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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
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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
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Hybrid deep learning and GARCH-family models for forecasting volatility of cryptocurrencies
Machine Learning with Applications, 2023 The combination of Deep Learning and GARCH-type models has been proved to be superior to the single models in forecasting of volatility in various markets such as energy, main metals, and especially stock markets.
Bahareh Amirshahi, Salim Lahmiri
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Challenges of integrated variance estimation in emerging stock markets [PDF]
Zbornik radova Ekonomskog fakulteta u Rijeci : časopis za ekonomsku teoriju i praksu, 2019 Estimating integrated variance, using high frequency data, requires modelling experience and data crunching skills. Although intraday returns have attracted much attention in recent years, handling these data is challenging because of their ...
Josip Arnerić, Mario Matković
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Multivariate GARCH Models [PDF]
SSRN Electronic Journal, 2008 This article contains a review of multivariate GARCH models. Most common GARCH models are presented and their properties considered. This also includes nonparametric and semiparametric models. Existing specification and misspecification tests are discussed.
Silvennoinen, Annastiina +1 more
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Modeling the volatility of Bitcoin returns using Nonparametric GARCH models
Academic Finance, 2022 Objective: The purpose of this paper is to demonstrate the effectiveness of the nonparametric GARCH model for the prediction of future Bitcoin prices. Methodology: The parametric GARCH models to characterize the volatility of Bitcoin returns are ...
Sami MESTIRI
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GARCH Modeling of Cryptocurrencies [PDF]
SSRN Electronic Journal, 2017 With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms of five criteria.
Chu, Jeffrey +3 more
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Comparing GARCH Models by Introducing Fuzzy Asymmetric Realized GARCH [PDF]
مدلسازی اقتصادسنجی, 2018 Estimation of conditional variance has lots of application reflecting economic, especially financial economics, social economics and political economics’ risk and volatility research.
Esmaiel Abounoori, Mohammad Amin Zabol
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GRG Non-Linear and ARWM Methods for Estimating the GARCH-M, GJR, and log-GARCH Models
JTAM (Jurnal Teori dan Aplikasi Matematika), 2022 Numerous variants of the basic Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have been proposed to provide good volatility estimating and forecasting. Most of the study does not work Excel’s Solver to estimate GARCH-type models.
Didit Budi Nugroho +5 more
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Regime Switching GARCH Models [PDF]
SSRN Electronic Journal, 2006 We develop univariate regime-switching GARCH (RS-GARCH) models wherein the conditional variance switches in time from one GARCH process to another. The switching is governed by a time-varying probability, specified as a function of past information. We provide sufficient conditions for stationarity and existence of moments.
Luc, BAUWENS +2 more
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