Relevant bibliographies by topics / GARCH-family models

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Academic literature on the topic 'GARCH-family models'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "GARCH-family models"

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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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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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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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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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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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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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Dissertations / Theses on the topic "GARCH-family models"

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Han, Yang. "Modeling and forecasting volatility of Shanghai Stock Exchange with GARCH family models." Thesis, Uppsala universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-155066.

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This paper discusses the performance of modeling and forecasting volatility ofdaily stock returns of A-shares in Shanghai Stock Exchange. The volatility is modeledby GARCH family models which are GARCH, EGARCH and GJR-GARCHmodels with three distributions, namely Gaussian distribution, student-t distributionand generalized error distribution (GED). In order to determine the performanceof forecasting volatility, we compare the models by using the Root MeanSquared Error (RMSE). The results show that the EGARCH models work so wellin most of daily stock returns and the symmetric GARCH models are be
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Nyssanov, Askar. "An empirical study in risk management: estimation of Value at Risk with GARCH family models." Thesis, Uppsala universitet, Statistiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-201397.

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In this paper the performance of classical approaches and GARCH family models are evaluated and compared in estimation one-step-ahead VaR. The classical VaR methodology includes historical simulation (HS), RiskMetrics, and unconditional approaches. The classical VaR methods, the four univariate and two multivariate GARCH models with the Student’s t and the normal error distributions have been applied to 5 stock indices and 4 portfolios to determine the best VaR method. We used four evaluation tests to assess the quality of VaR forecasts: -                     Violation ratio -                 
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Molin, Simon. "Volatility forecasting on global stock market indices : Evaluation and comparison of GARCH-family models forecasting performance." Thesis, Umeå universitet, Nationalekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184480.

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Volatility is arguably one of the most important measures in financial economics since it is often used as a rough measure of the total risk of financial assets. Many volatility models have been developed to model the process, where the GARCH-family models capture several characteristics that are observed in financial data. An accurate volatility forecast is of great value for monetary policymakers, risk managers, investors and for assessing the price and value of financial derivates. The purpose of this thesis is to evaluate if asymmetric or symmetric GARCH models generate better volatility f
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Grek, Åsa. "Forecasting accuracy for ARCH models and GARCH (1,1) family : Which model does best capture the volatility of the Swedish stock market?" Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-37495.

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Åstrand, Elias. "Volatility forecasting performance on OMX Stockholm 30 during a financial crisis : A comparison and evaluation of GARCH-family models." Thesis, Umeå universitet, Nationalekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-176291.

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Sung, Ching-Hsin, and 宋謹行. "Comparative Forecasting Volatility Performance of GARCH Family Models and Neural Networks." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/89161023232966795225.

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碩士<br>淡江大學<br>財務金融學系碩士班<br>97<br>We compare the predictive performance of various GARCH family models and Neural Networks. The models are compared out-of-sample using Taiwan Stock Exchange Capitalization Weighted Stock Index（TAIEX）data. We substitute the Realized Range-Based Volatility for the latent true volatility and choose six statistical loss functions to compare the predictive performance. We also use the forecasting volatilities into Black-Scholes formula to evaluate the theoretical option prices and compare with real option prices. To control for the fact that as the number of models i
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Tseng, Yen-Chi, and 曾彥錤. "A Comparison of the Forecasting Performance between GARCH family Models and VIX on TAIEX Options." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/47256603882931318283.

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碩士<br>淡江大學<br>財務金融學系碩士班<br>94<br>Volatility forecasting is very important to derivative pricing, hedging, and risk management. This paper using GARCH, GJR-GARCH models and the VIX index of TAIEX Options to compare their forecasting ability. The empirical evidence show that using daily data to forecast the performance, GJR-GARCH model is superior, while using intraday data, the explanatory power of all models are obviously enhanced and errors are also improved. Therefore, we approve that the more intensive data can obtain the higher explanatory power, lower forecasting errors and more infor
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Yu, Wei-Zheng, and 游為正. "An Application of Stochastic Volatility and GARCH family Models to Short-Term Interest Rate Forecasts." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/06141501663260785382.

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碩士<br>國立高雄應用科技大學<br>金融資訊研究所<br>94<br>Since interest rates have a widespread and profound impact on our society, any further understanding of its patterns or reliable forecasts will be valuable to us. However, due to its volatility, to forecast interest rate is not an easy task. And this difficulty has become obvious in the past two decades, especially after the deregulation of interest rates. The aim of this study, therefore, is to investigate the performance of alternative financial time series models in interest rate predictions. The models employed in this paper include GARCH , EGARCH , GJR
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Jánský, Ivo. "Value-at-risk forecasting with the ARMA-GARCH family of models during the recent financial crisis." Master's thesis, 2011. http://www.nusl.cz/ntk/nusl-297428.

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The thesis evaluates several hundred one-day-ahead VaR forecasting models in the time period between the years 2004 and 2009 on data from six world stock indices - DJI, GSPC, IXIC, FTSE, GDAXI and N225. The models model mean using the AR and MA processes with up to two lags and variance with one of GARCH, EGARCH or TARCH processes with up to two lags. The models are estimated on the data from the in-sample period and their forecasting ac- curacy is evaluated on the out-of-sample data, which are more volatile. The main aim of the thesis is to test whether a model estimated on data with lower vo
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Kao, Cheng-Yuan, and 高振原. "Value-at-Risk Estimations of Foreign Investments Involving Exchange Rate and Stock Market Risks - A Comparison between GARCH Family Models." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/c4p8an.

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碩士<br>國立臺灣科技大學<br>財務金融研究所<br>106<br>This thesis studies the effective measurement of Value-at-Risk (VaR) when investors hold positions involving duel risks from exchange rate market and foreign stock market. GARCH family models are used to incorporate the conditional heteroskedasticity of each individual asset. To further incorporate the correlation between assets, we apply the CCC-GARCH model to improve the performance of VaR estimation. The accuracy of VaR estimation is examined by penetration rates. The empirical results show that the CCC-GARCH model outperforms other competing models. In t
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Book chapters on the topic "GARCH-family models"

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Xu, Yue, and Sulin Pang. "Correlation Analysis of Yield and Volatility Based on GARCH Family Models." In Computational Risk Management. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18387-4_58.

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Liu, Xinran. "Volatility Modeling of S&P500 Returns: A Comparative Study of GARCH Family Models and VIX." In 2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1726-3_31.

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Du, Qingchuan. "Volatility in Chinese and European Stock Markets under the “Black swan” of the Russia-Ukraine War - An Empirical Test based on the GARCH Family Models and Investor Sentiment." In Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022). Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-036-7_217.

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Darmanto, Darmanto, Isnani Darti, Suci Astutik, and Nurjanah Nurjanah. "Identifying Instability in the G7 Stock Market Using Two-Regime MS-GARCH Family Model as Risk Measurement Forecasting." In Advances in Social Science, Education and Humanities Research. Atlantis Press SARL, 2025. https://doi.org/10.2991/978-2-38476-410-5_4.

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Ndlovu, Thabani, and Delson Chikobvu. "Estimating Extreme Value at Risk Using Bayesian Markov Regime Switching GARCH-EVT Family Models." In Cryptocurrencies - Financial Technologies of the Future [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1004124.

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In this study, the performance of the Bayesian Markov regime-switching GARCH-EVT in the estimation of extreme value at risk in the BitCoin/dollar (BTC/USD) and the South African Rand/dollar (ZAR/USD) exchange rates is investigated. The goal is to capture regime switches and extreme returns to exchange rates, all to explain and compare the riskiness of BitCoin and the Rand. The Markov chain Monte Carlo method is used to estimate parameters for the GARCH family models. Using the deviance information criterion, the two regime-switching GARCH models perform better than the single-regime GARCH mode
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"Other Financial Models: From ARMA to the GARCH Family." In Mathematics of Financial Markets. John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118818510.ch9.

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Sivaprakkash S. and Vevek S. "Price Volatility in Cryptocurrencies." In Emerging Insights on the Relationship Between Cryptocurrencies and Decentralized Economic Models. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-5691-0.ch002.

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Cryptocurrency is a digital currency which works as a medium of exchange through a computer network. This study aims at modelling the volatility of selected cryptocurrencies by adopting different models of the GARCH family and providing empirical evidence on the fit of conditional volatility. The research is based upon daily U.S. dollar price indexes of Bitcoin (BTC/USD). The data were collected for a time period from October 2021 to April 2022. The every-day price data was further drilled down to arrive at OHLC (open-high-low-close) price for every quarter of the day. The dataset for the anal
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Conference papers on the topic "GARCH-family models"

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Magris, Martin, and Alexandros Iosifidis. "Variational Inference for GARCH-family Models." In ICAIF '23: 4th ACM International Conference on AI in Finance. ACM, 2023. http://dx.doi.org/10.1145/3604237.3626863.

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LUO, YUANYUAN. "Using GARCH family models estimate the volatility of SSE 50ETF." In Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), edited by Shi Jin and Wanyang Dai. SPIE, 2023. http://dx.doi.org/10.1117/12.2671961.

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Xiao, Huihui. "A Hybrid Model Integrating LSTM with GARCH Family Models for the Ozone Concentration Prediction." In 2023 3rd International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI). IEEE, 2023. http://dx.doi.org/10.1109/cei60616.2023.10528100.

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Wang, Lun, and Zhigang Zhang. "Research on Shanghai Copper Futures Price Forecast Based on X12-ARIMA-GARCH Family Models." In 2020 International Conference on Computer Information and Big Data Applications (CIBDA). IEEE, 2020. http://dx.doi.org/10.1109/cibda50819.2020.00075.

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Lv, Donghui. "Volatility Research of Shanghai Stock Market Based on GARCH Model Family." In 2017 2nd International Conference on Education, Sports, Arts and Management Engineering (ICESAME 2017). Atlantis Press, 2017. http://dx.doi.org/10.2991/icesame-17.2017.423.

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Wenyuan Zhang and Yunyue Wang. "GARCH family model based on the Shanghai stock market shorting mechanism analysis." In 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC). IEEE, 2011. http://dx.doi.org/10.1109/aimsec.2011.6011101.

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Zhao, Chuanling, Ruilin Han, and Hao Liu. "Comparative Analysis of Stock Indexes Based on GARCH Family Model under GED Distribution." In Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2023, May 26–28, 2023, Nanjing, China. EAI, 2023. http://dx.doi.org/10.4108/eai.26-5-2023.2334463.

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Ting Li, Zhigang Zhang, Lutao Zhao, and Dongmei Ai. "GARCH family model and its application in calculating stock index future VaR in Chinese market." In 2011 International Conference on Multimedia Technology (ICMT). IEEE, 2011. http://dx.doi.org/10.1109/icmt.2011.6002533.

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Zhang, Yuqing, Zuoquan Zhang, and Dingyuan Fan. "The Risk Measurement and Empirical Study of China's CSI 300 Index Based on GARCH Model Family." In 2016 6th International Conference on Digital Home (ICDH). IEEE, 2016. http://dx.doi.org/10.1109/icdh.2016.053.

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Wang, Tian. "Stock Volatility Forecasting: Adopting LSTM Deep Learning Method and Comparing the Results with GARCH Family Model." In Proceedings of the International Conference on Financial Innovation, FinTech and Information Technology, FFIT 2022, October 28-30, 2022, Shenzhen, China. EAI, 2023. http://dx.doi.org/10.4108/eai.28-10-2022.2328447.

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