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Package CovRegpy : Regularised Covariance Regression and Forecasting in Python

van Jaarsveldt, Cole; Peters, Gareth; Ames, Matthew; … - 2023

. These seven scripts contain the Dynamic Conditional Correlation (DCC) framework, Instantaneous Frequency Forecasting (IFF … RCR framework to forecast covariance and correlation structures and finally apply portfolio weighting strategies based on …

Persistent link: https://www.econbiz.de/10014253907

Regularized Regression When Covariates are Linked on a Network : The 3cose Algorithm

Weber, Karl Matthias; Striaukas, Jonas; Schumacher, Martin - 2023

Covariates in regressions may be linked to each other on a network. Knowledge of the network structure can be incorporated into regularized regression settings via a network penalty term. However, when it is unknown whether the connection signs in the network are positive (connected covariates...

Persistent link: https://www.econbiz.de/10014357781

Variable selection in high dimensional linear regressions with parameter instability

Chudik, Alexander; Pesaran, M. Hashem; Sharifvaghefi, Mahrad - 2023

variable as well as the correlation of the covariates in the active set are allowed to vary over time, without committing to …

Persistent link: https://www.econbiz.de/10013494088

Kernel regression coefficients for practical significance

Vinod, Hrishikesh D. - In: Journal of risk and financial management : JRFM 15 (2022) 1, pp. 1-13

regressions for both examples to illustrate the computation of scale-free generalized partial correlation coefficients (GPCCs). We …

Persistent link: https://www.econbiz.de/10012814147

Testing the Diagonality of a Large Covariance Matrix in a Regression Setting

Lan, Wei - 2014

In multivariate analysis, the covariance matrix associated with a set of variables of interest (namely response variables) commonly contains valuable information about the dataset. When the dimension of response variables is considerably larger than the sample size, it is a non-trivial task to...

Persistent link: https://www.econbiz.de/10013054334

A Least Squares Regression Realized Covariation Estimation

Nolte, Ingmar; Vasios, Michalis; Voev, Valeri; Xu, Qi - 2019

We propose a least squares regression framework for the estimation of the realized covariation matrix using high frequency data. The new estimator is robust to market microstructure noise (MMS) and non-synchronous trading. Comprehensive simulation and empirical analysis show that our estimator...

Persistent link: https://www.econbiz.de/10014161679

Correlation, Regression, and Cointegration of Nonstationary Economic Time Series

Johansen, Soren - 2008

Yule (1926) introduced the concept of spurious or nonsense correlation, and showed by simulation that for some … distinguish between empirical and population correlation coefficients and show in a bivariate autoregressive model for … nonstationary variables that the empirical correlation and regression coefficients do not converge to the relevant population values …

Persistent link: https://www.econbiz.de/10012723932

Bridging factor and sparse models

Fan, Jianqing; Masini, Ricardo; Medeiros, Marcelo C. - 2021

Factor and sparse models are two widely used methods to impose a low-dimensional structure in high dimension. They are seemingly mutually exclusive. In this paper, we propose a simple lifting method that combines the merits of these two models in a supervised learning methodology that allows to...

Persistent link: https://www.econbiz.de/10012435974

On the impact of serial dependence on penalized regression methods

Tonini, Simone; Chiaromonte, Francesca; Giovannelli, … - 2022

regressions (PRs). Focusing on the direct relationship between the degree of cross-correlation of covariates and the estimation …

Persistent link: https://www.econbiz.de/10013336165

A penalized two-pass regression to predict stock returns with time-varying risk premia

Bakalli, Gaetan; Guerrier, Stéphane; Scaillet, Olivier - 2021

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...

Persistent link: https://www.econbiz.de/10012487589