Monte Carlo Valuation of natural gas investments

This paper deals with the valuation of energy assets related to natural gas. In particular, we evaluate a baseload Natural Gas Combined Cycle (NGCC) power plant and an ancillary instalation, namely a Liquefied Natural Gas (LNG) facility, in a realistic setting; specifically, these investments enjoy a long useful life but require some non-negligible time to build. Then we focus on the valuation of several investment options again in a realistic setting. These include the option to invest in the power plant when there is uncertainty concerning the initial outlay, or the option's time to maturity, or the cost of CO2 emission permits, or when there is a chance to double the plant size in the future. Our model comprises three sources of risk. We consider uncertain gas prices with regard to both the current level and the long-run equilibrium level; the current electricity price is also uncertain. They all are assumed to show mean reversion. The two-factor model for natural gas price is calibrated using data from NYMEX NG futures contracts. Also, we calibrate the one-factor model for electricity price using data from the Spanish wholesale electricity market, respectively. Then we use the estimated parameter values alongside actual physical parameters from a case study to value natural gas plants. Finally, the calibrated parameters are also used in a Monte Carlo simulation framework to evaluate several American-type options to invest in these energy assets. We accomplish this by following the least squares MC approach.

Employment comovements at the sectoral level over the business cycle

This paper extends the technique suggested by den Haan (2000) to investigate contemporaneous as well as lead and lag correlations among economic data for a range of forecast horizons. The technique provides a richer picture of the economic dynamics generating the data and allows one to investigate which variables lead or lag others and whether the lead or lag pattern is short term or long term in nature. The technique is applied to monthly sectoral level employment data for the U.S. and shows that among the ten industrial sectors followed by the U.S. Bureau of Labor Statistics, six tend to lead the other four. These six have high correlations indicating that the structural shocks generating the data movements are mostly in common. Among the four lagging industries, some lag by longer intervals than others and some have low correlations with the leading industries indicating that these industries are partially influenced by structural shocks beyond those generating the six leading industries.