This study investigates the effects of oil price shocks on volatility of selected agricultural and metal commodities. To achieve this goal, we decompose an oil price shock to its underlying components, including macroeconomics and oil specific shocks. The applied methodology is the structural vector autoregressive (SVAR) model and the time span is from April 1983 to December 2013. The investigation is divided into two subsamples, before and after 2006 for agricultures taking into account the 2006-2008 food crisis, and before and after 2008 for metals considering the recent global financial crisis. The validity of time divisions is confirmed by historical decomposition accomplishment. We find that, based on impulse response functions, the response of volatility of each commodity to an oil price shock differs significantly depending on the underlying cause of the shock for the both pre and post-crisis periods. moreover, according to variance decomposition the explanatory power of oil shocks becomes stronger after the crisis. The different responses of commodities are described in detail by investigating market characteristics in each period.

Linear sensor networks (LSNs) have recently attracted increasing attention due to the vast requirements on the monitoring and surveillance of a structure or area with a linear topology. However, there is little work on the network modeling and analysis based on a duty-cycling MAC protocol for LSNs. In this paper, we model a duty-cycling MAC with a pipelinedscheduling feature for an LSN, where each node is responsible for monitoring a certain area and can generate packets according to its sensed results. Based on the model, we analyze the network performance in terms of the system throughput, active time ratio per cycle of each node, and packet delivery latency. Through the extensive OPNET-based simulations, we validate the model and reveal the dependency of the network performance on various system parameters. Besides enabling the effective estimation of the protocol performance by using our model, we believe that our model and analysis could provide an insightful understanding on the behavior of a duty-cycling MAC protocol and aid its design and optimization for a multi-hop LSN.

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This paper provides an analysis of the link between the oil market and the U.S. stock market returns at the aggregate as well as industry levels. We empirically model oil price changes as driven by speculative demand shocks along with consumption demand and supply shocks in the oil market. We also take into account in our model all the factors that affect stock market price movements over and above the oil market, in order to quantify the pure effect of oil price shocks on returns. The results show that stock returns respond to oil price shocks differently, depending on the causes behind the shocks. Impulse response analysis suggests that consumption demand shocks are the most relevant drivers of the stock market return, relative to other oil market driven shocks. Industry level analysis is performed to control for the heterogeneity of the responses of returns to oil price changes. The results show that both cost side and demand side effects of oil price shocks matter for the responses of industries to oil price shocks. However, the main driver of the variation in industries' returns is the shock to aggregate stock market.

This paper provides an analysis of the link between the oil market and the U.S. stock market returns at the aggregate as well as industry levels. We empirically model oil price changes as driven by speculative demand shocks along with consumption demand and supply shocks in the oil market. We also take into account in our model all the factors that affect stock market price movements over and above the oil market, in order to quantify the pure effect of oil price shocks on returns. The results show that stock returns respond to oil price shocks differently, depending on the causes behind the shocks. Impulse response analysis suggests that consumption demand shocks are the most relevant drivers of the stock market return, relative to other oil market driven shocks. Industry level analysis is performed to control for the heterogeneity of the responses of returns to oil price changes. The results show that both cost side and demand side effects of oil price shocks matter for the responses of industries to oil price shocks. However, the main driver of the variation in industries' returns is the shock to aggregate stock market.