# Calculation of a 3:2:1 Crack Spread

### See here for the calculation.

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.

# March/April 2015 NG Calendar Spread -- Update

Below is an hourly chart of the March/April 2015 NG Calendar Spread.  It is at the contract lows with some below average weather coming this week.

# March/April 2015 Natural Gas Calendar Spread

The March (NGH5) minus April (NGJ5) 2015 calendar spread (aka the Widowmaker) is back down to \$0.20.  A hourly chart is below.  It looks like a good time to buy the spread, and wait for cold weather.

# New Working Paper: Parameter Variation & 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.

Abstract
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.

# March - April NG Spread is at Contract Lows

The spread has declined to \$0.21.  This shows market participants have a great amount of confidence that gas storage amounts will be sufficient to cover winter demand.

# EPA's Proposed Carbon Pollution Reductions by State

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.

The proposed reductions range from a maximum percent reduction of 71.82% in Washington state, to a minimum percent reduction of 10.58% in North Dakota.

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.