Implicit in industry standard option pricing models is the expectation that roughly 25% of stocks with 60% consistent volatility will septuple within 10 years, an extraordinary rate of appreciation. The exceptionally high equilibrium anticipated returns for an improbably large percentage of high...

This article attempts to adapt the Monte Carlo method to the quantitative risk management of environmental pollution. In this context, the feasibility of stochastic models to quantitatively evaluate the risk of chemical pollution is first discussed and then linked to a case study in which Monte...

I identify three sources of risk for the tax shields: two of them associated to the risk of debt and one associated to the operating risk. I present a set of conditions for defining risky debt associated to cash flow and not to accounting earnings. I explain why realization of tax shields for...

The Simulation-Based Excess Return Model (SERM) offers a simple, practical decision-making method for underwriting real estate development projects. It addresses the shortcomings of discounted cash flow modeling by taking into account the probabilistic distribution of outcomes and is based on...

We develop a general space-time filter applied to panel data models in order to control for heterogeneity as well as both time and spatial dependence. Treatment of initial period observations is analyzed when the number of time periods is small. A second issue relates to an implied restriction...

Although typically overlooked, many purchase datasets exhibit a high incidence of products with zero sales. We propose a new estimator for the Random-Coefficients Logit demand system for purchase datasets with zero-valued market shares. The identification of the demand parameters is based on a...

This paper extends the cross sectionally augmented panel unit root test proposed by Pesaran (2007) to the case of a multifactor error structure. The basic idea is to exploit information regarding the unobserved factors that are shared by other time series in addition to the variable under...

We analyze the construction of multivariate forecasting densities based on conditional models for each variable, given the other variables; a joint predictive density is obtained by iteratively simulating from the conditional models. This idea has been pursued in the context of missing data...

What are the main drivers of fluctuations in the aggregate US stock market? In this paper, we attempt to resolve the long-lasting debate surrounding this question by designing and solving a consumption-based asset pricing model which incorporates stochastic volatility, long-run risks in...