Introducing an Early Warning System of Exchange Rate Volatility in Iranian Exchange Market: Markov Switching GARCH Method

Document Type : Research Paper

Authors

1 PhD Student of Economics, Ferdowsi University of Mashhad

2 Professor of Economics, Ferdowsi University of Mashhad

3 Associate Professor of Economics, Ferdowsi University of Mashhad

Abstract

Forecasting exchange rate volatility is important step in exchange market policy making to avoid high volatility of exchange rate. Exchange rate volatility is important because it is an index of investment uncertainty in each economy. The aim of this paper is introducing an Early Warning System (EWS) of high volatility of exchange rate in Iranian exchange market. Therefore, by estimation a Markov switching GARCH model, exchange rate volatility has been modeled. In this paper, the daily data of market exchange rate during 25thOrdibehesht 1385 until 21thTir 1394 has been used. With estimation of this model, transition matrix of probabilities of high and low volatility regimes has been calculated. By using this matrix, probabilities of high and low volatility regimes in Iranian Exchange Market have been calculated for future horizons so, it is possible to achieve a suitable model for forecasting high volatility of exchange rate regime in Iranian Exchange rate Market. The results of this system indicate that the probability of staying in high volatility exchange rate regime, the probability of transition from high to low volatility, the probability of transition from low to high volatility and the probability of staying in low volatility exchange rate regime are 0.14, 0.03, 0.86 and 0.97 respectively.

Keywords


  1.  

    1. ابراهیمی، ایلناز، و حسین توکلیان (1391). طراحی یک سامانه هشداردهی زودهنگام بحران­های ارزی در ایران با استفاده از رویکرد مارکوف سوئیچینگ. بیست و دومین همایش سالانه سیاست­های پولی و ارزی، تهران، پژوهشکده پولی و بانکی.

    2. ابونوری، اسمعیل، و عرفانی، علیرضا (1387). الگوی چرخشی مارکف و پیش‏بینی احتمال بحران نقدینگی در کشورهای عضو اوپک. مجله پژوهشنامه اقتصادی وزارت امور اقتصادی و دارایی، 30، 153-174.

    3. خاشعی، مهدی، و بیجاری، مهدی (1386). به­کارگیری مدل میانگین متحرک خودرگرسیون انباشته فازی به منظور پیش­بینی نرخ ارز. مجله استقلال، سال 26، 2، 67-75.

    4. طیبی، سیدکمیل، موحدنیا، ناصر، و کاظمینی، معصومه (1387). بکارگیری شبکه­های عصبی مصنوعی در پیش­بینی متغیرهای اقتصادی و مقایسه آن با روشهای اقتصادسنجی: پیش­بینی روند نرخ ارز در ایران. مجله علمی پژوهشی شریف، 43، 99-104.

    5. رستم­زاده، مهدی (1390). ارزیابی مدل­های تعیین نرخ ارز با استفاده از الگوریتم ژنتیک (مطالعه موردی ایران و اتحادیه اروپا). رساله دکترای علوم اقتصادی، دانشگاه مازندران.

    6. مرزبان، حسین، اکبریان، رضا، و جواهری، بهنام (1384). یک مقایسه بین مدل­‌های اقتصاد سنجی ساختاری، سری زمانی و شبکه عصبی برای پیش ‌بینی نرخ ارز. تحقیقات اقتصادی، تابستان 1384، 69، 181-216.

    7. نادمی، یونس (1392). مدل­سازی نوسانات بازدهی بازار سهام تهران با روش مارکوف سوئیچینگ گارچ. رساله دکترای علوم اقتصادی دانشگاه مازندران.

     

    1. Anh, P. T. H. (2015). Early warning system of currency crisis based on exchange market pressure: The case of Vietnam. Available at SSRN 2595608.
    2. Armstrong, J. S. (2001). Principles of forecasting: A handbook for researchers and practitioners (Vol. 30). Springer.

    3. Candelon, B., Dumitrescu, E. I., & Hurlin, C. (2014). Currency crisis early warning systems: Why they should be dynamic. International Journal of Forecasting, 30(4), 1016-1029.

    4. Christofides, C., Eicher, T. S., & Papageorgiou, C. (2016). Did established early warning signals predict the 2008 Crises?. European Economic Review, 81, 103-114.

    5. Comelli, F. (2014). Comparing parametric and nonparametric early warning systems for currency crises in emerging market economies. Review of International Economics, 22(4), 700-721.

    6. Edison, Hali (2001). Do indicators of financial crises work? An evaluation of an early warning system. FRB International Finance Discussion Paper, 675.

    7. Granger, C. W. J. (1980). Forecasting in business and economics (Vol. 2). New York: Academic Press.

    8. Glosten, L.R., Jagannathan, R. & Runkle, D.E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48, 1779– 1801.

    9. Hamilton, J. D (1989). A new approach to the economic analysis of non-stationary time series and the business cycle. Econometrica, 57, 357-384.

    10. Hamilton, J.D. (1994). Time series analysis. Princeton University Press, Princeton, New Jersey.

    11. Kaminsky, Graciela, Saul Lizondo, and Carmen M. Reinhart (1998). Leading indicators of currency crises, Staff Papers-International Monetary Fund, 1-48.

    12. Klaassen, F. (2002). Improving GARCH volatility forecasts with regime-switching GARCH. Empirical Economics, 27, 363-394.

    13. Lang, M., & Schmidt, P. G. (2016). The early warnings of banking crises: nteraction of broad liquidity and demand deposits. Journal of International Money and Finance, 61, 1-29.

    14. Mark, N.C. (2000). International macroeconomics and finance, Oxford: Blackwell Publishers, 71.

    15. Marcucci, J. (2005). Forecasting stock market volatility with regime-switching GARCH models. Studies in Nonlinear Dynamics & Econometrics, 9(4), 1-55.

    16. Moosa, I. A. (2000). Exchange rate forecasting, techniques and Applications.Macmillan Business, London, 1(1), 31-39.

    17. Neely, C. J. (1997). Technical analysis in the foreign exchange market: A Layman´s guide. Federal Reserve Bank of St. Louis Review, 79, 23-38.

    18. Obstfeld, M., & Rogoff, K. (2000). New directions for stochastic Open economy models. Journal of International Economics, 50(2), 117−53.