Several new tests are proposed for examining the adequacy of a family of parametric models against large nonparametric alternatives. These tests formally check if the bias vector of residuals from ...
Bayesian nonparametric mixture models represent a powerful statistical framework that extends traditional mixture modelling by allowing the number of mixture components to be inferred from the data ...
Numerous advances in the modeling techniques of value-at-risk (VaR) have provided financial institutions with a wide range of market risk approaches. However, which model to use depends on the state ...
Mitchell Grant is a self-taught investor with over 5 years of experience as a financial trader. He is a financial content strategist and creative content editor. Timothy Li is a consultant, accountant ...
where y i is the ith observed response value, x i is the ith vector of explanatory values, and 's are uncorrelated random variables with zero mean and a common variance. If the form of the regression ...
We present a non-parametric method for calibrating jump–diffusion and, more generally, exponential Lévy models to a finite set of observed option prices. We show that the usual formulations of the ...
Parametric features are becoming more common in FEA packages. The key benefit of parametric features is that they let users see the effects of design changes quickly. With adequate planning, users can ...
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