Results 101 to 110 of about 15,849 (295)
Journal of Time Series AnalysisGeneralized Autoregressive Conditional Heteroscedasticity (GARCH) model and its variations have been widely adopted in the study of financial volatilities, while the extension of GARCH‐type models to high‐dimensional data is always difficult because of over‐parameterization and computational complexity. In this article, we propose a multi‐variate GARCH‐
Yue Pan, Jiazhu Pan
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A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets
Journal of Forecasting, EarlyView.ABSTRACT This study introduces a realized volatility fuzzy time series (RV‐FTS) model that applies a fuzzy c‐means clustering algorithm to estimate time‐varying c$$ c $$ latent volatility states and their corresponding membership degrees. These memberships are used to construct a fuzzified volatility estimate as a weighted average of cluster centroids.
Shafqat Iqbal, Štefan Lyócsa
wiley +1 more source
Journal of Forecasting, EarlyView.ABSTRACT We study the accuracy of a variety of parametric price duration‐based realized variance estimators constructed via various financial duration models and compare their forecasting performance with the performance of various nonparametric return‐based realized variance estimators.
Björn Schulte‐Tillmann +2 more
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The Effect of Exchange Rate Volatility on the private sector consumption in Iran (1352-90) [PDF]
فصلنامه پژوهشهای اقتصادی ایران, 2014 The real exchange rate is considered as a basic indicator in determining the level of international competition that explain the internal situation of the country. Instability in the performance of this Index implies imbalance in the economy. Instability
Hamid La'l Khezri +2 more
doaj
Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices
Journal of Forecasting, EarlyView.ABSTRACT This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models ...
Talha Omer +3 more
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Predicción de activos financieros usando modelos ARIMA y Redes Neuronales Autorregresivas
Revista Ingeniería, Matemáticas y Ciencias de la InformaciónEn este trabajo son aplicados diferentes métodos de pronóstico para predecir los precios y rendimientos de las acciones para dos de las principales empresas que transan en la bolsa de valores de Colombia: Bancolombia y Ecopetrol.
Johan Andrés Uribe Escudero +2 more
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A Hybrid Model Integrating LSTM with Multiple GARCH-Type Models for Volatility and Var Forecast [PDF]
, 2023 Jiayi Liu
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A Deep Learning Framework for Forecasting Medium‐Term Covariance in Multiasset Portfolios
Journal of Forecasting, EarlyView.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
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Copula multivariate GARCH model with constrained Hamiltonian Monte Carlo [PDF]
, 2019 Martin Burda, Louis Bélisle
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Coherent Forecasting of Realized Volatility
Journal of Forecasting, EarlyView.ABSTRACT The QLIKE loss function is the stylized favorite of the literature on volatility forecasting when it comes to out‐of‐sample evaluation and the state of the art model for realized volatility (RV) forecasting is the HAR model, which minimizes the squared error loss for in‐sample estimation of the parameters.
Marius Puke, Karsten Schweikert
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