Direct Nonparametric Estimation of State Price Density with Regularized Mixture

نویسنده

  • Yongho Jeon
چکیده

We consider the state price densities that are implicit in financial asset prices. In the pricing of an option, the state price density is proportional to the second derivative of the option pricing function and this relationship together with no arbitrage principle imposes restrictions on the pricing function such as monotonicity and convexity. Since the state price density is a proper density function and most of the shape constraints are caused by this, we propose to estimate the state price density directly by specifying candidate densities in a flexible nonparametric way and applying methods of regularization under extra constraints. The problem is easy to solve and the resulting state price density estimates satisfy all the restrictions required by economic theory.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

State Price Density Estimation via Nonparametric Mixtures

We consider nonparametric estimation of the state price density encapsulated in option prices. Unlike usual density estimation problems, we only observe option prices and their corresponding strike prices rather than samples from the state price density. We propose to model the state price density directly with a nonparametric mixture and estimate it using least squares. We show that although t...

متن کامل

Fully Nonparametric Probability Density Function Estimation with Finite Gaussian Mixture Models

Flexible and reliable probability density estimation is fundamental in unsupervised learning and classification. Finite Gaussian mixture models are commonly used to serve this purpose. However, they fail to estimate unknown probability density functions when used for nonparametric probability density estimation, as severe numerical difficulties may occur when the number of components increases....

متن کامل

Bayesian estimation of bandwidths for a nonparametric regression model with a flexible error density

We approximate the error density of a nonparametric regression model by a mixture of Gaussian densities with means being the individual error realizations and variance a constant parameter. We investigate the construction of a likelihood and posterior for bandwidth parameters under this Gaussian-component mixture density of errors in a nonparametric regression. A Markov chain Monte Carlo algori...

متن کامل

A sampling algorithm for bandwidth estimation in a nonparametric regression model with a flexible error density

We propose to approximate the unknown error density of a nonparametric regression model by a mixture of Gaussian densities with means being the individual error realizations and variance a constant parameter. This mixture density has the form of a kernel density estimator of error realizations. We derive an approximate likelihood and posterior for bandwidth parameters in the kernel–form error d...

متن کامل

Contour Level Estimation from Gaussian Mixture Models Applied to Nonlinear Bss

Probability density function estimation from limited data sets is a classical problem in pattern recognition. In this paper we propose a reformulation of the well-known nonparametric Parzen method as a parametrically regularized Gaussian Mixture Model, from which we can easily estimate density contour level. As an application illustration to the proposed contour level estimator, we also address...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2011