Relevant bibliographies by topics / GARCH

Academic literature on the topic 'GARCH'

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Published: 4 June 2021
Last updated: 4 May 2024

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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 (May 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(1.1) aggregation model. The results obtained indicate that the prediction of volatility using the GJR-GARCH(1.1) aggregation model is more accurate than the GACRH(1.1) aggregation model because it has a correct VaR value that is close to the given confidence level.
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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 (May 26, 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 from March 16, 2020, to January 31, 2022.The results of several models show that based on MAPE value the ARMAX GARCH model has better accuracy in forecasting stock price.
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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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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 (February 2005): 203–16. http://dx.doi.org/10.1080/0960310042000314214.

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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 (December 30, 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 against the presence of ARCH and GACH in all cases. On the other hand, the descriptive analysis of the risk premium and term premium variables show an asymmetric distribution. We used, therefore, the asymmetric GARCH model, E-GARCH. Our normality tests show, however, that the GARCH model neither allows for residuals smoothing nor improves the AIC.
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Necula, Ciprian. "A Copula-Garch Modelcopula-Garch Model." Economic Research-Ekonomska Istraživanja 23, no. 2 (January 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 (December 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 and two time-varying copula models combined with two GARCH models to form sixteen copula-GARCH models to depict the joint distribution of the two assets in the portfolio. We then computed corresponding one-day VaRs. Compared with the traditional VaR models, our results showed that the time-varying symmetrized Joe–Clayton (SJC) copula model combined with the GARCH Student-t innovation (tvSJC-copula–GARCHt) performed the best, regardless of the market situation. Hence, it could be served as a better way of detecting rare-event risk.
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Engle, Robert F., and Joshua V. Rosenberg. "GARCH Gamma." Journal of Derivatives 2, no. 4 (May 31, 1995): 47–59. http://dx.doi.org/10.3905/jod.1995.407924.

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Brownlees, Christian T. "Hierarchical GARCH." Journal of Empirical Finance 51 (March 2019): 17–27. http://dx.doi.org/10.1016/j.jempfin.2019.01.009.

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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 java with two models, namely the GARCH and gjr-GARCH model. By using real market data, performance of the algorithm are tested with various in-sample and out-of-sample measures, including backtesting of the widely used risk measure Value-at-Risk. The empirical studies conclude that the more complex gjr-sGARCH model with the conditional student’s t distribution was found to yield the most accurate results. However for the purpose of this paper the GARCH orgjr-GARCH seems more appropriate.
Denna uppsats undersöker möjligheten att automatisera approximationen av GARCH parametrar, där syftet är att använda algoritmen till ett automatiserat riskhanteringssystem. Med detta uppstår flera utmaningar som att garantera konvergens, kunna erhålla rimliga resultat oavsett datakvalitet, avvägning mellan algoritmens snabbhet och precision för att nämna några. Uppsatsen undersöker dessa problem och föreslår ett robust ramverk för en algoritm som innehåller transformationer av parameterrymden. Där dessa transformationer reducerar dimensionen av problemet samt reducerar antalet randvillkor. Algoritmen är implementerad i java med två modeller, GARCH och gjr-GARCH. Vidare så är algoritmen testad genom att använda riktig marknadsdata, där olika metoder använts för att utvärdera algoritmen. Modellerna som används backtestas på historisk data och det empiriska resultatet av detta talar för att gjr-sGARCH modellen med student’s t fördelning levererar noggrannast resultat. Det är dock den mest komplexa modellen som används i denna uppsats och för denna uppsats ändamål anses GARCH eller gjr-GARCH modellerna mer passande.
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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 estudados. Foram realizadas simulações dos três tipos de modelos com o objetivo de comparar o comportamento dos estimadores para diferentes valores dos parâmetros. Por fim, serão apresentadas aplicações em séries reais.
The goal of this project is to present and compare differents methods of modeling volatility (conditional variance) in financial time series. ARFIMA model is applied to capture long memory behavior of volatility in financial time series. GARCH model is used to model the temporal variation in financial volatility. Finally, FIGARCH model is used to model dynamic of financial time series returns as well as its volatility behavior. We present some estimators for the studied models. Estimators behavior of the three types of models for different parameters is assessed through a simulation study. At last, applications to real data are presented.
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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
Ph.D.
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 superiority when using both simulated data as well as real-world case studies.
Temple University--Theses
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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
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 dos modelos GARCH(1,1). Para validar a eficácia do sistema proposto, séries simuladas foram utilizadas. Os resultados derivados desse sistema são comparados com os modelos escolhidos pelos critérios de informação AIC e BIC. O desempenho das previsões dos modelos identificados por esses métodos são comparados utilizando-se séries reais.
ARCH and GARCH models have been largely explored technically and empirically since their creation in 1982 and 1986, respectively. However, the focus has always been on stylized facts of financial time series or volatility forecasts, where GARCH(1,1) has commonly been used. Studies on identification of GARCH models have been rare. In this context, this work aims to develop an intelligent system for improving the specification of GARCH models, thus avoiding the indiscriminate use of the GARCH(1,1) model. In order to validate the efficacy of the proposed system, simulated time series are used. Results are compared to chosen models through AIC and BIC criteria. Their performances are then compared by using real data.
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Books on the topic "GARCH"

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

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Engle, R. F. GARCH gamma. Cambridge, MA (1050 Massachusetts Avenue, Cambridge 02138): National Bureau of Economic Research, 1995.

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Kaiser, Thomas. Volatilitätsprognose mit Faktor-GARCH-Modellen. Wiesbaden: 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. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9778-7.

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

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

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

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

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

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1946-, Bernard Jean-Thomas, and Bank of Canada, eds. Forecasting commodity prices: GARCH, jumps, and mean reversion. [Ottawa]: Bank of Canada, 2006.

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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, 464–83. New York, NY: 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, 4114–33. New York, NY: 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, 363–95. New York, NY: 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, 477–504. New York, NY: 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, 405–52. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2614-5_14.

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Klaassen, Franc. "Improving GARCH volatility forecasts with regime-switching GARCH." In Advances in Markov-Switching Models, 223–54. Heidelberg: 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®, 461–98. New York, NY: 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®, 209–55. New York, NY: 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, 201–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-71297-8_9.

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Maercker, G. "Bootstrapping Garch(1,1) Models." In Decision Technologies for Computational Finance, 207–18. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5625-1_16.

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Conference papers on the topic "GARCH"

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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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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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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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Cohen, Israel. "Supergaussian GARCH models for speech signals." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-673.

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Jiratumpradub, Navaches, and Walailuck Chavanasporn. "Forecasting option price by GARCH model." In 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2016. http://dx.doi.org/10.1109/iciteed.2016.7863257.

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Jie Xu, Zhigang Zhang, Lutao Zhao, and Dongmei Ai. "The application review of GARCH model." In 2011 International Conference on Multimedia Technology (ICMT). IEEE, 2011. http://dx.doi.org/10.1109/icmt.2011.6002504.

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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. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3604237.3626863.

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Adamyan, Garik. "Consistent Clustering of ARMA-GARCH Processes." In Computer Science and Information Technologies 2023. Institute for Informatics and Automation Problems, 2023. http://dx.doi.org/10.51408/csit2023_50.

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Reports on the topic "GARCH"

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Engle, Robert, and Joshua Rosenberg. GARCH Gamma. Cambridge, MA: National Bureau of Economic Research, May 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. Cambridge, MA: National Bureau of Economic Research, October 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, January 2017. http://dx.doi.org/10.1920/wp.cem.2017.0517.

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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, September 2012. http://dx.doi.org/10.1920/wp.cem.2012.2512.

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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, May 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, G15. Keywords: Credit default swaps (CDS), CDS in Latin-American countries, sovereign risk, volatility, crisis, component GARCH models
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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. Cambridge, MA: National Bureau of Economic Research, December 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, May 2013. http://dx.doi.org/10.1920/wp.cem.2013.1813.

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