Presentation on theme: "THE INTEREST RATE PASS THROUGH FROM MARKET RATES TO COMMERCIAL BANKING RATES IN ROMANIA."— Presentation transcript:
THE INTEREST RATE PASS THROUGH FROM MARKET RATES TO COMMERCIAL BANKING RATES IN ROMANIA
CONTENTS INTEREST RATE PASS THROUGH AND ITS IMPORTANCE EMPIRICAL STUDIES CONCERNING THE INTEREST RATE PASS THROUGH THE MODEL THE DATA VECTOR ERROR CORECTION MODEL VAR TESTING CONCLUSIONS
THE IMPORTANCE OF INTEREST RATE PASS THROUGH Interest rate pass through is defined as the percentage change in the commercial rate due to one percentage change in the corresponding market rate. The process of pass through is very important for Central Banks in conducting monetary policy. It has grown in importance in Romania once the monetary authority has decided to implement inflation targeting regime, which has as its main instrument the monetary policy rate. The process of financial deepening of the Romanian economy has made the private sector vulnerable to interest rate movements.
EMPIRICAL STUDIES CONCERNING THE PASS THROUGH Single equation approach first developed by Cotarelli and Kourelis(1994) and then by BIS(1994) Error correction models – are now the most widely used : DeBondt(2005), Mojon(2005), Sandler and Kleiemeier(2000), Toolsema, Sturn si Haan (2002). They are most focused on the European market, as it is considered a bank orientated environment Asymmetric error correction models, developed by Sandler and Kleimeier, Hofmann and Mizen(2001), Thacz(2001), Lim(2001). They try to emphasize the different speed of adjustment due to raising or falling interest rates or to positive or negative deviations from the long run equilibrium Unrestricted VAR analysis, tries to derive the pass through from the impulse response function
THE MODEL The model I have used is a modification of the Monti Klein Model(1984) to an oligopolistic environment developed by Freixas and Rochet The model maximize the profit function of a bank, defined as the margin from loans minus the deposits intermediation +/ the proceeds from the position on the interbank market The Cournot equilibrum conditions are : r L * = γ 11 + β 11 r r D * = γ 21 + β 21 r, where r L * and r D * are the equilibrum rates for loans and deposits, r represents the interbank market and γ 11 and γ 21 are a function of the marginal cost and are known in the literature as markups Csaba More and Marton Nagy (2004) economists at the Hungarian Central Bank, have found evidence in the Hungarian banking market for a competition that relies between a perfect competition and a Cournot type competition
THE DATA The data used are the reported annualized average interest rates, weighted by volumes Depopf_36 Depopf_612 Depopf_15 Loanpj_13 Loanpj_36 Loanpj_612 Loanpj_15 LTloanpj LTloanpf Where pf – stands for individuals, pj – stands for companies, 36 – represents a maturity of 3 to 6 months, 612 – is a maturity of 6 to 12 months, 15 – represents a maturity from 1 to 5 years and LT stands for a maturity of over 5 years. All the variables are reported in levels.
Unit root tests I have applied the standard ADF and Phillips-Perron unit root tests and found evidence for a unit root process in all the variables tested I have decided to apply the Phillips-Perron procedure for testing the unit root in the presence of a structural break, being concerned about a large drop in the data that started in the middle of 2004 for some variables and for others at the beginning of 2005. I have found that when I applied this procedure, the short term variables for loans to companies loanpj_13, loanpj_36 where not unit-root processes but stationary variables. Also the medium term loan to company is not supported as a unit root variable by the Phillips-Perron procedure.
VECM PROCEDURE I introduced in the model 3 variables : the rate charged for loans, the rate for deposits (of comparable maturity) and the relevant market rate I have tested the number of cointegration relationship using the Johansen procedure with a dummy variable which accounts for the large drop in the data that appeared in February 2005 I have estimated in all cases a vector error correction model with 2 cointegration relations, which I have normalized with regards to the rate for loans and for deposits I have found evidence of strong cointegrating relations, suggested by the strong negative coefficients of adjustments (with t statistic close to 3), normality and lack of autocorrelation until lag 4 or more for residuals As a drawback, the relations couldn't support the weak exogeneity of the market rate, and sometimes it had a positive load coefficient
The procedure for the VECM I have estimated the VECM with one lag due to the short number of observations (44), in order to save the degrees of freedom In testing for the existing of cointegration relations I have used a dummy level variable, that takes the value of 0 until the rates started to drop and 1 after that. This is suggested by D.Hendry and K. Juselius(1999) I have found a number of 2 cointegrating vectors for all the relations estimated, supported both by trace test and maxim eigenvalue test I started to identify the cointegrating vector, which I have normalized with respect to the commercial rates, assuming that it is the market rate that drives the commercial rates
RESULTS Δ LOANPJ612 = -0.29*(LOANPJ612-0.98*BUBOR3M -4.98) -0.32* Δ LOANPJ612(-1) Δ DEPOPF36 = -0.53*(DEPOPF36-0.73*BUBOR3M-0.88) No weak exogeneity evidence for bubor3m. Residuals lack autocorrelation until lag 6 and they are normally distributed. T statistic for the load coefficients are over 3 Δ LOANPJ612 = -0.21*(LOANPJ612-BUBOR6M-4.09) -0.31* Δ LOANPJ612(-1) Δ DEPOPF612 = -0.15*(DEPOPF612-0.67*BUBOR6M +0.81) + 0.33* Δ BUBOR6M(-1) No weak exogeneity evidence for bubor6m. Residuals lack autocorrelation until lag 10 and they are normally distributed. T statistic for the load coefficients are over 2 Δ LOANPF15 = -0.3*(LOANPF15-1.14*BUBOR6M -5.92) Δ DEPOPF15 = -0.3*(DEPOPF15-0.64*BUBOR6M-2.13) No weak exogeneity evidence for bubor6m. Residuals lack autocorrelation until lag 4 and they are normally distributed. T statistic for the load coefficients are over 2 for the adjustment of deposits rate and over 4 for the adjustment of loan rate..
RESULTS Δ LTLOANPF = -0.31*(LTLOANPF-0.95*BUBOR6M -5.16) Δ DEPOPF15 = -0.35*(DEPOPF15-0.66*BUBOR6M-1.86) No weak exogeneity evidence for bubor6m. Residuals lack autocorrelation until lag 4 and they are normally distributed. T statistic for the load coefficients are over 2,5. Δ LTLOANPJ = -0.5*(LTLOANPF-1.01*BUBOR6M -4.34) – 0.42* Δ LTLOANPJ(-1) – 0.52* Δ LTLOANPJ(-2) Δ DEPOPF15 = -0.45*(DEPOPF15-0.65*BUBOR6M-2.144) No weak exogeneity evidence for bubor6m. Residuals lack autocorrelation for the 12 lags studied and they are normally distributed. T statistic for the load coefficients are over 3. This is the only model estimated with 2 lags, in order to eliminate the autocorrelation in residuals
VAR PROCEDURE Following DeBondt (2005) I decided to test the results from the VECM with the help of the impulse response function generated by a bivariate VAR specified in levels I choose a bivariate VAR, in which the endogenous variables are the commercial rate and the market rate. The literature recommends the use of this kind of approach when dealing with short samples, the overparametrization being a more important problem than underparametrization as it consumes the degrees of freedom For the problem of identification I have used the Choleski decomposition, with the market variable ordered first. This is in sense with the economic sense and, at least for this kind of specification has not been debated The number of lags chosen is based on the number of lags used when estimating the correspondent VECM
VAR RESULTS I have calculated the pass through coefficient as the percentage cumulated shock of the commercial rate due to one standard deviation in the market rate from the cumulated shock in the market rate from one standard deviation in itself The results are presented in the next 3 graphs :
CONCLUSIONS FROM VAR ANALYSIS The first graph indicates an overshooting of the rate charged to individuals for medium term, which is confirmed by the value of 1,14 of the coefficient of market rate in the cointegration relation. Long term rates for companies seem to reach faster to equilibrium, than the long term loans to individuals, an aspect which is revealed also by the VECM, through higher coefficients of adjustment The second graph reveals approximately equal values at equilibrium for deposit rates. Depopf36 reaches the equilibrium faster than the depopf612, a thing confirmed by different adjustment coefficients in the cointegrating relation. The results for depopf15 are cumbersome. This rate does the most part of its adjustment in the short-run, and after that adjusts more smoothly. The third graph reveals similar long term equilibrium relations for the short term rate applied to borrowing companies under the shocks of bubor3m and bubor6m. The VAR results contradict the VECM, by estimating that the loan rate adjust quicker to equilibrium under bubor6m shocks then under bubor3m shocks. Due to the fact that the VAR model from which I have drawn the impulse response function of the bubor6m shock seems to be miss specified, we can conclude that the bubor3m explains better the loanpj612 rate.
Conclusions I have analyzed the pass through of market rates to commercial rates for new operations, in a period which showed significant drops in all the rates. I have found that all the deposit rates paid to individuals are I(1), while some short term loan rates to companies where I(0) When applying a vector error correction model to the I(1) data, I discovered that the loan rates to companies have a one to one relation on long term with the relevant market rate. It appeared that longer maturity loans adjust faster to equilibrium than shorter maturity loans The long run relation for loan rates is 1 to 1, except for the medium term loan rate to individuals, which overshoots in the long run the corresponding market rates, but it adjusts at the same rate to equilibrium conditions as the long term rate charged to individuals Deposit rates adjust in the long run in an approximately 1 to 0,7 to the market ask rate. The adjustment process is faster in the short run, a thing explainable by the fact that we have analyzed a dropping interest rate environment
CONCLUSIONS The results are fairly comparable to the ones obtained from the impulse response function delivered by a bivariate VAR The results have to be interpreted with more than usual caution, because the period under study did not manage to cover a complete interest rate cycle. More than that, the market rates I have used, are only derived from the average quotations of banks on the interbank market. In an inflation report (2005), Romanian Central Bank stated that 90% of the money market operations are overnight. Thus, since the long term rates used are not a result of market operations, and only informative quotations, may explain why we did not find evidence for weak exogeneity The results used should be used with extreme caution when making predictions about further developments, as the Romanian financial market is continuing its nominal adjustment to the Euro levels. The end of 2006 marked the entry of the first Romanian bank on the international capital markets, to attract local currency long term funds by issuing corporate bonds. A further development in this area should be taken into account Further research should be taken in analyzing the interest rate pass through with respect to market power of banks, degree of capitalization, degree of competition on the market, etc