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.