I pushed a new development version of my EIAdata package to github. The main addition is a function 'wngsr' which will pull the latest Weekly Natural Gas Storage Report from the EIA. Instructions on how to install the development version are in the README file.
Here are my pdf slides for my presentation of 'Parameter Variation and the Components of Natural Gas Price Volatility'.
I have posted a new working paper of mine to the 'Working Paper' section. The paper, Parameter Variation & the Components of Natural Gas Price Volatility, hypothesizes that parameters linking natural gas returns to fundamental variables will tend to change as market participants learn. I therefore estimate the parameters using the Kalman filter. I also decompose conditional natural gas volatility into portions attributable to each variable. The abstract is below.
Estimating a static coefficient for a deseasoned gas storage or
weather variable implicitly assumes that market participants react
identically throughout the year (and over each year) to that variable.
In this analysis we model natural gas returns as a linear function of
gas storage and weather variables, and we allow the coefficients of this
function to vary continuously over time. This formulation takes into
account that market participants continuously try to improve their
forecasts of market prices, and this likely means they continuously
change the scale of their reaction to changes in underlying variables.
We use this model to also calculate conditional natural gas volatility
and the proportion of volatility attributable to each factor. We find
that return volatility is higher in the winter, and this increase is at-
tributable to increases in the proportion of volatility due to weather
and natural gas storage. We provide time series estimates of the chang-
ing proportion of volatility attributable to each factor, which is useful
for hedging and derivatives trading in natural gas markets.
My EIAdata R package is now available on CRAN. This means you can install the package with a simple,
install.packages("EIAdata") from within R.
A development version of the package is available on GitHub. This package gives you programmatic access, from within R, to over a million energy related time series available through the Energy Information Administration's API.
The EPA has recently announced a proposal to reduce carbon pollution. Specifically the EPA has set state targets for emission rates (the number of pounds of carbon emissions per megawatt hour of electricity produced). A summary of the percent reduction in proposed emission rate by state is below.
Of course, looking at percent reductions loses scale -- which is paramount in this case. If we look at total yearly proposed carbon reductions (in million metric tons) we see the largest reduction is by far in Texas (87 million metric tons per year) with Florida and Pennsylvania following.