Labys encountered many eventual Nobel laureates in Economics. He was coached through his first commodity model by Lawrence Klein, and made the acquaintance of Harry Johnston, Robert Mundell, James Meade, and Richard Stone.

Eventually Professor Labys moved back to the United States, opting for West Virginia University in Morgantown to be near his parents, rather than joining his doctoral advisor, Professor Granger, at the University of California in San Diego. His work continued to take him back to Europe, where he consulted with the United Nations in Geneva, the Food and Agriculture Organization in Rome, and the International Institute for Applied Systems Analysis in Vienna while maintaining academic affiliations with several French universities.

During his long career, Professor Labys served as advisor to many students both in the United States and abroad. His relationship with his doctoral students was characterized by strong support, with a willingness to help at any time and in any way possible. This collection of chapters is in part the result of such relationships.

W. PAUL LABYS CRA International, Salt Lake City, Utah

PETER V. SCHAEFFER West Virginia University, Morgantown, West Virginia

Acknowledgments

The Division of Resource Management and the Regional Research Institute financially supported the symposium that brought together the initial core of speakers and papers that led to the idea for this book. My West Virginia University colleagues Jerald J. Fletcher and Tim T. Phipps provided organizational support for the symposium, as did our secretary, Lisa Lewis. Randall W. Jackson, director of the Regional Research Institute, provided additional funding for technical support during the production of the manuscript. Walter Labys took pleasure and pride in this project and provided feedback and editorial advice on many issues and chapters. I benefited greatly from the advice and guidance of John Wiley & Sons’ Debra W. Englander, Executive Editor, Stacey Small, Editorial Assistant, Kelly O’Connor, Development Editor, and Michael Lisk, Senior Production Editor. Jacquelyn Strager created the base map of Indonesia that served as the basis for Figure 11.1 and Gloria Nestor assisted with Figure 14.1. Ika Rahmawati helped with translating statistical information from Indonesian into English. Weslie Boyd’s expert assistance was invaluable in getting the manuscript ready to meet the publisher’s guidelines.

Editing this book was interesting and rewarding, but also time consuming and at times, it interfered with family life. I therefore very gratefully acknowledge my wife’s love, support, and patience.

PETER V. SCHAEFFER

PART One

Dynamics of Commodity Price Behavior

At times the prices of many commodities display volatile behaviors. Since agricultural commodities and minerals, such as crude oil and metals, are among the fundamental inputs of our economies on the production and/or the consumption side, price volatility causes disruptions and can lead to crises. An improved understanding of the dynamics of price behavior is therefore highly desirable from a policy as well as from a consumer and supplier perspective.

Part One consists of four chapters, each written from a different point of view. In Chapter 1, the recently retired chief economist of Arcelor-Mittal and two colleagues from academia present a new method for estimating long memory processes from small samples, a common problem in industry, where forecasts frequently have to be made from very short series. This contribution provides a theoretically sound and interesting solution to a practical problem.

While the first chapter takes an industry and firm perspective, the second chapter analyzes time-series data to study the link between commodity price developments and business cycles. The question asked many times is whether commodity prices lead inflation or inflation leads commodity prices. The answer is not immediately visible from looking at the data, because trends can be obscured by short-run occurrences. This chapter’s analysis offers a method to uncover the true trend and provides evidence that, on balance, commodity prices are procyclical. The exceptions are the price of gold, which is countercyclical, and the price of sugar, which is acyclical.

Chapter 3 also studies the connection between inflation and commodity prices. The author uses a recently developed procedure to test the possible presence of nonlinearity in the comovement of commodity prices and the consumer price index. The results reveal interdependences between the different price series, with policy implications, for example, on how to combat inflation.

Chapter 4 also is focused on macroeconomic issues, but the issue of interest turns from domestic policy to the world market. The chapter deals with the relationship between the dollar and the oil price. The real price of the oil in every currency depends on a variety of factors, including OPEC policy. The price of crude oil is in U.S. dollars, but most of the imports of the largest oil-producing member countries originate in the euro zone or in Japan. Hence, the devaluation of the dollar lowers the purchase power of OPEC member countries, which they try to regain by adjusting the price of oil upward.

Together, these four chapters provide models, data, results, and insights that enhance our understanding of the dynamics of commodity price behavior. They use the most current models and techniques in time-series analysis and illustrate their application. The chapters complement each other by providing information at different levels of aggregation while dealing with the same general subject. Because of their mixed background in terms of professional experiences and geographic location, the authors also bring different perspectives to their respective tasks.

CHAPTER 1

Indirect Inference and Long Memory A New Truncated-Series Estimation Method

Armand Sadler, Jean-Baptiste Lesourd and Vêlayoudom Marimoutou

INTRODUCTION

Long-memory processes are an important and even fundamental advance in time-series modeling. More precisely, the so-called autoregressive fractionally integrated moving average (ARFIMA) model has been introduced by Granger and Joyeux (1980) and Hosking (1981). It is a generalization of the ARIMA model, which is a short memory process, by allowing the differencing parameter d to take any real value. The goal of this specification is to capture parsimoniously long-run multipliers that decay very slowly, which amounts to modeling long memories in a time series. ARFIMA processes, however, are associated with hyperbolically decaying autocorrelations, impulse response weights, and spectral density function exploding at zero frequency. As noted by Brockwell et al. (1998), while a long memory process can always be approximated by an ARMA(p, q) process, the orders p and q required to achieve a good approximation may be so large as to make parameter estimation extremely difficult. In any case, this approximation is not possible with small samples.

ARFIMA processes are defined as follows in their canonical form: where d ∈ (-0.5, 0.5) is the fractional difference operator and μ can be any deterministic function of time. If μ is zero, this process is called fractionally differenced autoregressive moving average (e.g., Fuller (1996)). The iid (independent and identically distributed) assumption is the strongest assumption; it implies mixing, that is, conditions on the dependence of the sequence. For a stationary sequence, mixing implies ergodicity (restrictions on the dependence of the sequence). Ergodic processes are not necessarily mixing; mixing conditions are stronger than ergodicity. For details, see White (1984) and Rosenblatt (1978).

For general overviews on long memory processes, surveys, and results, we refer the reader to Baillie (1996); Brockwell and Davis (1998); Fuller (1996); Gouriéroux and Monfort (1995); Gourieroux and Jasiak (1999); Hamilton (1994); Jasiak (1999, 2000); Lardic and Mignon (1999); Maddala and Kim (1998); and Sowell (1990) as well as to the discussions and comments by Bardet (1999), Bertrand (1999), Gourieroux (1999), Jasiak (1999), Lardic and Mignon (1999), Prat (1999), Renault (1999), Taqqu (1999), and Truong-Van (1999). Concerning recent research on the topic of long memory, we refer the reader to Andrews and Guggenberger (2003), Andrews and Sun (2004), and Davidson and Terasvirta (2002). Note also the presentation of a new stationarity test for fractionally integrated processes by Dolado, Gonzalo, and Mayoral (2002). Among the most important papers concerning estimation techniques for these ARFIMA model are Fox and Taqqu (1986), Geweke and Porter-Hudak (1983), Li and McLeod (1986), and Sowell (1992a). Tests for long memory across a variety of commodity spot and futures prices can be found in Barkoulas, Labys, and Onochie (1997, 1999) as well as in Cromwell et al. (2000).