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The stock market and the ringgit exchange rate: a note
Japan and the World Economy 14 (2002) 471–486
The stock market and the ringgit exchange rate: a note Ahmad Zubaidi Baharumshaha,*, A. Mansur M. Masihb, M. Azalia a
Department of Economics, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia b Department of Finance and Economics, King Fahd University of Petroleum & Minerals, KFUPM, P.O. Box 1764, Dhahran 31261, Saudi Arabia Received 23 May 2001; received in revised form 4 October 2001; accepted 11 April 2002
Abstract This paper presents and tests an augmented monetary model that includes the effect of stock prices on the bilateral exchange rates. The model is applied to the ringgit/US dollar (RM/US) and ringgit/Japanese yen (RM/JY) exchange rates. The empirical analysis is conducted by the Johansen method of cointegration. Using the data from the recent float that ends with 1996:Q4, the study is motivated, among others, by an interesting preliminary finding that although the augmented monetary model is cointegrated, it is subject to parameter instability and that the parameter time dependency can be attributed at least partly to a particular subset of the variables in the system including stock prices. We find that a restricted VAR model which imposes exogeneity restrictions on I(1) variables, such as stock prices, among others, exhibits both cointegration and parameter stability. In addition, we demonstrate that exchange rate adjusts to clear any disequilibrium in the long-run relationship. The empirical findings tend to suggest that the equity market is significant in affecting the exchange rate and in explaining at least in part the parameter instability evidenced in the cointegrating system. Hence, we conclude that models of equilibrium exchange rate should be extended to include equity markets in addition to bond markets. # 2002 Elsevier Science B.V. All rights reserved. JEL classification: F3 Keywords: Exchange rates; Equity market; Augmented monetary model; Cointegration
1. Introduction It is now well known that the models based mainly on the monetary theory of exchange rate determination work poorly in the floating exchange rate regime (Kearney and * Corresponding author. Tel.: þ60-3-89467625. E-mail address: [email protected] (A.Z. Baharumshah).
0922-1425/02/$ – see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 2 - 1 4 2 5 ( 0 2 ) 0 0 0 1 9 - 1
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MacDonald, 1990; Meese, 1986; McNown and Wallace, 1989; Baillie and Selover, 1987).1 This led economists to conclude that there was an important variable (or a set of variables) omitted from these models. The monetary approach considers the fundamental variables (money, income and the interest rates) as determinant of exchange rate.2 In this study, we extend the model to include the effect of stock prices in the exchange rate equation. Previous studies have shown that a significant relationship exists between equity prices and exchange rates (Smith, 1992a; Solnik, 1987; Abdalla and Murinde, 1997). The evidence of causal relationship between exchange rates and stock prices in the emerging market is documented in Abdalla and Murinde (1997) but they ignored the role of monetary factors in the exchange rate determination. Furthermore, it has also been suggested by Meese and Rogoff (1983) and Frankel (1984), among others, that the poor performance of monetary model in tracking exchange rate movements of the major currencies might be due to instability of the underlying structural relationship. The present study focuses on these two important issues in our attempt to model the behavior of the ringgit/US dollar (RM/US) and ringgit/Japanese yen (RM/JY) rates. The primary purpose of this paper is to identify the determinants of exchange rate. Particular attention is given to the role of stock prices in the exchange rates. Prior studies on the subject matter have mostly ignored the relationship between exchange rate and the stock markets. Indeed, actual currency activity is often explained in terms of macroeconomic factors. For example, the failure of the UK to maintain its place in the ERM has often been explained in terms of its underlying macroeconomic policy positions. The tight monetary stance of the Bundesbank in response to the effects of German unification in 1989 is often cited as the origin of ERM crisis in September 1992.3 This study has been motivated by at least three reasons. First, a number of papers in the 1990s that sought to link the relationship between macroeconomic variables and exchange rates have utilized Johansen’s method to deal with the non-stationary data. They have provided new empirical evidence in favor of the long-run monetary model of exchange rate determination (Chinn and Meese, 1995; MacDonald and Taylor, 1993, 1994a,b; Clarida and Gali, 1994; Moosa, 1994; Husted and MacDonald, 1999; Chinn, 2000).4 Hence, in this study the evaluation of the long-run properties of the model is pursued within the cointegration framework suggested by Johansen (1988) and its extension in Johansen and Juselius (1990). Second, compared to the major currencies, much less academic 1 These studies used the Engle and Granger (1987) two-step procedure to provide an explanation for the longrun behavior of exchange rates. 2 The monetary approach in its sticky price formulation states that the exchange rate, as the relative price of money, depends upon relative money supplies, and relative money demands (which are in turn functions of income levels and inflation rates). 3 Frankel and Rose (1995) and Dropsey (1996) provide a survey of the empirical research, which address the importance of macroeconomic factors in the determination of exchange rates. 4 Chinn and Meese (1995), MacDonald and Taylor (1993, 1994a) found that the monetary model outperforms a random walk model in terms of out-of-sample forecasting accuracy. Clarida and Gali (1994) also found monetary shocks explain a large portion of the Deutsche mark/dollar and yen/dollar exchange rate changes.
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research has been conducted on the properties of the currencies of emerging markets.5 This is probably based on the assumption that in countries where their currencies are tied to the dollar, empirical models have little relevance. This assumption applied to most of the East Asian countries where their currencies have been tied to the US dollar (Husted and MacDonald, 1999). Finally, the 1997 Asian financial crisis tends to suggest that the equity market and the exchange rate market are closely interrelated. The ringgit dropped by 37.40 percent over the period from 1 July to 30 September 1997. Over the same period we observed that the stock market plunged by 31.37 percent. In mid-December, the Korean won depreciated drastically from 800 wons against the US dollar to more than 2000 wons. The currency crisis set off a financial avalanche in its stock market, which witnessed a 50.3 percent fall. Similar debacles also occurred in other Asian financial markets. Few studies have attempted to include stock prices into conventional exchange rate models (except Sarantis, 1987; Smith, 1992a,b). Moreover, there is increasing evidence to indicate that the stock market has a significant effect on money demand (Friedman, 1988; Choudhry, 1996a,b; Thornton, 1998). If the stock market belongs to the money demand equation, then by implication it should also belong to the exchange rate equation in line with the monetary theory of exchange rate. This intuition prompted us to derive a monetary model augmented by the inclusion of a stock price variable. When we tested this augmented monetary model in the context of Malaysia using quarterly data, our study was further motivated by an interesting preliminary finding that although the augmented monetary model was cointegrated, it was subject to parameter instability. This study, therefore, makes an attempt to explain the parameter instability within a cointegrated system with a particular focus on the role that stock market plays. The remainder of this paper is organized as follows. Section 2 provides a review of the theoretical model. Section 3 provides a brief description on the methodology as well as the data employed in the analysis. The empirical results are presented and discussed in Sections 4 and 5 offers some concluding remarks.
2. The exchange rate model Typically, a long-run relationship for money demand will include the price level, income, the own rate of return of money and the yields on alternative assets. Following Friedman (1988), Choudhry (1996a,b) and Thornton (1998), we introduced stock prices in the demand for home and foreign money market.6 This produces the following relationship, where m is the nominal demand for money, p the price level, y the real income level, i
5 Most studies on the exchange rates have focused on the behavior of the major currencies. The work by Chinn (2000), Husted and MacDonald (1999), Diamandis et al. (1996), Makrydakis (1998), Georgoutsos and Kouretas (1997), Diamandis and Kouretas (1996) and Berg and Jayanetti (1993) are among the few exceptions. These studies differ from ours in that we have added stock prices in the exchange rate model. 6 No attempt has been made previously to include wealth variable in money demand for Malaysia. However, the results obtained by Thornton (1998) and Choudhry (1996a,b) show that real stock prices have significant wealth effect on the long-run demand for money balances in Germany, Canada and the US.
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the nominal rate of interest and s the real level of the stock market. All variables except the interest rate are in logarithms md ¼ pt þ ayt bit þ yst Assuming the same relationship holds for foreign money md
¼
pt
þ
ayt
bit
þ
(1) (md ),
we have
yst
(2)
Here the asterisk denotes a foreign variable. The demand for money may be positively or negatively related to stock prices, depending on whether substitution or income effects dominate (See Friedman, 1988). It is further assumed that the money market is in equilibrium so that md ¼ ms and md ¼ ms . It is assumed that absolute PPP holds, so pt ¼ pt þ et
(3)
where e is the log of the exchange rate (defined as the domestic price of foreign currency), pt and pt the domestic and foreign price levels, respectively. Substituting Eqs. (1) and (2), solving for pt and pt gives et ¼ ðmt mt Þ aðyt yt Þ þ bðit it Þ yðst st Þ
(4)
Therefore, the reduced form for the equilibrium exchange rate can be written as et ¼ b0 þ b1 ðmt mt Þ þ b2 ðyt yt Þ þ b3 ðit it Þ þ b4 ðst st Þ þ ut
(5)
where ut is a random error term. Defining Malaysia as the home country and the US and Japan as the foreign countries, the equation given will have b1 > 0, b2 < 0, b3 > 0 and b4 can be either positive or negative, depending on the relative strengths of the income and substitution effects. Hence, the model defined by Eq. (5) differs from the ‘pure’ monetary model in that it includes the influence of the stock market.
3. Data and methodology The data set consists of quarterly data on the RM/US and the RM/JY exchange rates.7 The exchange rates as well as the macroeconomic variable span from 1976:Q1 (1983:Q4 for the RM/JY model) to 1996:Q4, the later observations are omitted to avoid the complication arising out of the financial crisis that hit the region in July 1997 and the pegging of the ringgit to the US dollar. The income variable is measured by real gross domestic product, money supply is represented by M1 and the stock market is represented by the main stock index. In all cases, the short-term interest rate that is the 3-month Treasury bill is used in the analysis. The exchange rates and related fundamental variables were obtained from the IMF International Financial Statistics and Malaysia is regarded as the domestic country. Stock indices are from Morgan Stanley Capital International. All the variables apart from interest rates are expressed in logarithmic differentials (for variable x, it is ln x ln x, where the asterisk () denotes a foreign variable). 7 Under a floating exchange rate regime, the exchange rate will adjust to its fundamental determinants (provided no bubbles emerge), whereas under a fixed rate regime the fundamentals must adjust given the exchange rate.
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In carrying out the cointegration tests, the first step is to implement the unit root tests for order of integration. The standard augmented Dickey–Fuller and Phillip–Perron tests are commonly used to test for the order of integration of all the series. Since these procedures are well known in the literature, we do not explain them here. Once the order of integration of each series is established, the next step is to test for cointegration. The Johansen (1988) maximum likelihood technique is used to test for long-run cointegration. This procedure provides more robust results than other methods when there are more than two variables (Gonzalo, 1994). The Johansen procedure considers a p-dimensional vector autoregression (VAR) that can be written as DXt ¼ ut þ
k1 X
Gi DXti PXtk þ et ;
t ¼ 1; . . . ; T
(6)
i¼1
where X ¼ ½e; m-m ; y-y ; i-i ; s-s is a vector of non-stationary (in levels) variables, m a constant term, and the P matrix provides the long-run impact matrix. This matrix is important as the rank (r) of P indicates the number of vectors. When 0 < rank ðPÞ ¼ r < p, P ¼ ab0 , and b can be interpreted as a pxr matrix of cointegrating vector and a as pxr matrix of error correction parameters. The vector of constants in Eq. (6) allows for the possibility of deterministic drift in the data. Johansen (1988) derives the maximum likelihood estimates for a, b, and Gi and a test statistic for the hypothesis that there are at most r cointegrating vectors and t is the sample size. Johansen (1991) has also developed tests of hypothesis regarding individual elements of a and b. The selection of lag order of the unrestricted VAR is a prerequisite for the application of the Johansen procedure in this study, the number of lags in the VAR may be determined by Akaike Information criterion. In addition, residuals from the selected unrestricted VAR are tested for autocorrelation to verify the appropriateness of the lag order. As in the unit root tests, lags are not omitted if their exclusion introduces serial correlation. The Johansen procedure produces two tests for inferring the number of cointegrating vectors. The trace statistic is used for testing the null hypothesis of at most r cointegrating vector against the alternative of m cointegrating vectors. The maximal eigenvalue (l-max) is used in testing the null hypothesis of r1 against r cointegrating vectors. Detailed accounts of this cointegration methodology are found in Johansen (1988) and Johansen and Juselius (1990).8 Both statistics may be compared with the appropriate critical values provided by Johansen and Juselius (1990) and Osterwald-Lenum (1992).
4. Results and discussion A prerequisite of testing for cointegration is that all variables are non-stationary. For this purpose, we applied the standard Dickey–Fuller (1979, 1981) test with constant and time trend to check whether the variables contain a unit root. In applying the test, the optimal lag 8
The reader is also referred to Dickey et al. (1991) for an application of the methodology.
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Table 1 Augmented Dickey–Fuller unit root test results Levels
First difference
No trend
Trend
No trend
Trend
(A) RM/US e m-m y-y s-s i-i
1.92 2.60 0.68 1.65 1.22
(0) (0) (1) (0) (0)
1.80 0.69 0.80 1.99 3.01
(0) (0) (1) (0) (1)
7.45 1.70 4.08 7.95 7.14
(1) (3) (0) (0) (0)
7.60 6.61 4.61 7.90 7.09
(1) (0) (0) (0) (0)
(B) RM/JY e m-m y-y s-s i-i
1.68 0.40 0.93 0.69 0.65
(0) (0) (1) (0) (0)
0.72 1.76 1.38 1.95 3.08
(0) (0) (1) (0) (1)
6.29 8.61 3.00 7.48 6.46
(0) (0) (0) (0) (0)
6.44 8.88 3.77 7.84 6.45
(0) (0) (0) (0) (0)
Note: Lag length n was chosen based on Akaike Information Criteria (AIC). Asterisks () denote statistically significant at 5% significance level, 2.91 (no trend) and 3.49 (trend).
structure is determined by using Akaike Information criteria. Moreover, we require that the number of augmentation term used in the regression to eliminate serial correlation.9 The results of the ADF unit root tests are reported in Table 1. From these results, the null hypothesis that the series contain unit roots cannot be rejected in all cases. The hypothesis of a unit root is strongly rejected for the difference variables of all cases. The possibility that the series is integrated of higher order has also been explored by using the procedure suggested by Dickey and Pantula (1987) by assuming that each series is at most integrated of order 3. For every series the null hypothesis of exactly one unit root is accepted at the 5 percent level. Table 2 contains the results of Dickey and Pantula unit root tests. Given the consistency and unambiguity of results from all these testing approaches, we conclude that all the series under investigation are I(1). On the basis of the above conclusion, we proceeded to test whether the macroeconomic variables are cointegrated with the exchange rate on a bilateral basis vis-avis the US dollar (RM/US) and the Japanese yen (RM/JY). The Johansen–Juselius (1990) tests were performed on the exchange rate models. The procedure involves formulation of an unrestricted vector autoregressive (UVAR) in the variables of interest, that is, (e, i-m, y-y, i-i, s-s) in the present case. The system is constructed with three lags as this was established using the Akaike information criteria.10 Both the trace and the l-max tests reject the null hypothesis that there is zero cointegrating 9 For this purpose the Ljung–Box Q-statistics are computed to test the properties of the residual series and they are available from the authors upon request. 10 As pointed by Boswijk and Franses (1992) and Reimers (1992) the choice of the maximum lag length used in the specification of the VAR model can affect the determination of the number of cointegrating vectors. Boswijk and Franses found that insufficient lag length could lead to rejection of the null hypothesis of no cointegration too often, where over-parameterization of the dynamic structure would lead to loss of power.
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Table 2 Dickey–Pantula integration test results Tests for three unit roots
Tests for two unit roots
Tests for single unit roots
a3
LM(5)
a2
LM(5)
a1
LM(5)
(A) RM/US e m-m y-y s-s i-i
7.790 10.13 7.256 7.306 6.124
0.6375 1.5220 0.0019 1.5833 1.6683
6.003 3.247 3.020 5.316 3.150
0.1097 0.7494 0.5969 0.4547 0.8300
1.216 1.313 0.282 1.935 1.582
0.0078 0.7561 0.5625 0.3967 0.7407
(B) RM/JY e m-m y-y s-s i-i
9.572 8.673 6.063 6.824 5.034
1.3324 0.9451 0.9711 0.0249 1.0437
2.913 3.505 2.001 3.306 3.199
1.1357 0.3077 1.2812 0.0200 0.3306
1.898 0.353 0.521 0.593 0.479
1.4713 0.3505 1.2754 0.1249 0.6815
Note: Lag length n was chosen based on Akaike Information Criteria (AIC). Asterisks () denote statistically significant at 5% significance level, 2.91 (no trend) and 3.49 (trend).
vectors for the two-ringgit bilateral rates. The l-max statistics reveal one non-zero cointegrating vector (four common trends) while the trace test yields two non-zero cointegrating vectors among the I(1) variables for the two bilateral rates. However, these results are unreliable since the model failed both the single equation and the system diagnostic statistics which indicate problems of autocorrelation and nonnormal residuals. We sequentially experimented with higher lag structure and checked for white noise. Unfortunately, it proved impossible to meet the serial correlation standard based on the LM test for even those with six or more lags. To conserve space these results are not reported here.11 The VAR model is estimated recursively, and the stability of its parameters is tested by means of sequential Chow-type tests.12 Results for the RM/US model are plotted in Fig. 1a–e for each of the equations and results for the system as a whole appears in Fig. 1f. Results for the RM/JY model are graphed in Fig. 2a–f. Note that the critical values for this test appear in the plot as a straight line at unity. Visual inspection of the plots indicates that stock price levels exhibit instability in the mid 1980s for the RM/US model (Fig. 1d) and around 1994 for the RM/JY model (Fig. 2d). In addition, the behaviors of income and interest rate processes in the RM/JY model produced instability in the VAR model as a whole (Fig. 2c and e). 11
These results are available from the first author upon request. The test is conducted by using PCFIML. Briefly, sequential tests for parameter stability are conducted using the one-step ahead Chow-type tests. Starting with a minimum sample of size, we augment it by one recursively, thus, resulting in a sequence of F-statistics F (1, tk1), t ¼ T0 þ 1; . . . ; T, given by (RSSt RSSt1 )/RSSt1. The critical values appear in the plots to be a straight line at unity. This is because the statistics reported above are by one-off critical values from the F-distribution at the selected probability level, as an adjustment for changing degree of freedom (See Banerjee and Urga, 1995). 12
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Fig. 1. One-step Chow test (RM/US)—1%.
To further investigate whether the observed instability is attributed to the specific variables in the system, we re-estimated the stable part of the VAR model conditional on the variables found to be unstable.13 For the RM/JY model, the conditional variables are income, stock price level and interest rate while for the RM/US model only the stock price level seems to be unstable.14 A unique cointegrating vector is identified in the two bilateral rates by the trace statistics in the conditional VAR model. For the RM/US rate, the l-max test yielded two cointegrating vectors. To choose the number of cointegrating vectors in this case, we followed McNown and Wallace (1994) where the vectors revealed to be significant in both tests are considered. Having established the cointegrating rank, r ¼ 1, we proceeded with the exogeneity test. The null hypothesis that the variables are weakly exogenous with respect to the cointegrating vector is equivalent to the hypothesis that the variable is not error-correcting. 13
The break in 1985 is not surprising as it corresponds to the second oil shock as well as the financial reforms that took place after the 1984 recession. The second shock in 1994 corresponds to the portfolio and other capital inflows. In this period we observed that stock trading was very active with the stock price index reaching all time high in 1993:Q4 ðINDEX ¼ 1275Þ. In order to remove these effects from our data, we have added two shift dummies. However, the addition of these variables did not significantly change our empirical results. Further research will be needed to better model such breaks. 14 For the RM/US model the stable part of the model is estimated, namely the exchange rate, money supply, and income, conditional on the variable that was found to be unstable, that is stock price level. For the RM/JY model, the unstable variables are stock price level, income and interest rate. The results of the conditional model are not reported here to conserve space and are available from the authors upon request.
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Fig. 2. One-step Chow test (RM/JY)—1%.
Thus, imposing zero restriction on the coefficient of the error-correction term serves as a test of weakly exogeneity.15 The outcome of the test may be summarized as follows (results not reported here to conserve space). First, the RM/US model contains three endogenous variables: exchange rate, money supply and interest rate. The restricted model for the RM/ JY contains two endogenous variables, namely exchange rate and money supply. Second, it is noteworthy that the exogeneity of the exchange rate in the model would be inconsistent with the monetary model, since the adjustment toward equilibrium in this case would not be carried out by the exchange rate. In both systems the estimates of the adjustment coefficient clearly suggest that exchange rate and money supply respond to disequilibrium. Thus, they can be considered as driving the dynamics of the system as a whole.16 The resulting restricted VAR models contain three endogenous variables for the RM/US case and two endogenous variables for the RM/JY case and the results are shown in Table 3. The conditional VAR model is checked for parameter constancy, normality and absence of serial correlation. The empirical results obtained from the conditional VAR model showed marked improvement. In many respects, the results from the conditional VAR model are superior to the results obtained from the unrestricted VAR model. As shown in the Table 4, 15 If the coefficient of the dependent variable is weakly exogenous, the VEC model should be reformulated condition on this variable. 16 Hall and Milne (1994) show that weak exogeneity in a cointegrated system is equivalent to the notion of long-run causality. In general, the more cointegrating vectors (that is, less common trends) there are in the system, the more stable the system and the more constrained the long-run relationship among the five variable systems.
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Table 3 Testing for cointegration using the Johansen and Juselius method Tests H0
RM/US HA
RM/JY k¼5
C.V.
C.V.
k¼3
15.70 9.20
33.00 9.13
20.00 9.20
42.13 9.13
s-s
i-i
– –
21.79 (0.00) –
l-max r¼0 r 1 r 2
r¼1 r¼2 r¼3
22.00 15.70 9.20
30.08 18.21 1.495
r¼0 r 1 r 2
r¼1 r¼2 r¼3
34.90 20.00 9.20
49.79 19.71 1.495
e
m-m
Trace
RM/US RM/JY
6.89 (0.03) 6.44 (0.01)
Weak Exogeneity Tests y-y
15.87 (0.00) 13.48 (0.00)
– –
Note: Lag length n was chosen based on Akaike Information Criteria (AIC). Notes: Asterisks (), () denote statistically significant at 5 and 10% level, respectively. (Critical values are taken from Osterwald-Lenum, 1992.) The LR test is w2 distribution with one degree of freedom.
the models pass all the single equation diagnostic tests, except for the gdp equation in RM/ US system. The system diagnostic tests do not indicate any form of mis-specification in the model. Following Johansen–Juselius (1990), the exclusion restrictions on exchange rate and its determinants are formally tested. Table 5 reports the likelihood ratio statistics for the null hypothesis that the given variable does not belong to the cointegrating vector. With a single cointegrating vector this is asymptotically w2 with one degree of freedom. In each case, the hypothesis that exchange rate does not enter into the cointegrating relationship is easily rejected at the 5% significance level or better. In fact, the LR statistics show that all of the Table 4 Diagnostic checking Variable
Q-statistic
AR (5)
Norm (2)
ARCH (4)
HET (w2)
(A) RM/US (k ¼ 5) e m-m i-i System
4.989 8.054 13.23 45.15
3.64 3.62 9.32 66.17
0.03 0.35 17.24 11.30
2.76 (0.60) 1.82 (0.77) 17.70 (0.00) –
40.39 52.95 38.43 212.65
(B) RM/JY (k ¼ 3) e m-m System
4.598 20.58 26.91
6.79 (0.24) 7.25 (0.20) 21.00 (0.40)
(0.60) (0.61) (0.10) (0.02)
(0.98) (0.84) (0.00) (0.08)
0.61 (0.74) 1.97 (0.37) 4.14 (0.39)
5.43 (0.25) 6.54 (0.16) –
(0.21) (0.02) (0.28) (0.32)
13.45 (0.76) 18.39 (0.43) 54.85 (0.44)
Note: Asterisks () denote statistically significant at the 5% level. Figures in parenthesis are p-values. AR(n) is the nth order LM test for serial correlation, Norm (2) is a test for normality in the residuals; ARCH(m) is a mth order test for autoregressive conditional heteroskedasticity.
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Table 5 Tests of exclusions Variable e RM/US RM/JY
m-m
16.266 (0.000) 21.155 (0.000)
y-y
9.762 (0.008) 18.909 (0.000)
s-s
13.840 (0.001) 7.688 (0.006)
i-i
6.106 (0.047) 14.760 (0.000)
29.439 (0.000) 1.854 (0.173)
Constant
13.718 (0.001) 0.000 (0.989)
Note: Asterisks () denote statistically significant at the 5% level.
variables (except for interest rate in the RM/JY model) enter in the cointegration relationship.17 In general, these results seem to support the version of the augmented monetary model given by Eq. (5). Our contention that the pure monetary model may not be adequate to explain the movements of exchange rate for the emerging markets, specifically Malaysia has received some empirical support. Indeed, the results imply that money supply, income, interest rate and stock prices in the long-run determine the value of the ringgit. The results reflect the close relationship of the ringgit with the US dollar and the Japanese yen. This finding could stem from the fact that the ringgit was linked to the US dollar and Japanese yen for most of the sample period due to the importance of these two economies in international trade and capital accounts. These results are consistent with those of MacDonald and Taylor (1994a,b), Moosa (1994), Georgoutsos and Kouretas (1997), Husted and MacDonald (1999) and Chinn (2000), among others.18 They all have reported that at least a single vector is found in the exchange rate model. However, these results are in sharp contrast to previous studies that show the non-cointegration between exchange rate and the fundamental variables (Kearney and MacDonald, 1990; Meese, 1986; McNown and Wallace, 1989; Baillie and Selover, 1987). Visual inspection on the recursive estimation of the two conditional models reveals no parameter instability for each of the individual equation or for the system as a whole.19 The finding seems to suggest that the parameter instability of the model may be attributed to a subset of the variable(s) in the systems. It can be seen from the Figs. 3 and 4 that the eigenvalues in both models exhibit a stable behavior and mean reverting property. Thus, the conditional VAR model that incorporates stock prices is more appropriate for modeling the exchange rates behavior in Malaysia. Normalizing the cointegrating vector on exchange rates, the variable of our central interest can facilitate an econometric interpretation of the results. These normalized equations appear in Table 6. The numbers below the estimated parameters are the asymptotic standard errors. The coefficient of domestic–foreign money stock (narrow money) has the expected and statistically significant positive sign. The size of the coefficient is significantly different from unity as predicted by theory. In applying the monetary model to South Korea, Makrydakis (1998) found that all the parameter restrictions implied by theory are easily rejected by 17
Removing the interest rate variable in the RM/JY rate did not change our results significantly. The difference between our finding and that of these earlier works is that we found that the stock market enters in the long-run relationship. 19 To conserve space, these plots are not shown here but are available upon request. 18
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Fig. 3. Eigenvector and eigenvalue (RM/US).
Fig. 4. Eigenvector and eigenvalue (RM/JY).
Table 6 Estimated cointegrated vectors in Johansen estimation Variable
RM/US RM/JY
Constant
y-y
e
m-m
1.000 1.000
0.406 (0.155) 0.881 (0.231) 3.375 (0.624) 2.404 (0.841)
s-s
i-i
0.080 (0.033) 0.513 (0.093)
0.032 (0.004) 0.042 (0.029)
2.029 (0.656) 0.051 (3.615)
the data. Similarly, Miyakoshi (2000) found that the domestic–foreign money stock coefficient for the Korean won/German mark to be 2.587. These results are, however, in sharp contrast with Chinn (2000) who found that the coefficient is not significantly different from that implied by theory for four of the East Asian countries (Indonesia, Singapore, Thailand and Taiwan). The coefficient of domestic–foreign income differential is negative and statistically significant at conventional significant level for RM/US rate. This compares with 0.546 (1.056) reported by Chinn (2000) for Indonesia (South Korea) and the income elasticity
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of about 1.62–1.74 for Malaysia, for example Arize et al. (1999) and Tan (1997).20 Interest differential enters in with a positive sign and is statistically significant. In both equations, we find the coefficient to be very small. Earlier it is also shown that interest rate is important in money demand in Malaysia (e.g. Tan, 1997 and Arize et al., 1999). Hence, as the monetary model predicts, an increase in interest rates ceteris paribus, leads to an appreciation of the ringgit. In other words, an interest rate differential induces capital flows and movement in domestic currency. It can be seen from Table 6 that the estimated parameters on the stock price differential of 0.080 in the RM/US equation, is highly significant implying that a one percent point increase in the stock price differential induces a 0.080 percentage point appreciation of the ringgit against the dollar. For the RM/JY rate, the variable enters with an opposite sign (0.513), implying that a rise in the Malaysian share price index relative to Japan led to a depreciation of the ringgit against the yen. It is noteworthy to point out that empirical study on the relationship between exchange rates and stock prices yielded mixed results, depending on whether the income or substitution effect dominate. On one hand, it has been found that a significant positive relationship exist between equity prices and exchange rates (Smith, 1992a; Solnik, 1987). On the other hand, it has been shown that a strong negative relationship exists between stock prices and exchange rates (Soenen and Hennigar, 1988).
5. Summary conclusions and policy implications This paper presents and tests an augmented monetary model that includes the effect of stock prices on the bilateral exchange rates. The model is applied to the RM/US and RM/ JY. The empirical analysis is conducted by the Johansen method of cointegration. Using the data from the recent float that ends with 1996:Q4, the initial finding of the study is that although the augmented monetary model is cointegrated, it is subject to parameter instability and that the parameter time dependency can be attributed at least partly to a particular subset of the variables in the system including stock prices. The study further finds that a restricted VAR model which imposes exogeneity restrictions on I(1) variables, such as stock prices, among others, exhibits both cointegration and parameter stability. The empirical findings tend to suggest that the equity market is significant in affecting the exchange rate and in explaining at least in part the parameter instability evidenced in the cointegrating system Hence, we conclude that models of equilibrium exchange rate should be extended to include equity markets in addition to bond markets. Our findings are consistent with some of the recent studies done for the East Asian Economies (Chinn, 2000; Husted and MacDonald, 1999). Several interesting facts have emerged from the analysis. First, interest rate differential explains a small variation in the ringgit. This result is consistent with the results obtained for other countries, which found those interest rate differentials only accounted for a small 20 It should be noted that the coefficient of income in the exchange rate equation is predicted to be equal in sign to the income elasticity of demand, i.e. the coefficient of income in the money demand (see MacDonald and Taylor, 1994a). The hypothesis of unitary long-run income elasticity is often rejected because there is absence of economies of scale in money holding in LDCs (See Arize et al., 1999).
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portion of exchange rate movement. Second, domestic–foreign income differentials explain most of the variation in the ringgit. This result implies that rapid growth experience in the past two decades or so tends to strengthen the RM/US rate as predicted by the monetary approach. However, we observed that this is not the case for the RM/JY rate. We speculate that the reason for the failure could be due to the fact that the Malaysia and Japanese business cycles are not closely linked. Japan had been in recession for the most part of the 1990s while Malaysia had been experiencing robust growth before the crisis hit the region in 1997.21 We leave this for future work. Third, we find one long-run equilibrium path and domestic–foreign money stock differentials are endogenous in both systems. While domestic monetary policy may be helpful in the short-run in achieving some domestic policy objectives, it may not be fully successful in the long-run due to the endogeneity of money. The findings suggest that monetary policy has limited use as a stabilization policy in the long-run as it is not completely controlled by the monetary authorities. Finally, the evidence suggests that the stock market is an important determinant of exchange rate. This is consistent with a number of empirical papers that attempted to verify the relationship between stock prices and exchange rates (e.g. Solnik, 1987; Sarantis, 1987; Soenen and Hennigar, 1988; Smith, 1992a; Abdalla and Murinde, 1997). This paper endorses these findings within the broad framework of the monetary approach to exchange rate determination. However, authors like Bahmani-Oskooee and Sohrabian (1992) and Canova and De Nicolo (2000), on the other hand, failed to show any common trends between the exchange rate and the stock market. Nevertheless, from the statistical point of view, our results tend to suggest that the models of equilibrium exchange rate should be extended to include equity markets in addition to bond markets. The policy implication is that if indeed the equity market affects the foreign exchange market in the long-run, then the factors affecting the equity prices, such as the inflows of hot money (i.e. portfolio funds) are also likely to influence the movement of exchange rates. Hence, if equity market is moving erratically then the exchange rate market may also move in an erratic manner. The Asian financial crisis appears to be consistent with this contention.
Acknowledgements Useful comments were received from the editor of this Journal, Ryuzo Sato and the anonymous referees. We thank Evan Lau for the research assistance. This research has been funded by the Research in Priority Areas (IRPA) program and Universiti Putra Malaysia. All remaining errors are the authors’ responsibility. References Abdalla, I.S.A., Murinde, V., 1997. Exchange rate and stock price interaction in emerging financial markets: evidence on India, Korea, Pakistan and the Philippines. Applied Financial Economics 7, 25–35. 21 Husted and MacDonald (1999) also found evidence of misalignment in ringgit/yen rate based on the monetary model. Indeed, their results reveal the currency appeared overvalued for most part of the 1990s.
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Arize, A.C., Malindretos, J., Shwiff, S.S., 1999. Structural breaks, cointegration, and speed of adjustment. evidence from 12 LDCs money demand. International Review of Economics and Finance 8, 399–420. Baillie, R.J., Selover, D.D., 1987. Cointegration and models of exchange rate determination. International Journal of Forecasting 3, 43–52. Bahmani-Oskooee, M., Sohrabian, A., 1992. Stock prices and the effective exchange rate of the dollar. Applied Economics 24, 459–464. Banerjee, A., Urga, G., 1995. Looking for structural breaks in cointegrated systems. Discussion Paper No. DP 18-95, Center for Economic Forecasting, London Business School. Berg, H., Jayanetti, S.C., 1993. A novel test of the monetary approach using black market exchange rates and the Johansen–Juselius cointegration method. Economics Letters 41, 413–418. Boswijk, P., Franses, P.H., 1992. Dynamic specification and cointegration. Oxford Bulletin of Economics and Statistics 54, 368–381. Canova, F., De Nicolo, G., 2000. Stock returns, term structure, inflation and real activity and inflation: an international perspective. Macroeconomic Dynamics 4, 343–372. Chinn, M.D., 2000. Before the fall: were East Asian currencies overvalued? Emerging Market Review 1, 101–126. Chinn, M.D., Meese, R., 1995. Banking on currency forecasts: how predictable is the change in money. Journal of International Economics 38, 161–178. Choudhry, T., 1996a. Interdependence of stock markets: evidence from Europe during the 1920s and 1930s. Applied Financial Economics 6, 243–249. Choudhry, T., 1996b. Real stock prices and the long-run money demand function: evidence from Canada and the USA. Journal of International Money and Finance 15, 1–17. Clarida, R., Gali, J., 1994. Sources of real exchange rate fluctuations: how important are nominal shocks? Carnegie Rochester Conference Series on Public Policy 41, 1–56. Diamandis, P.F., Kouretas, G.P., 1996. The monetary approach to the exchange rate: long-run relationships, coefficient restrictions and temporal stability of the greek drachma. Applied Financial Economics 6, 351–362. Diamandis, P.F., Georgoutsos, D.A., Kouretas, G.D., 1996. Cointegration tests for the monetary exchange rate model: the Canadian/US dollar, 1970–1994. International Economic Journal 10, 83–97. Dickey, D.A., Fuller, W.A., 1979. Distribution of estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74, 427–431. Dickey, D.A., Fuller, W.A., 1981. The likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49, 1057–1072. Dickey, D., Pantula, S., 1987. Determining the order of differencing in autoregressive process. Journal of Business and Economic Statistics 5, 455–461. Dickey, D., Jansen, D., Thornton, D., 1991. A premier on cointegration with application to money and income. Federal Bank of St. Louis Review 73, 58–78. Dropsey, V., 1996. Macroeconomics determinants of exchange rates: a frequency-specific analysis. Applied Economics 28, 55–63. Engle, R.F., Granger, C.W.J., 1987. Cointegration and error correction: representation, estimation and testing, estimation and testing. Econometrica 55, 251–276. Frankel, J.A., 1984. Tests of monetary and portfolio balance models in exchange rate determination: sampling error misspecification. In: Frankel, J.A. (Ed.), Exchange Rate and International Macroeconomics. The University of Chicago Press, Chicago, pp. 67–105. Frankel, J.A., Rose, A.K., 1995. Empirical Research on Nominal Exchange Rates. In: Grossman, G., Rogoff, P.P. (Eds.), Handbook of International Economics, Vol. 3. Elsevier, Amsterdam, pp. 1689–1720. Friedman, M., 1988. Money and the stock market. Journal of Political Economy 96, 221–245. Georgoutsos, D.A., Kouretas, G., 1997. The monetary model of the exchange rate and the greek drachma in the 1920s. Applied Economics 7, 507–515. Gonzalo, J., 1994. Five alternative methods of estimating long-run equilibrium relationships. Journal of Econometrics 60, 203–233. Hall, S.G., Milne, A., 1994. The relevance of P-STAR analysis to UK monetary policy. The Economic Journal 104, 597–598. Husted, S., MacDonald, R., 1999. The Asian currency crash: were badly driven fundamentals to blame. Journal of Asian Economics 10, 537–550.
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Johansen, S., 1988. Statistical analysis of cointegrating vectors. Journal of Economic Dynamic and Control 12, 231–254. Johansen, S., 1991. Estimation and hypothesis testing of cointegration vectors. Econometrica 59, 1551–1580. Johansen, S., Juselius, K., 1990. Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics and Statistics 52, 169–210. Kearney, C., MacDonald, R., 1990. Rational expectations, bubbles and monetary models of the exchange rate: the Australian/US dollar rate during the recent float. Australian Economic Papers 29, 1–20. MacDonald, R., Taylor, M.P., 1993. The monetary approach to the exchange rate: rational expectation, long-run equilibrium and forecasting. International Monetary Fund Staff Papers 40, 89–102. MacDonald, R., Taylor, M.P., 1994a. The monetary model of the exchange rate: long-run relationships, short-run dynamics, and how to beat a random walk. Journal of International Money and Finance 13, 276–290. MacDonald, R., Taylor, M.P., 1994b. Re-examining the monetary approach to the exchange rate: the dollar/ franc, 1976–90. Applied Financial Economics 12, 167–176. Makrydakis, S., 1998. Testing the long-run validity of the monetary approach to the exchange rate: the won/US dollar case. Applied Economics Letters 5, 507–511. McNown, R., Wallace, M., 1989. Cointegration test for long-run equilibrium in the monetary exchange rate model. Economic Letters 31, 263–267. McNown, R., Wallace, M.S., 1994. Cointegration tests of the monetary exchange rate model for three high inflation economies. Journal of Money, Credit, and Banking 26, 396–411. Meese, R.A., 1986. Testing bubbles in exchange markets: a case of sparkling rates? Journal of Political Economy 94, 345–373. Meese, R.A., Rogoff, K.S., 1983. Empirical exchange rate models of the seventies: do they fit out of sample? Journal of International Economics 14, 3–24. Miyakoshi, T., 2000. The monetary approach to exchange rate: empirical observations from Korea. Applied Economics Letters 7, 791–794. Moosa, I.A., 1994. The monetary model of exchange rates revisited. Applied Financial Economics 4, 279–287. Osterwald-Lenum, M., 1992. A note with quartiles of the asymptotic distribution of the maximum likelihood cointegration rank test statistics: four cases. Oxford Bulletin of Economics and Statistics 54, 461–472. Reimers, H.E., 1992. Comparison of tests for multivariate cointegration. Statistical Papers 33, 335–359. Sarantis, N., 1987. A dynamic asset market model for the exchange rate of the pound sterling. Weltwirtschaftliches Archiv. 123, 24–38. Smith, C., 1992a. Stock markets and the exchange rate: a multicountry approach. Journal of Macroeconomics 14, 607–629. Smith, C., 1992b. Equities and the UK exchange rate. Applied Economics 24, 327–335. Soenen, L., Hennigar, E., 1988. An analysis of exchange rates and stock prices-the US experience between 1980 and 1986. Akron Business and Economic Review 19, 7–16. Solnik, B., 1987. Using financial price to test exchange rate models: a note. Journal of Finance 42, 114–119. Tan, E.C., 1997. Money demand amid financial sector development in Malaysia. Applied Economics 29, 1201–1215. Thornton, J., 1998. Real stock prices and the long-run demand for money in Germany. Applied Financial Economics 8, 513–517.
The stock market and the ringgit exchange rate: a note Ahmad Zubaidi Baharumshaha,*, A. Mansur M. Masihb, M. Azalia a
Department of Economics, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia b Department of Finance and Economics, King Fahd University of Petroleum & Minerals, KFUPM, P.O. Box 1764, Dhahran 31261, Saudi Arabia Received 23 May 2001; received in revised form 4 October 2001; accepted 11 April 2002
Abstract This paper presents and tests an augmented monetary model that includes the effect of stock prices on the bilateral exchange rates. The model is applied to the ringgit/US dollar (RM/US) and ringgit/Japanese yen (RM/JY) exchange rates. The empirical analysis is conducted by the Johansen method of cointegration. Using the data from the recent float that ends with 1996:Q4, the study is motivated, among others, by an interesting preliminary finding that although the augmented monetary model is cointegrated, it is subject to parameter instability and that the parameter time dependency can be attributed at least partly to a particular subset of the variables in the system including stock prices. We find that a restricted VAR model which imposes exogeneity restrictions on I(1) variables, such as stock prices, among others, exhibits both cointegration and parameter stability. In addition, we demonstrate that exchange rate adjusts to clear any disequilibrium in the long-run relationship. The empirical findings tend to suggest that the equity market is significant in affecting the exchange rate and in explaining at least in part the parameter instability evidenced in the cointegrating system. Hence, we conclude that models of equilibrium exchange rate should be extended to include equity markets in addition to bond markets. # 2002 Elsevier Science B.V. All rights reserved. JEL classification: F3 Keywords: Exchange rates; Equity market; Augmented monetary model; Cointegration
1. Introduction It is now well known that the models based mainly on the monetary theory of exchange rate determination work poorly in the floating exchange rate regime (Kearney and * Corresponding author. Tel.: þ60-3-89467625. E-mail address: [email protected] (A.Z. Baharumshah).
0922-1425/02/$ – see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 2 - 1 4 2 5 ( 0 2 ) 0 0 0 1 9 - 1
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MacDonald, 1990; Meese, 1986; McNown and Wallace, 1989; Baillie and Selover, 1987).1 This led economists to conclude that there was an important variable (or a set of variables) omitted from these models. The monetary approach considers the fundamental variables (money, income and the interest rates) as determinant of exchange rate.2 In this study, we extend the model to include the effect of stock prices in the exchange rate equation. Previous studies have shown that a significant relationship exists between equity prices and exchange rates (Smith, 1992a; Solnik, 1987; Abdalla and Murinde, 1997). The evidence of causal relationship between exchange rates and stock prices in the emerging market is documented in Abdalla and Murinde (1997) but they ignored the role of monetary factors in the exchange rate determination. Furthermore, it has also been suggested by Meese and Rogoff (1983) and Frankel (1984), among others, that the poor performance of monetary model in tracking exchange rate movements of the major currencies might be due to instability of the underlying structural relationship. The present study focuses on these two important issues in our attempt to model the behavior of the ringgit/US dollar (RM/US) and ringgit/Japanese yen (RM/JY) rates. The primary purpose of this paper is to identify the determinants of exchange rate. Particular attention is given to the role of stock prices in the exchange rates. Prior studies on the subject matter have mostly ignored the relationship between exchange rate and the stock markets. Indeed, actual currency activity is often explained in terms of macroeconomic factors. For example, the failure of the UK to maintain its place in the ERM has often been explained in terms of its underlying macroeconomic policy positions. The tight monetary stance of the Bundesbank in response to the effects of German unification in 1989 is often cited as the origin of ERM crisis in September 1992.3 This study has been motivated by at least three reasons. First, a number of papers in the 1990s that sought to link the relationship between macroeconomic variables and exchange rates have utilized Johansen’s method to deal with the non-stationary data. They have provided new empirical evidence in favor of the long-run monetary model of exchange rate determination (Chinn and Meese, 1995; MacDonald and Taylor, 1993, 1994a,b; Clarida and Gali, 1994; Moosa, 1994; Husted and MacDonald, 1999; Chinn, 2000).4 Hence, in this study the evaluation of the long-run properties of the model is pursued within the cointegration framework suggested by Johansen (1988) and its extension in Johansen and Juselius (1990). Second, compared to the major currencies, much less academic 1 These studies used the Engle and Granger (1987) two-step procedure to provide an explanation for the longrun behavior of exchange rates. 2 The monetary approach in its sticky price formulation states that the exchange rate, as the relative price of money, depends upon relative money supplies, and relative money demands (which are in turn functions of income levels and inflation rates). 3 Frankel and Rose (1995) and Dropsey (1996) provide a survey of the empirical research, which address the importance of macroeconomic factors in the determination of exchange rates. 4 Chinn and Meese (1995), MacDonald and Taylor (1993, 1994a) found that the monetary model outperforms a random walk model in terms of out-of-sample forecasting accuracy. Clarida and Gali (1994) also found monetary shocks explain a large portion of the Deutsche mark/dollar and yen/dollar exchange rate changes.
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research has been conducted on the properties of the currencies of emerging markets.5 This is probably based on the assumption that in countries where their currencies are tied to the dollar, empirical models have little relevance. This assumption applied to most of the East Asian countries where their currencies have been tied to the US dollar (Husted and MacDonald, 1999). Finally, the 1997 Asian financial crisis tends to suggest that the equity market and the exchange rate market are closely interrelated. The ringgit dropped by 37.40 percent over the period from 1 July to 30 September 1997. Over the same period we observed that the stock market plunged by 31.37 percent. In mid-December, the Korean won depreciated drastically from 800 wons against the US dollar to more than 2000 wons. The currency crisis set off a financial avalanche in its stock market, which witnessed a 50.3 percent fall. Similar debacles also occurred in other Asian financial markets. Few studies have attempted to include stock prices into conventional exchange rate models (except Sarantis, 1987; Smith, 1992a,b). Moreover, there is increasing evidence to indicate that the stock market has a significant effect on money demand (Friedman, 1988; Choudhry, 1996a,b; Thornton, 1998). If the stock market belongs to the money demand equation, then by implication it should also belong to the exchange rate equation in line with the monetary theory of exchange rate. This intuition prompted us to derive a monetary model augmented by the inclusion of a stock price variable. When we tested this augmented monetary model in the context of Malaysia using quarterly data, our study was further motivated by an interesting preliminary finding that although the augmented monetary model was cointegrated, it was subject to parameter instability. This study, therefore, makes an attempt to explain the parameter instability within a cointegrated system with a particular focus on the role that stock market plays. The remainder of this paper is organized as follows. Section 2 provides a review of the theoretical model. Section 3 provides a brief description on the methodology as well as the data employed in the analysis. The empirical results are presented and discussed in Sections 4 and 5 offers some concluding remarks.
2. The exchange rate model Typically, a long-run relationship for money demand will include the price level, income, the own rate of return of money and the yields on alternative assets. Following Friedman (1988), Choudhry (1996a,b) and Thornton (1998), we introduced stock prices in the demand for home and foreign money market.6 This produces the following relationship, where m is the nominal demand for money, p the price level, y the real income level, i
5 Most studies on the exchange rates have focused on the behavior of the major currencies. The work by Chinn (2000), Husted and MacDonald (1999), Diamandis et al. (1996), Makrydakis (1998), Georgoutsos and Kouretas (1997), Diamandis and Kouretas (1996) and Berg and Jayanetti (1993) are among the few exceptions. These studies differ from ours in that we have added stock prices in the exchange rate model. 6 No attempt has been made previously to include wealth variable in money demand for Malaysia. However, the results obtained by Thornton (1998) and Choudhry (1996a,b) show that real stock prices have significant wealth effect on the long-run demand for money balances in Germany, Canada and the US.
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the nominal rate of interest and s the real level of the stock market. All variables except the interest rate are in logarithms md ¼ pt þ ayt bit þ yst Assuming the same relationship holds for foreign money md
¼
pt
þ
ayt
bit
þ
(1) (md ),
we have
yst
(2)
Here the asterisk denotes a foreign variable. The demand for money may be positively or negatively related to stock prices, depending on whether substitution or income effects dominate (See Friedman, 1988). It is further assumed that the money market is in equilibrium so that md ¼ ms and md ¼ ms . It is assumed that absolute PPP holds, so pt ¼ pt þ et
(3)
where e is the log of the exchange rate (defined as the domestic price of foreign currency), pt and pt the domestic and foreign price levels, respectively. Substituting Eqs. (1) and (2), solving for pt and pt gives et ¼ ðmt mt Þ aðyt yt Þ þ bðit it Þ yðst st Þ
(4)
Therefore, the reduced form for the equilibrium exchange rate can be written as et ¼ b0 þ b1 ðmt mt Þ þ b2 ðyt yt Þ þ b3 ðit it Þ þ b4 ðst st Þ þ ut
(5)
where ut is a random error term. Defining Malaysia as the home country and the US and Japan as the foreign countries, the equation given will have b1 > 0, b2 < 0, b3 > 0 and b4 can be either positive or negative, depending on the relative strengths of the income and substitution effects. Hence, the model defined by Eq. (5) differs from the ‘pure’ monetary model in that it includes the influence of the stock market.
3. Data and methodology The data set consists of quarterly data on the RM/US and the RM/JY exchange rates.7 The exchange rates as well as the macroeconomic variable span from 1976:Q1 (1983:Q4 for the RM/JY model) to 1996:Q4, the later observations are omitted to avoid the complication arising out of the financial crisis that hit the region in July 1997 and the pegging of the ringgit to the US dollar. The income variable is measured by real gross domestic product, money supply is represented by M1 and the stock market is represented by the main stock index. In all cases, the short-term interest rate that is the 3-month Treasury bill is used in the analysis. The exchange rates and related fundamental variables were obtained from the IMF International Financial Statistics and Malaysia is regarded as the domestic country. Stock indices are from Morgan Stanley Capital International. All the variables apart from interest rates are expressed in logarithmic differentials (for variable x, it is ln x ln x, where the asterisk () denotes a foreign variable). 7 Under a floating exchange rate regime, the exchange rate will adjust to its fundamental determinants (provided no bubbles emerge), whereas under a fixed rate regime the fundamentals must adjust given the exchange rate.
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In carrying out the cointegration tests, the first step is to implement the unit root tests for order of integration. The standard augmented Dickey–Fuller and Phillip–Perron tests are commonly used to test for the order of integration of all the series. Since these procedures are well known in the literature, we do not explain them here. Once the order of integration of each series is established, the next step is to test for cointegration. The Johansen (1988) maximum likelihood technique is used to test for long-run cointegration. This procedure provides more robust results than other methods when there are more than two variables (Gonzalo, 1994). The Johansen procedure considers a p-dimensional vector autoregression (VAR) that can be written as DXt ¼ ut þ
k1 X
Gi DXti PXtk þ et ;
t ¼ 1; . . . ; T
(6)
i¼1
where X ¼ ½e; m-m ; y-y ; i-i ; s-s is a vector of non-stationary (in levels) variables, m a constant term, and the P matrix provides the long-run impact matrix. This matrix is important as the rank (r) of P indicates the number of vectors. When 0 < rank ðPÞ ¼ r < p, P ¼ ab0 , and b can be interpreted as a pxr matrix of cointegrating vector and a as pxr matrix of error correction parameters. The vector of constants in Eq. (6) allows for the possibility of deterministic drift in the data. Johansen (1988) derives the maximum likelihood estimates for a, b, and Gi and a test statistic for the hypothesis that there are at most r cointegrating vectors and t is the sample size. Johansen (1991) has also developed tests of hypothesis regarding individual elements of a and b. The selection of lag order of the unrestricted VAR is a prerequisite for the application of the Johansen procedure in this study, the number of lags in the VAR may be determined by Akaike Information criterion. In addition, residuals from the selected unrestricted VAR are tested for autocorrelation to verify the appropriateness of the lag order. As in the unit root tests, lags are not omitted if their exclusion introduces serial correlation. The Johansen procedure produces two tests for inferring the number of cointegrating vectors. The trace statistic is used for testing the null hypothesis of at most r cointegrating vector against the alternative of m cointegrating vectors. The maximal eigenvalue (l-max) is used in testing the null hypothesis of r1 against r cointegrating vectors. Detailed accounts of this cointegration methodology are found in Johansen (1988) and Johansen and Juselius (1990).8 Both statistics may be compared with the appropriate critical values provided by Johansen and Juselius (1990) and Osterwald-Lenum (1992).
4. Results and discussion A prerequisite of testing for cointegration is that all variables are non-stationary. For this purpose, we applied the standard Dickey–Fuller (1979, 1981) test with constant and time trend to check whether the variables contain a unit root. In applying the test, the optimal lag 8
The reader is also referred to Dickey et al. (1991) for an application of the methodology.
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Table 1 Augmented Dickey–Fuller unit root test results Levels
First difference
No trend
Trend
No trend
Trend
(A) RM/US e m-m y-y s-s i-i
1.92 2.60 0.68 1.65 1.22
(0) (0) (1) (0) (0)
1.80 0.69 0.80 1.99 3.01
(0) (0) (1) (0) (1)
7.45 1.70 4.08 7.95 7.14
(1) (3) (0) (0) (0)
7.60 6.61 4.61 7.90 7.09
(1) (0) (0) (0) (0)
(B) RM/JY e m-m y-y s-s i-i
1.68 0.40 0.93 0.69 0.65
(0) (0) (1) (0) (0)
0.72 1.76 1.38 1.95 3.08
(0) (0) (1) (0) (1)
6.29 8.61 3.00 7.48 6.46
(0) (0) (0) (0) (0)
6.44 8.88 3.77 7.84 6.45
(0) (0) (0) (0) (0)
Note: Lag length n was chosen based on Akaike Information Criteria (AIC). Asterisks () denote statistically significant at 5% significance level, 2.91 (no trend) and 3.49 (trend).
structure is determined by using Akaike Information criteria. Moreover, we require that the number of augmentation term used in the regression to eliminate serial correlation.9 The results of the ADF unit root tests are reported in Table 1. From these results, the null hypothesis that the series contain unit roots cannot be rejected in all cases. The hypothesis of a unit root is strongly rejected for the difference variables of all cases. The possibility that the series is integrated of higher order has also been explored by using the procedure suggested by Dickey and Pantula (1987) by assuming that each series is at most integrated of order 3. For every series the null hypothesis of exactly one unit root is accepted at the 5 percent level. Table 2 contains the results of Dickey and Pantula unit root tests. Given the consistency and unambiguity of results from all these testing approaches, we conclude that all the series under investigation are I(1). On the basis of the above conclusion, we proceeded to test whether the macroeconomic variables are cointegrated with the exchange rate on a bilateral basis vis-avis the US dollar (RM/US) and the Japanese yen (RM/JY). The Johansen–Juselius (1990) tests were performed on the exchange rate models. The procedure involves formulation of an unrestricted vector autoregressive (UVAR) in the variables of interest, that is, (e, i-m, y-y, i-i, s-s) in the present case. The system is constructed with three lags as this was established using the Akaike information criteria.10 Both the trace and the l-max tests reject the null hypothesis that there is zero cointegrating 9 For this purpose the Ljung–Box Q-statistics are computed to test the properties of the residual series and they are available from the authors upon request. 10 As pointed by Boswijk and Franses (1992) and Reimers (1992) the choice of the maximum lag length used in the specification of the VAR model can affect the determination of the number of cointegrating vectors. Boswijk and Franses found that insufficient lag length could lead to rejection of the null hypothesis of no cointegration too often, where over-parameterization of the dynamic structure would lead to loss of power.
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Table 2 Dickey–Pantula integration test results Tests for three unit roots
Tests for two unit roots
Tests for single unit roots
a3
LM(5)
a2
LM(5)
a1
LM(5)
(A) RM/US e m-m y-y s-s i-i
7.790 10.13 7.256 7.306 6.124
0.6375 1.5220 0.0019 1.5833 1.6683
6.003 3.247 3.020 5.316 3.150
0.1097 0.7494 0.5969 0.4547 0.8300
1.216 1.313 0.282 1.935 1.582
0.0078 0.7561 0.5625 0.3967 0.7407
(B) RM/JY e m-m y-y s-s i-i
9.572 8.673 6.063 6.824 5.034
1.3324 0.9451 0.9711 0.0249 1.0437
2.913 3.505 2.001 3.306 3.199
1.1357 0.3077 1.2812 0.0200 0.3306
1.898 0.353 0.521 0.593 0.479
1.4713 0.3505 1.2754 0.1249 0.6815
Note: Lag length n was chosen based on Akaike Information Criteria (AIC). Asterisks () denote statistically significant at 5% significance level, 2.91 (no trend) and 3.49 (trend).
vectors for the two-ringgit bilateral rates. The l-max statistics reveal one non-zero cointegrating vector (four common trends) while the trace test yields two non-zero cointegrating vectors among the I(1) variables for the two bilateral rates. However, these results are unreliable since the model failed both the single equation and the system diagnostic statistics which indicate problems of autocorrelation and nonnormal residuals. We sequentially experimented with higher lag structure and checked for white noise. Unfortunately, it proved impossible to meet the serial correlation standard based on the LM test for even those with six or more lags. To conserve space these results are not reported here.11 The VAR model is estimated recursively, and the stability of its parameters is tested by means of sequential Chow-type tests.12 Results for the RM/US model are plotted in Fig. 1a–e for each of the equations and results for the system as a whole appears in Fig. 1f. Results for the RM/JY model are graphed in Fig. 2a–f. Note that the critical values for this test appear in the plot as a straight line at unity. Visual inspection of the plots indicates that stock price levels exhibit instability in the mid 1980s for the RM/US model (Fig. 1d) and around 1994 for the RM/JY model (Fig. 2d). In addition, the behaviors of income and interest rate processes in the RM/JY model produced instability in the VAR model as a whole (Fig. 2c and e). 11
These results are available from the first author upon request. The test is conducted by using PCFIML. Briefly, sequential tests for parameter stability are conducted using the one-step ahead Chow-type tests. Starting with a minimum sample of size, we augment it by one recursively, thus, resulting in a sequence of F-statistics F (1, tk1), t ¼ T0 þ 1; . . . ; T, given by (RSSt RSSt1 )/RSSt1. The critical values appear in the plots to be a straight line at unity. This is because the statistics reported above are by one-off critical values from the F-distribution at the selected probability level, as an adjustment for changing degree of freedom (See Banerjee and Urga, 1995). 12
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Fig. 1. One-step Chow test (RM/US)—1%.
To further investigate whether the observed instability is attributed to the specific variables in the system, we re-estimated the stable part of the VAR model conditional on the variables found to be unstable.13 For the RM/JY model, the conditional variables are income, stock price level and interest rate while for the RM/US model only the stock price level seems to be unstable.14 A unique cointegrating vector is identified in the two bilateral rates by the trace statistics in the conditional VAR model. For the RM/US rate, the l-max test yielded two cointegrating vectors. To choose the number of cointegrating vectors in this case, we followed McNown and Wallace (1994) where the vectors revealed to be significant in both tests are considered. Having established the cointegrating rank, r ¼ 1, we proceeded with the exogeneity test. The null hypothesis that the variables are weakly exogenous with respect to the cointegrating vector is equivalent to the hypothesis that the variable is not error-correcting. 13
The break in 1985 is not surprising as it corresponds to the second oil shock as well as the financial reforms that took place after the 1984 recession. The second shock in 1994 corresponds to the portfolio and other capital inflows. In this period we observed that stock trading was very active with the stock price index reaching all time high in 1993:Q4 ðINDEX ¼ 1275Þ. In order to remove these effects from our data, we have added two shift dummies. However, the addition of these variables did not significantly change our empirical results. Further research will be needed to better model such breaks. 14 For the RM/US model the stable part of the model is estimated, namely the exchange rate, money supply, and income, conditional on the variable that was found to be unstable, that is stock price level. For the RM/JY model, the unstable variables are stock price level, income and interest rate. The results of the conditional model are not reported here to conserve space and are available from the authors upon request.
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Fig. 2. One-step Chow test (RM/JY)—1%.
Thus, imposing zero restriction on the coefficient of the error-correction term serves as a test of weakly exogeneity.15 The outcome of the test may be summarized as follows (results not reported here to conserve space). First, the RM/US model contains three endogenous variables: exchange rate, money supply and interest rate. The restricted model for the RM/ JY contains two endogenous variables, namely exchange rate and money supply. Second, it is noteworthy that the exogeneity of the exchange rate in the model would be inconsistent with the monetary model, since the adjustment toward equilibrium in this case would not be carried out by the exchange rate. In both systems the estimates of the adjustment coefficient clearly suggest that exchange rate and money supply respond to disequilibrium. Thus, they can be considered as driving the dynamics of the system as a whole.16 The resulting restricted VAR models contain three endogenous variables for the RM/US case and two endogenous variables for the RM/JY case and the results are shown in Table 3. The conditional VAR model is checked for parameter constancy, normality and absence of serial correlation. The empirical results obtained from the conditional VAR model showed marked improvement. In many respects, the results from the conditional VAR model are superior to the results obtained from the unrestricted VAR model. As shown in the Table 4, 15 If the coefficient of the dependent variable is weakly exogenous, the VEC model should be reformulated condition on this variable. 16 Hall and Milne (1994) show that weak exogeneity in a cointegrated system is equivalent to the notion of long-run causality. In general, the more cointegrating vectors (that is, less common trends) there are in the system, the more stable the system and the more constrained the long-run relationship among the five variable systems.
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Table 3 Testing for cointegration using the Johansen and Juselius method Tests H0
RM/US HA
RM/JY k¼5
C.V.
C.V.
k¼3
15.70 9.20
33.00 9.13
20.00 9.20
42.13 9.13
s-s
i-i
– –
21.79 (0.00) –
l-max r¼0 r 1 r 2
r¼1 r¼2 r¼3
22.00 15.70 9.20
30.08 18.21 1.495
r¼0 r 1 r 2
r¼1 r¼2 r¼3
34.90 20.00 9.20
49.79 19.71 1.495
e
m-m
Trace
RM/US RM/JY
6.89 (0.03) 6.44 (0.01)
Weak Exogeneity Tests y-y
15.87 (0.00) 13.48 (0.00)
– –
Note: Lag length n was chosen based on Akaike Information Criteria (AIC). Notes: Asterisks (), () denote statistically significant at 5 and 10% level, respectively. (Critical values are taken from Osterwald-Lenum, 1992.) The LR test is w2 distribution with one degree of freedom.
the models pass all the single equation diagnostic tests, except for the gdp equation in RM/ US system. The system diagnostic tests do not indicate any form of mis-specification in the model. Following Johansen–Juselius (1990), the exclusion restrictions on exchange rate and its determinants are formally tested. Table 5 reports the likelihood ratio statistics for the null hypothesis that the given variable does not belong to the cointegrating vector. With a single cointegrating vector this is asymptotically w2 with one degree of freedom. In each case, the hypothesis that exchange rate does not enter into the cointegrating relationship is easily rejected at the 5% significance level or better. In fact, the LR statistics show that all of the Table 4 Diagnostic checking Variable
Q-statistic
AR (5)
Norm (2)
ARCH (4)
HET (w2)
(A) RM/US (k ¼ 5) e m-m i-i System
4.989 8.054 13.23 45.15
3.64 3.62 9.32 66.17
0.03 0.35 17.24 11.30
2.76 (0.60) 1.82 (0.77) 17.70 (0.00) –
40.39 52.95 38.43 212.65
(B) RM/JY (k ¼ 3) e m-m System
4.598 20.58 26.91
6.79 (0.24) 7.25 (0.20) 21.00 (0.40)
(0.60) (0.61) (0.10) (0.02)
(0.98) (0.84) (0.00) (0.08)
0.61 (0.74) 1.97 (0.37) 4.14 (0.39)
5.43 (0.25) 6.54 (0.16) –
(0.21) (0.02) (0.28) (0.32)
13.45 (0.76) 18.39 (0.43) 54.85 (0.44)
Note: Asterisks () denote statistically significant at the 5% level. Figures in parenthesis are p-values. AR(n) is the nth order LM test for serial correlation, Norm (2) is a test for normality in the residuals; ARCH(m) is a mth order test for autoregressive conditional heteroskedasticity.
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Table 5 Tests of exclusions Variable e RM/US RM/JY
m-m
16.266 (0.000) 21.155 (0.000)
y-y
9.762 (0.008) 18.909 (0.000)
s-s
13.840 (0.001) 7.688 (0.006)
i-i
6.106 (0.047) 14.760 (0.000)
29.439 (0.000) 1.854 (0.173)
Constant
13.718 (0.001) 0.000 (0.989)
Note: Asterisks () denote statistically significant at the 5% level.
variables (except for interest rate in the RM/JY model) enter in the cointegration relationship.17 In general, these results seem to support the version of the augmented monetary model given by Eq. (5). Our contention that the pure monetary model may not be adequate to explain the movements of exchange rate for the emerging markets, specifically Malaysia has received some empirical support. Indeed, the results imply that money supply, income, interest rate and stock prices in the long-run determine the value of the ringgit. The results reflect the close relationship of the ringgit with the US dollar and the Japanese yen. This finding could stem from the fact that the ringgit was linked to the US dollar and Japanese yen for most of the sample period due to the importance of these two economies in international trade and capital accounts. These results are consistent with those of MacDonald and Taylor (1994a,b), Moosa (1994), Georgoutsos and Kouretas (1997), Husted and MacDonald (1999) and Chinn (2000), among others.18 They all have reported that at least a single vector is found in the exchange rate model. However, these results are in sharp contrast to previous studies that show the non-cointegration between exchange rate and the fundamental variables (Kearney and MacDonald, 1990; Meese, 1986; McNown and Wallace, 1989; Baillie and Selover, 1987). Visual inspection on the recursive estimation of the two conditional models reveals no parameter instability for each of the individual equation or for the system as a whole.19 The finding seems to suggest that the parameter instability of the model may be attributed to a subset of the variable(s) in the systems. It can be seen from the Figs. 3 and 4 that the eigenvalues in both models exhibit a stable behavior and mean reverting property. Thus, the conditional VAR model that incorporates stock prices is more appropriate for modeling the exchange rates behavior in Malaysia. Normalizing the cointegrating vector on exchange rates, the variable of our central interest can facilitate an econometric interpretation of the results. These normalized equations appear in Table 6. The numbers below the estimated parameters are the asymptotic standard errors. The coefficient of domestic–foreign money stock (narrow money) has the expected and statistically significant positive sign. The size of the coefficient is significantly different from unity as predicted by theory. In applying the monetary model to South Korea, Makrydakis (1998) found that all the parameter restrictions implied by theory are easily rejected by 17
Removing the interest rate variable in the RM/JY rate did not change our results significantly. The difference between our finding and that of these earlier works is that we found that the stock market enters in the long-run relationship. 19 To conserve space, these plots are not shown here but are available upon request. 18
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Fig. 3. Eigenvector and eigenvalue (RM/US).
Fig. 4. Eigenvector and eigenvalue (RM/JY).
Table 6 Estimated cointegrated vectors in Johansen estimation Variable
RM/US RM/JY
Constant
y-y
e
m-m
1.000 1.000
0.406 (0.155) 0.881 (0.231) 3.375 (0.624) 2.404 (0.841)
s-s
i-i
0.080 (0.033) 0.513 (0.093)
0.032 (0.004) 0.042 (0.029)
2.029 (0.656) 0.051 (3.615)
the data. Similarly, Miyakoshi (2000) found that the domestic–foreign money stock coefficient for the Korean won/German mark to be 2.587. These results are, however, in sharp contrast with Chinn (2000) who found that the coefficient is not significantly different from that implied by theory for four of the East Asian countries (Indonesia, Singapore, Thailand and Taiwan). The coefficient of domestic–foreign income differential is negative and statistically significant at conventional significant level for RM/US rate. This compares with 0.546 (1.056) reported by Chinn (2000) for Indonesia (South Korea) and the income elasticity
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of about 1.62–1.74 for Malaysia, for example Arize et al. (1999) and Tan (1997).20 Interest differential enters in with a positive sign and is statistically significant. In both equations, we find the coefficient to be very small. Earlier it is also shown that interest rate is important in money demand in Malaysia (e.g. Tan, 1997 and Arize et al., 1999). Hence, as the monetary model predicts, an increase in interest rates ceteris paribus, leads to an appreciation of the ringgit. In other words, an interest rate differential induces capital flows and movement in domestic currency. It can be seen from Table 6 that the estimated parameters on the stock price differential of 0.080 in the RM/US equation, is highly significant implying that a one percent point increase in the stock price differential induces a 0.080 percentage point appreciation of the ringgit against the dollar. For the RM/JY rate, the variable enters with an opposite sign (0.513), implying that a rise in the Malaysian share price index relative to Japan led to a depreciation of the ringgit against the yen. It is noteworthy to point out that empirical study on the relationship between exchange rates and stock prices yielded mixed results, depending on whether the income or substitution effect dominate. On one hand, it has been found that a significant positive relationship exist between equity prices and exchange rates (Smith, 1992a; Solnik, 1987). On the other hand, it has been shown that a strong negative relationship exists between stock prices and exchange rates (Soenen and Hennigar, 1988).
5. Summary conclusions and policy implications This paper presents and tests an augmented monetary model that includes the effect of stock prices on the bilateral exchange rates. The model is applied to the RM/US and RM/ JY. The empirical analysis is conducted by the Johansen method of cointegration. Using the data from the recent float that ends with 1996:Q4, the initial finding of the study is that although the augmented monetary model is cointegrated, it is subject to parameter instability and that the parameter time dependency can be attributed at least partly to a particular subset of the variables in the system including stock prices. The study further finds that a restricted VAR model which imposes exogeneity restrictions on I(1) variables, such as stock prices, among others, exhibits both cointegration and parameter stability. The empirical findings tend to suggest that the equity market is significant in affecting the exchange rate and in explaining at least in part the parameter instability evidenced in the cointegrating system Hence, we conclude that models of equilibrium exchange rate should be extended to include equity markets in addition to bond markets. Our findings are consistent with some of the recent studies done for the East Asian Economies (Chinn, 2000; Husted and MacDonald, 1999). Several interesting facts have emerged from the analysis. First, interest rate differential explains a small variation in the ringgit. This result is consistent with the results obtained for other countries, which found those interest rate differentials only accounted for a small 20 It should be noted that the coefficient of income in the exchange rate equation is predicted to be equal in sign to the income elasticity of demand, i.e. the coefficient of income in the money demand (see MacDonald and Taylor, 1994a). The hypothesis of unitary long-run income elasticity is often rejected because there is absence of economies of scale in money holding in LDCs (See Arize et al., 1999).
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portion of exchange rate movement. Second, domestic–foreign income differentials explain most of the variation in the ringgit. This result implies that rapid growth experience in the past two decades or so tends to strengthen the RM/US rate as predicted by the monetary approach. However, we observed that this is not the case for the RM/JY rate. We speculate that the reason for the failure could be due to the fact that the Malaysia and Japanese business cycles are not closely linked. Japan had been in recession for the most part of the 1990s while Malaysia had been experiencing robust growth before the crisis hit the region in 1997.21 We leave this for future work. Third, we find one long-run equilibrium path and domestic–foreign money stock differentials are endogenous in both systems. While domestic monetary policy may be helpful in the short-run in achieving some domestic policy objectives, it may not be fully successful in the long-run due to the endogeneity of money. The findings suggest that monetary policy has limited use as a stabilization policy in the long-run as it is not completely controlled by the monetary authorities. Finally, the evidence suggests that the stock market is an important determinant of exchange rate. This is consistent with a number of empirical papers that attempted to verify the relationship between stock prices and exchange rates (e.g. Solnik, 1987; Sarantis, 1987; Soenen and Hennigar, 1988; Smith, 1992a; Abdalla and Murinde, 1997). This paper endorses these findings within the broad framework of the monetary approach to exchange rate determination. However, authors like Bahmani-Oskooee and Sohrabian (1992) and Canova and De Nicolo (2000), on the other hand, failed to show any common trends between the exchange rate and the stock market. Nevertheless, from the statistical point of view, our results tend to suggest that the models of equilibrium exchange rate should be extended to include equity markets in addition to bond markets. The policy implication is that if indeed the equity market affects the foreign exchange market in the long-run, then the factors affecting the equity prices, such as the inflows of hot money (i.e. portfolio funds) are also likely to influence the movement of exchange rates. Hence, if equity market is moving erratically then the exchange rate market may also move in an erratic manner. The Asian financial crisis appears to be consistent with this contention.
Acknowledgements Useful comments were received from the editor of this Journal, Ryuzo Sato and the anonymous referees. We thank Evan Lau for the research assistance. This research has been funded by the Research in Priority Areas (IRPA) program and Universiti Putra Malaysia. All remaining errors are the authors’ responsibility. References Abdalla, I.S.A., Murinde, V., 1997. Exchange rate and stock price interaction in emerging financial markets: evidence on India, Korea, Pakistan and the Philippines. Applied Financial Economics 7, 25–35. 21 Husted and MacDonald (1999) also found evidence of misalignment in ringgit/yen rate based on the monetary model. Indeed, their results reveal the currency appeared overvalued for most part of the 1990s.
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