Fluctuation in commodity prices is a significant and timely issue to be studied. This study is to examine the impact of monetary policy and other macroeconomic shocks on the dynamics of agricultural commodity prices. The major contributions of this study are twofold. First, unlike other studies that use indexes, this study analyzes the commodities individually, affording the inclusion of commodity-specific fundamentals such as the level of inventory-an important determinant of commodity price-in a structural VAR framework. Second, it exploits a rich data set of agricultural commodity prices which includes commodities that are usually overlooked in the literature, and extracts a common factor using the dynamic factor model to understand the extent of comovement of the prices and to gauge the extent to which macroeconomic shocks drive the "comovement" in a factor-augmented VAR (FAVAR) framework. The findings show that monetary policy, global economic conditions, and the U.S. dollar exchange rates play an important role in the dynamics of agricultural commodity prices.JEL classifications: E32, E43, E52, Q02 14 Another reason for keeping the baseline model small is to identify our 'slow-to-fast' strategy precisely.

The impact of skewness in the hedger's objective function is tested using a model of hedging derived from a third-order Taylor Series approximation of expected utility. To determine the effect of price skewness upon hedging and speculation, analytical results are derived using an example of cotton storage. Findings suggest that when forward risk premiums and price skewness in the spot asset have opposite signs, speculation increases relative to the mean-variance model. When the signs are identical, speculation will decrease, contradicting findings of mean-variance models.

Reduced-rank restrictions can add useful parsimony to coefficient matrices of multivariate models, but their use is limited by the daunting complexity of the methods and their theory. The present work takes the easy road, focusing on unifying themes and simplified methods. For Gaussian and non-Gaussian (GLM, GAM, mixed normal, etc.) multivariate models, the present work gives a unified, explicit theory for the general asymptotic (normal) distribution of maximum likelihood estimators (MLE). MLE can be complex and computationally hard, but we show a strong asymptotic equivalence between MLE and a relatively simple minimum (Mahalanobis) distance estimator. The latter method yields particularly simple tests of rank, and we describe its asymptotic behavior in detail. We also examine the method's performance in simulation and via analytical and empirical examples.

Digital currencies, such as Bitcoin, have emerged as an alternative form of money, untethered to traditional money and largely unregulated. As such, digital currency represents a wild frontier for investors who might otherwise be shopping for gold or foreign currencies, with serious risks. The present work considers digital currency from a traditional asset pricing perspective. Setting aside risks of seller fraud or currency theft, we examine fluctuation and systematic risk in the price of Bitcoin. From this perspective, Bitcoin does not appear to carry much systematic risk --despite its high volatility --and so is a reasonable candidate for inclusion in investors' portfolios. Some illustrative examples suggest that the optimal amount of Bitcoin to include in investor portfolios may be tiny or instead substantial -as high as 21 percent of total financial assets.

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