One of the main challenges investors have to face is model uncertainty. Typically, the dynamic of the assets is modeled using two parameters: the drift vector and the covariance matrix, which are both uncertain. Since the variance/covariance parameter is assumed to be estimated with a certain...

We establish explicit socially optimal rules for an irreversible investment decision with time-to-build and uncertainty. Assuming a price sensitive demand function with a random intercept, we provide comparative statics and economic interpretations for three models of demand (arithmetic...

One of the main challenges investors have to face is model uncertainty. Typically, the dynamic of the assets is modeled using two parameters: the drift vector and the covariance matrix, which are both uncertain. Since the variance/covariance parameter is assumed to be estimated with a certain...

We propose a statistical methodology to quantify the financial implications of tropical cyclone-related physical risks implied by climate change. To address the sensitivity of disaster intensity to climate change, we provide a Monte Carlo methodology to generate synthetic cyclones consistent...

Risks and opportunities related to environmental transition are usually evaluated through the use of scenarios, produced and maintained by international bodies such as the International Energy Agency. This approach assumes perfect knowledge of the scenario by the agent, but in reality, scenario...

We develop a real-options approach to evaluate energy assets and potential investment projects under transition scenario uncertainty. Dynamic scenario uncertainty is modelled by assuming that the economic agent acquires the information about the scenario progressively by observing a signal. The...