Results 131 to 140 of about 13,808,600 (300)
Asymmetric Multivariate Normal Mixture GARCH [PDF]
An asymmetric multivariate generalization of the recently proposed class of normal mixture GARCH models is developed. Issues of parametrization and estimation are discussed.
Markus Haas +2 more
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A New Implementation of Network GARCH Model for Stock Volatility and Co‐Volatility Forecasting
Journal of Forecasting, EarlyView.ABSTRACT Volatility clustering and spillovers are key features of financial time series with many cross‐sectional assets. While network analysis links similar or correlated stocks and helps trace volatility spillovers, contemporary multivariate ARCH‐GARCH formulations struggle to represent structured network dependence and remain parsimonious.
Peiyi Zhou
wiley +1 more source
A Novel Text‐Based Framework for Forecasting Carbon Prices
Journal of Forecasting, EarlyView.ABSTRACT This study proposes a text‐based framework for predicting EU carbon prices. Using weekly data from 2020 to 2024, we construct a multivariate dataset combining financial indicators, commodity prices, Google Trends measures, and news‐based sentiment extracted using FinBERT.
Christian Oliver Ewald, Yaoyu Li
wiley +1 more source
Symmetric Normal Mixture GARCH [PDF]
Normal mixture (NM) GARCH models are better able to account for leptokurtosis in financial data and offer a more intuitive and tractable framework for risk analysis and option pricing than student’s t-GARCH models.
Emese Lazar, Carol Alexandra
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Beta Forecasting With Realized Beta Estimators and Machine Learning Algorithms
Journal of Forecasting, EarlyView.ABSTRACT This paper applies machine learning algorithms to the modeling of realized betas for the purposes of forecasting stock systematic risk. Higher levels of beta forecast accuracy are demonstrated, relative to other studies in the literature. These improvements are also highly significant, both statistically and economically.
Bao Doan +3 more
wiley +1 more source
Exact Maximum Likelihood estimation for the BL-GARCH model under elliptical distributed innovations [PDF]
In this paper, we discuss the class of Bilinear GATRCH (BL-GARCH) models which are capable of capturing simultaneously two key properties of non-linear time series : volatility clustering and leverage effects. It has been observed often that the marginal
Dominique Guegan +2 more
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Journal of Forecasting, EarlyView.ABSTRACT This paper develops Masked Asset–Regime Scenario Diffusion (MARS‐Diff), a leakage‐disciplined framework for probabilistic forecasting of multiday portfolio losses. The framework combines a regularized heterogeneous autoregressive model with exogenous predictors (HAR‐X) as its anchor, a train‐only masked representation of a high‐dimensional ...
Çağlar Sözen, Mervenur Sözen
wiley +1 more source
Wake me up before you GO-GARCH [PDF]
In this paper we present a new three-step approach to the estimation of Generalized Orthogonal GARCH (GO-GARCH) models, as proposed by van der Weide (2002).
Boswijk, H.P., Weide, R. van der
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The Role of Variance Risk Premium in Derivative Pricing: Modeling, Estimation and Impact
Journal of Futures Markets, EarlyView.ABSTRACT This paper estimates a model where variance risk premiums (VRP) is not fully explained by equity risk premiums (ERP). This separation can be detected thanks to a new breed of GARCH models with enough innovations to disconnect returns from variances. This type of risk‐neutralization is compatible with continuous‐time settings.
Marcos Escobar‐Anel +2 more
wiley +1 more source
Accurate Value-at-Risk Forecast with the (good old) Normal-GARCH Model [PDF]
A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal-GARCH model.
Stefan Mittnik +2 more
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