Nonparametric methods for volatility density estimation

@inproceedings{Es2009NonparametricMF,
  title={Nonparametric methods for volatility density estimation},
  author={Bert van Es and Peter Spreij and Harry van Zanten},
  year={2009}
}
Stochastic volatility modeling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and review several nonparametric methods for estimation of the density of the volatility process. Both models based on discretely sampled continuous-time processes and discrete-time models will be discussed. 
3 Citations

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References

SHOWING 1-10 OF 36 REFERENCES

Nonparametric volatility density estimation for discrete time models

We consider discrete time models for asset prices with a stationary volatility process. We aim at estimating the multivariate density of this process at a set of consecutive time instants. A

Nonparametric volatility density estimation

We consider a continuous-time stochastic volatility model. The model contains a stationary volatility process, the density of which, at a fixed instant in time, we aim to estimate. We assume that we

Nonparametric Estimation in a Stochastic Volatility Model

Nonparametric Modeling in Financial Time Series

TLDR
In this chapter, the main ideas of nonparametric kernel smoothing are explained in the rather simple situation of density estimation and regression in a continuous-time model such as a homogeneous diffusion model.

Option Valuation under Stochastic Volatility

This book provides an advanced treatment of option valuation. The general setting is that of 2D continuous-time models with stochastic volatility. Explicit equilibrium risk adjustments and many other

Penalized Projection Estimator for Volatility Density

Abstract.  In this paper, we consider a stochastic volatility model (Yt, Vt), where the volatility (Vt) is a positive stationary Markov process. We assume that (lnVt) admits a stationary density f

Limit theorems for discretely observed stochastic volatility models

A general set-up is proposed to study stochastic volatility models. We consider here a two-dimensional diffusion process (Yt, Vt) and assume that only (Yt) is observed at n discrete times with

On the relation between GARCH and stable processes