IDEAS home Printed from https://ideas.repec.org/a/spr/snbeco/v2y2022i9d10.1007_s43546-022-00317-z.html
   My bibliography  Save this article

Analysing exchange rate volatility in India using GARCH family models

Author

Listed:
  • Rachna Mahalwala

    (University of Delhi)

Abstract

Volatility in foreign exchange market is an important issue of concern for market participants and policy makers as higher the volatility the more unstable the foreign exchange market is. The volatility of the foreign exchange market in India is modelled using daily spot rate of the Indian rupee per US Dollar (USD/INR) obtained from the RBI’s website from January 1, 2010 to December 31, 2020. All empirical work is done on USD/INR return series which was first modelled with ARMA framework and tested for ARCH effects with ARCH-LM test for heteroscedasticity. When ARCH-LM test approved the use of GARCH family models for modelling volatility, both symmetric and asymmetric models namely, GARCH (1,1), EGARCH (1,1), TGARCH (1,1) and APARCH (1,1) were used. Post-estimation test for remaining ARCH effects were done to check the efficiency of the models. TGARCH (1,1) turned to be the best model using both the AIC and SIC criterions showing the presence of significant asymmetric response to positive and negative shocks but leverage effects could not be established. This refers that foreign exchange market in India responds differently to information depending whether it positive or negative. This information is helpful for market participants in making trade, investment and other economic decisions.

Suggested Citation

  • Rachna Mahalwala, 2022. "Analysing exchange rate volatility in India using GARCH family models," SN Business & Economics, Springer, vol. 2(9), pages 1-16, September.
  • Handle: RePEc:spr:snbeco:v:2:y:2022:i:9:d:10.1007_s43546-022-00317-z
    DOI: 10.1007/s43546-022-00317-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43546-022-00317-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s43546-022-00317-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Massimiliano Caporin & Michele Costola, 2019. "Asymmetry and leverage in GARCH models: a News Impact Curve perspective," Applied Economics, Taylor & Francis Journals, vol. 51(31), pages 3345-3364, July.
    2. Fidrmuc, Jarko & Horváth, Roman, 2008. "Volatility of exchange rates in selected new EU members: Evidence from daily data," Economic Systems, Elsevier, vol. 32(1), pages 103-118, March.
    3. Helmut Herwartz & Hans‐Eggert Reimers, 2002. "Empirical modelling of the DEM/USD and DEM/JPY foreign exchange rate: Structural shifts in GARCH‐models and their implications," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 18(1), pages 3-22, January.
    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. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    7. Eric Zivot, 2008. "Practical Issues in the Analysis of Univariate GARCH Models," Working Papers UWEC-2008-03-FC, University of Washington, Department of Economics.
    8. MacKinnon, James G, 1996. "Numerical Distribution Functions for Unit Root and Cointegration Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 601-618, Nov.-Dec..
    9. 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.
    10. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    11. Vilasuso, Jon, 2002. "Forecasting exchange rate volatility," Economics Letters, Elsevier, vol. 76(1), pages 59-64, June.
    12. 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.
    13. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    14. Hsieh, David A, 1989. "Modeling Heteroscedasticity in Daily Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(3), pages 307-317, July.
    15. Perekunah B. Eregha & Festus O. Egwaikhide & Emeka Osuji, 2020. "Modeling Exchange Rate Volatility in Selected WAMZ Countries: Evidence from Symmetric and Asymmetric GARCH Models," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 70(1-2), pages 58-80, January-J.
    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. 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.
    2. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    3. Li, Gang & Li, Yong, 2015. "Forecasting copper futures volatility under model uncertainty," Resources Policy, Elsevier, vol. 46(P2), pages 167-176.
    4. 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.
    5. Xekalaki, Evdokia & Degiannakis, Stavros, 2005. "Evaluating volatility forecasts in option pricing in the context of a simulated options market," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 611-629, April.
    6. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    7. 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.
    8. 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.
    9. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    10. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    11. Necula Ciprian & Radu Alina-Nicoleta, 2009. "Detecting Regime Switches In The Eur/Ron Exchange Rate Volatility," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 3(1), pages 610-615, May.
    12. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    13. DAVID G. McMILLAN & ALAN E. H. SPEIGHT, 2007. "Value‐at‐Risk in Emerging Equity Markets: Comparative Evidence for Symmetric, Asymmetric, and Long‐Memory GARCH Models," International Review of Finance, International Review of Finance Ltd., vol. 7(1‐2), pages 1-19, March.
    14. 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.
    15. Bei, Shuhua & Yang, Aijun & Pei, Haotian & Si, Xiaoli, 2023. "Price Risk Analysis using GARCH Family Models: Evidence from Shanghai Crude Oil Futures Market," Economic Modelling, Elsevier, vol. 125(C).
    16. Eskandar A. Tooma, 2003. "Modeling and Forecasting Egyptian Stock Market Volatility Before and After Price Limits," Working Papers 0310, Economic Research Forum, revised Apr 2003.
    17. Saker Sabkha & Christian de Peretti, 2018. "On the performances of Dynamic Conditional Correlation models in the Sovereign CDS market and the corresponding bond market," Working Papers hal-01710398, HAL.
    18. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    19. Saker Sabkha & Christian de Peretti, 2022. "On the performances of Dynamic Conditional Correlation models in the Sovereign CDS market and the corresponding bond market," Post-Print hal-01710398, HAL.
    20. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.

    More about this item

    Keywords

    Exchange rate; Volatility; ARCH; GARCH; EGARCH; TGARCH; APARCH;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:spr:snbeco:v:2:y:2022:i:9:d:10.1007_s43546-022-00317-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.