IDEAS home Printed from https://ideas.repec.org/p/udb/wpaper/uwec-2008-03-fc.html
   My bibliography  Save this paper

Practical Issues in the Analysis of Univariate GARCH Models

Author

Listed:
  • Eric Zivot

    (Department of Economics, University of Washington)

Abstract

This paper gives a tour through the empirical analysis of univariate GARCH models for financial time series with stops along the way to discuss various practical issues associated with model specification, estimation, diagnostic evaluation and forecasting.

Suggested Citation

  • Eric Zivot, 2008. "Practical Issues in the Analysis of Univariate GARCH Models," Working Papers UWEC-2008-03-FC, University of Washington, Department of Economics.
    • Handle: RePEc:udb:wpaper:uwec-2008-03-fc
    as

    Download full text from publisher

    File URL: http://faculty.washington.edu/ezivot/research/practicalgarchfinal.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:hum:wpaper:sfb649dp2014-050 is not listed on IDEAS
    2. Aliyev, Fuzuli & Ajayi, Richard & Gasim, Nijat, 2020. "Modelling asymmetric market volatility with univariate GARCH models: Evidence from Nasdaq-100," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    3. Rizvi, Syed Kumail Abbas & Naqvi, Bushra, 2008. "Asymmetric Behavior of Inflation Uncertainty and Friedman-Ball Hypothesis: Evidence from Pakistan," MPRA Paper 19488, University Library of Munich, Germany.
    4. Kola, Katlego & Kodongo, Odongo, 2017. "Macroeconomic risks and REITs returns: A comparative analysis," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1228-1243.
    5. Rachna Mahalwala, 2016. "A Study of Expiration-day Effects of Index Derivatives Trading in India," Metamorphosis: A Journal of Management Research, , vol. 15(1), pages 10-19, June.
    6. Beg, A.B.M. Rabiul Alam & Anwar, Sajid, 2012. "Sources of volatility persistence: A case study of the U.K. pound/U.S. dollar exchange rate returns," The North American Journal of Economics and Finance, Elsevier, vol. 23(2), pages 165-184.
    7. Ryszard Kokoszczyński & Paweł Sakowski & Robert Ślepaczuk, 2017. "Which Option Pricing Model Is the Best? HF Data for Nikkei 225 Index Options," Central European Economic Journal, Sciendo, vol. 4(51), pages 18-39.
    8. Syed Kumail Abbas Naqvi & Bushra Naqvi, 2010. "Asymmetric Behavior of Inflation Uncertainty and Friedman-Ball Hypothesis: Evidence from Pakistan," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 15(2), pages 1-33, Jul-Dec.
    9. Massimo PERI & Daniela VANDONE & Lucia BALDI, 2012. "Internet, noise trading and commodity prices," Departmental Working Papers 2012-07, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    10. Ryszard Kokoszczyński & Paweł Sakowski & Robert Ślepaczuk, 2017. "Which Option Pricing Model Is the Best? HF Data for Nikkei 225 Index Options," Central European Economic Journal, Sciendo, vol. 4(51), pages 18-39, December.
    11. Rizvi, Syed Kumail Abbas & Naqvi, Bushra, 2009. "Inflation Volatility: An Asian Perspective," MPRA Paper 19489, University Library of Munich, Germany.
    12. Thakolsri, Supachok & Sethapramote, Yuthana & Jiranyakul, Komain, 2015. "Asymmetric volatility of the Thai stock market: evidence from high-frequency data," MPRA Paper 67181, University Library of Munich, Germany.
    13. Aliyu, Shehu Usman Rano, 2011. "Reactions of stock market to monetary policy shocks during the global financial crisis: the Nigerian case," MPRA Paper 35581, University Library of Munich, Germany, revised 28 Dec 2011.
    14. Benschopa, Thijs & López Cabreraa, Brenda, 2014. "Volatility modelling of CO2 emission allowance spot prices with regime-switching GARCH models," SFB 649 Discussion Papers 2014-050, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    15. Haytem Troug & Matt Murray, 2020. "Crisis determination and financial contagion: an analysis of the Hong Kong and Tokyo stock markets using an MSBVAR approach," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 48(8), pages 1548-1572, December.
    16. Nyoni, Thabani, 2018. "Modeling and Forecasting Inflation in Zimbabwe: a Generalized Autoregressive Conditionally Heteroskedastic (GARCH) approach," MPRA Paper 88132, University Library of Munich, Germany.
    17. Troug, Haytem Ahmed & Murray, Matt, 2015. "Quantitative Easing in Japan and the UK An Econometric Evaluation of the Impacts of Unconventional Monetary Policy on the Returns of Aggregate Output and Price Levels," MPRA Paper 68707, University Library of Munich, Germany.
    18. Peri, Massimo & Vandone, Daniela & Baldi, Lucia, 2014. "Internet, noise trading and commodity futures prices," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 82-89.
    19. Peri, Massimo & Vandone, Daniela & Baldi, Lucia, 2012. "Information Demand and Agriculture Commodity Prices," 2012 International European Forum, February 13-17, 2012, Innsbruck-Igls, Austria 144973, International European Forum on System Dynamics and Innovation in Food Networks.
    20. Mhd Ruslan, Siti Marsila & Mokhtar, Kasypi, 2021. "Stock market volatility on shipping stock prices: GARCH models approach," The Journal of Economic Asymmetries, Elsevier, vol. 24(C).
    21. Rachna Mahalwala, 2022. "Analysing exchange rate volatility in India using GARCH family models," SN Business & Economics, Springer, vol. 2(9), pages 1-16, September.

    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.
    1. Hillebrand, Eric & Schnabl, Gunther & Ulu, Yasemin, 2009. "Japanese foreign exchange intervention and the yen-to-dollar exchange rate: A simultaneous equations approach using realized volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(3), pages 490-505, July.
    2. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    3. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    4. Woerner Jeannette H. C., 2003. "Variational sums and power variation: a unifying approach to model selection and estimation in semimartingale models," Statistics & Risk Modeling, De Gruyter, vol. 21(1), pages 47-68, January.
    5. Manabu Asai & Rangan Gupta & Michael McAleer, 2019. "The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures," Energies, MDPI, vol. 12(17), pages 1-17, September.
    6. 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.
    7. Christos Floros & Konstantinos Gkillas & Christoforos Konstantatos & Athanasios Tsagkanos, 2020. "Realized Measures to Explain Volatility Changes over Time," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    8. Chuong Luong & Nikolai Dokuchaev, 2016. "Modeling Dependency Of Volatility On Sampling Frequency Via Delay Equations," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 1-21, June.
    9. Hartwell, Christopher A., 2014. "The impact of institutional volatility on financial volatility in transition economies : a GARCH family approach," BOFIT Discussion Papers 6/2014, Bank of Finland, Institute for Economies in Transition.
    10. Dimitrios D. Thomakos & Michail S. Koubouros, 2011. "The Role of Realised Volatility in the Athens Stock Exchange," Multinational Finance Journal, Multinational Finance Journal, vol. 15(1-2), pages 87-124, March - J.
    11. repec:kap:iaecre:v:14:y:2008:i:1:p:112-124 is not listed on IDEAS
    12. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Christian Pierdzioch, 2021. "El Niño, La Niña, and the Forecastability of the Realized Variance of Heating Oil Price Movements," Sustainability, MDPI, vol. 13(14), pages 1-23, July.
    13. John M. Maheu & Thomas H. McCurdy, 2002. "Nonlinear Features of Realized FX Volatility," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 668-681, November.
    14. Dufour, Jean-Marie & García, René, 2008. "Measuring causality between volatility and returns with high-frequency data," UC3M Working papers. Economics we084422, Universidad Carlos III de Madrid. Departamento de Economía.
    15. Doureige J. Jurdi, 2020. "Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    16. Lee, Hwang Hee & Hyun, Jung-Soon, 2019. "The asymmetric effect of equity volatility on credit default swap spreads," Journal of Banking & Finance, Elsevier, vol. 98(C), pages 125-136.
    17. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Annals of Economics and Statistics, GENES, issue 123-124, pages 135-174.
    18. Wang, Fangfang, 2014. "Optimal design of Fourier estimator in the presence of microstructure noise," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 708-722.
    19. Almut Veraart & Luitgard Veraart, 2012. "Stochastic volatility and stochastic leverage," Annals of Finance, Springer, vol. 8(2), pages 205-233, May.
    20. Jonathan J. Reeves & Xuan Xie, 2014. "Forecasting stock return volatility at the quarterly frequency: an evaluation of time series approaches," Applied Financial Economics, Taylor & Francis Journals, vol. 24(5), pages 347-356, March.
    21. Sucarrat, Genaro, 2009. "Forecast Evaluation of Explanatory Models of Financial Variability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-33.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    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:udb:wpaper:uwec-2008-03-fc. 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: Michael Goldblatt (email available below). General contact details of provider: https://edirc.repec.org/data/deuwaus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.