Relevant bibliographies by topics / GARCH

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'GARCH'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "GARCH"

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Nurhayati, Nurhayati, Wiwin Apriani, and Ariestha Widyastuty Bustan. "Value at Risk Prediction for the GJR-GARCH Aggregation Model." Pattimura International Journal of Mathematics (PIJMath) 1, no. 1 (2022): 01–06. http://dx.doi.org/10.30598/pijmathvol1iss1pp01-06.

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Volatility is the level of risk faced due to price fluctuations. The greater the volatility brings, the greater the risk. We need a measure such as Value at Risk (VaR) and volatility modeling to overcome this. The most frequently used volatility model in the financial sector is GARCH. However, this model is still unable to accommodate the asymmetric nature, so the GJR-GARCH model was developed. In addition, this study also used aggregation returns with two assets in them. This study aimed to determine the VaR prediction for the GJR-GARCH(1.1) aggregation model and its comparison with the GARCH
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Parwati, Lusiana Sani, Endar Hasafah Nugrahani, and Retno Budiarti. "Forecasting Stock Price Using Armax-Garchx Model During The Covid-19 Pandemic." Mathline : Jurnal Matematika dan Pendidikan Matematika 8, no. 2 (2023): 489–502. http://dx.doi.org/10.31943/mathline.v8i2.413.

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The Covid-19 pandemic, which was proclaimed by the World Health Organization (WHO) on March 2020, has impacted stock risk on the capital market. Stock price forecasting can be used to provide future stock projection prices in order to reduce risk. The ARMA GARCH model and its development model can forecast stock prices by incorporating exogenous factors such as the ARMAX GARCH, ARMA GARCHX, and ARMAX GARCHX models. PT Mitra Keluarga Karyasehat Tbk's stock price is analyzed in this study, along with the exogenous factors of total daily positive cases and total daily fatalities cases of Covid-19
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Sucarrat, Genaro. "garchx: Flexible and Robust GARCH-X Modeling." R Journal 13, no. 1 (2021): 276. http://dx.doi.org/10.32614/rj-2021-057.

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KOUKI, Sonia. "Analysis of Risk Premium Behavior in the Tunisian Foreign Exchange Market During Crisis Period." Journal of Academic Finance 10, no. 2 (2019): 28–38. http://dx.doi.org/10.59051/joaf.v10i2.318.

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In this paper, we empirically examine time-varying risk premia in the Tunisian foreign exchange market by applying GARCH-M modeling to the TND/Euro and TND/USD parities for 1 to 12 months forecasting horizons. Our ultimate objective is to help better manage the parities and to help provide stability to a foreign exchange market where EMH is very weak. Our findings show 1) a heteroscedasticity of residuals for the TND/Euro (all forecasting horizons) and the TND/USD(for 1 month) indicating a lack of stability of their volatility; 2) a non-significant standard deviation of the risk premium agains
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Nugroho, D. B., D. Kurniawati, L. P. Panjaitan, Z. Kholil, B. Susanto, and L. R. Sasongko. "Empirical performance of GARCH, GARCH-M, GJR-GARCH and log-GARCH models for returns volatility." Journal of Physics: Conference Series 1307 (August 2019): 012003. http://dx.doi.org/10.1088/1742-6596/1307/1/012003.

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Hwang *, Soosung, and Steve E. Satchell. "GARCH model with cross-sectional volatility: GARCHX models." Applied Financial Economics 15, no. 3 (2005): 203–16. http://dx.doi.org/10.1080/0960310042000314214.

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Wu, Kejin, Sayar Karmakar, Rangan Gupta, and Christian Pierdzioch. "Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa." Climate 12, no. 5 (2024): 68. http://dx.doi.org/10.3390/cli12050068.

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Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS models to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called mod
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Necula, Ciprian. "A Copula-Garch Modelcopula-Garch Model." Economic Research-Ekonomska Istraživanja 23, no. 2 (2010): 1–10. http://dx.doi.org/10.1080/1331677x.2010.11517408.

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SO, LEH-CHYAN, and JUN-YANG YU. "IMPROVED DETECTION OF RARE-EVENT RISK OF A PORTFOLIO WITH U.S. REITs." Annals of Financial Economics 10, no. 02 (2015): 1550015. http://dx.doi.org/10.1142/s2010495215500153.

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In measuring the market risks of a portfolio, value-at-risk (VaR) is one of the most commonly used tools. In this paper, the copula-generalized autoregressive conditional heteroskedasticity (GARCH) method is used to determine whether it is a better alternative for estimating the VaR of portfolios containing U.S. real estate investment trusts (REITs). The FTSE NAREIT US Real Estate Index, all REITs and the S&P 500 index are used to construct a portfolio. In total, 2800 daily data covering the period of the subprime mortgage crisis of 2007–2009 are used in this paper. We used six constant an
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Iqbal, Teuku Achmad, Kusman Sadik, and I. Made Sumertajaya. "Pemodelan Pengukuran Luas Panen Padi Nasional Menggunakan Generalized Autoregressive Conditional Heteroscedastic Model (GARCH)." Jurnal Penelitian Pertanian Tanaman Pangan 33, no. 1 (2014): 17. http://dx.doi.org/10.21082/jpptp.v33n1.2014.p17-26.

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This study was aimed to build a model for the estimation of national harvested area of rice by incorporating element of variant heterogeneity and the influence of asymmetry factors on time series data using five types of GARCH models, namely: symmetric GARCH, exponential asymmetric GARCH, quadratic asymmetric GARCH, Threshold GARCH, and non-linear asymmetric GARCH. Those models were compared and evaluated, and then the best model was used to predict the accuracy of the national rice harvested area. The results showed that two types of GARCH had significant coefficient, indicating the validity
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Dissertations / Theses on the topic "GARCH"

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Sundström, Dennis. "Automatized GARCH parameter estimation." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213725.

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This paper is about automatizing parameter estimation of GARCH type conditional volatility models for the sake of using it in an automated risk monitoring system. Many challenges arise with this task such as guaranteeing convergence, being able to yield reasonable results regardless of the quality of the data, accuracy versus speed of the algorithm to name a few. These problems are investigated and a robust framework for an algorithm is proposed, containing dimension reducing and constraint relaxing parameter space transformations with robust initial values. The algorithm is implemented in jav
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Solda, Grazielle Yumi. "Modelos de memória longa, GARCH e GARCH com memória longa para séries financeiras." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-03052008-170204/.

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O objetivo deste trabalho é apresentar e comparar diferentes métodos de modelagem da volatilidade (variância condicional) de séries temporais financeiras. O modelo ARFIMA é empregado para capturar o comportamento de memória longa observado na volatilidade de séries financeiras. Por sua vez, o modelo GARCH é utilizado para modelar a volatilidade variando no tempo destas séries. Finalmente, o modelo FIGARCH é utilizado para modelar a dinâmica dos retornos de séries temporais financeiras juntamente com sua volatilidade. Serão apresentados alguns estimadores para os parâmetros dos modelos estudado
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Zheng, Lingyu. "Estimation of the linkage matrix in O-GARCH model and GO-GARCH model." Diss., Temple University Libraries, 2010. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/102486.

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Statistics<br>Ph.D.<br>We propose new estimation methods for the factor loading matrix in modeling multivariate volatility processes. The key step of the methods is based on the weighted scatter estimators, which does not involve optimizing any objective function and was embedded with robust estimation properties. The method can therefore be easily applied to high-dimensional systems without running into computational problems. The estimation is proved to be consistent and the asymptotic distribution is derived. We compare the performance with other estimation methods and demonstrate its super
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Shimizu, Kenichi. "Bootstrapping stationary ARMA-GARCH models." Wiesbaden Vieweg + Teubner, 2009. http://d-nb.info/996781153/04.

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He, Changli. "Statistical properties of GARCH processes." Doctoral thesis, Stockholm : Economic Research Insitute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1997. http://www.hhs.se/efi/summary/460.htm.

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Sepúlveda, Ana Margarida Queirós. "Modelos Heterocedásticos - ARCH e GARCH." Master's thesis, Faculdade de Economia da Universidade do Porto, 2010. http://hdl.handle.net/10216/57365.

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Sepúlveda, Ana Margarida Queirós. "Modelos Heterocedásticos - ARCH e GARCH." Dissertação, Faculdade de Economia da Universidade do Porto, 2010. http://hdl.handle.net/10216/57365.

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Hagerud, Gustaf E. "A new non-linear GARCH model." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1997. http://www.hhs.se/efi/summary/444.htm.

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許偉才 and Wai-choi Hui. "Optimal asset allocation under GARCH model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31222717.

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CALDEIRA, ANDRE MACHADO. "GARCH MODELS IDENTIFICATION USING COMPUTATIONAL INTELLIGENCE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14872@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>Os modelos ARCH e GARCH vêm sendo bastante explorados tanto tecnicamente quanto em estudos empíricos desde suas respectivas criações em 1982 e 1986. Contudo, o enfoque sempre foi na reprodução dos fatos estilizados das séries financeiras e na previsão de volatilidade, onde o GARCH(1,1) é o mais utilizado. Estudos sobre identificação dos modelos GARCH são muito raros. Diante desse contexto, este trabalho propõe um sistema inteligente para melhorar a identificação da correta especificação dos modelos GARCH, evitando assim o uso indiscriminado
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Books on the topic "GARCH"

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Francq, Christian, and Jean-Michel Zakoïan. GARCH Models. John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470670057.

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Engle, R. F. GARCH gamma. National Bureau of Economic Research, 1995.

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Kaiser, Thomas. Volatilitätsprognose mit Faktor-GARCH-Modellen. Deutscher Universitätsverlag, 1997. http://dx.doi.org/10.1007/978-3-322-97762-5.

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Shimizu, Kenichi. Bootstrapping Stationary ARMA-GARCH Models. Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9778-7.

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service), SpringerLink (Online, ed. Bootstrapping Stationary ARMA-GARCH Models. Vieweg+Teubner Verlag / Springer Fachmedien Wiesbaden GmbH, Wiesbaden, 2010.

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Zaffaroni, Paolo. Contemporaneous aggregation of GARCH processes. Banca d'Italia, 2002.

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Fiszeder, Piotr. Modele klasy Garch w empirycznych badaniach finansowych. Wydawn. Nauk. Uniwersytetu Mikołaja Kopernika, 2009.

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Francq, Christian. Modèles GARCH: Structure, inférence statistique et applications financières. Economica, 2009.

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Ahlstedt, Monica. Analysis of financial risks in a GARCH framework. Bank of Finland, 1998.

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Francq, Christian. GARCH models: Structure, statistical inference, and financial applications. Wiley, 2010.

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Book chapters on the topic "GARCH"

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Hafner, Christian M. "GARCH Modeling." In Complex Systems in Finance and Econometrics. Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-7701-4_26.

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Hafner, Christian M. "GARCH Modeling." In Encyclopedia of Complexity and Systems Science. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_242.

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Ruppert, David. "GARCH Models." In Springer Texts in Statistics. Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4419-6876-0_12.

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Ruppert, David. "GARCH Models." In Statistics and Data Analysis for Financial Engineering. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7787-8_18.

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Ruppert, David, and David S. Matteson. "GARCH Models." In Statistics and Data Analysis for Financial Engineering. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2614-5_14.

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Cruz Torres, Cristian A. "GARCH Models." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2025. https://doi.org/10.1007/978-3-662-69359-9_244.

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Klaassen, Franc. "Improving GARCH volatility forecasts with regime-switching GARCH." In Advances in Markov-Switching Models. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-642-51182-0_10.

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Zivot, Eric, and Jiahui Wang. "Multivariate GARCH Modeling." In Modeling Financial Time Series with S-Plus®. Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21763-5_13.

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Zivot, Eric, and Jiahui Wang. "Univariate GARCH Modeling." In Modeling Financial Time Series with S-Plus®. Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21763-5_7.

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Silvennoinen, Annastiina, and Timo Teräsvirta. "Multivariate GARCH Models." In Handbook of Financial Time Series. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-71297-8_9.

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Conference papers on the topic "GARCH"

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Bo, Shi, and Minheng Xiao. "Data-Driven Risk Measurement by SV-GARCH-EVT Model." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704237.

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Zhang, Linfang, Lei Dai, Fuyan Wang, Yuwen Zhao, Junxian Wu, and Qiuran Yu. "Research on Financial Prediction Based on LSTM-GARCH Hybrid Model." In 2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE). IEEE, 2024. https://doi.org/10.1109/iceace63551.2024.10898496.

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Siddiraju, Nithyasai, and Rashida Hasan. "A Hybrid LSTM-GARCH Network for Stock Market Volatility Prediction." In 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2025. https://doi.org/10.1109/ccwc62904.2025.10903807.

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LING, SHIQING, and MICHAEL MCALEER. "TESTING GARCH VERSUS E-GARCH." In Proceedings of the Hong Kong International Workshop on Statistics in Finance. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2000. http://dx.doi.org/10.1142/9781848160156_0013.

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Erdeljan, Jelena, and Jelena Ivetić. "GARCH MODELI ZA PROCENU VOLATILNOSTI VREMENSKIH SERIJA." In The 9th Conference on Mathematics in Engineering: Theory and Applications. Faculty of Technical Sciences, University of Novi Sad, 2024. http://dx.doi.org/10.24867/meta.2024.06.

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U radu su predstavljeni ARCH i GARCH modeli koji se koriste za procenu volatilnosti vremenskih serija, kao i određene modifikacije GARCH modela. Praktičan deo rada prikazuje primenu različitih GARCH modela na procenu volatilnosti relativnih dnevnih prinosa berzanskog indeksa BELEX15.
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Gani, Siti Mahirah Abdul, Zaidi Isa, and Munira Ismail. "Modeling volatility of SMR20: GARCH and Markov regime switching GARCH." In 4TH SYMPOSIUM ON INDUSTRIAL SCIENCE AND TECHNOLOGY (SISTEC2022). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0171637.

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Dehdari, V., M. Kariznovi, and M. R. Tenove. "Enhancing Short-Term Oil Forecasting in SAGD Operations Using ARIMAX-GARCH and Bi-directional LSTM Models." In SPE Canadian Energy Technology Conference and Exhibition. SPE, 2025. https://doi.org/10.2118/224013-ms.

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Abstract This study focuses on enhancing short-term oil forecasting in Steam-Assisted Gravity Drainage (SAGD) operations by leveraging advanced ARIMAX-GARCH and Bi-directional LSTM models. SAGD, a vital method for bitumen extraction, involves injecting steam into the reservoir to facilitate oil production through gravity drainage. Conventional analytical models often fail to account for critical operational parameters, such as subcool, and lack the sensitivity required to capture short-term variations and distinct SAGD phases (Ramp-up, Plateau, and Decline). To address these limitations, this
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Kanjamapornkul, Kabin, Boonserm Kijsirikul, and Jimson Mathew. "Minkowski Metric for GARCH (1,1)." In 2014 3rd International Conference on Eco-friendly Computing and Communication Systems (ICECCS). IEEE, 2014. http://dx.doi.org/10.1109/eco-friendly.2014.67.

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Lucheroni, Carlo, and Costantino Ragno. "Modeling of wind speed spatio-temporal series by multivariate-GARCH and copula/GARCH models." In 2017 14th International Conference on the European Energy Market (EEM). IEEE, 2017. http://dx.doi.org/10.1109/eem.2017.7982030.

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HUI, W. C., H. YANG, and K. C. YUEN. "OPTIMAL ASSET ALLOCATION UNDER GARCH MODEL." In Proceedings of the Hong Kong International Workshop on Statistics in Finance. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2000. http://dx.doi.org/10.1142/9781848160156_0019.

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Reports on the topic "GARCH"

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Engle, Robert, and Joshua Rosenberg. GARCH Gamma. National Bureau of Economic Research, 1995. http://dx.doi.org/10.3386/w5128.

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Engle, Robert, and Kevin Sheppard. Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH. National Bureau of Economic Research, 2001. http://dx.doi.org/10.3386/w8554.

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Wu, Jianbin, and Oliver Linton. A coupled component GARCH model for intraday and overnight volatility. The IFS, 2017. http://dx.doi.org/10.1920/wp.cem.2017.0517.

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Gamboa-Estrada, Fredy, and Jose Vicente Romero. Modelling CDS Volatility at Different Tenures: An Application for Latin-American Countries. Banco de la República de Colombia, 2022. http://dx.doi.org/10.32468/be.1199.

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Assessing the dynamics of risk premium measures and its relationship with macroeconomic fundamentals is important for both macroeconomic policymakers and market practitioners. This paper analyzes the main determinants of CDS in Latin-America at different tenures, focusing on their volatility. Using a component GARCH model, we decompose volatility between permanent and transitory components. We find that the permanent component of CDS volatility in all tenors was higher and more persistent in the global financial crisis than during the recent COVID-19 shock. JEL Classification: C22, C58, G01, G
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Dueker, Michael J. Markov Switching in GARCH Processes and Mean Reverting Stock Market Volatility. Federal Reserve Bank of St. Louis, 1994. http://dx.doi.org/10.20955/wp.1994.015.

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Shang, Dajing, Yang Yan, and Oliver Linton. Efficient estimation of conditional risk measures in a semiparametric GARCH model. Institute of Fiscal Studies, 2012. http://dx.doi.org/10.1920/wp.cem.2012.2512.

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Neely, Christopher J., and Paul A. Weller. Predicting Exchange Rate Volatility: Genetic Programming vs. GARCH and Risk Metrics™. Federal Reserve Bank of St. Louis, 2001. http://dx.doi.org/10.20955/wp.2001.009.

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Engle, Robert, and Joshua Rosenberg. Hedging Options in a GARCH Environment: Testing the Term Structure of Stochastic Volatility Models. National Bureau of Economic Research, 1994. http://dx.doi.org/10.3386/w4958.

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Neely, Christopher J., and Hui Guo. Investigating the Intertemporal Risk-Return Relation in International Stock Markets with the Component GARCH Model,. Federal Reserve Bank of St. Louis, 2006. http://dx.doi.org/10.20955/wp.2006.006.

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Han, Heejoon, and Dennis Kristensen. Asymptotic theory for the QMLE in GARCH-X models with stationary and non-stationary covariates. Institute for Fiscal Studies, 2013. http://dx.doi.org/10.1920/wp.cem.2013.1813.

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