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EXCHANGE RATE RISK CASE STUDY ROMANIA

EXCHANGE RATE RISK CASE STUDY ROMANIA. STUDENT : ŞUTA CORNELIA-MĂDĂLINA SUPERVISOR : PROF. MOIS Ă ALTĂR. CONTENT. Introduction Literature review Methodology Empirical assessment Conclusion Reference Appendix. Introduction.

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EXCHANGE RATE RISK CASE STUDY ROMANIA

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  1. EXCHANGE RATE RISKCASE STUDY ROMANIA STUDENT: ŞUTA CORNELIA-MĂDĂLINA SUPERVISOR: PROF. MOISĂ ALTĂR

  2. CONTENT • Introduction • Literature review • Methodology • Empirical assessment • Conclusion Reference Appendix

  3. Introduction • The exchange rate risk = the excess exchange rate volatility above the level associated with unbiased uncovered interest and purchasing power parity conditions. 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.

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

  5. 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:

  6. 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) - inflation differential (diff_infl = infl_ro - infl_eu) - interest rate differential (diff_ir = bubor3mo_r – euribor3mo_r)

  7. 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)

  8. 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 3rd one shows t-statistics

  9. The response of exchange rate to shocks in the other varibles

  10. TARCH (p,q,r) • The mean equation • The conditional variance equation

  11. 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).

  12. TARCH(1,2,2) • The mean equation

  13. The variance equation

  14. TARCH Conditional SD

  15. 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.

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