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Journal articles on the topic 'GARCH-family models'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Birău, Ramona, and Jatin Trivedi. "Estimating Emerging Stock Market Volatility Using Garch Family Models." Indian Journal of Applied Research 3, no. 9 (2011): 331–33. http://dx.doi.org/10.15373/2249555x/sept2013/99.

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2

Li, Dong, and Wuqing Wu. "RENORMING VOLATILITIES IN A FAMILY OF GARCH MODELS." Econometric Theory 34, no. 6 (2017): 1370–82. http://dx.doi.org/10.1017/s0266466617000470.

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This paper studies the weak convergence of renorming volatilities in a family of GARCH(1,1) models from a functional point of view. After suitable renormalization, it is shown that the limiting distribution is a geometric Brownian motion when the associated top Lyapunov exponent γ > 0 and is an exponential functional of the maximum process of a Brownian motion when γ = 0. This indicates that the volatility of the GARCH(1,1)-type model has a completely different random structure according to the sign of γ. The obtained results further strengthen our understanding of volatilities in GARCH-typ

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3

Zhao, Pengfei, Haoren Zhu, Wilfred Siu Hung NG, and Dik Lun Lee. "From GARCH to Neural Network for Volatility Forecast." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16998–7006. http://dx.doi.org/10.1609/aaai.v38i15.29643.

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Volatility, as a measure of uncertainty, plays a crucial role in numerous financial activities such as risk management. The Econometrics and Machine Learning communities have developed two distinct approaches for financial volatility forecasting: the stochastic approach and the neural network (NN) approach. Despite their individual strengths, these methodologies have conventionally evolved in separate research trajectories with little interaction between them. This study endeavors to bridge this gap by establishing an equivalence relationship between models of the GARCH family and their corres

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Bildirici, Melike, and Özgür Ersin. "Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns." Scientific World Journal 2014 (2014): 1–21. http://dx.doi.org/10.1155/2014/497941.

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The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN

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Tahira Bano Qsim, Masooma Fatima, Anam Javed, and Hina Ali. "Estimating and Forecasting Tax Revenues Using GARCH Family of Models: A Case of Pakistan." Journal for Social Science Archives 2, no. 2 (2024): 585–99. https://doi.org/10.59075/jssa.v2i2.101.

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Forecasting plays a vital role in effective planning and decision-making for policy formulation across a variety of fields of life. The Nonlinear models such as the GARCH family, including symmetric and asymmetric generalized autoregressive conditional heteroscedastic (GARCH) models with both normal and non-normal innovations are applied in this study to capture the dynamic and asymmetric features of the two tax revenue series, sales Tax and Direct Tax in Pakistan. Additionally, Autoregressive Moving Average (ARMA) model is used as the mean model. The prime objective of this research is to exa

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Dangal, Dil Nath, and Ram Prasad Gajurel. "Volatility of Daily Nepal Stock Exchange (Nepse) Index Return: A Garch Family Models." Tribhuvan University Journal 36, no. 01 (2021): 31–44. http://dx.doi.org/10.3126/tuj.v36i01.43514.

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The major intend of this study is to investigate the volatility clustering in NEPSE index. To reach the conclusion, 3392 annually observed time series data from 1 June 2006 to 7 April 2021 were obtained from various volume of annual trading report of Nepal Stock Exchange (NEPSE) and website of NEPSE and symmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models––GARCH (1,1), GARCH-M (1,1) and asymmetric GARCH family models––TGARCH (1,1), EGARCH (1,1), and PGARCH (1,1) were employed. The stylized facts confirm that the volatility clustering and leverage effect on the ret

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He, Changli, Timo Teräsvirta, and Hans Malmsten. "MOMENT STRUCTURE OF A FAMILY OF FIRST-ORDER EXPONENTIAL GARCH MODELS." Econometric Theory 18, no. 4 (2002): 868–85. http://dx.doi.org/10.1017/s0266466602184039.

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In this paper we consider the moment structure of a class of first-order exponential generalized autoregressive conditional heteroskedasticity (GARCH) models. This class contains as special cases both the standard exponential GARCH model and the symmetric and asymmetric logarithmic GARCH model. Conditions for the existence of any arbitrary moment are given. Furthermore, the expressions for the kurtosis and the autocorrelations of positive powers of absolute-valued observations are derived. The properties of the autocorrelation structure are discussed and compared to those of the standard first

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8

Ou, Jishun, Xiangmei Huang, Yang Zhou, Zhigang Zhou, and Qinghui Nie. "Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework." Entropy 24, no. 10 (2022): 1392. http://dx.doi.org/10.3390/e24101392.

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Traffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process. A few generalized autoregressive conditional heteroscedastic (GARCH) models have been developed to capture and hence forecast the volatility of traffic flow. Although these models have been confirmed to be capable of producing more reliable forecasts than traditional point forecasting models, the more or less imposed restrictions on parameter estimations may make the asymmetric property of traffic volatility be not

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9

Dinku, Tirngo, Worku Gardachw, and Ngozi Adeleye. "Price Volatility for Selected Agricultural Commodities in Ethiopia: Evidence from GARCH Models." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 18 (November 11, 2021): 1380–88. http://dx.doi.org/10.37394/23207.2021.18.127.

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This study models the volatility of returns for selected agricultural commodity prices in Ethiopia using the generalized autoregressive conditional heteroskedasticity (GARCH) approach. GARCH family models, specifically threshold GARCH and exponential GARCH were employed to analyze the time varying volatility of selected agricultural commodities prices from 2010 to 2021. The data analysis results revealed that, out of the GARCH specifications, the EGARCH model with the normal distributional assumption of residuals was a better fit model for the price volatility of “teff” and “red pepper” in whi

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10

Lee, O., and H. M. Kim. "Covariance stationary GARCH-family models with long memory property." Journal of the Korean Statistical Society 37, no. 1 (2008): 29–35. http://dx.doi.org/10.1016/j.jkss.2007.07.001.

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11

Birău, Ramona, and Jatin Trivedi. "Modeling Return Volatility of Bric Emerging Stock Markets Using Garch Family Models." Indian Journal of Applied Research 3, no. 11 (2011): 119–21. http://dx.doi.org/10.15373/2249555x/nov2013/39.

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12

Mahmud, Mahreen. "The Forecasting Ability of GARCH Models for the 2003–07 Crisis: Evidence from S&P500 Index Volatility." Lahore Journal of Business 1, no. 1 (2012): 37–58. http://dx.doi.org/10.35536/ljb.2012.v1.i1.a3.

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This article studies the ability of the GARCH family of models to accurately forecast the volatility of S&P500 stock index returns across the financial crisis that affected markets in 2003–07. We find the GJR-GARCH (1,1) model to be superior in its ability to forecast the volatility of the initial crisis period (2003– 06) compared to its realized volatility, which acts as a proxy for the actual. This model is then extended to make forecasts for the crisis period. We conclude that the model’s ability to forecast volatility across the crisis is not substantially affected, thus supporting the

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13

Abebe, T. H. "Using Models of the GARCH Family to Estimate the Level of Food and Non-Food Inflation in Ethiopia." Journal of Applied Economic Research 20, no. 4 (2021): 726–49. http://dx.doi.org/10.15826/vestnik.2021.20.4.028.

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An increase in inflation volatility implies higher uncertainty about future prices. As a result, producers and consumers can be affected by the increased inflation volatility, because it increases the uncertainty and the risk in the market. Thus, inflation volatility attracts the attention of researchers to find a suitable model which can predict the future conditions of the market. This study aims to fit appropriate ARMA-GARCH family models for food and non-food inflation rate of from the period January 1971 through June 2020. Since the main objective of the study is identifying an appropriat

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14

Naka, Atsuyuki, and Ece Oral. "Stock Return Volatility And Trading Volume Relationships Captured With Stable Paretian GARCH And Threshold GARCH Models." Journal of Business & Economics Research (JBER) 11, no. 1 (2012): 47. http://dx.doi.org/10.19030/jber.v11i1.7522.

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<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify;" class="MsoNormal"><span style="font-size: 10pt; mso-fareast-language: JA;"><span style="font-family: Times New Roman;">This paper examines the volatility of Dow Jones Industrial Average stock returns and the trading volume by employing stable Paretian GARCH and Threshold GARCH (TGARCH) models. Our results indicate that the trading volume significantly contributes to the volatility of stock returns. Additionally, strong leverage effects exist

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15

Kim, Jeehye, and Kook-Hyun Chang. "A Study on the Empirical Performance of the Volatility Estimation Models." Journal of Derivatives and Quantitative Studies 23, no. 1 (2015): 73–97. http://dx.doi.org/10.1108/jdqs-01-2015-b0004.

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In this paper, we examine which volatility estimation model best explains KOSPI200-realized volatility in the Korean stock market, which has both heteroscedasticity and jump risk. The sample covers from July 1, 2010 to July 31, 2014, which is a low-volatility period in Korean stock market by which time the effects of the global crisis had almost vanished. We use the intra-day return of KOSPI200, which has been measured by 5-minute intervals. This study finds GARCH-family models are efficient estimators compared to historical volatility and EWMA. Also, among the GARCH-family models, Jump-Diffus

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Kumar, Arya, and Uma Sankar Mishra. "Testing the Volatility and Model Designing for International Tourist Footfalls in India: Applying GARCH Family Models." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 212–20. http://dx.doi.org/10.5373/jardcs/v11sp10/20192793.

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17

Kim, Jong-Min, Chulhee Jun, and Junyoup Lee. "Forecasting the Volatility of the Cryptocurrency Market by GARCH and Stochastic Volatility." Mathematics 9, no. 14 (2021): 1614. http://dx.doi.org/10.3390/math9141614.

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This study examines the volatility of nine leading cryptocurrencies by market capitalization—Bitcoin, XRP, Ethereum, Bitcoin Cash, Stellar, Litecoin, TRON, Cardano, and IOTA-by using a Bayesian Stochastic Volatility (SV) model and several GARCH models. We find that when we deal with extremely volatile financial data, such as cryptocurrencies, the SV model performs better than the GARCH family models. Moreover, the forecasting errors of the SV model, compared with the GARCH models, tend to be more accurate as forecast time horizons are longer. This deepens our insight into volatility forecast m

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18

Wang, Yan, Pingzeng Liu, Ke Zhu, Lining Liu, Yan Zhang, and Guangli Xu. "A Garlic-Price-Prediction Approach Based on Combined LSTM and GARCH-Family Model." Applied Sciences 12, no. 22 (2022): 11366. http://dx.doi.org/10.3390/app122211366.

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The frequent and sharp fluctuations in garlic prices seriously affect the sustainable development of the garlic industry. Accurate prediction of garlic prices can facilitate correct evaluation and scientific decision making by garlic practitioners, thereby avoiding market risks and promoting the healthy development of the garlic industry. To improve the prediction accuracy of garlic prices, this paper proposes a garlic-price-prediction method based on a combination of long short-term memory (LSTM) and multiple generalized autoregressive conditional heteroskedasticity (GARCH)-family models for

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19

Sharma, Prateek, and Vipul _. "Forecasting stock index volatility with GARCH models: international evidence." Studies in Economics and Finance 32, no. 4 (2015): 445–63. http://dx.doi.org/10.1108/sef-11-2014-0212.

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Purpose – The purpose of this paper is to compare the daily conditional variance forecasts of seven GARCH-family models. This paper investigates whether the advanced GARCH models outperform the standard GARCH model in forecasting the variance of stock indices. Design/methodology/approach – Using the daily price observations of 21 stock indices of the world, this paper forecasts one-step-ahead conditional variance with each forecasting model, for the period 1 January 2000 to 30 November 2013. The forecasts are then compared using multiple statistical tests. Findings – It is found that the stand

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20

Ivanov, Mikhail A., and Yanina A. Roshchina. "A mixture GARCH-based recurrent neural network for financial volatility forecasting." Journal Of Applied Informatics 19, no. 5 (2024): 30–47. https://doi.org/10.37791/2687-0649-2024-19-5-30-47.

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This paper is devoted to the development of mathematical models of stock price volatility in financial markets, with a focus on the GARCH family models. The paper proposes to consider these models from a new perspective: as recurrent rather than autoregressive. The main idea is that GARCH econometric models can be interpreted as recurrent neural networks, especially after introducing an activation function into the equation of variance dynamics. The relevance of the study stems from the constant need to improve the accuracy of volatility forecasting in modern financial markets, especially in t

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21

Ayele, Amare Wubishet, Emmanuel Gabreyohannes, and Hayimro Edmealem. "Generalized Autoregressive Conditional Heteroskedastic Model to Examine Silver Price Volatility and Its Macroeconomic Determinant in Ethiopia Market." Journal of Probability and Statistics 2020 (May 25, 2020): 1–10. http://dx.doi.org/10.1155/2020/5095181.

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Like most commodities, the price of silver is driven by supply and demand speculation, which makes the price of silver notoriously volatile due to the smaller market, lower market liquidity, and fluctuations in demand between industrial and store value use. The concern of this article was to model and forecast the silver price volatility dynamics on the Ethiopian market using GARCH family models using data from January 1998 to January 2014. The price return series of silver shows the characteristics of financial time series such as leptokurtic distributions and thus can suitably be modeled usi

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22

Birău, Ramona, and Jatin Trivedi. "Investigating Long-Term Volatility of Warsaw Stock Exchange Based on Garch Family Models." International Journal of Scientific Research 2, no. 9 (2012): 239–41. http://dx.doi.org/10.15373/22778179/sep2013/79.

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23

Létourneau, Pascal. "An Improved Estimation Method for a Family of GARCH Models." Journal of Derivatives 27, no. 1 (2019): 67–91. http://dx.doi.org/10.3905/jod.2019.1.081.

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24

Hentschel, Ludger. "All in the family Nesting symmetric and asymmetric GARCH models." Journal of Financial Economics 39, no. 1 (1995): 71–104. http://dx.doi.org/10.1016/0304-405x(94)00821-h.

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25

Hang, Wenqian. "Modeling RMB Exchange Rate Volatility – Application of GARCH Family Models." SHS Web of Conferences 154 (2023): 02016. http://dx.doi.org/10.1051/shsconf/202315402016.

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The exchange rate risk caused by the two-way fluctuation of the RMB exchange rate will bring many effects. The volatility of the foreign exchange market is the most common feature of the financial market. Therefore, the research on the volatility of the RMB exchange rate is of great significance in economic and financial aspects. Through statistical analysis of the RMB exchange rate data, an ARMA model was established to eliminate the auto-correlation of the sequence, and the GARCH family model was combined to fit the data. Comparing different distribution hypotheses, the EGARCH model under th

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26

Kumar, Dilip. "Structural breaks in unbiased volatility estimator: Modeling and forecasting." Journal of Prediction Markets 11, no. 1 (2017): 27–50. http://dx.doi.org/10.5750/jpm.v11i1.1239.

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The study provides a framework to model the unbiased extreme value volatility estimator (The AddRS estimator) in presence of structural breaks. We observe that the structural breaks in the volatility based on the AddRS estimator can partly explain its long memory property. We evaluate the forecasting performance of the proposed framework and compare the results with the corresponding results of the models from the GARCH family. The forecasts evaluation exercises consider the cases when future breaks are known as well as unknown. Our findings indicate that the proposed framework outperform the

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Nugroho, Didit Budi, Tundjung Mahatma, and Yulius Pratomo. "GARCH Models under Power Transformed Returns: Empirical Evidence from International Stock Indices." Austrian Journal of Statistics 50, no. 4 (2021): 1–18. http://dx.doi.org/10.17713/ajs.v50i4.1075.

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This study evaluates the empirical performance of four power transformation families: extended Tukey, Modulus, Exponential, and Yeo--Johnson, in modeling the return in the context of GARCH(1,1) models with two error distributions: Gaussian (normal) and Student-t. We employ an Adaptive Random Walk Metropolis method in Markov Chain Monte Carlo scheme to draw parameters. Using 19 international stock indices from the Oxford-Man Institute and basing on the log likelihood, Akaike Information Criterion, Bayesian Information Criterion, and Deviance Information Criterion, the use of power transformatio

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28

Aktan, Bora, Renata Korsakienė, and Rasa Smaliukienė. "TIME‐VARYING VOLATILITY MODELLING OF BALTIC STOCK MARKETS." Journal of Business Economics and Management 11, no. 3 (2010): 511–32. http://dx.doi.org/10.3846/jbem.2010.25.

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As time‐varying volatility has found applications in roughly all time series modelling in economics, it largely draws attention in the areas of financial markets. This study also examines the characteristics of conditional volatility in the Baltic Stock Markets (Estonia, Latvia and Lithuania) by using a broad range of GARCH volatility models. Correctly forecasting the volatility leads to better understanding and managing financial market risk. Daily returns from four Baltic stock indexes are used; Estonia (TALSE index), Latvia (RIGSE index), Lithuania (VILSE index) and synthetic BALTIC benchma

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29

Seo, Monghwan, Sungchul Lee, and Geonwoo Kim. "Forecasting the Volatility of Stock Market Index Using the Hybrid Models with Google Domestic Trends." Fluctuation and Noise Letters 18, no. 01 (2019): 1950006. http://dx.doi.org/10.1142/s0219477519500068.

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In order to improve the forecasting accuracy of the volatilities of the markets, we propose the hybrid models based on artificial neural networks with multi-hidden layers in this paper. Specifically, the hybrid models are built using the estimated volatilities obtained from GARCH family models and Google domestic trends (GDTs) as input variables. We further carry out many experiments varying the number of layers and activation functions to obtain the accurate hybrid model for forecasting volatility. The proposed models are applied to forecast weekly and monthly volatilities of S&P 500 inde

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Kumar, Arya, Jyotirmayee Sahoo, Jyotsnarani Sahoo, Subhashree Nanda, and Devi Debyani. "Exploring Asymmetric GARCH Models for Predicting Indian Base Metal Price Volatility." Folia Oeconomica Stetinensia 24, no. 1 (2024): 105–23. http://dx.doi.org/10.2478/foli-2024-0007.

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Abstract Research background Many studies have been done in the field of predicting the Volatility of Commodities; however, very little or no analysis has been conducted on any sector, industry, or indices to identify which model is best to understand the asset’s characteristics, as there is a hypothesis that all financial time series can be interpreted by implementing the same model. Purpose The primary objective is to identify different tools developed by the researchers in estimating impulsive clustering and leverage effects. A comparison will be made among the available tools of the GARCH

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31

Asif, Muhammad, and Abdul Aziz. "Equity market volatility using garch models- evidence from Pakistan stock exchange (kse-100 index)." International Journal of Accounting and Economics Studies 4, no. 2 (2016): 96. http://dx.doi.org/10.14419/ijaes.v4i2.6200.

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Purpose – The purpose of this paper is to investigate the cluster volatility of return distribution in the Pakistan Stock exchange (PSX) formerly named Karachi stock exchange (KSE-100 Index). GARCH model for characterizing financial market volatility is discussed.Design/methodology/approach –This study used daily time series of the market index PSX (KSE-100) data over the period from January 1st, 2008 to December 31st, 2015, 1983 observations have been collected from KSE website.ARCH family models have been used, such as GARCH, EGARCH, PGARCH and TARCH models, to estimate cluster volatility. S

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Jatau, Monica, Moses Abanyam Chiawa, and David Adugh Kuhe. "Modeling Stock Returns Volatility in Nigeria: Applications of GARCH Family Models." Asian Journal of Economics, Business and Accounting 9, no. 1 (2018): 1–12. http://dx.doi.org/10.9734/ajeba/2018/39861.

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Rana, Surya Bahadur. "Dynamics of Time Varying Volatility in Stock Returns: Evidence from Nepal Stock Exchange." Journal of Business and Social Sciences Research 5, no. 1 (2020): 15–34. http://dx.doi.org/10.3126/jbssr.v5i1.30196.

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This study examines the properties of time varying volatility of daily stock returns in Nepal over the period 2011-2020 using 2059 observations on daily returns of NEPSE index series. The study examines various symmetric and asymmetric GARCH family models using several specifications of error distribution. The results of symmetric GARCH (1,1) and GARCH-M (1, 1) models indicate that there is volatility persistence in daily returns on composite NEPSE index series over the sampled period. However, the estimated results for GARCH-M (1, 1) models show that the stock returns in Nepal offer no signif

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Poignard, Benjamin, and Jean-David Fermanian. "DYNAMIC ASSET CORRELATIONS BASED ON VINES." Econometric Theory 35, no. 1 (2018): 167–97. http://dx.doi.org/10.1017/s026646661800004x.

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We develop a new method for generating dynamics of conditional correlation matrices of asset returns. These correlation matrices are parameterized by a subset of their partial correlations, whose structure is described by a set of connected trees called “vine”. Partial correlation processes can be specified separately and arbitrarily, providing a new family of very flexible multivariate GARCH processes, called “vine-GARCH” processes. We estimate such models by quasi-maximum likelihood. We compare our models with DCC and GAS-type specifications through simulated experiments and we evaluate thei

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AL-Najjar, Dana Mohammad. "Modelling and Estimation of Volatility Using ARCH/GARCH Models in Jordan’s Stock Market." Asian Journal of Finance & Accounting 8, no. 1 (2016): 152. http://dx.doi.org/10.5296/ajfa.v8i1.9129.

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<p>Financials have been concerned constantly with factors that have impact on both taking and assessing various financial decisions in firms. Hence modelling volatility in financial markets is one of the factors that have direct role and effect on pricing, risk and portfolio management. Therefore, this study aims to examine the volatility characteristics on Jordan’s capital market that include; clustering volatility, leptokurtosis, and leverage effect. This objective can be accomplished by selecting symmetric and asymmetric models from GARCH family models. This study applies; ARCH, GARCH

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Mohammad, Naim Azimi. "Rationalizing an Econometric Test Model: An Empirical Investigation of ARCH Family Models." Journal of Research in Business, Economics and Management 5, no. 4 (2016): 625–34. https://doi.org/10.5281/zenodo.3965517.

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Selecting an appropriate econometric testing model is of high value to scholars of this field. The central focus of this paper is to empirically investigate the rationality and appropriateness of an econometric testing model for time series macroeconomic variables that exhibit clustering volatility. We test the India’s Producer Price Index (PPI) covering the period January 01, 1947 to October 30, 2015 arranged on monthly basis by using the ARCH family models. The empirical investigation and statistical analysis show that among ARCH, GARCH, TARCH, PARCH and EGARCH models, the most rationa

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JAFARI, G. R., A. BAHRAMINASAB, and P. NOROUZZADEH. "WHY DOES THE STANDARD GARCH(1, 1) MODEL WORK WELL?" International Journal of Modern Physics C 18, no. 07 (2007): 1223–30. http://dx.doi.org/10.1142/s0129183107011261.

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The AutoRegressive Conditional Heteroskedasticity (ARCH) and its generalized version (GARCH) family of models have grown to encompass a wide range of specifications, each of them is designed to enhance the ability of the model to capture the characteristics of stochastic data, such as financial time series. The existing literature provides little guidance on how to select optimal parameters, which are critical in efficiency of the model, among the infinite range of available parameters. We introduce a new criterion to find suitable parameters in GARCH models by using Markov length, which is th

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Spulbar, Cristi, Ramona Birau, Jatin Trivedi, Iqbal Thonse Hawaldar, and Elena Loredana Minea. "Testing volatility spillovers using GARCH models in the Japanese stock market during COVID-19." Investment Management and Financial Innovations 19, no. 1 (2022): 262–73. http://dx.doi.org/10.21511/imfi.19(1).2022.20.

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This paper investigates volatility spillovers in the stock market in Japan during the COVID-19 pandemic by using GARCH family models. The empirical analysis is focused on the dynamics of the NIKKEI 225 stock market index during the sample period from July 30, 1998, to January 24, 2022. In other words, the sample period covers both the period of the global financial crisis (GFC) and the COVID-19 pandemic. The econometrics includes GARCH (1,1), GJR (1,1), and EGARCH (1,1) models. By applying GARCH family models, this empirical study also examines the long-term behavior of the Japanese stock mark

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Hefnawy, Fatma, and V. Shaker. "Measuring the Wheat Price Volatility in Global Commodity Market: GARCH Family Models." Journal of Agricultural Economics and Social Sciences 12, no. 12 (2021): 1205–8. http://dx.doi.org/10.21608/jaess.2022.118255.1022.

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Atabani Adi, Agya. "Modeling exchange rate return volatility of RMB/USD using GARCH family models." Journal of Chinese Economic and Business Studies 17, no. 2 (2019): 169–87. http://dx.doi.org/10.1080/14765284.2019.1600933.

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Bildirici, Melike, and Özgür Ömer Ersin. "Forecasting oil prices: Smooth transition and neural network augmented GARCH family models." Journal of Petroleum Science and Engineering 109 (September 2013): 230–40. http://dx.doi.org/10.1016/j.petrol.2013.08.003.

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Khan, Maaz, Umar Nawaz Kayani, Mrestyal Khan, Khurrum Shahzad Mughal, and Mohammad Haseeb. "COVID-19 Pandemic & Financial Market Volatility; Evidence from GARCH Models." Journal of Risk and Financial Management 16, no. 1 (2023): 50. http://dx.doi.org/10.3390/jrfm16010050.

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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 applied the GARCH (1, 1), GJR-GARCH (1, 1), and EGARCH (1, 1) econometric models on the daily time series returns data ranging from 27 November 2018 to 15 June 2021. The empirical findings show a high level of volatility persistence in all the financial markets during the COVID-19 pandemic. Moreover, the Crude Oil and S&P 500 index sho

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Nadarajah, Saralees, Jules Clement Mba, Patrick Rakotomarolahy, and Henri T. J. E. Ratolojanahary. "Ensemble Learning and an Adaptive Neuro-Fuzzy Inference System for Cryptocurrency Volatility Forecasting." Journal of Risk and Financial Management 18, no. 2 (2025): 52. https://doi.org/10.3390/jrfm18020052.

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The purpose of this study is to conduct an empirical comparative study of volatility models for three of the most popular cryptocurrencies. We study the volatility of the following cryptocurrencies: Bitcoin, Ethereum, and Litecoin. We consider the GARCH-type, boosting-family-tree-based ensemble learning, and ANFIS volatility models for these financial crypto-assets, which some have claimed capture stylized facts about cryptocurrency volatility well. We conduct comparative studies on in-sample and out-of-sample empirical analyses. The results show that tree-based ensemble learning delivers bett

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Salgado, Roberto J. Santillán, Marissa Martínez Preece, and Francisco López Herrera. "Modeling the risk-return characteristics of the SB1 Mexican private pension fund index." Global Journal of Business, Economics and Management: Current Issues 5, no. 2 (2016): 70. http://dx.doi.org/10.18844/gjbem.v5i2.370.

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This paper analyzes the returns and variance behavior of the largest specialized private pension investment funds index in Mexico, the SIEFORE Básica 1 (or, SB1). The analysis was carried out with time series techniques to model the returns and volatility of the SB1, using publicly available historical data for SB1. Like many standard financial time series, the SB1 returns show non-normality, volatility clusters and excess kurtosis. The econometric characteristics of the series were initially modeled using three GARCH family models: GARCH (1,1), TGARCH and IGARCH. However, due to the presence

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Zamrus, Nurul Asyikin, Mohd Hirzie Mohd Rodzhan, and Nurul Najihah Mohamad. "Forecasting Model of Air Pollution Index using Generalized Autoregressive Conditional Heteroskedasticity Family (GARCH)." Malaysian Journal of Fundamental and Applied Sciences 18, no. 2 (2022): 184–96. http://dx.doi.org/10.11113/mjfas.v18n2.2279.

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The Air Pollution Index (API) of Malaysia has increased consistently in recent decades, becoming a serious environment issue concern. In this paper, we analyzed daily integer value time series data for API in Sarawak from January to June in 2019 using generalized autoregressive conditional heteroskedasticity (GARCH) family for discrete case namely poisson integer value GARCH (INGARCH), negative binomial integer value GARCH (NBINGARCH) and integer value autoregressive conditional heteroskedasticity (INARCH) models. The parameters of the models will be estimated using quasi likelihood estimator

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Alex, Dhanya, and Roshna Varghese. "Derivative Trading and Spot Market Volatility: Evidence from Indian Market." International Journal Of Innovation And Economic Development 1, no. 3 (2015): 23–34. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.13.2003.

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The present study tries to estimate the effect of introduction of individual stock derivatives on the underlying stock volatility in Indian stock market. To estimate the effect of introduction of derivatives on stock market, GARCH family models which are known for their ability to model volatility. The return series of the ten companies were tested using methods like, unit root test and descriptive statistics to confirm that GARCH models could be used. Using these models, the asymmetric nature of stock returns and the volatility of stock returns on the introduction of derivatives are checked.

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Rahman, Md Habibur, and A. H. M. Ziaul Haq. "Forecasting Index Return Volatility of The Chittagong Stock Exchange of Bangladesh using GARCH Models." Journal of Business Studies 03, no. 01 (2022): 169–96. http://dx.doi.org/10.58753/jbspust.3.1.2022.11.

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Purpose: The aim of this research is to identify the best-fitted model(s) for estimating and forecasting the return volatility of the Chittagong Stock Exchange (CSE) in Bangladesh. Methodology: The study analyzes the returns of the Chittagong Stock Exchange's (CSE) daily Selective Categories Index (CSCX) from February 4, 2013 to December 31, 2021 (as a full sample) and from July 1, 2021 to December 30, 2021 (for forecasting). The researcher used GARCH family approaches considering different error distributions, to find the well-suited model(s) for the CSCX index. The researchers used ARMA to d

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Mbwambo, Haika Andrew, and Laban Gaspe Letema. "Forecasting volatility in oil returns using asymmetric GARCH models: evidence from Tanzania." International Journal of Research in Business and Social Science (2147- 4478) 12, no. 1 (2023): 204–11. http://dx.doi.org/10.20525/ijrbs.v12i1.2308.

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Crude oil is, without a doubt, one of the most significant commodities in the modern world. The highly contagious coronavirus, the conflict between Ukraine and Russia, and not to mention the unusual turn of events worldwide have all significantly impacted crude oil prices. Since oil is required for all critical economic activities, such as production and transportation, a forecast for crude oil prices is essential. Using a range of GARCH models at such an intense time, this study attempted to close this gap by forecasting crude oil volatility. To forecast the returns of Brent crude oil prices

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Szolgayová, Elena Peksová, Michaela Danačová, Magda Komorniková, and Ján Szolgay. "Hybrid Forecasting of Daily River Discharges Considering Autoregressive Heteroscedasticity." Slovak Journal of Civil Engineering 25, no. 2 (2017): 39–48. http://dx.doi.org/10.1515/sjce-2017-0011.

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AbstractIt is widely acknowledged that in the hydrological and meteorological communities, there is a continuing need to improve the quality of quantitative rainfall and river flow forecasts. A hybrid (combined deterministic-stochastic) modelling approach is proposed here that combines the advantages offered by modelling the system dynamics with a deterministic model and a deterministic forecasting error series with a data-driven model in parallel. Since the processes to be modelled are generally nonlinear and the model error series may exhibit nonstationarity and heteroscedasticity, GARCH-typ

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Riza, Putri Pratama, and Viverita. "Modeling Volatility Asymmetry in Government-Owned Stocks: Evidence from Value at Risk Estimation in Indonesia." International Journal of Current Science Research and Review 08, no. 05 (2025): 2681–93. https://doi.org/10.5281/zenodo.15560828.

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Abstract : This study evaluates the comparative effectiveness of four volatility models—EWMA, GARCH(1,1), EGARCH, and TGARCH—in estimating daily Value at Risk (VaR) for a portfolio of Indonesian state-owned enterprise (SOE) stocks over the period 2019–2024. Motivated by the rapid growth of retail investor participation and increasing exposure to market risk in Indonesia’s emerging capital market, the research addresses a critical gap in empirical risk modeling for government-owned equities. A key contribution of this study lies in the integration of asymmetric GARCH-fam

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