CENTRAL BANK INTERVENTION IN THE ROMANIAN FOREIGN EXCHANGE MARKET. ESTIMATING A REACTION FUNCTION M.Sc. Student: Bogdan Radulescu Supervisor: Prof. Moisa Altar ACADEMY OF ECONOMIC STUDIES DOCTORAL SCHOOL OF FINANCE AND BANKING

2/21 Contents  Romanian FOREX market  Model of optimal intervention  Data and stylized facts  Empirical study GARCH model for conditional variance Structural breaks Estimated reaction function oLinear reaction function oProbit models and asymmetries oOrdered probit reaction function  Conclusions

3/21 Romanian FOREX Market  Managed float regime since 1997 ‘USD market’ 1997 – 28 Feb 2003 ‘EUR market’ starting with 3 Mar 2003  Since 2002 NBR is following a trade weighted basket of EUR and USD Initially weights 60% EUR - 40% USD Updated to 75% EUR - 25% USD in 2004  Conflicting objectives for exchange rate policy Depreciation for external competitiveness Nominal anchor to fix inflation expectations  NBR systematically intervened to achieve exchange rate stability

4/21 Model of Optimal Intervention  In most empirical papers the reaction function is assumed rather than derived  Almekinders and Eijffinger (1996), Frenkel and Stadtmann (2001), Frenkel, Pierdzioch and Stadtmann (2002) and Ito and Yabo (2004) derive an optimal reaction function by minimizing the loss function of the central bank  We follow their approach to derive the reaction function; in addition to the exchange rate level target in these papers, we explicitly introduce a volatility target Loss function Exchange rate model

5/21 Model of Optimal Intervention (2)  Optimal intervention is a function of the deviation from target, conditional variance and other variables that impact the exchange rate  Exchange rate target (similar to Ito and Yabo (2004)) Short term target Medium term target Long term target  Optimal intervention

6/21 Data and Stylized Facts  Daily data  Intervention frequency decreased 60-80% of trading days for , 36% in 2002-Feb 2003 and 18% in Mar 2003 – Mar 2004 probability of continued intervention: 50-70% in , 18% in 2002-Feb 2003 and 2.33% in 2003 – 2004)  were influenced by some difficulties were low liquidity years for the interbank market Aug Apr 1999 was a period of fast depreciation related to the Russian crises and anticipation of difficulties with the peak of external debt service in 1999 (ROL lost 70% against USD in less than 9 months) In the second half of 1999 a peak of external debt service led to near depletion of NBR reserves and high risk of default  We restrict the study to Jan – Mar. 2004

7/21 Data and Stylized Facts (2)

8/21 GARCH Model of Volatility Dependent variable: RUSD Dependent variable: REUR VariableCoefficientProbability VariableCoefficientProbability C C RUSD(-1) REUR(-1) RUSD(-2) MA(4) RUSD(-5) INT RUSD(-10) CL INT REU INT(-1) CL D02*REU Conditional variance equation C C ARCH(1) ARCH(1) GARCH(1) GARCH(1) INT INT INT(-1) D02*REU

9/21 GARCH Model of Volatility (2) StatisticUSD equationEUR equation No. observations R-squared Box-Pierce Q for residuals [probability] Q[1] [0.082] [0.496] Q[5] [0.492] [0.590] Q[10] [0.571] [0.141] Box-Pierce Q for squared residuals [probability] Q[1] [0.155] [0.244] Q[5] [0.823] [0.653] Q[10] [0.971] [0.300] ARCH – LM test [probability] LM[1] [0.1559] [0.7686] LM[5] [0.8068] [0.7495]

10/21 Structural Breaks  We use the Andrews (1993) Sup(Wald(t)) test to search for an unknown structural break over Feb 2003  The structural break is identified on 27 Feb 2001 Below, all models are estimated for four samples:  full USD sample (2000 – Feb 2003)  pre-Feb 2001 (2000 – 27 Feb 2001)  post-Feb 2001 (28 Feb 2001 – 28 Feb 2003)  full EUR sample (Mar 2003 – Mar 2004)  Structural change on 28 Feb/ 3 Mar 2003 (the interbank market switched from trading USD to trading EUR)

11/21 Linear Reaction Function Full USD samplePre-Feb 2001Post-Feb 2001Full EUR sample Intercept [0.0000] [0.3714] [0.0001] [0.3166] DTGT [0.0533] [0.1562] [0.0540] [0.0244] DMA [0.0109] [0.4372] [0.0318] [0.0129] RUSD/REUR [0.0000] [0.0047] [0.0000] [0.0016] VOL [0.4942] [0.3629] [0.3663] [0.3227] VOL*DUP [0.9208] [0.7419] [0.0687] CL [0.0000] [0.0000] [0.0000] [0.0007] INT(-1) [0.0179] [0.3141] [0.1847] - INT(-3) [0.0130] REU [0.0028] No. observations R-squared [Adjusted] [0.2593] [0.4850] [0.1828] [0.2281] Log likelihood BG serial corr. LM(1) [0.1153] [0.1708] [0.3309] [0.6125] LM(5) [0.1644] [0.1393] [0.6867] [0.8668] White heteroscedasticity [0.0100] [0.0391] [0.3468] [0.0272] * White covariance matrix when the White test rejects the null of no heteroscedasticity

12/21 Linear Reaction Function (2)  LR test for the null of no structural break in Feb 2001 is (8 df) - rejection of the null at 1%  The weights of different horizons in the overall target can be recovered from the estimated parameters USDROL marketEURROL market Full samplePre-Feb 2001Post-Feb 2001Full sample DTGT 2.68% [0.39]- 3.36% [0.39] 2.90% [0.39] DMA11.71% [0.27]-17.43% [0.18]28.17% [0.08] RUSD/REUR85.61% [0.00]100%79.21% [0.00]68.93% [0.00] * Where the coefficient in the reaction function is insignificant, weight has been restricted to zero * Standard errors computed with the ‘delta’ method

13/21 Probit Reaction Functions  Estimation of the reaction function as a discrete choice model is recommended because intervention is a ‘zero inflated’ process  Some researchers estimate separate probit models for ‘buy interventions’ and ‘sell interventions’ to search for possible asymmetries (Frenkel and Stadtmann (2001), Frenkel, Pierdzioch and Stadtmann (2002), Kim and Sheen (2000))  We assume the decision to intervene can be written:  LR test for the null of no structural break in Feb 2001 Buy interventions: (8 df) – significance level 0.00 Sell interventions: (8 df) – significance level 0.00

14/21 Probit for ‘Buy Interventions’ Full USD samplePre-Feb 2001Post-Feb 2001 Full EUR sample Constant [0.0000] [0.9934] [0.0000] [0.0004] DTGT [0.3576] [0.0032] [0.6722] [0.0162] DMA [0.0026] [0.3761] [0.0171] [0.3040] RUSD/REUR [0.0004] [0.0004] [0.0065] [0.2167] GARCH [0.4779] [0.3826] [0.9419] [0.7076] GARCH*DUP [0.8136] [0.5456] [0.7262] CL [0.0000] [0.0000] [0.0000]-2.7E-08 [0.0170] INTBUY[-1] [0.0000] [0.0000]- INTBUY[-2] [0.0434] [0.0024]- INTBUY[-3] [0.0063] REU [0.0688] REU [0.0324] Observations % 0s/ % 1s 51.62%/ 48.38%40.88%/ 59.12%57.91%/ 42.09% 89.49%/ 10.51% LogL LogL Chi-square [probability] [0.0000] [0000] [0.0000] [0.0000] McFadden LRI Cramer * Marginal effects have the same signs as estimated coefficients

15/21 Probit for ‘Sell Interventions’ Full USD samplePre-Feb 2001Post-Feb 2001Full EUR sample Constant [0.0000] [0.6445] [0.0000] [0.0153] DTGT [0.0012] [0.0017] [0.2690] [0.0513] DMA [0.0011] [0.3528] [0.0061] [0.0139] RUSD/REUR [0.0020] [0.0449] [0.0176] [0.0037] GARCH [0.0685] [0.0465] [0.9971] [1.0000] GARCH*DUP [0.2767] [0.5363] [1.0000] CL [0.0000] [0.0000] [0.0073]3.84E-08 [0.0366] INTSELL[-1] [0.0107] [0.0429]- REU [0.0176] REU [0.3604] Observations % 0s/ % 1s 86.53%/ 13.47%76.69%/ 23.31%92.29%/ 7.71%95.33%/ 4.67% LogL LogL Chi-square [probability] [0.0000] [0.0000] [0.0000] [0.0000] McFadden LRI Cramer * Marginal effects have the same signs as estimated coefficients

16/21 Predictions from probit models Full USD samplePre-Feb 2001Post-Feb 2001Full EUR sample Buy interventions Actual/ Predicted % correct 72.53% 70.80%72.81%79.12%72.44%65.46%91.24%83.33% % of interventions predicted 70.62%82.29%59.62%18.52% % of no interventions predicted 72.71%68.59%77.13%99.56% Sell interventions % correct 88.53%57.14%86.29%72.92%92.64%66.67%96.44%75.00% % of interventions predicted 18.52%50.72%5.13%25.00% % of no interventions predicted 97.84%94.27%99.79%99.59% Gain over naïve prediction Naïve predictions % naïve correct 51.62%59.12%57.91%89.49% % buy correct 71.70%76.69%69.76%91.05% % buy gain 20.07%17.57%11.86%1.56% % sell correct 87.16%84.12%92.49%96.11% % sell gain 0.62%7.43%0.20%0.78%

17/21 Ordered Probit Reaction Function  We assume that NBR compares benefits of reducing loss of no intervention to fixed costs of intervention and intervenes only when benefits are higher than costs  gives a neutral band of no intervention

18/21 Ordered Probit Reaction Function (2) Full USD samplePre-Feb 2001Post-Feb 2001Full EUR sample DTGT [0.1103] [0.0007] [0.4880] [0.0014] DMA [0.8838] [0.1988] [0.4468] [0.0449] RUSD/REUR [0.0002] [0.0018] [0.0012] [0.0090] GARCH [0.4291] [0.2028] [0.7212] [0.4425] GARCH*DUP [0.5058] [0.8735] [0.8305] CL [0.0000] [0.0000] [0.0000] [0.0006] CL[-1] [0.0064]-- INTORD[-1] [0.0000] [0.0000]- INTORD[-2] [0.0393]- REU [0.0018] REU [0.1398] Lower limit [0.0000] [0.3445] [0.0004] [0.0009] Higher limit [0.0000] [0.1482] [0.0000] [0.0000] Observations % 0s / % 1s / %2s13.4% / 38.1% / 48.3%23.3% / 17.5% / 59.1%7.7% / 50.1% / 42%4.6% / 84.8% / 10.5% LogL LogL Chi-square [probability] [0.0000] [0.0000] [0.0000] [0.0000] McFadden LRI * Marginal effects have the same signs as estimated coefficients

19/21 Ordered Probit Reaction Function (3)  LR test for no structural break in Feb 2001 is (9 df)  The weights of different horizons in the overall target  Predictions of ordered probit reaction function Full USD samplePre-Feb 2001Post-Feb 2001Full EUR sample DTGT- 4.19% [0.39]- 6.62% [0.38] DMA % [0.14] RUSD/REUR100%95.81% [0.00]100%68.47% [0.00] Actual012% correct % interventions correctly predicted Full USD sample % 63.10% % % Pre-Feb % 86.89% % Post-Feb % 52.78% % % Full EUR sample % % %

20/21 Conclusions  The exchange rate level target had a highly significant effect on NBR interventions  NBR intervened mainly to smooth out exchange rate fluctuations (“leaning-against-the-wind”) Short sighted – focused on daily fluctuations Monthly fluctuations gained importance in the last year  NBR also pursued a nominal depreciation policy, but depreciation had a small weight in its objectives  Volatility had an insignificant effect on interventions  Net purchases of clients from the banks had a significant impact on interventions; NBR used interventions to cover a part of the FX market’s deficit with clients  Asymmetry between buying interventions and selling interventions

21/21 Future Developments  Further study of the influence of costs on intervention behavior  Objective of building foreign exchange reserves  Links to money market and NBR’s sterilization operations