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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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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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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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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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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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Quantitative Finance and Economics, 2022 The aim of this paper is to examine exchange rate volatility using GARCH models with a new innovation distribution, the Normal Tempered Stable. We estimated daily exchange rate volatility using different distributions (Normal, Student, NIG) in order to ...
Sahar Charfi, Farouk Mselmi
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Modeling and Forecasting the Volatility of NIFTY 50 Using GARCH and RNN Models
Economies, 2022 The stock market is constantly shifting and full of unknowns. In India in 2000, technological advancements led to significant growth in the Indian stock market, introducing online share trading via the internet and computers.
Vanshu Mahajan +2 more
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Volatility regimes of selected central European stock returns: a Markov switching GARCH approach
Journal of Business Economics and Management, 2022 This paper investigates the weekly stock market data of the Hungarian stock index BUX, the Czech stock index PX and the Polish stock index WIG20 spanning from January 7, 2001 to April 18, 2021.
Michaela Chocholatá
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