# Semiparametric estimation in the normal variance-mean mixture model

## TL;DR

This paper introduces a semiparametric estimation method for variance-mean mixture models, focusing on estimating the normal mean and the mixing distribution density, with demonstrated effectiveness on simulated and real data.

## Contribution

It presents a novel two-step semiparametric estimation procedure for variance-mean mixtures, combining parametric mean estimation with nonparametric mixing density recovery.

## Key findings

- Effective estimation demonstrated on simulated data
- Successful application to real financial data
- Improved understanding of mixture model parameters

## Abstract

In this paper we study the problem of statistical inference on the parameters of the semiparametric variance-mean mixtures. This class of mixtures has recently become rather popular in statistical and financial modelling. We design a semiparametric estimation procedure that first estimates the mean of the underlying normal distribution and then recovers nonparametrically the density of the corresponding mixing distribution. We illustrate the performance of our procedure on simulated and real data.