This paper provides a comprehensive analysis of the degree of co-movement among the nominal price returns of 11 major energy, agricultural, and food commodities using monthly data between 1970 and 2013. The authors study the extent and the time evolution of unconditional and conditional correlations using a uniform-spacings testing approach, a multivariate dynamic conditional correlation model and a rolling regression procedure.
Commodities have become an important component of many investors' portfolios and the focus of much political controversy over the past decade. This book utilizes structural models to provide a better understanding of how commodities' prices behave and what drives them. It exploits differences across commodities and examines a variety of predictions of the models to identify where they work and where they fail. The findings of the analysis are useful to scholars, traders and policy makers who want to better understand often puzzling - and extreme - movements in the prices of commodities from aluminium to oil to soybeans to zinc.
Factor models have become the most successful tool in the analysis and forecasting of high-dimensional time series. This monograph provides an extensive account of the so-called General Dynamic Factor Model methods. The topics covered include: asymptotic representation problems, estimation, forecasting, identification of the number of factors, identification of structural shocks, volatility analysis, and applications to macroeconomic and financial data.
Amidst a sharp rise in commodity investing, many have asked whether commodities nowadays move in sync with traditional financial assets. The authors provide evidence that challenges this idea. Using dynamic correlation and recursive co-integration techniques, they found that the relation between the returns on investable commodity and U.S. equity indices has not changed significantly in the last fifteen years. The authors also find no evidence of any secular increase in co-movement between the returns on commodity and equity investments during periods of extreme returns.
Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical macroeconomics. These models have been developed to address the fact that most questions of interest to empirical macroeconomists involve several variables and must be addressed using multivariate time series methods. Many different multivariate time series models have been used in macroeconomics, but Vector Autoregressive (VAR) models have been among the most popular. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics reviews and extends the Bayesian literature on VARs, TVP-VARs and TVP-FAVARs with a focus on the practitioner. The authors go beyond simply defining each model, but specify how to use them in practice, discuss the advantages and disadvantages of each and offer tips on when and why each model can be used.
As commodity markets have continued their expansion an extensive and complex financial industry has developed to service them. This industry includes hundreds of participating firms, including asset managers, brokers, consultants, verification agencies and a myriad of other institutions. Universities and other training institutions have responded to this rapid expansion of commodity markets as well as their substantial future growth potential by launching specialized courses on the subject. The Economics of Commodity Markets attempts to bridge the gap between academics and working professionals by way of a textbook that is both theoretically informative and practical. Based in part on the authors’ teaching experience of commodity finance at the University Paris Dauphine, the book covers all important commodity markets topics and includes coverage of recent topics such as financial applications and intuitive economic reasoning. The book is composed of three parts that cover: commodity market dynamics, commodities and the business cycle, and commodities and fundamental value. The key original approach to the subject matter lies in a shift away from the descriptive to the econometric analysis of commodity markets. Information on market trends of commodities is presented in the first part, with a strong emphasis on the quantitative treatment of that information in the remaining two parts of the book. Readers are provided with a clear and succinct exposition of up-to-date financial economic and econometric methods as these apply to commodity markets. In addition a number of useful empirical applications are introduced and discussed. This book is a self-contained offering, discussing all key methods and insights without descending into superfluous technicalities. All explanations are structured in an accessible manner, permitting any reader with a basic understanding of mathematics and finance to work their way through all parts of the book without having to resort to external sources.
An Introduction to Wavelets and Other Filtering Methods in Finance and Economics presents a unified view of filtering techniques with a special focus on wavelet analysis in finance and economics. It emphasizes the methods and explanations of the theory that underlies them. It also concentrates on exactly what wavelet analysis (and filtering methods in general) can reveal about a time series. It offers testing issues which can be performed with wavelets in conjunction with the multi-resolution analysis. The descriptive focus of the book avoids proofs and provides easy access to a wide spectrum of parametric and nonparametric filtering methods. Examples and empirical applications will show readers the capabilities, advantages, and disadvantages of each method. - The first book to present a unified view of filtering techniques - Concentrates on exactly what wavelets analysis and filtering methods in general can reveal about a time series - Provides easy access to a wide spectrum of parametric and non-parametric filtering methods
Featuring new contributions by leading globalization scholars, this timely volume analyzes the organization, geography, politics, and power dynamics of international trade and production networks understood as global commodity chains.
This introduction to wavelet analysis 'from the ground level and up', and to wavelet-based statistical analysis of time series focuses on practical discrete time techniques, with detailed descriptions of the theory and algorithms needed to understand and implement the discrete wavelet transforms. Numerous examples illustrate the techniques on actual time series. The many embedded exercises - with complete solutions provided in the Appendix - allow readers to use the book for self-guided study. Additional exercises can be used in a classroom setting. A Web site offers access to the time series and wavelets used in the book, as well as information on accessing software in S-Plus and other languages. Students and researchers wishing to use wavelet methods to analyze time series will find this book essential.
