No abstract

No abstract

In this paper we develop a stochastic version of a dynamic Cournot model. The model is dynamic because firms are slow to adjust output in response to changes in their economic environment. The model is stochastic because management may make errors in identifying the best course of action in a dynamic setting. We capture these behavioral errors with Brownian motion. The model demonstrates that the limiting output level of the game is a random variable, rather than a constant that is found in the non-stochastic case. In addition, the limiting variance in firm output is smaller with more firms. Finally, the model predicts that firm failure is more likely in smaller markets and for firms that are smaller and less efficient at managing errors.

Energy is closely related to environmental risk. A rising fuel price in the 1970s had hurt consumers and caused disturbance to the natural environment. Households could not afford high imported energy prices and thus resorted to fuel wood. Land competed for fuel wood and agricultural crops, and thus high fuel prices strained the environment with respect to the use of land. If human health and safe housing were included in environmental risk, a high energy price would induce broader environmental risk. Households with limited income would not be able to use expensive fossil energy to warm their houses and would depend on only electric mats or blankets to keep warm. Such insufficient warming methods would not only threaten their health but would also worsen the condition of their houses. The abrupt increase in energy prices in 2007 and 2008 had significantly impacted environmental risk. It forced low income households to spend more on energy, leaving less for other expenditure segments, but had left high income households generally intact. This contrasting effect between different income groups had increased the sustainability of the energy risks at the high prices. This study shows how risks associated with the household economy have increased in response to the recent dramatic increases in energy prices. We develop a method for assessing risk by using the variance of ratios of energy expenditure to current income. We then examine how differently the economic change has increased risk across expenditure segments. We find energy expenditure as the biggest contributor to the risk. In addition, we illustrate how energy expenditure has changed the risk profile for each income group, with the first group (i.e., the lowest income group) experiencing the greatest increase. This group hurts the most during days of high energy prices.