Results 71 to 80 of about 1,114 (191)

Robust Estimation and Inference for Time‐Varying Unconditional Volatility

Journal of Time Series Analysis, EarlyView.
ABSTRACT We derive a general and robust estimator of a large class of parametric specifications of time‐varying unconditional volatility of financial returns, both univariate and multivariate, and establish the Consistency and Asymptotic Normality (CAN) of the estimator.
Adam Lee, Rickard Sandberg, Genaro Sucarrat   +2 more
wiley   +1 more source

Empirical‐Process Limit Theory and Filter Approximation Bounds for Score‐Driven Time Series Models

Journal of Time Series Analysis, EarlyView.
ABSTRACT This article examines the filtering and approximation‐theoretic properties of score‐driven time series models. Under specific Lipschitz‐type and tail conditions, new results are derived, leading to maximal and deviation inequalities for the filtering approximation error using empirical process theory.
Enzo D'Innocenzo
wiley   +1 more source

On Diagnostic Checking of Vector ARMA-GARCH Models with Gaussian and Student-t Innovations

Econometrics, 2013
This paper focuses on the diagnostic checking of vector ARMA (VARMA) models with multivariate GARCH errors. For a fitted VARMA-GARCH model with Gaussian or Student-t innovations, we derive the asymptotic distributions of autocorrelation matrices of the ...
Yongning Wang, Ruey S. Tsay
doaj   +1 more source

On Testing for Independence Between Generalized Error Models of Several Time Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT We define generalized innovations associated with generalized error models having arbitrary distributions, that is, distributions that can be mixtures of continuous and discrete distributions. These models include stochastic volatility models and regime‐switching models with possibly zero‐inflated regimes.
Kilani Ghoudi, Bouchra R. Nasri, Bruno N. Rémillard   +2 more
wiley   +1 more source

Financial Uncertainty and Gold Market Volatility: Evidence from a Generalized Autoregressive Conditional Heteroskedasticity Variant of the Mixed-Data Sampling (GARCH-MIDAS) Approach with Variable Selection

Econometrics
We analyze the predictive effect of monthly global, regional, and country-level financial uncertainties on daily gold market volatility using univariate and multivariate GARCH-MIDAS models, with the latter characterized by variable selection.
O-Chia Chuang, Rangan Gupta, Christian Pierdzioch, Buliao Shu   +3 more
doaj   +1 more source

European Markets’ Reactions to Exogenous Shocks: A High Frequency Data Analysis of the 2005 London Bombings

International Journal of Financial Studies, 2013
Terrorist incidents exert a negative, albeit usually short-lived, impact on markets and equity returns. Given the integration of global financial markets, mega-terrorist events also have a high contagion potential with their shock waves being transmitted
Christos Kollias, Stephanos Papadamou, Costas Siriopoulos   +2 more
doaj   +1 more source

Penalized Convex Estimation in Dynamic Location Models

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper studies L1$$ {L}^1 $$‐penalized estimation for location models yt=mt+ϵt$$ {y}_t={m}_t+{\epsilon}_t $$, where mt$$ {m}_t $$ is defined by a possibly non‐Markovian recursion and ϵt$$ {\epsilon}_t $$ is a martingale difference sequence with possibly time‐varying conditional variance.
Reda Alami Chentoufi
wiley   +1 more source

Multivariate GARCH models with spherical parameterizations: an oil price application

Financial Innovation
In popular Baba-Engle-Kraft-Kroner (BEKK) and dynamic conditional correlation (DCC) multivariate generalized autoregressive conditional heteroskedasticity models, the large number of parameters and the requirement of positive definiteness of the ...
Luca Vincenzo Ballestra, Riccardo De Blasis, Graziella Pacelli   +2 more
doaj   +1 more source

Normalising Flow Enhanced GARCH Models: A Two-Stage Framework for Flexible Innovation Modelling in Financial Time Series

Risks
We introduce the Normalising Flow GARCH (NF-GARCH), a two-stage hybrid framework that enhances traditional GARCH models by replacing restrictive parametric innovation distributions with learned densities via normalising flows.
Abdullah Hassan, Farai Mlambo, Wilson Tsakane Mongwe   +2 more
doaj   +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