Results 11 to 20 of about 40,912 (303)
Closing the GARCH gap: Continuous time GARCH modeling [PDF]
Journal of Econometrics, 1996 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Feike C Drost, Bas J M Werker
exaly +9 more sources
Multivariate GARCH Models: A Survey [PDF]
SSRN Electronic Journal, 2003 AbstractThis paper surveys the most important developments in multivariate ARCH‐type modelling. It reviews the model specifications and inference methods, and identifies likely directions of future research. Copyright © 2006 John Wiley & Sons, Ltd.
BAUWENS, Luc +2 more
openaire +4 more sources
Improving GARCH volatility forecasts with regime-switching GARCH [PDF]
Empirical Economics, 2002 Many researchers use GARCH models to generate volatility forecasts. Using data on three major U.S. dollar exchange rates we show that such forecasts are too high in volatile periods. We argue that this is due to the high persistence of shocks in GARCH forecasts.
Franc Klaassen, Klaassen Franc
exaly +7 more sources
Empirical performance of GARCH, GARCH-M, GJR-GARCH and log-GARCH models for returns volatility
Journal of Physics: Conference Series, 2019 Abstract Volatility plays an important role in the field of financial econometrics as one of the risk indicators. Many various models address the problem of modeling the volatilities of financial asset returns. This study provides a new empirical performance comparison of the four different GARCH-type models, namely GARCH, GARCH-M, GJR ...
D B Nugroho +5 more
openaire +2 more sources
Bitcoin, gold and the dollar – A GARCH volatility analysis
Finance Research Letters, 2016 Anne Haubo Dyhrberg
exaly +2 more sources
Revista de Administração Contemporânea, 2021 ABSTRACT Context: modeling volatility is an advanced technique in financial econometrics, with several applications for academic research. Objective: in this tutorial paper, we will address the topic of volatility modeling in R. We will discuss the underlying logic of GARCH models, their representation and estimation process, along with a descriptive
Marcelo Scherer Perlin +3 more
openaire +5 more sources
LSTM–GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios
Computational Economics, 2023 In the present work, the volatility of the leading cryptocurrencies is predicted through generalised autoregressive conditional heteroskedasticity (GARCH) models, multilayer perceptron (MLP), long short-term memory (LSTM), and hybrid models of the type ...
A. García-Medina, Ester Aguayo-Moreno
semanticscholar +1 more source
Forecasting gains by using extreme value theory with realised GARCH filter
IIMB Management Review, 2021 Early empirical evidence suggests that the realised generalised autoregressive conditional heteroskedasticity (GARCH) model provides significant forecasting gains over the standard GARCH models in volatility forecasting.
Samit Paul, Prateek Sharma
doaj +1 more source
COVID-19 Pandemic & Financial Market Volatility; Evidence from GARCH Models
Journal of Risk and Financial Management, 2023 Across the globe, COVID-19 has disrupted the financial markets, making them more volatile. Thus, this paper examines the market volatility and asymmetric behavior of Bitcoin, EUR, S&P 500 index, Gold, Crude Oil, and Sugar during the COVID-19 pandemic. We
Maaz Khan +4 more
semanticscholar +1 more source
Reviews of Management Sciences, 2022 Purpose: The purpose of this paper is to predict the volatility of the KSE-100 index using econometric and machine learning models. It also designs hybrid models for volatility forecasting by combining these two models in three different ways ...
Komal Batool +2 more
doaj +1 more source