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Contagion Phenomenon among Central and Eastern European Currencies

ACADEMY OF ECONOMIC STUDIES DOCTORAL SCHOOL OF FINANCE AND BANKING. Contagion Phenomenon among Central and Eastern European Currencies. Student: Roteanu Cosmina Georgiana. Bucharest, July 2009. Dissertation paper outline. The importance of contagion among CEE currencies

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Contagion Phenomenon among Central and Eastern European Currencies

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  1. ACADEMY OF ECONOMIC STUDIES DOCTORAL SCHOOL OF FINANCE AND BANKING Contagion Phenomenon among Central and Eastern European Currencies Student: Roteanu Cosmina Georgiana Bucharest, July 2009

  2. Dissertation paper outline • The importance of contagion among CEE currencies • The aims of the present paper • Brief review of the literature on contagion • Model specifications • Data, estimation and results • Conclusions • References

  3. The importance of contagion among CEE currencies • Contagion was at the heart of the global financial crisis. • September 2008 – critical stage of the crisis. • Effects on CEE countries: heightened volatility, an increase in risk premia and plummeting currencies. • Shift in the attitude towards adopting Euro: “ Secure public finances and a quick adoption of the euro are the best way out of the crisis for Poland” – Finance Minister, February 2009. In January 2009 Czech prime minister announced that the government will determine a date for adopting the euro in November 2009, the most realistic target being 2013. “For countries in the EU, euroisation offers the largest benefits in terms of resolving the foreign currency debt overhang, removing uncertainty and restoring confidence.” – IMF, April 2009. • Before adopting the euro, every country has to be part of ERM II, for at least two years, but meeting the convergence criteria appears more difficult in the light of the crisis events.

  4. Aims of the paper • This paper focuses on the exchange rate behavior ofCzech Koruna, Polish Zloty and Romanian Leu during normal and heightened volatility periods. • The objectives are: • to isolate different sources of exchange rate volatility : common and idiosyncratic • to compute a measure of contagion represented by the spillover effects from unanticipated local shocks from one foreign exchange market to another after conditioning on common factors.

  5. Broad definition of contagion(World Bank) “Contagion is the cross-country transmission of shocks or the general cross-country spillover effects” • Contagion takes place both during “good” and “bad” times. • Most of the literature distinguishes ‘fundamental’ linkages from contagion: • Calvo and Reinhart (1996) : fundamentals – based contagion “true” contagion • Kaminsky et al.(2000) : fundamentals – based contagion common cause contagion pure contagion

  6. Restrictive Definition(World Bank) “Contagion is the transmission of shocks to other countries or the cross-country correlation, beyond any fundamental link among the countries and beyond common shocks.” • Fundamental linkages across countries include: - Financial links - Real links

  7. Very Restrictive Definition(World Bank) “Contagion occurs when cross-country correlations increase during ‘crisis times’ relative to correlations during ‘tranquil times.” • Only increases in correlation are recognized as contagion • Forbes and Rigobon (2002) : “ … a significant increase in cross-market linkages after a shock to one country.” • It needs to control for general volatility rising during financial crises

  8. Our version Dungey et al. (2005) definition of contagion as “unexpected shocks” or news • Other terms: “pure contagion”, “shift contagion” • Contagion takes place because transmission arises over and above the anticipated links, so the reaction is beyond what could have been expected on the basis of fundamental linkages.

  9. Measure of Contagion Latent Factor Model proposed by Dungey et al. (2005) • It includes global and country factors to capture market fundamentals and also additional movements over and above market fundamentals during crisis period to account for contagion • This modeling strategy is shown to encompass many of the existing approaches to measuring contagion, including • the correlation analysis proposed by Forbes and Rigobon (2002) • the vector autoregression (VAR) approach of Favero and Giavazzi (2002) • the probability models of Eichengreen et al. (1996) • the co-exceedance approach of Bae et al. (2003)

  10. DFGM - A Model of Interdependence • Latent factor model of asset returns during non-crisis period • Based on Arbitrage Pricing Theory x1,t = λ1wt + γ1u1,t x2,t = λ2wt + γ2u2,t (1) x3,t = λ3wt + γ3u3,t xt – returns during non-crisis period wt – world factor (common shock) ui,t – idiosyncratic factor λi, γi >0 – factor loadings that determine the contribution of each shock to the volatility of asset markets • Assumptions: • the factors are stochastic processes with zero mean and constant variance: wt ~ (0,1) ui,t~ (0,1) • all factors are independent:

  11. DFGM - A Model of Contagion The factor model is augmented to allow an avenue for contagion between the foreign exchange markets in all directions: y1,t = λ1wt + γ1u1,t + δ1,2 u2,t+ δ1,3 u3,t y2,t = λ2wt + γ2u2,t + δ2,1 u1,t + δ2,3 u3,t (2) y3,t = λ3wt + γ3u3,t + δ3,1 u1,t+ δ3,2 u2,t yi,t = returns during crisis period δi,j = effects of unanticipated local shocks from the asset j to i (strength of contagion across markets) The parameters of the model are estimated by GMM: q = arg G’WG (Hamilton, 1994) (3)Θ The model in (1) is just identified as there are six unknown parameters and six unique variances and covariances, whilst the model in (2) is unidentified as there are N (N+1) / 2 = 6 unique moment conditions and 12 unknown parameters. In this special case, the model becomes block-recursive with identification of the factor loadings using the pre-crisis period moments (the first block), whilst the parameters capturing the effect of contagion are identified by the empirical moments from the variance-covariance matrix of the crisis period.

  12. Variance - Covariance Decompositions • The covariances and variances between asset returns during the period of tranquility are given by: • During the period of turbulence, the variances and covariances of asset returns become: A practical interpretation of asset returns volatility is provided by the decomposition of the variance-covariance matrix into the contributions of each shock. • The change in volatility between the two periods is due to the existence of contagion: • In addition, the change in covariance between the two periods is given by:

  13. The Data • Daily nominal exchange rates of three CEE currencies against the euro, namely the Czech koruna (CZK), the Polish zloty (PLN) and the Romanian new leu (RON). The data is obtained from the website of the European Central Bank . • The sampling period covers August 1st, 2005 to March 31st, 2009, considering: - tranquil period: from August 1st, 2005 to August 29th, 2008 - turbulent period: from September 1st, 2008 to March 31st, 2009 • The choice for the beginning of the sample is motivated by the rationale of constant monetary policy regime so as to exclude any shifts in the links between currencies that could be generated by a shift in the monetary policy of one of the countries considered.

  14. Preliminary Analysis Daily percentage returns. The vertical line splits the sample into the two periods analyzed. • ADF test results indicate that all series in levels display a unit root . Consequently, the series are transformed into log-differences and continuously compounded percentage exchange rate returns are obtained (which are I(0)): yt=100*( ln(St) − ln(St−1)), where St is the spot rate.

  15. Empirical Results • The unconstrained system of equations (1) and (2) by GMM using (3) as a criterion, with the moment conditions equal to the differences between the sample and the theoretical variances and covariances. The objective function is minimized using the OPTMUM procedure in Gauss. • We prefiltered the data by using a trivariate VAR(1) in the currencies returns (Dungey (2009)). • We have also adopted the approach of Forbes and Rigobon (2002), who considered in their empirical application US returns as common variable control, and estimated a VAR containing one lag and EUR/USD returns. This lead us to similar results. • The parameters estimates are presented bellow along with their corresponding standard errors and t-statistics.

  16. Volatility Decomposition – Tranquil Period • The decompositions are based on GMM estimates. • The country-specific factors such as macroeconomic fundamentals account for about 85% of the volatility in the individual foreign exchange markets in Romania and Czech Republic, whilst in this group of countries PLN/EUR acts to a great extent as a common factor, which suggest a greater financial integration of the latter pair in the regional economy. • The funding that the volatility of Czech koruna is driven mainly by factors different from the ones influencing the other CEE exchange rates might reveal the CZK role of a funding currency for investments in other currencies of the region, as suggested by Pramor and Tamirisa (2006).

  17. Volatility Decomposition – Turbulent Period • The variance decompositions show that asset return volatility in the post-Lehman period was dominated by contagion with much smaller contributions from the world and idiosyncratic factors. The PLN/EUR returns experienced the greatest increase in variance over the two periods with an important contribution of contagion from both RON/EUR (72%) and CZK/EUR (18%). The absolute levels effects are greatest in the transmission from Romanian leu to the Polish zloty. Significant effects of contagion are observed in the other returns as well, both direct and reverse, the only exception being the absence of a contagious channel from PLN/EUR to RON/EUR. • The increase in covariances over the two periods can be also considered evidence of contagion. • The Romanian leu was the most important source of volatility in the region, while the Polish zloty acted as the main shock absorber (consistent with Borghijs and Kuijs (2004) ).

  18. Contagion Tests Bivariate results of tests of contagion - Wald tests of the null hypothesis of no contagion H0: δi,j = 0 *1% significance level;**5% significance level; ***10%significance level; distribution χ2 with df=1 Multivariate tests of contagion *1% significance level; distribution χ2 with df=6 *1% significance level; distribution χ2 with df=2

  19. Remarks • The results of bivariate testing of each contagious channel within the region confirms the existence of contagion initially identified from the variance - covariance decompositions. The null hypothesis of no contagion is accepted only in case of the transmission from PLN/EUR to RON/EUR. • The multivariate tests examine whether a shock emerging in one particular foreign exchange market is transmitted to the others countries in the system. The significance results are consistent with the information from the bivariate tests as they find significant contagion from each asset market individually to the other two. • We also tested for the presence of contagion anywhere in the region, without a priori specifying a point of origin for that contagion. The null hypothesis of no contagion is rejected even at a significance level of 1%.

  20. Concluding remarks Studying the propagation of shocks among the Romanian Leu, Polish Zloty and Czech Koruna , we were able to reject the null hypothesis of no contagion over the Post-Lehman period. We found that during periods of heightened volatility, in addition to common shocks and spillovers from some identifiable local channel, a new channel of volatility transmission emerge: contagion. Consequently, the linkages between the three currencies strengthen over turbulent periods, the result being consistent with that of Kóbor and Székely. We also discover that RON represents the most important source of volatility, whilst PLN only acts as a shock absorber within the region. The conclusions laid above have very important implications for the conduct of monetary policy. Policy makers should take into account other countries’ actions when making their own decisions. This is most obvious when considering the objective of euro adoption by the countries included in the analysis. In order to meet the Maastricht exchange rate stability criterion, the central banks will probably undertake intramarginal interventions in the foreign exchange markets to keep their currencies within the band. Having in view that these operations are almost by definition carried out in periods of heightened volatility, our results suggest that the intervention in one FX market will have strong and valuable influences on the other exchange rates. This calls for increased cooperation and coordination of monetary policy within the region. From a different perspective, the detection of contagion in the three foreign exchange markets suggests that a solution to how these countries can reduce their vulnerability to external shocks during periods of high volatility consists of short-term strategies, like foreign exchange intervention.

  21. References Bae, K.H., Karolyi, G.A. and Stulz, R.M. (2003), “A new approach to measuring financial contagion”, Review of Financial Studies, 16(3), 717–763. Bekaert, G., Harvey, C.R. and Ng, A (2005), “Market integration and contagion”, Journal of Business, 78(1), 39–69. Borghijs, A. and Kuijs, L. (2004), “Exchange Rates in Central Europe: A Blessing or a Curse?”, IMF Working Paper 04/2. Brooks, C. (2002), “Introductory Econometrics for Finance”, Cambridge University Press Calvo, S. and Reinhart, C.M. (1996), “Capital Flows to Latin America: Is There Evidence of Contagion Effects?” in Calvo, G.A., Goldstein, M. and Hochreitter, E. eds. Private Capital Flows to Emerging Markets. Dornbusch, R., Park, Y.C. and Claessens, S. (2000), “Contagion: Understanding How it Spreads”, The World Bank Research Observer, Vol.1 5(2), pp. 1 77–97. Dungey, M., Martin, V.L. and Pagan, A.R. (2000), “A multivariate latent factor decomposition of international bond yield spreads”, Journal of Applied Econometrics, 15(6), 697–715. Dungey, M. and Martin, V.L. (2004), “A multifactor model of exchange rates with unanticipated shocks: measuring contagion in the east Asian currency crisis”, Journal of Emerging Markets Finance, 3(3), 305–330. (2007), “Unraveling financial market linkages during crises”, Journal of Applied Econometrics, 22(1). Dungey, M., Fry, R. Gonzales-Hermosillo, B. and Martin, V. (2005), “Empirical Modeling of Contagion: A Review of Methodologies”, Quantitative Finance, Vol. 5, No. 1, 9–24. Dungey M. and Tambakis, D. (2005), Identifying International Financial Contagion: Progress and Challenges, edited by Oxford University Press. Dungey, M. (2009), “The Tsunami: Measures of Contagion in the 2007-08 Credit Crunch”, CESifo Forum, 9, (4) pp. 33-43. Eichengreen, B., Rose, A.K. and Wyplosz, C. (1996), “Contagious currency crises”, NBER Working Paper 5681. Favero, C.A. and Giavazzi, F.(2002), “Is the international propagation of financial shocks non-linear? Evidence from the ERM”, Journal of International Economics, 57(1), 231–246. Forbes, K. and Rigobon, R. (2002), “No contagion, only interdependence: measuring stock market co-movements”, Journal of Finance, 57(5), 2223–2261. Gravelle, T. Kichian, M. and Morley, J. (2006), “Detecting shift-contagion in currency and bond markets”, Journal of International Economics 68 , 409– 423. Gourieroux, C., Monfort, A. and Renault, E. (1993), "Indirect Inference," Journal of Applied Econometrics, S85-118

  22. References Habib, M.M. (2002), “Financial Contagion, Interest Rates and the Role of the Exchange Rate as Shock Absorber in Central and Eastern Europe”, BOFIT Discussion Paper No. 7/2002. Hamilton, J.D. (1994), “Time series analysis”, Princeton University Press, 799 pp. Kaminsky, G.L. and Reinhart, C.M.(2000a), “On crises, contagion and confusion”, Journal of International Economics, 51(1), 145–168. (2000b), “Financial markets in times of stress”, NBER Working Paper 8569. (2003) “The Center and the Periphery: The Globalization of Financial Turmoil”, NBER Working Paper 9479 . Kóbor, Á. and Székely, I.P. (2004), “Foreign Exchange Market Volatility in EU Accession Countries in the Run-Up to Euro Adoption: Weathering Uncharted Waters”, IMF Working Paper 04/16. Mahieu, R. and Schotman, P.(1994), “Neglected common factors in exchange rate volatility”, Journal of Empirical Finance, 1, 279–311. Masson, P.(1999a), “Contagion: macroeconomic models with multiple equilibria”, Journal of International Money and Finance, 18, 587–602. (1999b), “Contagion: monsoonal effects, spillovers, and jumps between multiple equilibria. In The Asian Financial Crisis: Causes, Contagion and Consequences”, Cambridge University Press. (1999c), “Multiple equilibria, contagion and the emerging market crises”, IMF Working Paper, 99/164. Pericoli, M. and Sbracia, M. (2003), “A primer on financial contagion”, Journal of Economic Surveys, 17(4), 571–608. Pramor, M. and Tamirisa, N.T. (2006), “Common Volatility Trends in the Central and Eastern European Currencies and the Euro”, IMF Working Paper 06/206. Rigobon, R. (2003a), “Identification through heteroskedasticity”, The review of Economics and Statistics, 85(4), 777–792. (2003b), “On the measurement of the international propagation of shocks: is the transmission stable?”, Journal of International Economics, 61, 261–283. Ross (1976), “Arbitrage Theory of Capital Asset Pricing", Journal of Economic Theory 13, 341-360. Sharpe, W. (1964), “Capital asset prices: a theory of market equilibrium under conditions of risk”, Journal of Finance, 19, 425–442. Solnik, B.H., “The international pricing of risk: an empirical investigation of the world capital market structure”, Journal of Finance, 29(2), 365–378. www.ecb.int

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