Results 81 to 90 of about 3,586 (227)

Semiparametric Estimation of Multivariate GARCH Models [PDF]

SSRN Electronic Journal, 2015
The paper introduces a new simple semiparametric estimator of the conditional variance covariance and correlation matrix (SP-DCC). While sharing a similar sequential approach to existing dynamic conditional correlation (DCC) methods, SP-DCC has the advantage of not requiring the direct parameterization of the conditional covariance or correlation ...
openaire   +2 more sources

Wavelet‐Based Hurst Exponent Estimation

WIREs Computational Statistics, Volume 18, Issue 3, September 2026.
The review explores how wavelet‐based methods for estimating the Hurst parameters have developed from their theoretical roots to real‐world applications in fields like biology, engineering, and telecommunications. The review aims to highlight key techniques, compare their strengths and limitations, and point out challenges that still need to be ...
Dixon Vimalajeewa, Fabrizio Ruggeri, Brani Vidakovic   +2 more
wiley   +1 more source

A Deep Learning Framework for Forecasting Medium‐Term Covariance in Multiasset Portfolios

Journal of Forecasting, Volume 45, Issue 4, Page 1797-1828, July 2026.
ABSTRACT Forecasting the covariance matrix of asset returns is central to portfolio construction, risk management, and asset pricing. However, most existing models struggle at medium‐term horizons, several weeks to months, where shifting market regimes and slower dynamics prevail.
Pedro Reis, Ana Paula Serra, João Gama
wiley   +1 more source

Bayesian Estimation of R-Vine Copula with Gaussian-Mixture GARCH Margins: An MCMC and Machine Learning Comparison

Mathematics
This study proposes Bayesian estimation of multivariate regular vine (R-vine) copula models with generalized autoregressive conditional heteroskedasticity (GARCH) margins modeled by Gaussian-mixture distributions.
Rewat Khanthaporn, Nuttanan Wichitaksorn
doaj   +1 more source

Electricity Price Prediction Using Multikernel Gaussian Process Regression Combined With Kernel‐Based Support Vector Regression

Journal of Forecasting, Volume 45, Issue 4, Page 2059-2077, July 2026.
ABSTRACT This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out‐of‐sample data is not ...
Abhinav Das, Stephan Schlüter, Lorenz Schneider   +2 more
wiley   +1 more source

Econometrics at the Extreme: From Quantile Regression to QFAVAR1

Journal of Economic Surveys, Volume 40, Issue 3, Page 1672-1686, July 2026.
ABSTRACT This paper surveys quantile modelling from its theoretical origins to current advances. We organize the literature and present core econometric formulations and estimation methods for: (i) cross‐sectional quantile regression; (ii) quantile time series models and their time series properties; (iii) quantile vector autoregressions for ...
Stéphane Goutte, Konstantinos N. Konstantakis, Dimitris Konstantios, Panayotis G. Michaelides, Arsenios‐Georgios N. Prelorentzos   +4 more
wiley   +1 more source

Automated Bandwidth Selection for Inference in Linear Models With Time‐Varying Coefficients

Journal of Time Series Analysis, Volume 47, Issue 4, Page 854-875, July 2026.
ABSTRACT The problem of selecting the smoothing parameter, or bandwidth, for kernel‐based estimators of time‐varying coefficients in linear models with possibly endogenous explanatory variables is considered. We examine automated bandwidth selection by means of cross‐validation, a nonparametric variant of Akaike's information criterion, and bootstrap ...
Charisios Grivas, Zacharias Psaradakis
wiley   +1 more source

Copula multivariate GARCH model with constrained Hamiltonian Monte Carlo

Dependence Modeling, 2019
The Copula Multivariate GARCH (CMGARCH) model is based on a dynamic copula function with time-varying parameters. It is particularly suited for modelling dynamic dependence of non-elliptically distributed financial returns series.
Burda Martin, Bélisle Louis
doaj   +1 more source

Audit‐Ready Machine Learning for Short‐Horizon Equity Prediction: A Dual‐Target Benchmark With Fold‐Isolated Preprocessing

Engineering Reports, Volume 8, Issue 6, June 2026.
This study introduces a rigorous, walk‐forward protocol to evaluate next‐day return and volatility‐proxy forecasting across matched model families. By enforcing fold‐isolated preprocessing and causal feature construction on US mega‐caps, the study eLectively mitigates performance inflation.
Abdul Kadar Muhammad Masum, Md. Abul Kalam Azad, Mirza Nadim Saad, Al‐Amain, Mohammad Azam Khan, A. B. M. Yasir Arafat   +5 more
wiley   +1 more source

Semiparametric multivariate GARCH models [PDF]

, 2003
This paper studies voting over quadratic taxation when income is fixed and taxation non distortionary. The set of feasible taxes is compact and self-interested voters have corner preferences. We first show that, if a majority winning tax policy exists, it involves maximum progressivity.
HAFNER, Christian, ROMBOUTS, Jeroen
openaire   +1 more source