We adopt a family of nonparametric Cressie-Read estimators to price options based on relative pricing using the underlying asset returns. We use option models with stochastic volatility and jumps to investigate the ability of each member in this family to price options with different moneynesses...

This paper proposes a simple and crude way of approximating the XVA sensitivities. In short, the idea is simply to recycle the existing base simulated portfolio values for the bumped ones. This is done by re-simulating the risk factors for the bumped market and finding out which other base state...

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 primer offers a hands-on accessible guide to writing and estimating structural models. We review commonly-used methodologies, including dynamic programming, maximum likelihood, generalized and simulated method of moments, conditional choice probabilities as well as tools to compute standard...

We develop a method to estimate a game’s primitives in complex dynamic environments. Because of the environment’s complexity, agents may not know or understand some key features of their interaction. Instead of equilibrium assumptions, we impose an asymptotic ε-regret (ε-AR) condition on...

In this paper we propose a new IV estimator to be used in detecting vertical property tax inequity. We conduct Monte Carlo experiments to evaluate the bias of this estimator in comparison to traditional linear and log-linear regression based estimators. We find that the new estimator is more...

The literature on panel models has made considerable progress in the last few decades, integrating non-stationary data both in the time and spatial domain. However, there remains a gap in the literature that simultaneously models non-stationarity and cointegration in both the time and spatial...

This paper studies the computational complexity of Bayesian and quasi-Bayesian estimation in large samples carried out using a basic Metropolis random walk. The framework covers cases where the underlying likelihood or extremum criterion function is possibly non-concave, discontinuous, and of...

The aim of this study is to present an efficient and easy framework for the application of the Least Squares Monte Carlo methodology to the pricing of gas or power facilities as detailed in Boogert and de Jong. As mentioned in the seminal paper by Longstaff and Schwartz, the convergence of the...