EXCHANGE RATE RISK CASE STUDY ROMANIA STUDENT: ŞUTA CORNELIA-MĂDĂLINA SUPERVISOR: PROF. MOISĂ ALTĂR

CONTENT 1. Introduction 2. Literature review 3. Methodology 4. Empirical assessment 5. Conclusion ReferenceAppendix

Introduction  The exchange rate risk = the excess exchange rate volatility above the level associated with unbiased uncovered interest and purchasing power parity conditions. UIP: UIP:PPP:  Why? Exchange rate risks represents one of the most important sources of uncertainty in transition countries since these are usually small open economies, vulnerable to exchange rate fluctuations.

Different approaches  Papers on volatility of the exchange rate investigate either the sources like: openness of the economy (Hau 2002) openness of the economy (Hau 2002) unpredictable circumstances (Frenkel 1981) unpredictable circumstances (Frenkel 1981) Exchange rate regime (Koncenda and Valachy 2003) Exchange rate regime (Koncenda and Valachy 2003) either the impact on different variables.  relationship between exchange rate risk and stock market (Jorion 1991, )  relationship between exchange rate risk and stock market (Jorion 1991, Derviz 2004)  Relationship between exchange rate risk and convergence to euro (Orlowski 2004)

The model  The construction of the model is based on purchasing power parity condition and uncovered interest parity;  It incorporates conditions that are inherent to the process of monetary convergence to a common currency area;  It designs a policy instrument rule that includes exchange rate risk:  The empirical analysis of interactions between the movement in the nominal exchange rate as a function of expected domestic inflation differential and the lagged interest rate differentials relative to EU is based on:

Data  Initial data series: nominal monthly EUR/ROL exchange rate (eur_n), consumer price index (CPI), harmonised index of consumer price (HICP), 3 months maturity bubor (bubor3mo), 3 months maturity euribor (euribor3mo);  Time length: 1999:01 – 2006:4;  All data series are seasonally adjusted, using Census X12 procedure, utilise by the US Census Bureau;  The variables included in the model are: - change in nominal exchange rate (dl_eur_n_sa) - change in nominal exchange rate (dl_eur_n_sa) - inflation differential (diff_infl = infl_ro - infl_eu) - inflation differential (diff_infl = infl_ro - infl_eu) - interest rate differential - interest rate differential (diff_ir = bubor3mo_r – euribor3mo_r)

The VAR framework  To identify the optimum lags between changes in the spot exchange rate, inflation and short-term interest rates differentials with respect to Euro area  Verify stationarity of series using ADF and Phillips-Perron unit root tests: change in nominal exchange rate and inflation differential series are stationary at any significance level, while interest rate differential is stationary at 5% significance level. (Appendix 3)  check the relationship between the three variables using Granger Causality and cross correlation (Appendix 6)  Select numbers of lags to include using Lag Length Criteria; Akaike information criterion, Final Prediction error, LR and Hannan-Quinn information criterion, Schwarz information criterion (Appendix 4)

Var(4)  Check VAR stability (Appendix 5);  Check if residuals are white noise;  The significant lags in the VAR(4) are: Note: the second line shows VAR coefficient, the 3 rd one shows t- statistics OnOf Δlog of spot ex. rate InflationDiff. Interest rate diff. Δlog of spot ex. Rate 1 month [4.74] 1 month [2.83] 1 month [-2.33] InflationDiff. 2 months [1.94] 3 months 0.42 [3.296] 1 month [1.87] 3 months [4.01] 4months [-3.016] Interest rate diff. 2 months [2.55] 4 months [-3.18] 2 months [1.946] 1 month 1.76 [5.937] 2 months [-3.062]

The response of exchange rate to shocks in the other varibles

TARCH (p,q,r)  The mean equation  The conditional variance equation

Why tarch?  the p-order ARCH term reflects the impact of news (innovation) from the previous periods on the conditional variance;  the q-order GARCH term allows to measure the degree of persistency in volatility;  the asymmetric TARCH term captures the leverage effect;  A negative value of the coefficient γ would imply that negative news (innovation) increases the subsequent volatility of the exchange rate more than positive news (innovation).

TARCH(1,2,2)  The mean equation Dependent Variable: DL_EUR_N_SA CoefficientStd. Errorz-StatisticProb. LOG(GARCH) C DIFF_INFL DIFF_IR(-4) AR(1)

The variance equation CoefficientStd. Errorz-StatisticProb. C7.59E E RESID(-1)^ RESID(-1)^2*(RESID(-1)<0) RESID(-2)^2*(RESID(-2)<0) GARCH(-1) GARCH(-2) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) Inverted AR Roots.37

TARCH Conditional SD

Conclusion  The exchange rate risk was assessed for Romania using a TARCH-M model.  The estimated coefficient for the proxy of exchange rate risk, although does not have a high value, it is highly significant, suggesting that the problem of excessive exchange rate ought to be taken into consideration.  Even if Romania is joining the EMU in a couple of years or more, a diminishing exchange rate risk is a sign of a ‘healthy’ economy, so NBR can take it into account when choosing the right monetary policy.