In this paper, we introduce a new approach for volatility modeling in discrete and continuous time. We follow the … stochastic volatility literature by assuming that the variance is a function of a state variable. However, instead of assuming …

industry investment occurs earlier than socially optimal and the first entrant takes more risk than socially optimal. While …, taking the form of postponed capacity investment, may occur in Markov Perfect Equilibrium. Volatility and the expected speed …

probability sessions, suggesting that this bias robustly persists in environments including both risk and future uncertainty, and …We elicit subjects' willingness to pay to reduce future risk. In our experiments, subjects are given a cash endowment … of uncertainty in our experiments. In two additional treatments, we control for future uncertainty with a continuation …

We consider the effect of an increase in the risk from pollution. We show that in the case of a flow pollution, when … the number of players is sufficiently large, the result of Bramoulle and Treich, showing that a marginal increase of risk … in the neighborhood of a risk-free world is welfare-improving, holds even when we consider non-marginal increases in risk …

scalar diffusion. Among other examples, Stein equation implies that the mean of Hermite polynomials is zero. The GMM approach … we adopted is well suited for two reasons. It allows us to study in detail the parameter uncertainty problem, i.e., when … that are robust against parameter uncertainty and show that Hermite polynomials are special examples. This is the main …

. We establish the direct links between the usual parametric estimation methods, namely the QMLE, the GMM and the M …-estimation. The usual univariate QMLE is, under non-normality, less efficient than the optimal GMM estimator. However, the bivariate … QMLE based on the dependent variable and its square is as efficient as the optimal GMM one. A Monte Carlo analysis confirms …