Fitting Vast Dimensional Time-Varying Covariance Models

By Cavit Pakel, N. Shephard, Kevin Sheppard, Robert F. Engle. Journal of business & economic statistics, 2020

116 citations recorded by the source corpus.

Abstract Estimation of time-varying covariances is a key input in risk management and asset allocation. ARCH-type multivariate models are used widely for this purpose. Estimation of such models is computationally costly and parameter estimates are meaningfully biased when applied to a moderately large number of assets. Here, we propose a novel estimation approach that suffers from neither of these issues, even when the number of assets is in the hundreds. The theory of this new method is developed in some detail. The performance of the proposed method is investigated using extensive simulation studies and empirical examples. Supplementary materials for this article are available online.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4d2cf19e9f6bf7bb4629e4dec7b17485091740bd

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Citations in the corpus

The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation (2020, unknown): “The work of Stein (1956) and James and Stein (1961) was so revolutionary that it took a while for it to be digested and embraced by the academic community; this process was aided by Efron and Morris (1973, 1975, 1977) who provided a more in-depth analysis of Stein’s shrinkage method that included th”
Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly (2019, methodology): “…of Engle, Ledoit, and Wolf (2019) manages to do just that by combining two key innovations: first, it uses the composite-likelihood method of Pakel et al. (2017) which makes estimation in large dimensions feasible; second, it uses the NL shrinkage method of Ledoit and Wolf (2012, 2015) for…”
Interconnectedness in the global financial market (2017, unknown): “However, models of this kind are difficult to use with a large number of stocks (see also Engle et al., 2008).”
MGARCH models: Trade-off between feasibility and flexibility (2018, background): “Pakel et al. (2014) propose the composite likelihood (CL) estimator which is based on summing up the quasi-likelihood functions of subsets of assets, allowing to estimate models even when the cross-sectional dimension is larger than the sample size.”
Sparse Bayesian time-varying covariance estimation in many dimensions (2016, background): “lti-step approaches or rolling-window-like estimates. Suggested solutions include Engle and Kelly (2012) who propose an estimator assuming that pairwise correlations are equal at every point in time, Pakel et al. (2014) who consider composite likelihood estimation, Gruber and West (2016) who use a d”
Large Dynamic Covariance Matrices (2019, unknown): “Fortunately, a way to overcome this computational hurdle has been found by Pakel et al. (2014). The basic idea is that, instead of looking at all assets together jointly, it is easier to look at pairs of assets. They call it the composite likelihood method: The composite log-likelihood is computed b”