IDEAS home Printed from https://ideas.repec.org/a/noa/journl/y2017i3p1-10.html
   My bibliography  Save this article

Time Series Modeling of Inflation and its Volatility in Croatia

Author

Listed:
  • Igor Živko

    (Faculty of Economics and Business,University of Mostar)

  • Mile BoÅ¡njak

    (Faculty of Economics and Business,University of Zagreb)

Abstract

Croatian National Bank is not targeting inflation but exchange rate as the nominal anchor or intermediary goal of monetary policy and inflation in Croatia is a dominantly foreign driven phenomenon. Using monthly data on CPI in Croatia from January 1997 up to November 2015, ARIMA (0,1,1) x (0,1,1)12 model is fitted asthe one describing CPI behavior pattern and therefore reliable for CPI forecasting. Furthermore, to establish its volatility pattern several ARCH family models are tested and ARCH (1) model is found to be the best fitted one in explaining CPI volatility development in Croatia.

Suggested Citation

  • Igor Živko & Mile BoÅ¡njak, 2017. "Time Series Modeling of Inflation and its Volatility in Croatia," Notitia - journal for economic, business and social issues, Notitia Ltd., vol. 1(3), pages 1-10, December.
    • Handle: RePEc:noa:journl:y:2017:i:3:p:1-10
    as

    Download full text from publisher

    File URL: http://www.notitia.hr/RePEc/noa/journl/01_2017.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. repec:onb:oenbwp:y::i:73:b:1 is not listed on IDEAS
    2. Poulos, Laurette & Kvanli, Alan & Pavur, Robert, 1987. "A comparison of the accuracy of the Box-Jenkins method with that of automated forecasting methods," International Journal of Forecasting, Elsevier, vol. 3(2), pages 261-267.
    3. Maruška Vizek & Tanja Broz, 2009. "Modeling Inflation in Croatia," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 45(6), pages 87-98, November.
    4. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Mance Davor & Žiković Saša & Mance Diana, 2015. "Econometric Analysis of Croatia’s Proclaimed Foreign Exchange Rate," South East European Journal of Economics and Business, Sciendo, vol. 10(1), pages 7-17, April.
    7. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    8. Tomislav Globan & Vladimir Arčabić & Petar Sorić, 2016. "Inflation in New EU Member States: A Domestically or Externally Driven Phenomenon?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(1), pages 154-168, January.
    9. Payne, James E., 2002. "Inflationary dynamics of a transition economy: the Croatian experience," Journal of Policy Modeling, Elsevier, vol. 24(3), pages 219-230, June.
    10. 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.
    11. Ikechukwu Kelikume & Adedoyin Salami, 2014. "Time Series Modeling and Forecasting Information: Evidence from Nigeria," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 8(2), pages 41-51.
    12. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    13. Michael Joyce & David Miles & Andrew Scott & Dimitri Vayanos, 2012. "Quantitative Easing and Unconventional Monetary Policy – an Introduction," Economic Journal, Royal Economic Society, vol. 122(564), pages 271-288, November.
    14. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    15. Poncela, Pilar & Senra, Eva, 2002. "Forecasting monthly us consumer price indexes through a disaggregated I(2) analysis," DES - Working Papers. Statistics and Econometrics. WS ws020301, Universidad Carlos III de Madrid. Departamento de Estadística.
    16. Marvin Goodfriend, 2007. "How the World Achieved Consensus on Monetary Policy," Journal of Economic Perspectives, American Economic Association, vol. 21(4), pages 47-68, Fall.
    17. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    18. Texter, Pamela A. & Ord, J. Keith, 1989. "Forecasting using automatic identification procedures: A comparative analysis," International Journal of Forecasting, Elsevier, vol. 5(2), pages 209-215.
    19. Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).
    20. Valerija Botrić & Boris Cota, 2006. "Sources Of Inflation In Transition Economy: The Case Of Croatia," Ekonomski pregled, Hrvatsko društvo ekonomista (Croatian Society of Economists), vol. 57(12), pages 835-854.
    Full references (including those not matched with items on IDEAS)

    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. Ender Su & John Bilson, 2011. "Trading asymmetric trend and volatility by leverage trend GARCH in Taiwan stock index," Applied Economics, Taylor & Francis Journals, vol. 43(26), pages 3891-3905.
    2. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    3. Turan Bali & Panayiotis Theodossiou, 2007. "A conditional-SGT-VaR approach with alternative GARCH models," Annals of Operations Research, Springer, vol. 151(1), pages 241-267, April.
    4. Výrost, Tomáš & Baumöhl, Eduard, 2009. "Asymmetric GARCH and the financial crisis: a preliminary study," MPRA Paper 27939, University Library of Munich, Germany.
    5. Muhammad Sheraz & Imran Nasir, 2021. "Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach," Risks, MDPI, vol. 9(5), pages 1-20, May.
    6. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    7. Köksal, Bülent, 2009. "A Comparison of Conditional Volatility Estimators for the ISE National 100 Index Returns," MPRA Paper 30510, University Library of Munich, Germany.
    8. Li, Gang & Li, Yong, 2015. "Forecasting copper futures volatility under model uncertainty," Resources Policy, Elsevier, vol. 46(P2), pages 167-176.
    9. Tae-Hwy Lee & Yong Bao & Burak Saltoğlu, 2007. "Comparing density forecast models Previous versions of this paper have been circulated with the title, 'A Test for Density Forecast Comparison with Applications to Risk Management' since October 2003;," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(3), pages 203-225.
    10. Dennis Kristensen, 2009. "On stationarity and ergodicity of the bilinear model with applications to GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(1), pages 125-144, January.
    11. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    12. Azimi, Mohammad Naim, 2015. "Modelling the Clustering Volatility of India's Wholesales Price Index and the Factors Affecting it," MPRA Paper 70267, University Library of Munich, Germany.
    13. Brooks, Robert D. & Faff, Robert W. & McKenzie, Michael D. & Mitchell, Heather, 2000. "A multi-country study of power ARCH models and national stock market returns," Journal of International Money and Finance, Elsevier, vol. 19(3), pages 377-397, June.
    14. Jamal Bouoiyour & Refk Selmi, 2015. "Exchange volatility and export performance in Egypt: New insights from wavelet decomposition and optimal GARCH model," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 24(2), pages 201-227, March.
    15. Amare Wubishet Ayele & Emmanuel Gabreyohannes & Yohannes Yebabe Tesfay, 2017. "Macroeconomic Determinants of Volatility for the Gold Price in Ethiopia: The Application of GARCH and EWMA Volatility Models," Global Business Review, International Management Institute, vol. 18(2), pages 308-326, April.
    16. El Jebari, Ouael & Hakmaoui, Abdelati, 2018. "GARCH Family Models vs EWMA: Which is the Best Model to Forecast Volatility of the Moroccan Stock Exchange Market? || Modelos de la familia GARCH vs EWMA: ¿cuál es el mejor modelo para pronosticar la ," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 237-249, Diciembre.
    17. Marcel Bräutigam & Marie Kratz, 2019. "Bivariate FCLT for the Sample Quantile and Measures of Dispersion for Augmented GARCH(p, q) processes," Working Papers hal-02176276, HAL.
    18. Vacca, Gianmarco & Zoia, Maria Grazia & Bagnato, Luca, 2022. "Forecasting in GARCH models with polynomially modified innovations," International Journal of Forecasting, Elsevier, vol. 38(1), pages 117-141.
    19. Tissaoui, Kais & Ftiti, Zied, 2016. "Liquidity, liquidity risk, and information flow: Lessons from an emerging market," Research in International Business and Finance, Elsevier, vol. 37(C), pages 28-48.
    20. Algieri, Bernardina, 2014. "The influence of biofuels, economic and financial factors on daily returns of commodity futures prices," Energy Policy, Elsevier, vol. 69(C), pages 227-247.

    More about this item

    Keywords

    CPI; ARIMA; ARCH; Croatia;
    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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • F39 - International Economics - - International Finance - - - Other

    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:noa:journl:y:2017:i:3:p:1-10. 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: Vlatka Bilas (email available below). General contact details of provider: http://www.notitia.hr .

    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.