Merits and drawbacks of variance targeting in GARCH models
Variance targeting estimation is a technique used to alleviate the numerical difficulties encountered in the quasi-maximum likelihood (QML) estimation of GARCH models.
Francq, Christian +2 more
core
Nickel price forecasting based onempirical mode decomposition and deep learning model with expansion mechanism. [PDF]
PLoS OneLi J, Yu Z, Zhang J, Meng W.
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Enhancing Prediction by Incorporating Entropy Loss in Volatility Forecasting. [PDF]
Entropy (Basel)Urniezius R +9 more
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Intelligent system for portfolio optimization for novel volatility forecasting using machine learning. [PDF]
Sci RepBiswas T, Dey A, Mandal G, Ghosh N.
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An Asymmetric Block Dynamic Conditional Correlation Multivariate GARCH Model
The Block DCC model for determining dynamic correlations within and between groups of financial asset returns is extended to account for asymmetric effects.
Vargas, Gregorio A.
core
Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis. [PDF]
BMC Public HealthTeja MD, Rayalu GM.
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Early warning of regime switching in a financial time series: A heteroskedastic network model. [PDF]
PLoS OneWang L, An S, Dong Z, Dong X, Li J.
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Perception and Prediction of Factors Influencing Carbon Price: Multisource, Spatiotemporal, Hierarchical Federated Learning Framework with Cross-Modal Feature Fusion. [PDF]
Sensors (Basel)Wang P, Zhou X.
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Recurrent Neural Network GO-GARCH Model for Portfolio Selection. [PDF]
J Time Ser EconomBurda M, Schroeder AK.
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Selected Topics in Time Series Forecasting: Statistical Models vs. Machine Learning. [PDF]
Entropy (Basel)Tjøstheim D.
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