Coal has been an important source of energy in the USA for centuries. Coal prices can be quite uncertain and highly volatile, often experiencing large changes. Understanding the data-generating process of coal prices would seem critical, both from a market perspective and from a policy perspective. This study investigates the appropriate stochastic process underlying coal prices. Commonly assumed processes, such as geometric Brownian motion fail to properly account for the arrival of unanticipated information which inflicts rapid changes – or jumps – in energy markets. Such discontinuities can manifest ‘fat tails’ in the distribution of returns. To investigate the possibility of time-varying volatility, generalized autoregressive conditional heteroscedastic models are also incorporated into the analysis. We find compelling empirical evidence that discontinuities must not be ignored, with US coal prices experiencing jumps every few days. The result has implications for the potential closure of coal-fired plants in response to cheaper alternatives or climate-based regulations.
- Coal prices
- jump diffusion