KernelDensity2D: Two-Dimensional Kernel Estimation

Description Usage Arguments Value References See Also

View source: R/DSFM.R

Description

This function computes the two-dimensional kernel estimation needed for the DSFM2D funtion to work. Following Fengler and al. (2007), it is using the product of two QuarticKernel1D functions.

Usage

1
KernelDensity2D(y, I, J, x1, x2, u, U, h)

Arguments

y

a list of numeric matrix of the data at each time t.

I

the total number of matrix in y.

J

a list of the total number of covariates in each matrix of y.

x1

a list of numeric vector for the first dimension at each time t.

x2

a list of numeric vector for the second dimension at each time t.

u

a numeric matrix of the estimation grid.

U

the length of u.

h

a numeric matrix of the bandwidth.

Value

It returns \hat{p}_t(u), \hat{q}_t(u) and the couples J_t\hat{p}_t(u) and J_t\hat{q}_t(u).

References

Fengler, Matthias R, Wolfgang K Haerdle, and Enno Mammen (2007). "A Semiparametric Factor Model for Implied Volatility Surface Dynamics". In: Journal of Financial Econometrics 5.2, pp. 189-218.

Borak, Szymon, Matthias R. Fengler, and Wolfgang K. Haerdle (2005)."DSFM Fitting of Implied Volatility Surfaces". In: 5th International Conference on Intelligent Systems Design and Applications (ISDA'05), pp. 526-531. IEEE.

See Also

Other kernel.functions: KernelDensity1D, NormalKernel1D, QuarticKernel1D


MarcGumowski/dysefamor documentation built on May 7, 2019, 2:47 p.m.