Asset Value, Interest Rates and Oil Price Volatility

Simulations from a standard two-region model where producers respond to changes in interest rates are better able to match observed data than an identical model without supply-side responses. This indicates that incorporating the supply-side behaviour of oil producers is quantitatively important when endogenously modeling oil prices. These results have two implications. First, adding supply-side responses can change the oil price/output relationship, which is a continuing topic of research interest. Second, if production is unable to adjust to interest rate changes, an important explanatory factor of oil price volatility may be missing.

Arbitrage and the Price of Oil

The model simulated in this paper shows that falling interest rates contribute to rising oil prices. This occurs because oil producers treat oil in the ground as an asset and attempt to arbitrage differences between its rate of return and the interest rate. When calibrated to match observed data over the last two decades, model results indicate that this arbitrage behaviour may have made the largest contribution to the pre-crisis boom in oil prices. Productivity driven growth shocks raise the oil price by about 70 percent, but this rises to 150 percent when falling interest rates are included.

Aggregate Productivity under an Energy-Based Approach

Obtaining reliable data on capital is a recurring challenge when estimating economy-wide productivity growth, especially for developing countries. In this paper I construct energy-based productivity series which use energy consumption instead of capital when making such estimates. I first show that—for the U.S. and select OECD countries—growth in the energy-based series are strongly correlated with other sources historically. I then estimate energy-based productivity growth for other OECD and non-OECD countries where data on capital and productivity is more limited

How important are real interest rates for oil prices?

Using a recursive vector autoregression (VAR), this paper considers the relation between the U.S. real interest rate and the real oil price. Theoretically, as outlined in Hotelling (1931) and Working (1949), a lower real interest rate results in reduced production and increased storage, implying a higher oil price. The results presented here show that the robustness of this relationship depends crucially on how the real interest rate is calculated, and the time-frame of the sample. Consistent with earlier studies, the oil price falls with an innovation to the ex-ante U.S. real interest rate. However, this is not true if the real interest rate is calculated ex-post. In this case, the oil price only falls in response to an innovation in short-term U.S. real interest rates (three months or less). Additionally, the response of the oil price to longer-term ex-ante U.S. real interest rates must include the period through 2006 for this relationship to appear. The oil price consistently responds to innovations in short-term rates throughout the entire sample. We draw two conclusions from the results. The first is that the oil price is consistently responsive to short-term U.S. real interest rates, underlying the importance of storage. Second, oil prices have become more responsive to longer-term U.S. real interest rates. The reasons behind this change are unclear and require further study.Oil price, Real interest rate, VAR, Hotelling, Storage

Testing for Explosive Behaviour in Relative Inflation Measures: Implications for Monetary Policy

In this paper we test for large deviations in headline measures of the price level relative to core measures using the recently proposed test of Phillips et al. (2011a). We find evidence of explosive behaviour in the headline price index of personal consumption expenditures (PCE) relative to the core PCE (less food and energy prices) on three occasions from 1982-2010. Two of these episodes correspond to energy supply shocks (OPEC price collapse of 1986 and Hurricane Katrina). The third one is during March 2008 through September 2008 which seems to be driven by both food and energy prices as these indices exhibit explosive behaviour. We also find evidence suggesting that inflation expectations behave differently under normal and explosive periods. In particular, unemployment and interest rates also help predict inflation expectations during explosive episodes relative to normal times. Furthermore, explosive episodes in the relative measure between headline and core inflation is found to be more important than the relative volatile periods implied by a Markov-switching model when studying inflation expectations. The findings of this paper suggest that explosive behaviour of headline versus core PCE should be taken into account when conducting monetary policy as it is a key determinant in consumers’ inflation expectations.Explosive behaviour, core inflation, relative measure, inflation expectations

A Repayment Model of House Prices Oil Price Dynamics in a Real Business Cycle Model

We show the importance of endogenous oil prices and production in the real business cycle framework. Endogenising these variables improves the model's predictions of business cycle statistics, oil related and non-oil related, relative to a situation where either is exogenous. This result is robust to the standard extensions (variable capacity utilisation and monopolistic competition) used in the literature. In particular, we first show that with either exogenous oil prices or production the standard real business cycle model and variants cannot match the oil-related and business cycle facts. In contrast, when both of these variables are endogenous, we can substantially improve the corresponding co-movements and slightly improve standard business cycle properties for consumption and investment.Oil price, two regions, variable capacity utilization

UML Modeling of Network Topologies for Distributed Computer System

Nowadays distributed computer systems have become very popular approach due to its availability at low cost and high performance computers, which are connected through a communication network. For connection of the distributed computer systems, network topologies are must for the communication lines. In the present paper a detailed study of network topologies is done for the distributed computer systems. A most popular Unified Modeling Language (UML) is used for designing the different network topologies. A comparative study is done for 2D Mesh, Torus, and Hypercube topologies and performance is evaluated after designing the UML Class, Sequence, and Activity diagrams for these topologies

Electricity Use as an Indicator of U.S. Economic Activity

We argue for the resurrection of an old idea: electricity use as an indicator of U.S. economic activity. Our analysis relies on associations–the 40-year correlation between growth rates in real GDP and electricity use can be as high as 89% –and intuition. Electricity use and economic conditions should move together. The vast majority of goods and services are still produced using electricity; services may require less electricity, but they still require some. Electricity use also has other strengths –it is broad-based and the data are available weekly, possibly hourly by 2015

A Network-based View of the U.S. Energy Sector

We describe portions of the U.S input-output tables through the tools of networks analysis—focusing on either energy intensive industries or those that are part of the separate and distinct energy sector. We first represent both energy intensive and energy sector industries visually through network diagrams for the years 1997, 2002, and 2007. Next, we show that the energy sector is generally more densely connected than either energy intensive industries or all industries over those years, and is more likely to have groups of three sub-sectors all linked as well. We then move to the level of individual industries within the broad sectors and find that energy intensive industries have the most in-coming connections on average for these tables. Energy sector ones have fewer, but the number grows over time, as do outgoing connections. Other measures of centrality—closeness and betweenness—vary over time for both the energy sector and energy intensive industries. Specifically, petroleum refining and electricity generation stand out for their centrality, drilling oil and gas wells for its lack of centrality