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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
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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
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Chapter 8 Forecasting economic variables with nonlinear models
Teräsvirta, Timo - 2006
building are parameter estimation and evaluation that are also briefly considered. There are two possibilities of generating …
Persistent link: https://www.econbiz.de/10014023698
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Long/Short Equity Risk Premia Parity Portfolios via Implicit Factors in Regularized Covariance Regression
van Jaarsveldt, Cole; Peters, Gareth; Ames, Matthew; … - 2023
A time-series basis decomposition and trend extraction technique known as Empirical Mode Decomposition (EMD), designed for multi-scale time-frequency decomposition in non-stationary time-series settings, will be combined with Regularised Covariance Regression (RCR) methods to produce a framework...
Persistent link: https://www.econbiz.de/10014348857
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Time-Localized Wavelet Multiple Regression and Correlation : Eurozone Stock Markets Across Scales and Time
Fernandez-Macho, Javier - 2020
regression on wavelet coefficients. The concept of wavelet local multiple correlation is used to produce one single set of multi …-scale correlations along time, in contrast with the large number of wavelet correlation maps that need to be compared when using standard … century. It is shown how the evolution of the correlation structure in these markets has been far from homogeneous both along …
Persistent link: https://www.econbiz.de/10012854086
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A Dimensionality-Robust Test in Multiple Predictive Regression
Xu, Ke-Li - 2020
-variables based tests which involve enforcement or partial enforcement of the null hypothesis in variance estimation and analyze their …
Persistent link: https://www.econbiz.de/10012847644
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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
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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
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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 …This paper characterizes the impact of serial dependence on the non-asymptotic estimation error bound of penalized … general case of weakly cross-correlated non Gaussian AR processes of any autoregressive order. To improve the estimation …
Persistent link: https://www.econbiz.de/10013336165
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Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts
De Gooijer, Jan G. - 2019
We propose a hybrid penalized averaging for combining parametric and non-parametric quantile forecasts when faced with a large number of predictors. This approach goes beyond the usual practice of combining conditional mean forecasts from parametric time series models with only a few predictors....
Persistent link: https://www.econbiz.de/10012859663