Portfolio selection: shrinking the time-varying inverse conditional covariance matrix

2018 ◽  
Vol 61 (6) ◽  
pp. 2583-2604 ◽  
Author(s):  
Ruili Sun ◽  
Tiefeng Ma ◽  
Shuangzhe Liu
Keyword(s):  
Covariance Matrix ◽  
Portfolio Selection ◽  
Time Varying ◽  
Conditional Covariance

THE DEMAND FOR LIQUID ASSETS: EVIDENCE FROM THE MINFLEX LAURENT DEMAND SYSTEM WITH CONDITIONALLY HETEROSKEDASTIC ERRORS

2018 ◽  
Vol 23 (07) ◽  
pp. 2941-2958
Author(s):  
Dongfeng Chang ◽  
Apostolos Serletis
Keyword(s):  
United States ◽  
Covariance Matrix ◽  
Financial Econometrics ◽  
The United States ◽  
Demand System ◽  
Demand Systems ◽  
Time Varying ◽  
Conditional Covariance ◽  
Demand For Money ◽  
Monetary Assets

We investigate the demand for money and the degree of substitutability among monetary assets in the United States using the generalized Leontief and the Minflex Laurent (ML) models as suggested by Serletis and Shahmoradi (2007). In doing so, we merge the demand systems literature with the recent financial econometrics literature, relaxing the homoskedasticity assumption and instead assuming that the covariance matrix of the errors of flexible demand systems is time-varying. We also pay explicit attention to theoretical regularity, treating the curvature property as a maintained hypothesis. Our findings indicate that only the curvature constrained ML model with a Baba, Engle, Kraft, and Kroner (BEKK) specification for the conditional covariance matrix is able to generate inference consistent with theoretical regularity.

scholarly journals NEGATIVE VOLATILITY SPILLOVERS IN THE UNRESTRICTED ECCC-GARCH MODEL

2009 ◽  
Vol 26 (3) ◽  
pp. 838-862 ◽  
Author(s):  
Christian Conrad ◽  
Menelaos Karanasos
Keyword(s):  
Covariance Matrix ◽  
Garch Model ◽  
Positive Definiteness ◽  
Time Varying ◽  
Conditional Covariance ◽  
Conditional Correlation ◽  
Economic Theories ◽  
Model Builder ◽  
Volatility Spillovers ◽  
Volatility Feedback

This paper considers a formulation of the extended constant or time-varying conditional correlation GARCH model that allows for volatility feedback of either the positive or negative sign. In the previous literature, negative volatility spillovers were ruled out by the assumption that all the parameters of the model are nonnegative, which is a sufficient condition for ensuring the positive definiteness of the conditional covariance matrix. In order to allow for negative feedback, we show that the positive definiteness of the conditional covariance matrix can be guaranteed even if some of the parameters are negative. Thus, we extend the results of Nelson and Cao (1992) and Tsai and Chan (2008) to a multivariate setting. For the bivariate case of order one, we look into the consequences of adopting these less severe restrictions and find that the flexibility of the process is substantially increased. Our results are helpful for the model-builder, who can consider the unrestricted formulation as a tool for testing various economic theories.

PORTFOLIO SELECTION WITH CONDITIONAL COVARIANCE MATRIX AND NONLINEAR PROGRAMMING

2016 ◽  
Vol 49 (5) ◽  
pp. 343-355
Author(s):  
Julio César Martínez Sánchez ◽  
David Sotres-Ramos ◽  
Martha Elva Ramírez Guzmán
Keyword(s):  
Nonlinear Programming ◽  
Covariance Matrix ◽  
Portfolio Selection ◽  
Conditional Covariance

scholarly journals A suggestion for constructing a large time-varying conditional covariance matrix

2017 ◽  
Vol 156 ◽  
pp. 110-113 ◽  
Author(s):  
Heather D. Gibson ◽  
Stephen G. Hall ◽  
George S. Tavlas
Keyword(s):  
Covariance Matrix ◽  
Large Time ◽  
Time Varying ◽  
Conditional Covariance

scholarly journals Rates of convergence in conditional covariance matrix with nonparametric entries estimation

2019 ◽  
Vol 49 (18) ◽  
pp. 4536-4558 ◽  
Author(s):  
Jean-Michel Loubes ◽  
Clément Marteau ◽  
Maikol Solís
Keyword(s):  
Covariance Matrix ◽  
Rates Of Convergence ◽  
Conditional Covariance

scholarly journals Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 228
Author(s):  
Rasoul Shafipour ◽  
Gonzalo Mateos
Keyword(s):  
Inverse Problem ◽  
Covariance Matrix ◽  
A Priori ◽  
Dynamic Network ◽  
Streaming Data ◽  
Optimal Time ◽  
Time Varying ◽  
Graph Learning ◽  
Topology Inference ◽  
Diffusion Dynamics

We develop online graph learning algorithms from streaming network data. Our goal is to track the (possibly) time-varying network topology, and affect memory and computational savings by processing the data on-the-fly as they are acquired. The setup entails observations modeled as stationary graph signals generated by local diffusion dynamics on the unknown network. Moreover, we may have a priori information on the presence or absence of a few edges as in the link prediction problem. The stationarity assumption implies that the observations’ covariance matrix and the so-called graph shift operator (GSO—a matrix encoding the graph topology) commute under mild requirements. This motivates formulating the topology inference task as an inverse problem, whereby one searches for a sparse GSO that is structurally admissible and approximately commutes with the observations’ empirical covariance matrix. For streaming data, said covariance can be updated recursively, and we show online proximal gradient iterations can be brought to bear to efficiently track the time-varying solution of the inverse problem with quantifiable guarantees. Specifically, we derive conditions under which the GSO recovery cost is strongly convex and use this property to prove that the online algorithm converges to within a neighborhood of the optimal time-varying batch solution. Numerical tests illustrate the effectiveness of the proposed graph learning approach in adapting to streaming information and tracking changes in the sought dynamic network.

scholarly journals An Iterative Approach to Ill-Conditioned Optimal Portfolio Selection

2019 ◽  
Vol 56 (4) ◽  
pp. 773-794 ◽  
Author(s):  
Mårten Gulliksson ◽  
Stepan Mazur
Keyword(s):  
Covariance Matrix ◽  
Portfolio Selection ◽  
Numerical Study ◽  
Asset Returns ◽  
Positive Definite ◽  
Convergence Properties ◽  
Good Convergence ◽  
Optimal Portfolio Selection ◽  
Singular Covariance Matrix ◽  
Better Than

AbstractCovariance matrix of the asset returns plays an important role in the portfolio selection. A number of papers is focused on the case when the covariance matrix is positive definite. In this paper, we consider portfolio selection with a singular covariance matrix. We describe an iterative method based on a second order damped dynamical systems that solves the linear rank-deficient problem approximately. Since the solution is not unique, we suggest one numerical solution that can be chosen from the iterates that balances the size of portfolio and the risk. The numerical study confirms that the method has good convergence properties and gives a solution as good as or better than the solutions that are based on constrained least norm Moore–Penrose, Lasso, and naive equal-weighted approaches. Finally, we complement our result with an empirical study where we analyze a portfolio with actual returns listed in S&P 500 index.

Sign in / Sign up

Export Citation Format

Share Document