In assessing drivers of commodity prices and volatility at this stage of the current super-cycle in commodities (year 12 of a projected 25), it is vital to understand that production cost is a fundamental. Moreover, marginal production costs are among the most powerful drivers of commodity...

The paper develops a price discovery model for commodity futures markets that accounts for two forms of limits to arbitrage caused by transaction costs and noise trader risk. Four market regimes are identified: (1) effective arbitrage, (2) transaction costs but no noise trader risk, (3) no...

To compare the impact of fundamental news with the publication of traders' positions in an event study framework, a generalized autoregressive conditional heteroscedasticity (GARCH) model with t-distributed error terms is applied to corn, soybean, and wheat futures returns from January 1996 to...

The COVID-19 pandemic induced numerous supply chain shocks in U.S. agricultural markets though few empirical studies have sought to disentangle commodity price impacts caused by unique changes in food and non-food agricultural product demand. Using a data-modified version of the...

We use weekly survey data on short-term and medium-term sentiment of German investors in order to study the causal relationship between investors' mood and subsequent stock price changes. In contrast to extant literature for other countries, a tri-variate vector autoregression for short-run...

We propose a new approach to model high and low frequency components of equity correlations. Our framework combines a factor asset pricing structure with other specifications capturing dynamic properties of volatilities and covariances between a single common factor and idiosyncratic returns....

We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix block-wise and regularize it. A data-driven...

We introduce a regularization and blocking estimator for well-conditioned high-dimensional daily covariances using high-frequency data. Using the Barndorff-Nielsen, Hansen, Lunde, and Shephard (2008a) kernel estimator, we estimate the covariance matrix block-wise and regularize it. A data-driven...

This paper examines the effect of macroeconomic releases on stock market volatility through a Poisson-Gaussian-GARCH process with time varying jump intensity, which is allowed to respond to such information. It is found that the day of the announcement, per se, has little impact on jump...