TVMVP-package: TVMVP: Time-Varying Minimum Variance Portfolio Optimization

In TVMVP: Time-Varying Minimum Variance Portfolio

TVMVP: Time-Varying Minimum Variance Portfolio Optimization

Description

The TVMVP package provides tools for estimating time-dependent covariance matrices using kernel-weighted principal component analysis. These estimates can then be used for portfolio optimization in a dynamic setting.

Details

The method involves five steps: (1) determining the number of factors, (2) estimating kernel-weighted PCA, (3) regularizing the idiosyncratic error covariance, (4) estimating the total covariance matrix, and (5) computing optimal portfolio weights.

An optional step includes a hypothesis test to check whether the covariance matrix is time-invariant.

The local PCA method, method for determining the number of factors, and associated hypothesis test are based on Su and Wang (2017). The approach to time-varying portfolio optimization follows Fan et al. (2024). The regularisation applied to the residual covariance matrix adopts the technique introduced by Chen et al. (2019).

The methodology implemented in this package closely follows Fan et al. (2024). The original authors provide a Matlab implementation at https://github.com/RuikeWu/TV-MVP.

The default kernel function in the package is the Epanechnikov kernel. Other kernel functions can also be used, however these are not implemented in the package. In order to do this, write an R function with an integrable kernel function and use this as input in the functions with argument kernel_func. It should be constructed as custom_kernel <- function(u){...}.

Similarly, the bandwidth function which is implemented in the package is the Silverman's rule of thumb. For most functions, simply set bandwidth to your preferred bandwidth, however for expanding_tvmvp, only Silverman's is implemented in this version of the package.

Authors and Maintainer

Authors: Erik Lillrank and Yukai Yang
Maintainer: Erik Lillrank
Department of Statistics, Uppsala University
[e-mail], [e-mail]

Functions

determine_factors

Selects the optimal number of factors via an information criterion.

hyptest

Hypothesis test for time-invariant covariance matrices. Bootstrap p-values supported.

predict_portfolio

Optimizes portfolio weights for out-of-sample prediction of portfolio performance.

expanding_tvmvp

Evaluates MVP performance in a expanding window framework.

time_varying_cov

Estimates the time-varying covariance matrix.

silverman

Silverman's rule of thumb bandwidth formula.

Class

TVMVP

Time Varying Minimum Variance Portfolio (TVMVP) Class.

References

Lillrank, E. (2025). A Time-Varying Factor Approach to Covariance Estimation

Su, L., & Wang, X. (2017). On time-varying factor models: Estimation and testing. Journal of Econometrics, 198(1), 84–101.

Fan, Q., Wu, R., Yang, Y., & Zhong, W. (2024). Time-varying minimum variance portfolio. Journal of Econometrics, 239(2), 105339.

Chen, J., Li, D., & Linton, O. (2019). A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables. Journal of Econometrics, 212(1), 155–176.

Author(s)

Maintainer: Erik Lillrank [e-mail] (ORCID)

Authors:

• Yukai Yang [e-mail] (ORCID)

See Also

Useful links:

• https://github.com/erilill/TV-MVP

• Report bugs at https://github.com/erilill/TV-MVP/issues

TVMVP documentation built on June 28, 2025, 1:08 a.m.