garchx: Flexible and Robust GARCH-X Modelling
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Drost, Feike C & Nijman, Theo E, 1993.
"Temporal Aggregation of GARCH Processes,"
Econometrica, Econometric Society, vol. 61(4), pages 909-927, July.
- Drost, F.C. & Nijman, T.E., 1990. "Temporal Aggregation Of Garch Processes," Papers 9066, Tilburg - Center for Economic Research.
- Drost, F.C. & Nijman, T.E., 1992. "Temporal aggregation of GARCH processes," Other publications TiSEM afe8fdcf-5f83-44b5-8da3-5, Tilburg University, School of Economics and Management.
- Drost, F.C. & Nijman, T.E., 1992. "Temporal Aggregation of Garch Processes," Papers 9240, Tilburg - Center for Economic Research.
- Drost, F.C. & Nijman, T.E., 1993. "Temporal aggregation of GARCH processes," Other publications TiSEM 0642fb61-c7f4-4281-b484-4, Tilburg University, School of Economics and Management.
- Drost, F.C. & Nijman, T.E., 1992. "Temporal aggregation of GARCH processes," Discussion Paper 1992-40, Tilburg University, Center for Economic Research.
- Drost, F.C. & Nijman, T.E., 1990. "Temporal aggregation of GARCH processes," Other publications TiSEM 929bb665-083a-4d60-906d-e, Tilburg University, School of Economics and Management.
- Drost, F.C. & Nijman, T.E., 1994. "Temporal aggregation of GARCH processes," Other publications TiSEM b6718003-2fa5-43bb-a690-d, Tilburg University, School of Economics and Management.
- Drost, F.C. & Nijman, T.E., 1990. "Temporal aggregation of GARCH processes," Discussion Paper 1990-66, Tilburg University, Center for Economic Research.
- Escanciano, Juan Carlos, 2009. "Quasi-Maximum Likelihood Estimation Of Semi-Strong Garch Models," Econometric Theory, Cambridge University Press, vol. 25(2), pages 561-570, April.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Hansen, Bruce E, 1994.
"Autoregressive Conditional Density Estimation,"
International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
- Hansen, B.E., 1992. "Autoregressive Conditional Density Estimation," RCER Working Papers 322, University of Rochester - Center for Economic Research (RCER).
- Tom Doan, 2025. "RATS programs to replicate Hansen's GARCH models with time-varying t-densities," Statistical Software Components RTZ00086, Boston College Department of Economics.
- Tom Doan, 2025. "LOGSKEWTGARCH: RATS procedure to compute the log density of skew-t distribution for use with GARCH," Statistical Software Components RTS00260, Boston College Department of Economics.
- Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
- Peter Reinhard Hansen & Zhuo Huang & Howard Howan Shek, 2012. "Realized GARCH: a joint model for returns and realized measures of volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 877-906, September.
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Werge, Nicklas & Wintenberger, Olivier, 2022.
"AdaVol: An Adaptive Recursive Volatility Prediction Method,"
Econometrics and Statistics, Elsevier, vol. 23(C), pages 19-35.
- Nicklas Werge & Olivier Wintenberger, 2020. "AdaVol: An Adaptive Recursive Volatility Prediction Method," Papers 2006.02077, arXiv.org, revised Jan 2021.
- Nicklas Werge & Olivier Wintenberger, 2022. "AdaVol: An Adaptive Recursive Volatility Prediction Method," Post-Print hal-02733439, HAL.
- Kejin Wu & Sayar Karmakar & Rangan Gupta, 2023.
"GARCHX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables,"
Papers
2308.13346, arXiv.org, revised Sep 2024.
- Kejin Wu & Sayar Karmakar & Rangan Gupta, 2024. "GARCHX-NoVaS: A Model-Free Approach to Incorporate Exogenous Variables," Working Papers 202425, University of Pretoria, Department of Economics.
- Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2021. "Choosing the frequency of volatility components within the Double Asymmetric GARCH–MIDAS–X model," Econometrics and Statistics, Elsevier, vol. 20(C), pages 12-28.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
- Todd, Prono, 2010. "Simple GMM Estimation of the Semi-Strong GARCH(1,1) Model," MPRA Paper 20034, University Library of Munich, Germany.
- Chao Wang & Qian Chen & Richard Gerlach, 2017. "Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution," Papers 1707.03715, arXiv.org.
- Tingguo Zheng & Han Xiao & Rong Chen, 2021. "Generalized Autoregressive Moving Average Models with GARCH Errors," Papers 2105.05532, arXiv.org.
- Takahashi, Makoto & Watanabe, Toshiaki & Omori, Yasuhiro, 2016.
"Volatility and quantile forecasts by realized stochastic volatility models with generalized hyperbolic distribution,"
International Journal of Forecasting, Elsevier, vol. 32(2), pages 437-457.
- Makoto Takahashi & Toshiaki Watanabe & Yasuhiro Omori, 2014. "Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution," CIRJE F-Series CIRJE-F-921, CIRJE, Faculty of Economics, University of Tokyo.
- Makoto Takahashi & Toshiaki Watanabe & Yasuhiro Omori, 2015. "Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution," CIRJE F-Series CIRJE-F-975, CIRJE, Faculty of Economics, University of Tokyo.
- Makoto Takahashi & Toshiaki Watanabe & Yasuhiro Omori, 2014. "Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution," CIRJE F-Series CIRJE-F-949, CIRJE, Faculty of Economics, University of Tokyo.
- Zhao, Zifeng & Zhang, Zhengjun & Chen, Rong, 2018. "Modeling maxima with autoregressive conditional Fréchet model," Journal of Econometrics, Elsevier, vol. 207(2), pages 325-351.
- Xiufeng Yan, 2021. "Autoregressive conditional duration modelling of high frequency data," Papers 2111.02300, arXiv.org.
- Chen, Cathy W.S. & Hsu, Hsiao-Yun & Watanabe, Toshiaki, 2023. "Tail risk forecasting of realized volatility CAViaR models," Finance Research Letters, Elsevier, vol. 51(C).
- Benjamin Beckers & Helmut Herwartz & Moritz Seidel, 2017. "Risk forecasting in (T)GARCH models with uncorrelated dependent innovations," Quantitative Finance, Taylor & Francis Journals, vol. 17(1), pages 121-137, January.
- Todd, Prono, 2009. "Simple, Skewness-Based GMM Estimation of the Semi-Strong GARCH(1,1) Model," MPRA Paper 30994, University Library of Munich, Germany, revised 30 Jul 2011.
- CHEN, Cathy W.S. & WENG, Monica M.C. & WATANABE, Toshiaki & 渡部, 渡部, 2015. "Employing Bayesian Forecasting of Value-at-Risk to Determine an Appropriate Model for Risk Management," Discussion paper series HIAS-E-16, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
- Demos, Antonis & Sentana, Enrique, 1998.
"Testing for GARCH effects: a one-sided approach,"
Journal of Econometrics, Elsevier, vol. 86(1), pages 97-127, June.
- Antonis Demos & Enrique Sentana, 1996. "Testing for GARCH Effects: A One-Sided Approach," Working Papers wp1996_9611, CEMFI.
- Xiufeng Yan, 2021. "Multiplicative Component GARCH Model of Intraday Volatility," Papers 2111.02376, arXiv.org.
- Huang, Wen & Huang, Zhuo & Matei, Marius & Wang, Tianyi, 2012. "Price Volatility Forecast for Agricultural Commodity Futures: The Role of High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 83-103, December.
- Font, Begoña, 1998. "Modelización de series temporales financieras. Una recopilación," DES - Documentos de Trabajo. EstadÃstica y EconometrÃa. DS 3664, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- De Lira Salvatierra, Irving & Patton, Andrew J., 2015.
"Dynamic copula models and high frequency data,"
Journal of Empirical Finance, Elsevier, vol. 30(C), pages 120-135.
- Irving Arturo De Lira Salvatierra & Andrew J. Patton, 2013. "Dynamic Copula Models and High Frequency Data," Working Papers 13-28, Duke University, Department of Economics.
- Chen, Bin & Hong, Yongmiao, 2014.
"A unified approach to validating univariate and multivariate conditional distribution models in time series,"
Journal of Econometrics,
Elsevier, vol. 178(P1), pages 22-44.
- Bin Chen & Yongmiao Hong, 2013. "A Unified Approach to Validating Univariate and Multivariate Conditional Distribution Models in Time Series," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
- Richard Gerlach & Chao Wang, 2016. "Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Realized Measures," Papers 1612.08488, arXiv.org.
- Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, C.E.P.R. Discussion Papers.
- Amendola, A. & Candila, V. & Cipollini, F. & Gallo, G.M., 2024.
"Doubly multiplicative error models with long- and short-run components,"
Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
- Alessandra Amendola & Vincenzo Candila & Fabrizio Cipollini & Giampiero M. Gallo, 2020. "Doubly Multiplicative Error Models with Long- and Short-run Components," Papers 2006.03458, arXiv.org.
More about this item
Keywords
; ; ; ; ;JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ETS-2020-06-08 (Econometric Time Series)
- NEP-ORE-2020-06-08 (Operations Research)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:100301. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.
Printed from https://ideas.repec.org/p/pra/mprapa/100301.html