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Browsing Publications by Author "Energy Economics, publisher"

Now showing 1 - 5 of 5
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    A model for energy pricing with stochastic emission costs
    (Colorado State University. Libraries, 2009-07-23) Elliott, Robert J., author; Lyle, Matthew R., author; Miao, Hong, author; Energy Economics, publisher
    We use a supply-demand approach to value energy products exposed to emission cost uncertainty. We find closed form solutions for a number of popularly traded energy derivatives such as: forwards, European call options written on spot prices and European Call options written on forward contracts. Our modeling approach is to first construct noisy supply and demand processes and then equate them to find an equilibrium price. This approach is very general while still allowing for sensitivity analysis within a valuation setting. Our assumption is that, in the presence of emission costs, traditional supply growth will slow down causing output prices of energy products to become more costly over time. However, emission costs do not immediately cause output price appreciation, but instead expose individual projects, particularly those with high emission outputs, to much more extreme risks through the cost side of their profit stream. Our results have implications for hedging and pricing for producers operating in areas facing a stochastic emission cost environment.
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    An examination of the flow characteristics of crude oil: evidence from risk-neutral moments
    (Colorado State University. Libraries, 2015-10-10) Chatrath, Arjun, author; Miao, Hong, author; Ramchander, Sanjay, author; Wang, Tianyang, author; Energy Economics, publisher
    This paper examines the information content of risk-neutral moments to explain crude oil futures returns. Implied volatility and higher moments are extracted from observed crude oil option prices using a model-free implied volatility framework and the Black–Scholes model. We find a tenuous and time-varying association between returns and implied volatility and its innovations. Specifically, changes in implied volatility are found to be meaningfully associated with crude returns only over the period spanning the recent financial crisis. The results lead us to conclude that crude oil prices are determined primarily in a flow demand/supply environment. Finally, we document that oil risk is priced into the cross-section of stock returns in the oil and transportation sectors.
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    Crude oil moments and PNG stock returns
    (Colorado State University. Libraries, 2014-04-24) Chatrath, Arjun, author; Miao, Hong, author; Ramchander, Sanjay, author; Energy Economics, publisher
    We examine the risk-neutral moments of crude oil and their relationship to stock returns in the Petroleum and Natural Gas (PNG) industry. We find substantial overlaps in the association between returns and S&P 500- and crude oil higher moments. Net of these overlaps, PNG stocks share a significant negative relationship with crude volatility and positive relationships with crude skewness and kurtosis. Large cap stocks and those with a history of hedging exhibit negative loadings on crude volatility. However, after controlling for S&P 500- and crude oil returns and their risk-neutral moments, there is little evidence that PNG stocks systematically and significantly price either S&P 500- or crude oil volatility. We document a weak pricing of crude skewness, but find no evidence for the pricing of the implied higher moments of market returns.
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    Influential factors in crude oil price forecasting
    (Colorado State University. Libraries, 2017-07-12) Miao, Hong, author; Ramchander, Sanjay, author; Wang, Tianyang, author; Yang, Dongxiao, author; Energy Economics, publisher
    This paper identifies factors that are influential in forecasting crude oil prices. We consider six categories of factors (supply, demand, financial market, commodities market, speculative, and geopolitical) and test their significance in the context of estimating various forecasting models. We find that the Least Absolute Shrinkage and Selection Operator (LASSO) regression method provides significant improvements in the forecasting accuracy of prices compared to alternative benchmarks. Relative to the no-change and futures-based models, LASSO forecasts at the 8-step ahead horizon yield significant reductions in Mean Squared Prediction Error (MSPE), with MSPE ratios of 0.873 and 0.898, respectively. We also document substantial improvements in forecasting performance of the factor-based model that employs only a subset of variables chosen by LASSO. Finally, the time-varying nature of the relationship between factors and oil prices is used to explain recent movements in crude oil prices.
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    Price discovery in crude oil futures
    (Colorado State University. Libraries, 2014-09-15) Elder, John, author; Miao, Hong, author; Ramchander, Sanjay, author; Energy Economics, publisher
    This study examines price discovery among the two most prominent price benchmarks in the market for crude oil, WTI sweet crude and Brent sweet crude. Using data on the most active futures contracts measured at the one-second frequency, we find that WTI maintains a dominant role in price discovery relative to Brent, with an estimated information share in excess of 80%, over a sample from 2007 to 2012. Our analysis is robust to different decompositions of the sample, over pit-trading sessions and non-pit trading sessions, segmentation of days associated with major economic news releases, and data measured to the millisecond. We find no evidence that the dominant role of WTI in price discovery is diminished by the price spread between Brent that emerged in 2008.