IDEAS home Printed from https://ideas.repec.org/p/fem/femwpa/2003.43.html
   My bibliography  Save this paper

STAR-GARCH Models for Stock Market Interactions in the Pacific Basin Region, Japan and US

Author

Listed:
  • Giorgio Busetti

    (Quantitative Methods, Monte Paschi Alternative Investment, Milano, Italy)

  • Matteo Manera

    (Department of Statistics, University of Milano-Bicocca, Italy and Fondazione Eni Enrico Mattei, Milano, Italy)

Abstract

We investigate the financial interactions between countries in the Pacific Basin region (Korea, Singapore, Malaysia, Hong Kong and Taiwan), Japan and US. The originality of the paper is the use of STAR-GARCH models, instead of standard correlation-cointegration techniques. For each country in the Pacific Basin region, we find statistically adequate STAR-GARCH models for the series of stock market daily returns, using Nikkei225 and S&P500 as alternative threshold variables. We provide evidence for the leading role of Japan in the period 1988-1990 (pre-Japanese crisis years), whereas our results suggest that the Pacific Basin region countries are more closely linked with the US during the period 1995-1999 (post- Japanese crisis years).

Suggested Citation

  • Giorgio Busetti & Matteo Manera, 2003. "STAR-GARCH Models for Stock Market Interactions in the Pacific Basin Region, Japan and US," Working Papers 2003.43, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2003.43
    as

    Download full text from publisher

    File URL: https://feem-media.s3.eu-central-1.amazonaws.com/wp-content/uploads/NDL2003-043.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    2. Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(2), pages 280-310, April.
    3. M. B. Priestley, 1980. "State‐Dependent Models: A General Approach To Non‐Linear Time Series Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 47-71, January.
    4. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Felix Chan & Michael McAleer, 2003. "Estimating smooth transition autoregressive models with GARCH errors in the presence of extreme observations and outliers," Applied Financial Economics, Taylor & Francis Journals, vol. 13(8), pages 581-592.
    7. 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.
    8. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    9. Alvaro Escribano & Oscar Jorda, "undated". "Improved Testing And Specification Of Smooth Transition Regression Models," Department of Economics 97-26, California Davis - Department of Economics.
    10. Franses, Philip Hans & Ghijsels, Hendrik, 1999. "Additive outliers, GARCH and forecasting volatility," International Journal of Forecasting, Elsevier, vol. 15(1), pages 1-9, February.
    11. Terasvirta, Timo & Tjostheim, Dag & W.J. Granger, Clive, 1986. "Aspects of modelling nonlinear time series," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 48, pages 2917-2957, Elsevier.
    12. Stephen Leybourne & Paul Newbold & Dimitrios Vougas, 1998. "Unit roots and smooth transitions," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(1), pages 83-97, January.
    13. Hansen Bruce E., 1997. "Inference in TAR Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(1), pages 1-16, April.
    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. Melike Bildirici & Özgür Ömer Ersin, 2014. "Nonlinearity, Volatility and Fractional Integration in Daily Oil Prices: Smooth Transition Autoregressive ST-FI(AP)GARCH Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 108-135, October.
    2. Fuzuli Aliyev, 2019. "Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul," IJFS, MDPI, vol. 7(2), pages 1-11, June.
    3. Mubariz Hasanov & Tolga Omay, 2008. "Nonlinearities in emerging stock markets: evidence from Europe's two largest emerging markets," Applied Economics, Taylor & Francis Journals, vol. 40(20), pages 2645-2658.

    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. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    2. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, September.
    3. Mohamed CHIKHI & Claude DIEBOLT, 2022. "Testing the weak form efficiency of the French ETF market with the LSTAR-ANLSTGARCH approach using a semiparametric estimation," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 13, pages 228-253, June.
    4. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911, October.
    5. Mohamed Chikhi & Claude Diebolt, 2019. "Testing Nonlinearity through a Logistic Smooth Transition AR Model with Logistic Smooth Transition GARCH Errors," Working Papers of BETA 2019-06, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    6. Chan, Felix & Marinova, Dora & McAleer, Michael, 2004. "Modelling the asymmetric volatility of electronics patents in the USA," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(1), pages 169-184.
    7. Carnero, María Ángeles, 2004. "Spurious and hidden volatility," DES - Working Papers. Statistics and Econometrics. WS ws042007, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Param Silvapulle & Titi Kanti Lestari & Jae Kim, 2004. "Nonlinear Modelling of Purchasing Power Parity in Indonesia," Econometric Society 2004 Australasian Meetings 316, Econometric Society.
    9. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871, October.
    10. Martinez Oscar & Olmo Jose, 2012. "A Nonlinear Threshold Model for the Dependence of Extremes of Stationary Sequences," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-39, September.
    11. Rinke Saskia & Sibbertsen Philipp, 2016. "Information criteria for nonlinear time series models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(3), pages 325-341, June.
    12. Maringer Dietmar G. & Meyer Mark, 2008. "Smooth Transition Autoregressive Models -- New Approaches to the Model Selection Problem," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(1), pages 1-21, March.
    13. Jieye Qin & Christopher J. Green & Kavita Sirichand, 2019. "Determinants of Nikkei futures mispricing in international markets: Dividend clustering, currency risk, and transaction costs," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(10), pages 1269-1300, October.
    14. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    15. Gilles Dufrenot & Laurent Mathieu & Valerie Mignon & Anne Peguin-Feissolle, 2006. "Persistent misalignments of the European exchange rates: some evidence from non-linear cointegration," Applied Economics, Taylor & Francis Journals, vol. 38(2), pages 203-229.
    16. Scharth, Marcel & Medeiros, Marcelo C., 2009. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," International Journal of Forecasting, Elsevier, vol. 25(2), pages 304-327.
    17. Meitz, Mika & Saikkonen, Pentti, 2011. "Parameter Estimation In Nonlinear Ar–Garch Models," Econometric Theory, Cambridge University Press, vol. 27(6), pages 1236-1278, December.
    18. Jawadi Fredj & Koubaa Yousra, 2004. "Threshold Cointegration between Stock Returns : An application of STECM Models," Econometrics 0412001, University Library of Munich, Germany.
    19. Junru Zhang & Hadrian Geri Djajadikerta & Zhaoyong Zhang, 2018. "Does Sustainability Engagement Affect Stock Return Volatility? Evidence from the Chinese Financial Market," Sustainability, MDPI, vol. 10(10), pages 1-21, September.
    20. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.

    More about this item

    Keywords

    STAR-GARCH models; stock market integration; Pacific-Basin capital markets; outliers;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration

    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:fem:femwpa:2003.43. 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: Alberto Prina Cerai (email available below). General contact details of provider: https://edirc.repec.org/data/feemmit.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.