reformulate its estimation into a double-constrained optimization problem. We implement our approach in R and evaluate it using …

This chapter deals with the estimation of risk neutral distributions for pricing index options resulting from the … hypothesis of the risk neutral valuation principle. After justifying this hypothesis, we shall focus on parametric estimation ….r.t. reviewed parametric statistical families used for direct estimation. Additionally, we shall introduce the Fast Fourier …

orhistorical and Monte Carlo simulation methods. Although these approaches to overall VaR estimation have receivedsubstantial … proposed estimation approach pairs intuitiveappeal with computational efficiency. We evaluate various alternative estimation …

-Gaussian dependency structures with a small number of parameters. In this paper we develop a novel adaptive estimation technique of the … ; Archimedean copula ; adaptive estimation …

functions are rank-based estimators whose inflated estimation errors are known to converge weakly to a Gaussian process that is …

This paper proposes methods for estimation and inference in multivariate, multi-quantile models. The theory can …

We propose marginal integration estimation and testing methods for the coefficients of varying coefficient multivariate … regression model. Asymptotic distribution theory is developed for the estimation method which enjoys the same rate of convergence … as univariate function estimation. For the test statistic, asymptotic normal theory is established. These theoretical …

-Gaussian dependency structures with a small number of parameters. In this paper we develop a novel adaptive estimation technique of the …