sqrt_matrix

Computes the square root of a symmetric matrix via eigen decomposition.
Negative eigenvalues are handled by taking the square root of their absolute values.

internal

Provides the estimation of a time-dependent covariance matrix of returns with the intended use for portfolio optimization. The package offers methods for determining the optimal number of factors to be used in the covariance estimation, a hypothesis test of time-varying covariance, and user-friendly functions for portfolio optimization and rolling window evaluation. The local PCA method, method for determining the number of factors, and associated hypothesis test are based on Su and Wang (2017) <doi:10.1016/j.jeconom.2016.12.004>. The approach to time-varying portfolio optimization follows Fan et al. (2024) <doi:10.1016/j.jeconom.2022.08.007>. The regularisation applied to the residual covariance matrix adopts the technique introduced by Chen et al. (2019) <doi:10.1016/j.jeconom.2019.04.025>.

Erik Lillrank

TVMVP

Time-Varying Minimum Variance Portfolio

Yukai Yang

sqrt_matrix function

<dl><dt>Amat</dt>
<dd>A numeric symmetric matrix.</dd></dl>

Arguments

Compute the Square Root of a Matrix — sqrt_matrix

<dl>

<dt>Amat</dt>
<dd>A numeric symmetric matrix.</dd>

</dl>

sqrt_matrix: Compute the Square Root of a Matrix

Description

Usage

Value

Arguments