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.
This timely volume addresses three important recent trends in the internationalization of United States equity markets: extensive market integration through foreign investment and links among stock prices around the world; increasing securitization as countries such as Japan come to rely more than ever before on markets in equities and bonds at the expense of banks; and the opening of national financial systems of newly industrializing countries to international financial flows and institutions, as governments remove capital controls and other barriers. Eight essays examine such issues as the current extent of international market integration, gains to U.S. investors through international diversification, home-country bias in investing, the role of time and location around the world in stock trading, and the behavior of country funds. Other, long-standing questions about equity markets are also addressed, including market efficiency and the accuracy of models of expected returns, with a particular focus on variances, covariances, and the price of risk according to the Capital Asset Pricing Model.
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.
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.
We investigate how corporate stock returns respond to geopolitical risk in the case of South Korea, which has experienced large and unpredictable geopolitical swings that originate from North Korea. To do so, a monthly index of geopolitical risk from North Korea (the GPRNK index) is constructed using automated keyword searches in South Korean media. The GPRNK index, designed to capture both upside and downside risk, corroborates that geopolitical risk sharply increases with the occurrence of nuclear tests, missile launches, or military confrontations, and decreases significantly around the times of summit meetings or multilateral talks. Using firm-level data, we find that heightened geopolitical risk reduces stock returns, and that the reductions in stock returns are greater especially for large firms, firms with a higher share of domestic investors, and for firms with a higher ratio of fixed assets to total assets. These results suggest that international portfolio diversification and investment irreversibility are important channels through which geopolitical risk affects stock returns.
The April 2012 issue of the World Economic Outlook assesses the prospects for the global economy, which has gradually strengthened after a major setback during 2011. The threat of a sharp global slowdown eased with improved activity in the United States and better policies in the euro area. Weak recovery will likely resume in the major advanced economies, and activity will remain relatively solid in most emerging and developing economies. However, recent improvements are very fragile. Policymakers must calibrate policies to support growth in the near term and must implement fundamental changes to achieve healthy growth in the medium term. Chapter 3 examines how policies directed at real estate markets can accelerate the improvement of household balance sheets and thus support otherwise anemic consumption. Chapter 4 examines how swings in commodity prices affect commodity-exporting economies, many of which have experienced a decade of good growth. With commodity prices unlikely to continue growing at the recent elevated pace, however, these economies may have to adapt their fiscal and other policies to lower potential output growth in the future.
How much does speculation contribute to oil price volatility? We revisit this contentious question by estimating a sign-restricted structural vector autoregression (SVAR). First, using a simple storage model, we show that revisions to expectations regarding oil market fundamentals and the effect of mispricing in oil derivative markets can be observationally equivalent in a SVAR model of the world oil market à la Kilian and Murphy (2013), since both imply a positive co-movement of oil prices and inventories. Second, we impose additional restrictions on the set of admissible models embodying the assumption that the impact from noise trading shocks in oil derivative markets is temporary. Our additional restrictions effectively put a bound on the contribution of speculation to short-term oil price volatility (lying between 3 and 22 percent). This estimated short-run impact is smaller than that of flow demand shocks but possibly larger than that of flow supply shocks.
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 based on monthly data between 1970 and 2013. A uniform-spacings testing approach, a multivariate dynamic conditional correlation model and a rolling regression procedure are used to study the extent and the time-evolution of unconditional and conditional correlations. The results indicate that (i) the price returns of energy and agricultural commodities are highly correlated; (ii) the overall level of co-movement among commodities increased in recent years, especially between energy and agricultural commodities and in particular in the cases of maize and soybean oil, which are important inputs in the production of biofuels; and (iii) particularly after 2007, stock market volatility is positively associated with the co-movement of price returns across markets.
