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The behavior of secondary market prices of LDC syndicated loans
E~EVIER
Journal of Banking& Finance20 (1996) 537-554
Journal of BANKING & FINANCE
The behavior of secondary market prices of LDC syndicated loans Suk Hun Lee a,*, Hyun Mo Sung b, Jorge L. Urrutia a a School of Business Administration, Department of Finance, Loyola Unieersity of Chicago, 820 N. Michigan Ace., Chicago, IL 60611, USA b Summer Institute of Linguistics, 7500 W. Camp, Wisdom Road, Dallas, TX 75236. USA
Received 15 May 1994; accepted 15 January 1995
Abstract The paper investigates the time series properties and the efficiency of the secondary market for LDCs syndicated loans. The data corresponding to weekly secondary market prices for 26 LDCs syndicated loans are divided into two subperiods: from July 1988 to June 1990 and from July 1990 to May 1992. Tests of the random walk hypothesis are conducted based on the Augmented Dickey-Fuller methodology and the variance-ratio test of Lo and MacKinlay. The efficiency of the market is tested by means of filter rule tests. The empirical findings suggest that the secondary market for LDCs syndicated loans has become more efficient over time and that loan prices follow random walks. JEL classification: F34; G14 Keywords: LDC debt; Randomwalk; Market efficiency
1. Introduction The less developed countries (LDCs) debt crisis became obvious for the first time in August 1982, when Mexico announced it could not meet scheduled repayments on its almost $100 billion of external debt. During the three years following that announcement, over 35 debtor nations renegotiated their debt
* Correspondingauthor. Tel.: 312-915-7071;fax: 312-915-6118. 0378-4266/96/$15.00 © 1996 Elsevier Science B.V. All rights reserved SSDI 0378-4266(95)00009-7
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service obligations with their creditors. 1 Although the debt crisis is now less dramatic, it is still a major economic concern for the LDCs. The debt crisis led to the development of a secondary market for developing country syndicated loans during the 1980s. Initially, the role of this market was to facilitate commercial banks to adjust their credit exposure to highly indebted countries. Since then the market has grown rapidly and has attracted non-bank market participants. In effect, trading volume has increased from $2 billion in 1984 to an estimated volume of around $500 billion in 1992. Furthermore, according to the February 13th-19th, 1993 issue of the Economist, this market was projected to reach $1 trillion in 1993. 2 More recently, the secondary market for LDCs syndicated loans has been used as one of the major vehicles for reducing LDCs 'debt overhang.' Under the Brady Initiative, the Philippines signed the debt and debt service reduction (DDSR) agreement on January 3, 1990 and retired $1.337 billion of its debt through cash buyback at 50% discount. Costa Rica signed its DDSR agreement on May 6, 1990 and retired $991 million of its debt through cash buyback at 84% discount. Other countries, such as Uruguay and Nigeria, have also retired their debt through debt buybacks. The secondary market for LDCs syndicated loans has been the subject of numerous academic studies (Laney, 1987; Sachs and Huizinga, 1987; Berg and Sachs, 1988; Purcell and Orlanski, 1988; Vatnick, 1988; Hajivassiliou, 1989; Boehmer and Megginson, 1990; Stone, 1991; Ozler and Huizinga, 1991; Aramburn and Grosse, 1992; Lee and Sung, 1993). Authors have examined the determinants of secondary market prices for developing countries syndicated loans and the reactions of these prices to debt conversions and the announcement of the Brady Plan. Boehmer and Megginson (1990) examine 32 monthly (from July 1985 to July 1988) secondary loan prices for ten LDCs. They find that the secondary market prices of LDCs loans are related to the ratio of total long-term debt to GNP, the ratio of total long-term debt to total exports, the level of incurred payment arrears, the unilateral moratoria by Brazil and Peru, and the loan-loss provisions by U.S. banks. Furthermore, the adoption of legislation for a debt-conversion program is negatively related to the secondary market loan prices. 3 Stone (1991) uses monthly price data to examine the impact of the announcement of the
a See Lee (1991) for further discussion on the debt reschedulings during the 1980s. 2 The trading volumes for the years 1984 through 1991 are 2, 4, 7, 12, 50, 108.1, 137.5, and 150 billion U.S. dollars, respectively. According to the February 13th-19th, 1993 issue of the Economist, trading in all sorts of 'emerging-market' debt was approximately $500 billion in 1992 and it was projected to top $1 trillion in 1993. 3 Some argue that the negative relationship between the debt-conversion program and the secondary market loan prices is due to the misspecification of the debt conversion variable. For example, Aramburu and Grosse (1992) state that after correctly specifying the debt conversion variable, they find a statistically significant positive relationship between the debt conversion variable and the loan prices.
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
539
Brady Plan on secondary market prices. He finds that during the month of March 1989, the loan prices for Honduras, Morocco, Uruguay, and Zaire declined significantly while Mexico was the only country with a statistically significant increase in its loan price. The remainder of this paper is organized as follows: Section 2 discusses the relevance and purpose of the research; Section 3 presents the methodology; Section 4 describes the data; Sections 5 and 6 report and discuss the empirical results; finally, Section 7 briefly summarizes and concludes the paper.
2. Relevance and purpose of this research study The market efficiency and the random walk hypotheses have been extensively studied for the U.S. equity markets. In effect, tests of these hypotheses for the American markets have been conducted, among others, by authors such as Fama (1965, 1970, 1991), Shiller (1981), Shiller and Perron (1985), French and Roll (1986), Summers (1986), Fama and French (1986, 1988), O'Brien (1987), Ball and Kothari (1989), and Lo and MacKinlay (1988). The research has been extended to major foreign markets, such as the Japanese and European markets, as these markets have become important to international investors. Examples of this research are papers published by Solnik (1974), Hillard (1979), Gultekin and Gultekin (1983), Poterba and Summers (1988), and Von Furstenberg and Jeon (1989). In general, most of the research on the stochastic behavior of stock prices has centered on the equity markets of the world's largest economies, such as the United States, Japan, United Kingdom, and Germany. However, capital markets have become more integrated and investing has become more global, owing to the advances in telecommunications, the deregulation of capital markets, multiple listing, and the boom of international mutual funds. In this context the issues of efficiency and randomness of smaller capital markets and the financial markets of third world countries have also become relevant. Some research has been conducted for equity markets of developing countries by authors such as Errunza (1977, 1983), Errunza and Losq (1985, 1987), Errunza and Padmanabhan (1988), Harvey (1993), Claessen et al. (1993), and Urrutia (1994). This paper analyzes the time series properties of the secondary market for LDCs syndicated loans. Even though this market has grown rapidly since its creation and has been the subject of several studies, its efficiency and stochastic properties have not yet been investigated. The issues of efficiency and randomness of this market are important in the context of markets integration and globalization. The study of market efficiency and time series properties of the LDCs syndicated loans will also help us to enhance our understanding of this fast-growing and increasingly important market. The issue of efficiency is also important for the evaluation of previous studies, which have relied on the assumption of market
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
540
efficiency. If the secondary market for LDCs syndicated loans was not efficient during the time periods these studies were conducted, it follows that their empirical results should be interpreted with care. Our basic hypotheses are that the prices of secondary loans for LDCs follow random walks and that the market has become more efficient over time, mainly because of the larger volume of trade and market maturity. Tests of random walks are conducted based on the Dickey and Fuller (1979) methodology and the variance-ratio test of Lo and MacKinlay (1988). The efficiency of the market is tested by means of filter rule tests.
3. Methodology This section describes briefly the several tests of random walk and market efficiency conducted in the paper. 3.1. Augmented D i c k e y - F u l l e r tests o f random walk
Our first test of random walk is the Augmented Dickey and Fuller test, ADF, developed by Dickey and Fuller (1979). It can be mathematically expressed as follows: X t -X,_ 1 = ao +boX,_ 1 + ~bi(Xt_
i -X,_i_l)
-~- E.t
(1)
i=1
where X t is the natural logarithm of a time series, a 0 is a constant, and Et is an error term. The null hypothesis is H0: b 0 = 0; that is, the time series follows a random walk process. 3.2. Variance-ratio tests o f random walk
The second test of the random walk hypothesis used in this paper is the variance-ratio test of Lo and MacKinlay (1988), which can be expressed as follows: o'2(q,1) = o 2 ( q ) / c r 2 ( 1 )
(2)
where o.2(q) is 1 / q times the variance of the q-difference, o'2(1) the variance of the first differences, and o" 2(q,1) the variance ratio. In order to test the null of random walk, Lo and MacKinlay (1988) have developed formulas to compute o'2(q) and O"2(1). They have also derived an asymptotic standard normal test-statistic, Z *(q), which is heteroscedasticity consistent and controls for volatility changes over time: Z*(q)
= [o.2(q,1) - 1]/~f~-Z(q) ~ N ( 0 , 1 )
(3)
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
541
where ~b* (q) corresponds to the asymptotic variance of the variance ratio (see Lo and MacKinlay, 1988).
3.3. Filter rule tests of market efficiency Following Sweeney (1986, 1988), an x-percent filter rule divides an asset's weekly return series into two samples: 'in-week' returns and 'out-week' returns. On in-weeks an investor holds a long position, and on out-weeks an investor holds a short position (Fama and Blume, 1966). Since under a buy-and-hold strategy an investor holds a long position on both in-weeks and out-weeks, the only difference between a filter rule strategy and a buy-and-hold strategy is due to loan price performance on out-weeks. The mean in-week return, /~in, and the mean out-week return, Rout are given by:
Rm = ~..Rt/Nin, tel
Rout =
~-, Rt/Nout
(4)
t~O
where gin and Nout are the number of in-weeks and out-weeks, respectively. The difference in sample means, Rin -/~out, is an estimate of the variation in expected weekly returns predictable by a filter rule. A test of statistical significance for a difference between in-week and out-week returns is the two-sample t-test:
T=
¢
N'nN°u' (nin -- Rout)//S(R),
(5)
Ni. + gout
where the two-sample standard deviation
¢
S(R)=
Z(R,
-
_2
Rin) + Z ( R ,
S(R) is calculated as: - R- o u t )
2
,co Ni. +Nou _ 2
(6)
yielding a t-statistic with Nin + No~t - 2 degrees of freedom.
4. Data
The data used in this study correspond to weekly bid and ask secondary market prices of 26 developing countries syndicated loans for the time period July 7, 1988 to May 27, 1992. These prices refer to public and publicly guaranteed debt in percentages of face value (i.e., number of cents per one U.S. dollar). According to the LDCs debt traders, these quoted prices represent benchmark prices for LDCs'
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largest external liabilities. The prices have been collected from the L D C Debt Report. 4 There are eight weeks for which the L D C Debt Report did not provide prices of syndicated loans for any of the 26 countries in the sample. 5 These missing values have been replaced with average prices calculated using prices from the weeks immediately before and after the missing week. Also, three countries, Costa Rica, Dominican Republic, and Senegal, have 5, 12, and 1 weeks of missing prices, respectively. 6 In this case, the missing weekly prices have been treated as missing values. For purpose of analysis and in order to investigate the issue o f efficiency of the LDCs syndicated loans market, the sample has been divided in two subsamples of equal size: from July 1988 to June 1990 and from July 1990 to May 1992. 7
5. Analysis of the empirical results A first step in examining the time series properties and the efficiency of LDCs syndicated loan prices is to analyze the spread term over the sample period. The spread term, defined as [(offer - b i d ) / o f f e r ] X 100, is plotted in Fig. 1. From Fig. 1 we conclude the following: First, for the 26 countries included in the sample, there was a steady decline in the spread term during the sample period. The average spread term declined from 9.59% in July 1988 to 7.15% in May 1992. Second, the spread term for the five major Latin American debtor countries whose debts are the most actively traded in the secondary market, was much smaller than the spread for the other countries included in the sample. 8 5.1. Results o f D i c k e y - F u l l e r and variance-ratio tests o f random walk Since the A D F tests yield similar results for the two sample periods, only the results for the second subperiod are provided here. Table 1 shows that the null
4 LDC Debt Report is a weekly publication on issues related to debt of LDCs published by American Banker. It began providing weekly bid and offer secondary market prices for 28 countries. By the beginning of May 1989, it stopped reporting price data for Romania and Turkey and started providing price data for South Africa. These three countries are not included in the sample; hence, our sample contains 26 countries. 5These eight weeks are the weeks ending on 11/29/88, 12/29/88, 8/17/89, 8/24/89, 11/15/89, 12/28/89, 1/4/90, and 1/18/90. 6 The five missing weeks for Costa Rica are weeks ending on 5/24/90, 6/7/90, 6/20/90, 7/3/90, and 7/25/90. The 12 missing weeks for the Dominican Republic cover the time period 9/26/90 through 12/26/90. The missing week for Senegal is the week ending on 10/4/90. 7 We understand these are short time periods and that the split of the data in two subperiods may create a small-samples problem. However, we have divided the data in two subsamples because a main purpose of this research is to examine the behavior of the LDCs syndicated loans market over time. 8 These five countries are Argentina, Brazil, Chile, Mexico, and Venezuela.
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
8t
543
18-
It
--
14-
6 4
Older Counld~ (21)
Major I.mln A n m t ¢ ~ Debtor Counlfl~ (5)
0-1 """ 7/88
12/88
"
"J 6/89
l?J~
.% . . . . . .!. . . .. . .". ". .".". ' " ~ 6/90 12/90
.,. .... . . .. . .'.-. ". .. . . . . . . 6/91 17./91 5t92
Time
Fig. 1. L D C debt spreads. * Spread = [ ( o f f e r - b i d ) / o f f e r ] × 100. a N u m b e r s in parentheses represent n u m b e r of countries included.
hypothesis of random walk cannot be rejected at the 5 percent significance level for all countries. That is, the ADF tests strongly suggest that the secondary prices of developing countries syndicated loan follow random walk processes. The second test of the random walk hypothesis is the variance-ratio test of Lo and MacKinlay (1988). The base observation interval is one week. Variance-ratio estimates, ty2(q,l), asymptotic variances of the variance ratio, ~b*(q), and variance-ratio test-statistics, Z * ( q ) , are computed for each of the intervals q = 2,4,8,12, and 16. Thus, by using one week as the base interval, several parameters are computed for each q by comparing the variance of the one-week base interval with those of 2-week, 4-week, 8-week, 12-week, and 16-week observation intervals. Table 2 reports the heteroscedasticity-robust variance ratios for the first time period, July 7, 1988 to June 31, 1990. The results in Table 2 show that the null hypothesis of random walk cannot generally be rejected at the 5 percent significance level for Algeria, Argentina, Brazil, Chile, Colombia, Ecuador, Honduras, Ivory Coast, Jamaica, Mexico, Morocco, Panama, the Philippines, Uruguay, Venezuela, and Yugoslavia. The null hypothesis of random walk is partially rejected, especially for low values of the q-intervals, for the following countries: Bolivia, Costa Rica, Dominican Republic, Nicaragua, Nigeria, Peru, Poland, Senegal, Sudan, and Zaire. The rejection of the random walk hypothesis for these ten countries is heteroscedasticity robust and suggests that the variance ratios are different from one due to autocorrelation of weekly prices. Moreover, for all of these ten countries, except Poland, the variance ratios are less than one, indicating that the price variance increases less than proportionally with time. That is, they exhibit negative autocorrelation, which indicates the presence of a mean-reversion process. Table 3 shows the heteroscedasticity-robust variance-ratios for the second time period, July 1, 1990 to May 27, 1992. The null hypothesis of random walk is
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Table 1 Dickey-Fuller tests of random walk of secondary market prices of LDCs syndicated loans. Time period: July 1, 1990-May 27, 1992. Model: l n P t - l n P t_ 1 ao q'- bolnPt- 1 + bl(lnPt- l - l n P t - 2 ) + b2(lnPt- 2 - l n P t - 3)+ b3(lnPt-3 - l n P t - 4 ) + b4(lnPt- 4 - lnP/- 5)+ et =
Country
a0
- 0.020 (0.053) (-0.378) Argentina 0.038 (0.056) (0.677) 0.006 Bolivia (0.049) (0.119) Brazil 0.087 (0.074) (1.178) -0.010 Chile (0.011) (-0.864) Columbia - 0.008 (0.023) ( - 0.324) Costa Rica 0.013 (0.032) (0.393) - 0.005 Dora. Rep. (0.005) ( - 1.000) 0.009 Ecuador (0.055) (0.172) Honduras 0.003 (0.031) (0.104) Ivory Coast 0.049 (0.141) (0.346) Jamaica - 0.042 (0.035) ( - 1.214) Mexico - - 0.006 (0.022) ( - 0.287) Morocco 0.050 (0.041) (1.210) 0.045 Nicaragua (0.097) (0.468) Algeria
bo
b1
b2
b3
b4
R2
0.007 (0.019) (0.385) - 0.010 (0.020) ( - 0.495) - 0.002 (0.018) (-0.093) - 0.028 (0.026) (-1.069) 0.004 (0.004) (1.091) 0.004 (0.008) (0.427) - 0.003 (0.012) (-0.236) 0.000 (.) (.) - 0.001 (0.020) (-0.062) 0.000 (0.011) (0.042) - 0.017 (0.050) (-0.332) 0.017 (0.012) (1.382) 0.004 (0.008) (0.529) - 0.018 (0.015) (-- 1.198) - 0.015 (0.035) (--0.421)
- 0.323 (0.107) (-3.008)* 0.006 (0.107) (0.059) - 0.034 (0.107) (-0.321) - 0.038 (0.106) (-0.362) -0.019 (0.103) (-0.181) 0.005 (0.107) (0.047) 0.086 (0.111) (0.780) - 0.167 (0.441) ( - 0.378) - 0.102 (0.110) (-0.928) -0.219 (0.107) ( - 2.048) * 0.077 (0.107) (0.716) 0.042 (0.107) (0.394) 0.002 (0.106) (0.020) - 0.050 (0.108) (-0.465) 0.072 (0.107) (0.676)
0.050 (0.109) (0.457) - 0.006 (0.104) ( - 0.055) 0.023 (0.107) (0.216) -0.186 (0.104) - 1.781) 0.343 (0.101) (3.401)* - 0.003 (0.107) -0.026) - 0.002 (0.111) -0.018) 0.000
0.269 (0.109) (2.465)* 0.254 (0.103) (2.456)* 0.000 (0.107) (0.001) 0.134 (0.103) (1.303) - 0.066 (0.101) (-0.658) - 0.015 (0.107) (-0.138) - 0.021 (0.111) (-0.194) 0.000
0.003 (0.107) (0.028) - 0.028 (0.107) (-0.262) - 0.002 (0.106) ( - 0.023) -0.133 (0.104) ( - 1.275) -0.211 (0.101) ( - 2.078)* -0.019 (0.107) (-0.174) -0.017 (0.110) (-0.158) 0.000 (.)
O.169 (3.540)
(.) (.)
(.) (.)
0.131 (0.110) (1.189) - 0.093 (0.109) ( - 0.859) - 0.223 (0.107) ( - 2.087)* 0.011 (0.107) (0.099) - 0.099
- 0.092 (0.111) (-0.830) 0.138 (0.109) (1.270) - 0.116 (0.107) ( - 1.089) - 0.009 (0.107) (-0.081) - 0.047
(0.106)
(--0.930) 0.098 (0.108) (0.903) 0.019 (0.107) (0.177)
(0.106)
(-0.450) - 0.082 (0.108) ( - 0.757) 0.011 (0.107) (0.105)
0.068 (1.272) 0.002 (0.033) 0.082 (1.560) 0.146 (2.971) 0.003 (0.045) 0.009 (0.148) 0.028 (0.143)
(.) - 0.055 (0.112) ( - 0.494) 0.029 (0.107) (0.274) -0.001 (0.103) (-0.013) 0.027 (0.096)
0.041 (0.743) 0.075 (1.413) 0.075 (1.407) 0.028 (0.507)
(0.280) - 0.140 (0.106) ( - 1.327) 0.017 (0.108)
0.032 (0.578) 0.037 (0.673)
(o.158) -
0.006 (0.103)
( - 0.058)
0.008 (0.145)
545
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554 Table 1 (continued) Country
a0
Nigeria
- 0.023 (0.037) (-0.636) Panama 0.065 (0.073) (0.889) Peru 0.008 (0.100) (0.083) Philippines 0.013 (0.040) (0.322) Poland 0.040 (0.070) (0.578) Senegal 0.025 (0.079) (0.317) Sudan 0.061 (0.128) (0.474) Uruguay -0.024 (0.033) (-0.723) Venezuela - 0.044 (0.027) ( - 1.610) Yugoslavia - 0.041 (0.063) (0.652) Zaire - 0.008 (0.059) ( - 0.130)
b0
bI
b2
0.010 (0.013) (0.779) - 0.019 (0.026) (-0.743) 0.OOl (0.036) (0.025) - 0.004 (0.014) (-0.289) - 0.013 (0.025) (-0.529) - 0.008 (0.027) (-0.304) - 0.025 (0.046) (-0.536) 0.011 (0.012) (0.893) 0.017 (0.010) (1.717) 0.010 (0.023) (0.438) - 0.001 (0.021) ( - 0.002)
-0.118 0.078 (0.106) (0.107) (-1.111) (-0.726) 0.039 0.105 (0.105) (0.105) (0.366) (1.002) 0.019 0.037 (0.107) (0.107) (0.177) (0.344) 0.128 -0.158 (O.lO3) (0.105) (1.241) 1.509) - 0.029 0.178 (0.107) (O.lO7) -0.271) (1.663) -O.OOl 0.155 (0.118) (0.119) (1.308) 0.OO8) - 0.010 (0.108) (0.108) - 0.097) ( - 0.097) -0.192 - O.O8O (0.107) (0.107) ( - 0.748) ( - 1.797) 0.122 -0.152 (0.108) (0.107) (1.125) ( - 1.419) -0.172 0.084 (0.105) (O.lO5) (0.802) ( - 1.639) - 0.027 0.028 (0.110) (O.lO9) (-0.251) (-0.253) -
-
-
-
0
-
.
0
1
0
R2
b3
b4
-0.177 (0.106) ( - 1.669) -0.016 (0.105) (-0.152) -0.101 (O.109J ( - 0.925) 0.140 (0.104) (1.347) 0.221 (0.107) (2.064)* - 0.002 (0.119) -0.018) -O.OlO (0.108) - 0.097) - 0.070 (0.107) 0,654 ) 0.043 (0.108) (0.401) 0.034 (0.105) - 0.326 ) 0.013 (0.110) (0.119)
0.048 0.054 (0.108) (0.986) (0.442) - 0.222 0.070 (0.105) (1.304) (-2.122)* 0.006 0.011 (0.111) (0.198) (0.050) O.lO3 0.067 (0.104) (1.245) (0.993) -0.131 0.080 (0.108) (1.517) ( - 1.209) - 0.027 0.025 (0.110) (0.367) ( - 0.244) 0.002 0.003 (0.069) (0.059) (0.027) 0.048 0.046 (0.107) (0.845) (-0.450) - 0.048 0.075 (0.107) (1.416) ( - 0.445) - 0.219 0.077 (0.109) (1.453) ( - 2.008) * 0.004 0.002 (0.109) (0.030) (0.034)
-
-
-
The null hypothesis is b 0 = 0 (returns follow a random walk). The t-critical at the 5 percent level is - 2 . 8 8 [from Dickey and Fuller (1979)]. An asterisk indicates that the individual coefficient is different from zer~ at the 5 percent significance level. Standard errors and t-statistics are in parentheses.
c o n f i r m e d f o r all c o u n t r i e s , e x c e p t S e n e g a l a n d S u d a n . T h e r e f o r e , t h e v a r i a n c e - r a t i o t e s t s r e p o r t a d r a m a t i c c h a n g e in t h e t i m e s e r i e s p r o p e r t i e s o f t h e L D C s s y n d i c a t e d l o a n s m a r k e t f r o m t h e f i r s t to t h e s e c o n d p e r i o d . In sum, the results of the variance-ratio tests for the first time period, July 1988 to J u n e
1990,
developing
confirm
the null hypothesis
of random
walk
for 16 of the 26
countries under analysis. For the other ten countries, the secondary
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S.H. Lee et al. //Journal of Banking & Finance 20 (1996) 537-554
Table 2 Variance-ratio estimates and variance-ratio test statistics under heteroscedasticity of the random walk hypothesis for secondary market prices of LDCs syndicated loans. Time period: July 7, 1988-June 31, 1990 Country
Algeria Argentina Bolivia Brazil Chile Columbia Costa Rica Dom. Rep. Ecuador Honduras Ivory Coast Jamaica Mexico Morocco Nicaragua Nigeria Panama Peru Philippines Poland Senegal Sudan
Number q of base observations aggregated to form variance ratio 2
4
8
12
16
0.370 ( - 3.361)" 1.106 (1.243) 0.105 ( - 5.738)" 1.002 (0.016) 0.973 (-0.246) 0.411 (-4.197)" 0.283 ( - 3.502)* 0.171 ( - 5.999)' 0.359 ( - 3.699)" 0.095 (-3.017)" 0.173 ( - 3.200)" 0.205 ( -3.645)" 1.074 (0.566) 0.366 (-3.418)" 0.100 ( - 6.400)" 0.090 (-4.520)" 0.636 ( - 2.650)" 0.154 (-4.117)* 1.042 (0.260) 1.094 (0.933) 0.097 ( - 6.624)* 0.076 ( - 6.272)"
0.346 ( - 1.863) 1.218 (1.363) 0.099 ( - 3.090)* 1.199 (0.910) 1.187 (0.907) 0.516 ( - 1.845) 0.207 ( - 2.070)* 0.140 ( - 3.325)" 0.470 ( - 1.635) 0.099 ( - 1.606) 0.158 (-1.742) 0.213 ( - 1.929) 1.264 (1.072) 0.346 ( - 1.884) 0.069 ( - 3.539)" 0.090 (-2.418)' 0.856 ( - 0.560) 0.141 (-2.232)" 1.270 (0.899) 1.435 (2.298)" 0.093 ( - 3.555)" 0.054 ( - 3.433)"
0.465 ( - 0.964) 1.024 (0.096) 0.084 ( - 1.990) 1.204 (0.591) 1.234 (0.718) 0.621 (-0.914) 0.157 ( - 1.392) 0.109 ( - 2.179)* 0.597 ( - 0.787) 0.115 (-0.997) 0.146 (-1.117) 0.118 ( - 1.369) 1.379 (0.975) 0.359 ( - 1.167) 0.067 ( - 2.244)" 0.100 (-1.541) 0.901 ( - 0.243) 0.130 (-1.430) 1.230 (0.485) 2.009 (3.369)" 0.096 ( - 2.242)' 0.032 ( - 2.220)"
0.594 ( - 0.577) 0.766 ( - 0.730) 0.069 ( - 1.603) 0.946 (-0.124) 1.067 (0.164) 0.613 (-0.737) 0.165 ( - 1.089) 0.084 ( - 1.770) 0.647 ( - 0.543) 0.095 (-0.805) 0.134 (-0.894) 0.075 ( - 1.141) 1.219 (0.444) 0.376 (-0.897) 0.065 ( - 1.786) 0.103 (-1.262) 0.689 ( - 0.604) 0.137 (-1.119) 1.095 (0.159) 2.469 (3.870)* 0.095 ( - 1.774) 0.021 ( - 1.772)
0.710 ( - 0.351) 0.769 ( - 0.614) 0.065 ( - 1.378) 0.743 (-0.500) 0.969 (-0.064) 0.645 (-0.575) 0.154 ( - 0.941) 0.075 ( - 1.522) 0.690 ( - 0.407) 0.083 (-0.695) 0.115 (-0.778) 0.062 ( - 1.007) 1.134 (0.232) 0.349 (-0.796) 0.066 ( - 1.538) 0.113 (-1.118) 0.591 ( - 0.677) 0.146 (-0.944) 1.081 (0.115) 2.757 (3.943)" 0.101 ( - 1.508) 0.021 ( - 1.509)
S.H. Lee et aL /Journal of Banking & Finance 20 (19961 537-554
547
Table 2 (continued) Country
Number q of base observationsaggregated to form varianceratio 2
Uruguay Venezuela Yugoslavia Zaire
0.391 - 2.983)* 1.179 (0.858) 0.446 -3.552)* O.137 -4.794)*
4
8
12
16
0.408 ( - 1.550) 1.210 (0.539) 0.493 ( - 1.737) O.118 (-2.618)*
0.329 ( - 1.11O) 1.464 (0.753) 0.613 (-0.842) O.112 ( - 1.666)
0.247 ( - 0.984) 1.528 (0.676) 0.731 (-(I.475) 0.093 ( - 1,343)
0.170 ( - 0.924) 1.615 (0.671) 0.794 (-0.3211 (I.072 ( - 1.1711
The data consist of average weekly bid and offer secondary market prices of 26 developingcountry syndicated loans from July 7, 1988 to June 31, 1990. One week is taken as base observationinterval.The variance-ratio0-~-(q,1) is definedas 0-Z(q)/0- 2( 1), where 0-Z(q) is an unbiased estimator of 1/q of the variance of the qth difference of the natural logarithm of the syndicated loan price, and o-2(1) is an unbiased estimator of the variance of the first-difference of the natural logarithm of the syndicatedloan price. The variance-ratioo-2(q,11 is reported in the main row, and the heteroscedasticity-robustvariance-ratio test statistic Z" (q) is in parentheses. The null hypothesis is that o'2(q,1) is equal to one (prices follow a random walk). An asterisk indicates rejectionof the null at the 5 percent significancelevel. market prices of syndicated loans exhibit mean-reversion. The finding of mean-reversion agrees with Fama and French (1988), and French and Roll (1986), who reported negative serial correlation for American stocks, and with Poterba and Summers (1988), who have found mean-reversion for a sample of the European and Asian national stock index. For the second time period, July 1990 to May 1992, the random walk hypothesis is generally confirmed by the variance-ratio tests. Our findings of a random walk for secondary market prices of syndicated loans for LDCs for the second period, differ from Urrutia (1994), who reject the random walk for several Latin American equity markets, Lo and MacKinlay (1988), who reject the null of random walk for N Y S E - A M E X stock prices, and from Poterba and Summers (1988), who also reject the random walk hypothesis for stocks of several national equity markets, including European and Asian markets. On the other hand, our findings of random walk agree with Claessen et al. (1993), who do not reject the null of random walk for a sample of 20 emerging markets. These apparently contradictory results should not be surprising. In effect, Solnik (1974) in his investigation of the market structure of stock prices for several European markets concludes that prices are strongly affected by the specific characteristics of the individual markets. 5.2. Results o f tests o f market efficiency For the time period July 7, 1988 to June 31, 1990, the variance-ratio tests have partially rejected the random walk in favor of a mean-reversion process. However,
S,H. Lee et al. / J o u r n a l of Banking & Finance 20 (1996) 537-554
548
Table 3 Variance-ratio estimates and variance-ratio test statistics under heteroscedasticity of the random walk hypothesis for secondary market prices of LDCs syndicated loans. Time period: July 1, 1990-May 27, 1992 Country
Algeria Argentina Bolivia Brazil Chile Columbia Costa Rica Dora. Rep. Ecuador Honduras Ivory Coast Jamaica Mexico Morocco Nicaragua Nigeria Panama Peru Philippines Poland Senegal Sudan
Number q of base observations aggregated to form variance ratio 2
4
8
0.689 (-0.863) 1.009 (0.091) 0.962 ( - 0.607) 0.952 (-0.591) 1.046 (0.457) 1.014 (0.543) 1.084 (1.088) 0.978 (-0.153) 0.875 ( - 0.857) 0.799 ( - 1.463) 1.052 (0.707) 0.981 ( - 0.290) 1.027 (0.216) 0.953 (-0.353) 1.044 (0.339) 0.955 (-0.515) 1.125 (1.246) 1.026 (0.240) 0.896 ( - 0.534) 1.151 (0.991) 0.939 (-5.382)* 0.719 ( - 70.720) *
0.731 ( - 0.400) 1.156 (0.823) 0.982 (-0.154) 0.858 ( - 0.942) 1.357 (1.889) 1.030 (0.619) 1.109 (0.749) 0.589 ( - 1.507) 0.897 (-0.375) 0.713 ( - 1.117) 0.809 ( - 1.390) 1.024 (0.199) 0.942 0.246) 0.972 -0.111) 1.115 (0.472) 0.745 ( - 1.544) 1.238 (1.271) 1.049 (0.240) 1.025 (0.067) 1.351 (1.299) 1.059 (2.783) * O.582 ( - 56.180) *
0.582 ( - 0.393) 1.379 (1.268) 0.884 ( - 0.627) 0.738 ( - 1.096) 1.435 (1.460) 0.857 ( - 1.888) 1.089 (0.388) 0.458 ( - 1.258) 0.817 ( - 0.423) 0.716 ( - 0.700) 0.659 ( - 1.565) 1.191 (0.999) 0.797 ( - 0.544) 1.096 (0.241) 1.196 (0.509) 0.583 ( - 1.602) 0.882 ( - 0.400) 1.096 (0.299) 1.329 (0.570) 1.495 (1.097) 1.129 (3.907)* 0.522 (-42.551)"
-
12
16
0.423 ( - 0.428) 1.164 (0.432) 0.609 ( - 1.669) 0.595 ( - 1.341) 1.664 (1.773) 0.751 ( - 2.594)* 1.149 (0.512)
0.296 ( - 0.444) 0.816 (-0.414) 0.434 ( - 2.059)* 0.534 ( - 1.321) 1.783 (1.797) 0.882 ( - 1.049) 1.295 (0.863)
(.) 0.625 ( - 0.684) 0.692 ( - 0.599) 0.537 ( - 1.677) 1.141 (0.583) 0.866 ( - 0.284) 1.167 (0.337) 1.388 (0.793) 0.604 ( - 1.201) 0.843 ( - 0.420) 1.152 (0.374) 1.427 (0.584) 1.541 (0.946) 1.133 (3.201)* 0.467 (-39.126)*
(.) 0.487 ( - 0.797) 0.682 ( - 0.528) 0.406 ( - 1.835) 1.070 (0.248) 0.814 ( - 0.337) 1.241 (0.426) 1.432 (0.754) 0.680 ( - 0.825) 0.643 ( - 0.812) 1.143 (0,299) 1,316 (0.370) 1.638 (0.950) 1.147 (3.031)" 0.477 (-33.766)*
549
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
Table 3 (continued) Country Uruguay Venezuela Yugoslavia Zaire
Number q of base observations aggregated to form variance ratio 2
4
8
12
16
0.961 (-0.363) 0.884 (-0.895) 1.103 (0.887) 0.980 (-0.911)
0.759 ( - 1.197) 1.006 (0.024) 0.990 (-0.045) 0.965 (-0.865)
0.743 (-0.809) 1.057 (0.149) 0.667 (-0.972) 0.971 (-0.450)
0.765 (-/).583) 1.087 (0.180) 0.560 ( - 1.013) 1.014 (0.170)
0.774 (-0.478) 1.016 (0.029) 0.489 ( - 1.001) 1.086 (0.902)
The data consist of average weekly bid and offer secondary market prices of 26 developing country syndicated loans from July 1, 1990 to May 27, 1992. One week is taken as base observation interval. The variance-ratio cr 2(q, 1) is defined as o": (q)/o- z( 1), where o-2(q) is an unbiased estimator of 1 / q of the variance of the qth difference of the natural logarithm of the syndicated loan price, and o'2(1) is an unbiased estimator of the variance of the first-difference of the natural logarithm of the syndicated loan price. The variance-ratio o-2(q,1) is reported in the main row, and the heteroscedasticity-robust variance-ratio test statistic Z *(q) is in parentheses. The null hypothesis is that o'2(q,1) is equal to one (prices follow a random walk). An asterisk indicates rejection of the null at the 5 percent significance level.
the existence of autocorrelation in the secondary market prices of syndicated loans does not necessarily imply market inefficiency (Lucas, 1978; Levitch, 1979). In effect, spurious autocorrelation m a y also be due to infrequent or n o n s y n c h r o n o u s trading (Poterba and S u m m e r s , 1988; Scholes and W i l l i a m s , 1977). It has also been suggested that small securities trade less frequently than larger securities and that noise trading may also provide explanations for the presence of transitory c o m p o n e n t s of prices (Cutler et al., 1988). In this section we present specific tests of market efficiency. Tables 4 and 5 present the basic statistics o f w e e k l y returns of the secondary markets for L D C s syndicated loans for the first and second period, respectively. 9 It can be seen that the series exhibit kurtosis and skewness. Also, studentized ranges are larger than 6.0, i m p l y i n g that the sample does not c o m e f r o m a normal distribution. ~o A test for i n d e p e n d e n c e b e t w e e n successive returns w h i c h does not require normality is the filter rule test. E m p l o y i n g a 2.5% filter rule, the results of the filter tests are presented in Table 6.
'~For details about the formulas to compute mean, standard deviation, skewness and kurtosis (and their standard errors), and the studentized range, see Fama (1976). l0 See Fama (1976).
550
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
Table 4 Summary statistics of weekly returns of secondary markets of LDCs syndicated loans. Time period: July 7, 1988-June 31, 1990 Country
Mean
Standard deviation
Skewness
Kurtosis
Skewness std. error
Kurtosis std. error
Studentized range
Algeria Argentina Bolivia Brazil Chile Columbia Costa Rica Dom. Rep. Ecuador Honduras Ivory Coast Jamaica Mexico Morocco Nicaragua Nigeria Panama Peru Philippines Poland Senegal Sudan Uruguay Venezuela Yugoslavia Zaire
-0.13284 -0.50891 0.08747 -0.74656 0.08679 -0.02388 0.62221 -0.12840 -0.42279 -0.18371 - 1.40729 0.0358 -0.16379 -0.09548 0.49118 0.01702 -0.52166 -0.21902 -0.04769 -0.98301 -0.27010 -0.17531 -0.18570 -0.18359 0.28818 -0.04691
2.4579 6.2141 6.9730 5.5738 1.6468 3.1394 8.8119 13.3722 7.4553 20.1373 24.0219 10.5614 3.0267 3.7671 58.5701 10.2434 10.0490 16.5682 2.9693 4.2313 8.0005 75.0975 2.4034 3.4739 2.4583 5.8663
-0.25909 0.71279 -0.04935 -0.36488 -0.23175 0.32336 0.12728 -0.13136 0.31048 -0.20757 -0.57491 0.07646 -0.54528 -0.70849 0.03500 -0.14324 0.24264 0.18228 0.20236 - 1.18022 -0.06038 0.00857 0.10518 -0.45667 0.74939 -0.06997
2.65881 1.54597 -0.11169 0.97987 2.54396 0.96733 2.23686 -0.53571 1.61358 7.50094 6.98035 4.18026 4.23172 3.17969 -0.48723 1.74917 1.78341 2.20615 2.87139 5.15220 -0.61897 -0.57218 3.94672 4.36828 1.72737 1.14976
0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24744 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019
0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.49487 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038
6.80372 6.33801 4.20487 5.42603 6.81225 6.29514 6.86071 4.47792 5.72944 6.88422 8.31350 6.60133 7.52495 6.45555 4.43668 5.15740 5.81741 5.98123 6.58364 7.75535 4.74039 3.20643 6.69237 7.77407 6.40687 5.37402
Weekly returns are computed as R, = 1001n(Pt / P t - I), where the price is the average of the bid and offer prices. Standard errors are computed as: V/6/N for skewness and 2V/-~-/N for kurtosis. The studentized range is computed as: (maximum return- minimum return)/standard deviation. The number of observations N = 104, except for Costa Rica N = 98.
E m p i r i c a l e v i d e n c e o f m a r k e t i n e f f i c i e n c y is f o u n d for the first time p e r i o d , July 1988 to J u n e 1990. T h e s e results a g r e e w i t h t h o s e r e p o r t e d by H a r v e y (1993) and C l a e s s e n et al. (1993), w h o r e p o r t e d i n e f f i c i e n c y in e m e r g i n g markets. O n the o t h e r h a n d , the null o f m a r k e t e f f i c i e n c y g e n e r a l l y c a n n o t be r e j e c t e d for the m o r e r e c e n t t i m e p e r i o d , July 1990 to M a y 1992. T h e s e results agree w i t h Urrutia (1994) and c o n f i r m our h y p o t h e s i s o f an i n c r e a s e in e f f i c i e n c y o f the L D C s
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
551
Table 5 Summary statistics of weekly returns of secondary markets of LDCs syndicated loans. Time period: July 1, 1990-May 27, 1992 Country
Mean
Standard deviation
Skewness
Kurtosis
Skewness std. error
Kurtosis std. error
Studentized range
Algeria Argentina Bolivia Brazil Chile Columbia Costa Rica Dom. Rep. Ecuador Honduras Ivory Coast Jamaica Mexico Morocco Nicaragua Nigeria Panama Peru Philippines Poland Senegal Sudan Uruguay Venezuela Yugoslavia Zaire
0.03121 1.29820 0.19392 0.51319 0.31814 0.23570 0.48098 - 0.26352 0.50724 0.40451 0.50355 0.48097 0.38350 0.08151 0.72311 0.36441 1.03610 1.21053 0.03764 0.47603 0.27508 0.52125 0.37660 (I.29225 - 1.01433 -0.66829
4.7812 4.7315 4.0943 6.4050 1.0143 1.9271 2.6703 0.8740 4.6081 2.6574 12.4937 3.1211 1.8655 3.4807 8.3080 3.0925 6.1904 8.3364 3.4965 6.0057 3.8101 16.0689 2.7932 2.2367 5.4089 4.4487
-2.68785 0.00902 2.16818 - 1.13675 1.45396 6.72884 3.47697 - 3.31662 0.44446 1.87302 2.66697 3.28568 0.77163 0.20635 0.15657 1.25969 2.32351 0.74020 0.10264 1.18163 5.70011 4.53549 0.94325 0.00613 0.02475 -5.76321
31.7167 1.9929 28.0341 5.4739 4.5939 50.5629 19.6094 11.0000 2.6990 16.1513 29.2533 24.3699 3.4717 2.1805 5.4535 4.5752 10.8477 4.2676 5.1572 4.1563 41.7395 36.4601 12.5542 2.5283 4.1682 42.1855
0.24744 0.24744 0.24744 0.24744 0.24744 0.24744 0.24871 0.73855 0.24744 0.24744 (}.24744 0.24744 (I.24744 0.24744 0.24744 0.24744 (I.24744 0.24744 (/.24744 (I.24744 0.27386 (/.24744 0.24744 0.24744 0.24744 0.24744
0.49487 0.49487 0.49487 0.49487 0.49487 0.49487 0.49742 1.47710 (I.49487 (I.49487 (/.49487 0.49487 0.49487 0.49487 0.49487 0.49487 0.49487 0.49487 (I.49487 (I.49487 0.54772 0.49487 (/.49487 (I.49487 (I.49487 (I.49487
11.9315 6.5082 11.3747 7.3254 6.4432 9.8031 9.(/708 3.3166 6.2787 9.9862 11.5236 10.7103 7.3120 5.9894 7.(175(I 6.7831 7.6245 6.6301 7.440 6.9992 9.5550 1(t.4175 9.4585 7.1251 6.6332 10.4613
Weekly returns are computed as Rt = 1001n(P~ ~P t-1 )' where the price is the average of the bid and offer prices. Standard errors are computed as: v ~ / N for skewness and 2 ~ / N for kurtosis. Thc studentized range is computed as: (maximum r e t u r n - m i n i m u m return)/standard deviation. The number of observations N = 98, except for Costa Rica N 97, for Dom. Rep. N - l 1, and for Senegal N = 80.
syndicated ciency
loans market over time. A plausible explanation
is a l a r g e r t r a d i n g v o l u m e
and increase
in m a r k e t
for the higher effimaturity
during
the
s e c o n d p e r i o d . A s it w a s s t a t e d e a r l i e r , t h e t r a d i n g v o l u m e in t h e s e c o n d a r y m a r k e t f o r L D C s y n d i c a t e d l o a n s i n c r e a s e d f r o m $ 2 b i l l i o n in 1 9 8 4 to a r o u n d $ 5 0 0 b i l l i o n in 1 9 9 2 .
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
552
Table 6 Nominal annual returns for in-weeks and out-weeks for 26 countries based on the 2.5% filter rule Countries
July 1 9 8 8 - J u n e 1990
Ri.(%) Algeria Argentina
July 1 9 9 0 - M a y 1992
Ro.t(%)
T
Rin(%)
Rout(%)
T
- 39.38 27.14
26.84 -57.32
- 2.73 * 1.40
2.87 73.72
- 3.25 51.98
0.05 0.41
- 188.06 - 2.63
353.32 - 61.44
- 10.96 * 0.96
16.69 48.77
4.70 - 16.82
0.25 0.74
Chile Columbia Costa Rica Dom. Rep. Ecuador Honduras Ivory Coast Jamaica Mexico Morocco
- 0.20 - 38.40 - 200.59 - 346.71 -184.24 - 529.02 - 1183.26 - 177.65 - 7.03 - 47.77
7.22 47.77 295.68 560.05 117.09 435.71 398.23 489.12 - 12.36 164.20
- 0.42 - 2.65 * - 6.33 * - 8.16 * -4.33* - 5.22 * -6.21" - 5.78 * 0.14 - 4.08 *
16.54 24.75 28.52 - 15.07 16.57 13.79 -39.03 25.01 13.25 23.10
- 3.72 19.57 0.0 50.85 368.95 71.16 144.50 - 32.19
1.58 0.34 -0.51 - 26.77 * -0.82 - 2.83 * 1.43
Nicaragua Nigeria Panama Peru Philippines Poland Senegal
- 2757.90 - 193.82 - 180.57 - 594.06 - 10.75 - 26.61 - 240.56
1836.99 384.73 94.57 399.35 46.57 76.58 348.37
*
29.21 5.75 86.19 52.06 15.76 66.57 6.77
52.06 28.05 - 6.95 93.08 - 30.89 - 35.88 18.59
- 0.25 -0.66 1.31 - 0.43 1.02 1.67 - 0.28
Sudan Uruguay Venezuela Yugoslavia Zaire Average
- 2866.22 - 38.63 22.72 2.12 - 133.07 -382.12
2536.31 14.23 - 26.63 41.48 175.69 328.42
- 9.73 * - 2.21 * 1.37 - 1.37 - 5.87 * -4.17"
105.80 11.23 8.90 - 73.27 - 15.96 21.10
- 22.74 93.07 38.28 - 46.80 - 45.19 32.90
0.66 - 1.75 - 0.84 - 0.36 0.74 -1.10
Bolivia Brazil
- 11.20 -6.12 - 2.82 - 6.98 - 0.80 1.16 - 9.56
* * * *
Mean returns on in-weeks (Rin) and out-weeks (Ro.t) selected by 2.5% filter rule. In-week and out-week two-sample t-statistics are denoted by T. An asterisk indicates significance at the 5% level.
6. Summary and conclusions This
paper
secondary time
series
increasing enhance
empirically
market
properties market
investigates
for LDCs
syndicated
of the LDCs
integration
our understanding
and
the sample
is d i v i d e d
1990, and from July
loans. The
syndicated globalization
of this fast-growing
In order to facilitate the analysis time,
the randomness
and to evaluate
in t w o
1990 through
time
May
loans
the efficiency
is i m p o r t a n t
of security
the evolution from
July
of the
efficiency
and
in a w o r l d
markets.
and increasingly
periods:
1992.
and
study of market
It w i l l
important
of also
market.
of this market
over
1988
June
through
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
553
The Augmented Dickey-Fuller tests confirm the random walk hypothesis for the secondary market LDCs syndicated loan prices for the two subperiods. However, the more powerful variance-ratio tests reject the random walk for about half of the countries in the first period but confirms the random walk for the second period. The filter rule tests find evidence of market inefficiency in the first time period, but the null of market efficiency cannot be rejected for the second time period. Our findings indicate that the secondary market for LDC syndicated loans has become more efficient over time. The improvement in market efficiency is probably due to the increase in trading volume and market maturity. References Aramburu, D. and R. Grosse, 1992, A valuation model for Latin American loans in the secondary market, Recent Development in International Banking and Finance 6, 50-74. Ball, R. and S.P. Kothari, 1989, Non-stationary expected returns: Implications for tests of market efficiency and serial correlation in returns, Journal of Financial Economics 25, 51-74. Berg, A. and J. Sachs, 1988, The debt crisis: Structural explanations of country performance, Journal of Development Economics 29, 271-306. Boehmer, K. and W. Megginson, 1990, Determinants of secondary market prices for developing country syndicated loans, Journal of Finance 45, 1517-1564. Claessen, S., S. Dasgupta and J. Glen, 1993, Stock price behavior in emerging stock markets, World Bank, Working Paper. Cutler, D., J. Poterba and L. Summers, 1988, What moves stock prices'?, Journal of Portfolio Management 15, 4-12. Dickey, D. and W. Fuller, 1979, Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association 74, 427-431. Errunza, V., 1977, Gains from portfolio diversification into less developed countries' securities, Journal of International Business Studies, 83-99. Errunza, V., 1983, Emerging markets - a new opportunity for improving global portfolio performance, Financial Analysts Journal, 51-58. Errunza, V. and E. Losq, 1985, The behavior of stock prices on LDC markets, Journal of Banking and Finance, 561-575. Errunza, V. and E. Losq, 1987, How risky are emerging markets? Myths and perceptions versus theory and evidence, Journal of Portfolio Management, 62-66. Errunza, V. and P. Padmanabhan, 1988, Further evidence on the bcnefits of portfolio investments in emerging markets, Financial Analysts Journal 44, 76-78. Fama, E.F., 1965, The behavior of stock market prices, Journal of Business, 34-105. Fama, E.F., 1970, Efficient capital markets: A review of theory and empirical work, Journal of Finance 25, 383-417. Fama, E.F., 1976, Foundations of finance (Basic Books, New York). Fama, E.F., 1991, Efficient markets: II, Journal of Finance 46, 1575-1617. Fama, E. and M. Blume, 1966, Filter rules and stock market trading, Journal of Business 39, 226-241. Fama, E. and K. French, 1986, Common factors in the serial correlation of stock returns, Center for Research in Security prices, Working Paper No. 200, University of Chicago, Chicago. Fama, E. and K. French, 1988, Permanent and temporary components of stock prices, Journal of Political Economy 96, 246-273. French, K. and R. Roll, 1986, Stock return variances: The arrival of information and the reaction of traders, Journal Financial Economics 17, 5-26.
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Gultekin, M. and B. Gultekin, 1983, Stock market seasonality: International evidence, Journal of Financial Economics 12, 469-481. Hajivassiliou, V., 1989, Do the secondary markets believe in life after debt?, in: I. Husain and I. Diwan, eds., Dealing with the debt crisis (World Bank, Washington, DC) 276-291. Harvey, C., 1993, Predictable risk and returns in emerging markets, Working Paper Series, Duke University. Hillard, J.E., 1979, The relationship between equity indices on world exchanges, Journal of Finance 34, 103-114. Laney, L., 1987, The secondary market in developing country debt: Some observations and implications, Federal Reserve Bank of Dallas Economic Review (July), 1-12. LDC Debt Report, American Bankers, Various issues. Lee, S., 1991, Ability and willingness to service debt as explanation for commercial and official rescheduling cases, Journal of Banking and Finance 15, 5-27. Lee, S. and H. Sung, 1993, The reactions of secondary market prices of developing country syndicated loans to the Brady Plan, Working Paper, Loyola University of Chicago, Chicago. Levitch, R., 1979, On efficiency of markets for foreign exchange, in: R. Dornbusch and J. Frenkel, eds., International economic policy (Johns Hopkins, Baltimore, MD). Lo, A. and C. MacKinlay, 1988, Stock market prices do not follow random walks: Evidence from a simple specification test, Review of Financial Studies 1, 41-66. Lucas, R., 1978, Asset prices in an exchange economy, Econometrica 66, 1429-1445. O'Brien, J., 1987, Testing for transitory elements in stock prices, Mimeo, Board of Governors of the Federal Reserve System, Washington, DC. Ozler, S. and H. Huizinga, 1991, Bank exposure, capital and secondary market discounts on developing country debt, Working Paper, November. Poterba, J. and L. Summers, 1988, Mean-reversion in stock prices: Evidence and implications, Journal of Financial Economics 22, 27-59. Purcell, A. and D. Orlanski, 1988, Developing country loans: A new valuation model for secondary market trading, Corporate bond research (Salomon Brothers, New York). Sachs, J. and H. Huizinga, 1987, U.S. commercial banks and the developing-country debt crisis, Brookings Papers on Economic Activity. Scholes, M. and J. Williams, 1977, Estimating betas from nonsynchronous data, Journal of Financial Economics 5, 309-328. ShiUer, R. and P. Perron, 1985, Testing the random walk hypothesis: Power versus frequency of observations, Economic Letters 18, 381-386. Solnik, B.H., 1974, The international pricing of risk: An empirical investigation of the world capital market structure, Journal of Finance 29, 365-378. Stone, M., 1991, Are sovereign debt secondary market returns sensitive to macroeconomic fundamentals? Evidence from the contemporary and interwar markets, Journal of International Money and Finance 10, S100-S122. Summers, L., 1986, Does the stock market rationally reflect fundamental values?, Journal of Finance 41, 591-601. Sweeney, R., 1986, Beating the foreign exchange market, Journal of Finance 41, 163-182. Sweeney, R., 1988, Some new filter rule tests: Methods and results, Journal of Financial and Quantitative Analysis 23, 285-300. Urrutia, J., 1994, Tests of random walk and market efficiency for Latin American emerging equity market, Working Paper, Loyola University of Chicago, Chicago. Vatnick, S., 1988, The secondary market for debt: A possible explanation of how LDC debt prices are determined (World Bank, Washington, DC). Von Furstenberg, G. and B.N. Jeon, 1989, International stock price movements: Links and messages, Brookings Papers on Economic Activity.
Journal of Banking& Finance20 (1996) 537-554
Journal of BANKING & FINANCE
The behavior of secondary market prices of LDC syndicated loans Suk Hun Lee a,*, Hyun Mo Sung b, Jorge L. Urrutia a a School of Business Administration, Department of Finance, Loyola Unieersity of Chicago, 820 N. Michigan Ace., Chicago, IL 60611, USA b Summer Institute of Linguistics, 7500 W. Camp, Wisdom Road, Dallas, TX 75236. USA
Received 15 May 1994; accepted 15 January 1995
Abstract The paper investigates the time series properties and the efficiency of the secondary market for LDCs syndicated loans. The data corresponding to weekly secondary market prices for 26 LDCs syndicated loans are divided into two subperiods: from July 1988 to June 1990 and from July 1990 to May 1992. Tests of the random walk hypothesis are conducted based on the Augmented Dickey-Fuller methodology and the variance-ratio test of Lo and MacKinlay. The efficiency of the market is tested by means of filter rule tests. The empirical findings suggest that the secondary market for LDCs syndicated loans has become more efficient over time and that loan prices follow random walks. JEL classification: F34; G14 Keywords: LDC debt; Randomwalk; Market efficiency
1. Introduction The less developed countries (LDCs) debt crisis became obvious for the first time in August 1982, when Mexico announced it could not meet scheduled repayments on its almost $100 billion of external debt. During the three years following that announcement, over 35 debtor nations renegotiated their debt
* Correspondingauthor. Tel.: 312-915-7071;fax: 312-915-6118. 0378-4266/96/$15.00 © 1996 Elsevier Science B.V. All rights reserved SSDI 0378-4266(95)00009-7
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service obligations with their creditors. 1 Although the debt crisis is now less dramatic, it is still a major economic concern for the LDCs. The debt crisis led to the development of a secondary market for developing country syndicated loans during the 1980s. Initially, the role of this market was to facilitate commercial banks to adjust their credit exposure to highly indebted countries. Since then the market has grown rapidly and has attracted non-bank market participants. In effect, trading volume has increased from $2 billion in 1984 to an estimated volume of around $500 billion in 1992. Furthermore, according to the February 13th-19th, 1993 issue of the Economist, this market was projected to reach $1 trillion in 1993. 2 More recently, the secondary market for LDCs syndicated loans has been used as one of the major vehicles for reducing LDCs 'debt overhang.' Under the Brady Initiative, the Philippines signed the debt and debt service reduction (DDSR) agreement on January 3, 1990 and retired $1.337 billion of its debt through cash buyback at 50% discount. Costa Rica signed its DDSR agreement on May 6, 1990 and retired $991 million of its debt through cash buyback at 84% discount. Other countries, such as Uruguay and Nigeria, have also retired their debt through debt buybacks. The secondary market for LDCs syndicated loans has been the subject of numerous academic studies (Laney, 1987; Sachs and Huizinga, 1987; Berg and Sachs, 1988; Purcell and Orlanski, 1988; Vatnick, 1988; Hajivassiliou, 1989; Boehmer and Megginson, 1990; Stone, 1991; Ozler and Huizinga, 1991; Aramburn and Grosse, 1992; Lee and Sung, 1993). Authors have examined the determinants of secondary market prices for developing countries syndicated loans and the reactions of these prices to debt conversions and the announcement of the Brady Plan. Boehmer and Megginson (1990) examine 32 monthly (from July 1985 to July 1988) secondary loan prices for ten LDCs. They find that the secondary market prices of LDCs loans are related to the ratio of total long-term debt to GNP, the ratio of total long-term debt to total exports, the level of incurred payment arrears, the unilateral moratoria by Brazil and Peru, and the loan-loss provisions by U.S. banks. Furthermore, the adoption of legislation for a debt-conversion program is negatively related to the secondary market loan prices. 3 Stone (1991) uses monthly price data to examine the impact of the announcement of the
a See Lee (1991) for further discussion on the debt reschedulings during the 1980s. 2 The trading volumes for the years 1984 through 1991 are 2, 4, 7, 12, 50, 108.1, 137.5, and 150 billion U.S. dollars, respectively. According to the February 13th-19th, 1993 issue of the Economist, trading in all sorts of 'emerging-market' debt was approximately $500 billion in 1992 and it was projected to top $1 trillion in 1993. 3 Some argue that the negative relationship between the debt-conversion program and the secondary market loan prices is due to the misspecification of the debt conversion variable. For example, Aramburu and Grosse (1992) state that after correctly specifying the debt conversion variable, they find a statistically significant positive relationship between the debt conversion variable and the loan prices.
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539
Brady Plan on secondary market prices. He finds that during the month of March 1989, the loan prices for Honduras, Morocco, Uruguay, and Zaire declined significantly while Mexico was the only country with a statistically significant increase in its loan price. The remainder of this paper is organized as follows: Section 2 discusses the relevance and purpose of the research; Section 3 presents the methodology; Section 4 describes the data; Sections 5 and 6 report and discuss the empirical results; finally, Section 7 briefly summarizes and concludes the paper.
2. Relevance and purpose of this research study The market efficiency and the random walk hypotheses have been extensively studied for the U.S. equity markets. In effect, tests of these hypotheses for the American markets have been conducted, among others, by authors such as Fama (1965, 1970, 1991), Shiller (1981), Shiller and Perron (1985), French and Roll (1986), Summers (1986), Fama and French (1986, 1988), O'Brien (1987), Ball and Kothari (1989), and Lo and MacKinlay (1988). The research has been extended to major foreign markets, such as the Japanese and European markets, as these markets have become important to international investors. Examples of this research are papers published by Solnik (1974), Hillard (1979), Gultekin and Gultekin (1983), Poterba and Summers (1988), and Von Furstenberg and Jeon (1989). In general, most of the research on the stochastic behavior of stock prices has centered on the equity markets of the world's largest economies, such as the United States, Japan, United Kingdom, and Germany. However, capital markets have become more integrated and investing has become more global, owing to the advances in telecommunications, the deregulation of capital markets, multiple listing, and the boom of international mutual funds. In this context the issues of efficiency and randomness of smaller capital markets and the financial markets of third world countries have also become relevant. Some research has been conducted for equity markets of developing countries by authors such as Errunza (1977, 1983), Errunza and Losq (1985, 1987), Errunza and Padmanabhan (1988), Harvey (1993), Claessen et al. (1993), and Urrutia (1994). This paper analyzes the time series properties of the secondary market for LDCs syndicated loans. Even though this market has grown rapidly since its creation and has been the subject of several studies, its efficiency and stochastic properties have not yet been investigated. The issues of efficiency and randomness of this market are important in the context of markets integration and globalization. The study of market efficiency and time series properties of the LDCs syndicated loans will also help us to enhance our understanding of this fast-growing and increasingly important market. The issue of efficiency is also important for the evaluation of previous studies, which have relied on the assumption of market
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
540
efficiency. If the secondary market for LDCs syndicated loans was not efficient during the time periods these studies were conducted, it follows that their empirical results should be interpreted with care. Our basic hypotheses are that the prices of secondary loans for LDCs follow random walks and that the market has become more efficient over time, mainly because of the larger volume of trade and market maturity. Tests of random walks are conducted based on the Dickey and Fuller (1979) methodology and the variance-ratio test of Lo and MacKinlay (1988). The efficiency of the market is tested by means of filter rule tests.
3. Methodology This section describes briefly the several tests of random walk and market efficiency conducted in the paper. 3.1. Augmented D i c k e y - F u l l e r tests o f random walk
Our first test of random walk is the Augmented Dickey and Fuller test, ADF, developed by Dickey and Fuller (1979). It can be mathematically expressed as follows: X t -X,_ 1 = ao +boX,_ 1 + ~bi(Xt_
i -X,_i_l)
-~- E.t
(1)
i=1
where X t is the natural logarithm of a time series, a 0 is a constant, and Et is an error term. The null hypothesis is H0: b 0 = 0; that is, the time series follows a random walk process. 3.2. Variance-ratio tests o f random walk
The second test of the random walk hypothesis used in this paper is the variance-ratio test of Lo and MacKinlay (1988), which can be expressed as follows: o'2(q,1) = o 2 ( q ) / c r 2 ( 1 )
(2)
where o.2(q) is 1 / q times the variance of the q-difference, o'2(1) the variance of the first differences, and o" 2(q,1) the variance ratio. In order to test the null of random walk, Lo and MacKinlay (1988) have developed formulas to compute o'2(q) and O"2(1). They have also derived an asymptotic standard normal test-statistic, Z *(q), which is heteroscedasticity consistent and controls for volatility changes over time: Z*(q)
= [o.2(q,1) - 1]/~f~-Z(q) ~ N ( 0 , 1 )
(3)
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
541
where ~b* (q) corresponds to the asymptotic variance of the variance ratio (see Lo and MacKinlay, 1988).
3.3. Filter rule tests of market efficiency Following Sweeney (1986, 1988), an x-percent filter rule divides an asset's weekly return series into two samples: 'in-week' returns and 'out-week' returns. On in-weeks an investor holds a long position, and on out-weeks an investor holds a short position (Fama and Blume, 1966). Since under a buy-and-hold strategy an investor holds a long position on both in-weeks and out-weeks, the only difference between a filter rule strategy and a buy-and-hold strategy is due to loan price performance on out-weeks. The mean in-week return, /~in, and the mean out-week return, Rout are given by:
Rm = ~..Rt/Nin, tel
Rout =
~-, Rt/Nout
(4)
t~O
where gin and Nout are the number of in-weeks and out-weeks, respectively. The difference in sample means, Rin -/~out, is an estimate of the variation in expected weekly returns predictable by a filter rule. A test of statistical significance for a difference between in-week and out-week returns is the two-sample t-test:
T=
¢
N'nN°u' (nin -- Rout)//S(R),
(5)
Ni. + gout
where the two-sample standard deviation
¢
S(R)=
Z(R,
-
_2
Rin) + Z ( R ,
S(R) is calculated as: - R- o u t )
2
,co Ni. +Nou _ 2
(6)
yielding a t-statistic with Nin + No~t - 2 degrees of freedom.
4. Data
The data used in this study correspond to weekly bid and ask secondary market prices of 26 developing countries syndicated loans for the time period July 7, 1988 to May 27, 1992. These prices refer to public and publicly guaranteed debt in percentages of face value (i.e., number of cents per one U.S. dollar). According to the LDCs debt traders, these quoted prices represent benchmark prices for LDCs'
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largest external liabilities. The prices have been collected from the L D C Debt Report. 4 There are eight weeks for which the L D C Debt Report did not provide prices of syndicated loans for any of the 26 countries in the sample. 5 These missing values have been replaced with average prices calculated using prices from the weeks immediately before and after the missing week. Also, three countries, Costa Rica, Dominican Republic, and Senegal, have 5, 12, and 1 weeks of missing prices, respectively. 6 In this case, the missing weekly prices have been treated as missing values. For purpose of analysis and in order to investigate the issue o f efficiency of the LDCs syndicated loans market, the sample has been divided in two subsamples of equal size: from July 1988 to June 1990 and from July 1990 to May 1992. 7
5. Analysis of the empirical results A first step in examining the time series properties and the efficiency of LDCs syndicated loan prices is to analyze the spread term over the sample period. The spread term, defined as [(offer - b i d ) / o f f e r ] X 100, is plotted in Fig. 1. From Fig. 1 we conclude the following: First, for the 26 countries included in the sample, there was a steady decline in the spread term during the sample period. The average spread term declined from 9.59% in July 1988 to 7.15% in May 1992. Second, the spread term for the five major Latin American debtor countries whose debts are the most actively traded in the secondary market, was much smaller than the spread for the other countries included in the sample. 8 5.1. Results o f D i c k e y - F u l l e r and variance-ratio tests o f random walk Since the A D F tests yield similar results for the two sample periods, only the results for the second subperiod are provided here. Table 1 shows that the null
4 LDC Debt Report is a weekly publication on issues related to debt of LDCs published by American Banker. It began providing weekly bid and offer secondary market prices for 28 countries. By the beginning of May 1989, it stopped reporting price data for Romania and Turkey and started providing price data for South Africa. These three countries are not included in the sample; hence, our sample contains 26 countries. 5These eight weeks are the weeks ending on 11/29/88, 12/29/88, 8/17/89, 8/24/89, 11/15/89, 12/28/89, 1/4/90, and 1/18/90. 6 The five missing weeks for Costa Rica are weeks ending on 5/24/90, 6/7/90, 6/20/90, 7/3/90, and 7/25/90. The 12 missing weeks for the Dominican Republic cover the time period 9/26/90 through 12/26/90. The missing week for Senegal is the week ending on 10/4/90. 7 We understand these are short time periods and that the split of the data in two subperiods may create a small-samples problem. However, we have divided the data in two subsamples because a main purpose of this research is to examine the behavior of the LDCs syndicated loans market over time. 8 These five countries are Argentina, Brazil, Chile, Mexico, and Venezuela.
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
8t
543
18-
It
--
14-
6 4
Older Counld~ (21)
Major I.mln A n m t ¢ ~ Debtor Counlfl~ (5)
0-1 """ 7/88
12/88
"
"J 6/89
l?J~
.% . . . . . .!. . . .. . .". ". .".". ' " ~ 6/90 12/90
.,. .... . . .. . .'.-. ". .. . . . . . . 6/91 17./91 5t92
Time
Fig. 1. L D C debt spreads. * Spread = [ ( o f f e r - b i d ) / o f f e r ] × 100. a N u m b e r s in parentheses represent n u m b e r of countries included.
hypothesis of random walk cannot be rejected at the 5 percent significance level for all countries. That is, the ADF tests strongly suggest that the secondary prices of developing countries syndicated loan follow random walk processes. The second test of the random walk hypothesis is the variance-ratio test of Lo and MacKinlay (1988). The base observation interval is one week. Variance-ratio estimates, ty2(q,l), asymptotic variances of the variance ratio, ~b*(q), and variance-ratio test-statistics, Z * ( q ) , are computed for each of the intervals q = 2,4,8,12, and 16. Thus, by using one week as the base interval, several parameters are computed for each q by comparing the variance of the one-week base interval with those of 2-week, 4-week, 8-week, 12-week, and 16-week observation intervals. Table 2 reports the heteroscedasticity-robust variance ratios for the first time period, July 7, 1988 to June 31, 1990. The results in Table 2 show that the null hypothesis of random walk cannot generally be rejected at the 5 percent significance level for Algeria, Argentina, Brazil, Chile, Colombia, Ecuador, Honduras, Ivory Coast, Jamaica, Mexico, Morocco, Panama, the Philippines, Uruguay, Venezuela, and Yugoslavia. The null hypothesis of random walk is partially rejected, especially for low values of the q-intervals, for the following countries: Bolivia, Costa Rica, Dominican Republic, Nicaragua, Nigeria, Peru, Poland, Senegal, Sudan, and Zaire. The rejection of the random walk hypothesis for these ten countries is heteroscedasticity robust and suggests that the variance ratios are different from one due to autocorrelation of weekly prices. Moreover, for all of these ten countries, except Poland, the variance ratios are less than one, indicating that the price variance increases less than proportionally with time. That is, they exhibit negative autocorrelation, which indicates the presence of a mean-reversion process. Table 3 shows the heteroscedasticity-robust variance-ratios for the second time period, July 1, 1990 to May 27, 1992. The null hypothesis of random walk is
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S.H. Lee et al. / J o u r n a l of Banking & Finance 20 (1996) 537-554
Table 1 Dickey-Fuller tests of random walk of secondary market prices of LDCs syndicated loans. Time period: July 1, 1990-May 27, 1992. Model: l n P t - l n P t_ 1 ao q'- bolnPt- 1 + bl(lnPt- l - l n P t - 2 ) + b2(lnPt- 2 - l n P t - 3)+ b3(lnPt-3 - l n P t - 4 ) + b4(lnPt- 4 - lnP/- 5)+ et =
Country
a0
- 0.020 (0.053) (-0.378) Argentina 0.038 (0.056) (0.677) 0.006 Bolivia (0.049) (0.119) Brazil 0.087 (0.074) (1.178) -0.010 Chile (0.011) (-0.864) Columbia - 0.008 (0.023) ( - 0.324) Costa Rica 0.013 (0.032) (0.393) - 0.005 Dora. Rep. (0.005) ( - 1.000) 0.009 Ecuador (0.055) (0.172) Honduras 0.003 (0.031) (0.104) Ivory Coast 0.049 (0.141) (0.346) Jamaica - 0.042 (0.035) ( - 1.214) Mexico - - 0.006 (0.022) ( - 0.287) Morocco 0.050 (0.041) (1.210) 0.045 Nicaragua (0.097) (0.468) Algeria
bo
b1
b2
b3
b4
R2
0.007 (0.019) (0.385) - 0.010 (0.020) ( - 0.495) - 0.002 (0.018) (-0.093) - 0.028 (0.026) (-1.069) 0.004 (0.004) (1.091) 0.004 (0.008) (0.427) - 0.003 (0.012) (-0.236) 0.000 (.) (.) - 0.001 (0.020) (-0.062) 0.000 (0.011) (0.042) - 0.017 (0.050) (-0.332) 0.017 (0.012) (1.382) 0.004 (0.008) (0.529) - 0.018 (0.015) (-- 1.198) - 0.015 (0.035) (--0.421)
- 0.323 (0.107) (-3.008)* 0.006 (0.107) (0.059) - 0.034 (0.107) (-0.321) - 0.038 (0.106) (-0.362) -0.019 (0.103) (-0.181) 0.005 (0.107) (0.047) 0.086 (0.111) (0.780) - 0.167 (0.441) ( - 0.378) - 0.102 (0.110) (-0.928) -0.219 (0.107) ( - 2.048) * 0.077 (0.107) (0.716) 0.042 (0.107) (0.394) 0.002 (0.106) (0.020) - 0.050 (0.108) (-0.465) 0.072 (0.107) (0.676)
0.050 (0.109) (0.457) - 0.006 (0.104) ( - 0.055) 0.023 (0.107) (0.216) -0.186 (0.104) - 1.781) 0.343 (0.101) (3.401)* - 0.003 (0.107) -0.026) - 0.002 (0.111) -0.018) 0.000
0.269 (0.109) (2.465)* 0.254 (0.103) (2.456)* 0.000 (0.107) (0.001) 0.134 (0.103) (1.303) - 0.066 (0.101) (-0.658) - 0.015 (0.107) (-0.138) - 0.021 (0.111) (-0.194) 0.000
0.003 (0.107) (0.028) - 0.028 (0.107) (-0.262) - 0.002 (0.106) ( - 0.023) -0.133 (0.104) ( - 1.275) -0.211 (0.101) ( - 2.078)* -0.019 (0.107) (-0.174) -0.017 (0.110) (-0.158) 0.000 (.)
O.169 (3.540)
(.) (.)
(.) (.)
0.131 (0.110) (1.189) - 0.093 (0.109) ( - 0.859) - 0.223 (0.107) ( - 2.087)* 0.011 (0.107) (0.099) - 0.099
- 0.092 (0.111) (-0.830) 0.138 (0.109) (1.270) - 0.116 (0.107) ( - 1.089) - 0.009 (0.107) (-0.081) - 0.047
(0.106)
(--0.930) 0.098 (0.108) (0.903) 0.019 (0.107) (0.177)
(0.106)
(-0.450) - 0.082 (0.108) ( - 0.757) 0.011 (0.107) (0.105)
0.068 (1.272) 0.002 (0.033) 0.082 (1.560) 0.146 (2.971) 0.003 (0.045) 0.009 (0.148) 0.028 (0.143)
(.) - 0.055 (0.112) ( - 0.494) 0.029 (0.107) (0.274) -0.001 (0.103) (-0.013) 0.027 (0.096)
0.041 (0.743) 0.075 (1.413) 0.075 (1.407) 0.028 (0.507)
(0.280) - 0.140 (0.106) ( - 1.327) 0.017 (0.108)
0.032 (0.578) 0.037 (0.673)
(o.158) -
0.006 (0.103)
( - 0.058)
0.008 (0.145)
545
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554 Table 1 (continued) Country
a0
Nigeria
- 0.023 (0.037) (-0.636) Panama 0.065 (0.073) (0.889) Peru 0.008 (0.100) (0.083) Philippines 0.013 (0.040) (0.322) Poland 0.040 (0.070) (0.578) Senegal 0.025 (0.079) (0.317) Sudan 0.061 (0.128) (0.474) Uruguay -0.024 (0.033) (-0.723) Venezuela - 0.044 (0.027) ( - 1.610) Yugoslavia - 0.041 (0.063) (0.652) Zaire - 0.008 (0.059) ( - 0.130)
b0
bI
b2
0.010 (0.013) (0.779) - 0.019 (0.026) (-0.743) 0.OOl (0.036) (0.025) - 0.004 (0.014) (-0.289) - 0.013 (0.025) (-0.529) - 0.008 (0.027) (-0.304) - 0.025 (0.046) (-0.536) 0.011 (0.012) (0.893) 0.017 (0.010) (1.717) 0.010 (0.023) (0.438) - 0.001 (0.021) ( - 0.002)
-0.118 0.078 (0.106) (0.107) (-1.111) (-0.726) 0.039 0.105 (0.105) (0.105) (0.366) (1.002) 0.019 0.037 (0.107) (0.107) (0.177) (0.344) 0.128 -0.158 (O.lO3) (0.105) (1.241) 1.509) - 0.029 0.178 (0.107) (O.lO7) -0.271) (1.663) -O.OOl 0.155 (0.118) (0.119) (1.308) 0.OO8) - 0.010 (0.108) (0.108) - 0.097) ( - 0.097) -0.192 - O.O8O (0.107) (0.107) ( - 0.748) ( - 1.797) 0.122 -0.152 (0.108) (0.107) (1.125) ( - 1.419) -0.172 0.084 (0.105) (O.lO5) (0.802) ( - 1.639) - 0.027 0.028 (0.110) (O.lO9) (-0.251) (-0.253) -
-
-
-
0
-
.
0
1
0
R2
b3
b4
-0.177 (0.106) ( - 1.669) -0.016 (0.105) (-0.152) -0.101 (O.109J ( - 0.925) 0.140 (0.104) (1.347) 0.221 (0.107) (2.064)* - 0.002 (0.119) -0.018) -O.OlO (0.108) - 0.097) - 0.070 (0.107) 0,654 ) 0.043 (0.108) (0.401) 0.034 (0.105) - 0.326 ) 0.013 (0.110) (0.119)
0.048 0.054 (0.108) (0.986) (0.442) - 0.222 0.070 (0.105) (1.304) (-2.122)* 0.006 0.011 (0.111) (0.198) (0.050) O.lO3 0.067 (0.104) (1.245) (0.993) -0.131 0.080 (0.108) (1.517) ( - 1.209) - 0.027 0.025 (0.110) (0.367) ( - 0.244) 0.002 0.003 (0.069) (0.059) (0.027) 0.048 0.046 (0.107) (0.845) (-0.450) - 0.048 0.075 (0.107) (1.416) ( - 0.445) - 0.219 0.077 (0.109) (1.453) ( - 2.008) * 0.004 0.002 (0.109) (0.030) (0.034)
-
-
-
The null hypothesis is b 0 = 0 (returns follow a random walk). The t-critical at the 5 percent level is - 2 . 8 8 [from Dickey and Fuller (1979)]. An asterisk indicates that the individual coefficient is different from zer~ at the 5 percent significance level. Standard errors and t-statistics are in parentheses.
c o n f i r m e d f o r all c o u n t r i e s , e x c e p t S e n e g a l a n d S u d a n . T h e r e f o r e , t h e v a r i a n c e - r a t i o t e s t s r e p o r t a d r a m a t i c c h a n g e in t h e t i m e s e r i e s p r o p e r t i e s o f t h e L D C s s y n d i c a t e d l o a n s m a r k e t f r o m t h e f i r s t to t h e s e c o n d p e r i o d . In sum, the results of the variance-ratio tests for the first time period, July 1988 to J u n e
1990,
developing
confirm
the null hypothesis
of random
walk
for 16 of the 26
countries under analysis. For the other ten countries, the secondary
546
S.H. Lee et al. //Journal of Banking & Finance 20 (1996) 537-554
Table 2 Variance-ratio estimates and variance-ratio test statistics under heteroscedasticity of the random walk hypothesis for secondary market prices of LDCs syndicated loans. Time period: July 7, 1988-June 31, 1990 Country
Algeria Argentina Bolivia Brazil Chile Columbia Costa Rica Dom. Rep. Ecuador Honduras Ivory Coast Jamaica Mexico Morocco Nicaragua Nigeria Panama Peru Philippines Poland Senegal Sudan
Number q of base observations aggregated to form variance ratio 2
4
8
12
16
0.370 ( - 3.361)" 1.106 (1.243) 0.105 ( - 5.738)" 1.002 (0.016) 0.973 (-0.246) 0.411 (-4.197)" 0.283 ( - 3.502)* 0.171 ( - 5.999)' 0.359 ( - 3.699)" 0.095 (-3.017)" 0.173 ( - 3.200)" 0.205 ( -3.645)" 1.074 (0.566) 0.366 (-3.418)" 0.100 ( - 6.400)" 0.090 (-4.520)" 0.636 ( - 2.650)" 0.154 (-4.117)* 1.042 (0.260) 1.094 (0.933) 0.097 ( - 6.624)* 0.076 ( - 6.272)"
0.346 ( - 1.863) 1.218 (1.363) 0.099 ( - 3.090)* 1.199 (0.910) 1.187 (0.907) 0.516 ( - 1.845) 0.207 ( - 2.070)* 0.140 ( - 3.325)" 0.470 ( - 1.635) 0.099 ( - 1.606) 0.158 (-1.742) 0.213 ( - 1.929) 1.264 (1.072) 0.346 ( - 1.884) 0.069 ( - 3.539)" 0.090 (-2.418)' 0.856 ( - 0.560) 0.141 (-2.232)" 1.270 (0.899) 1.435 (2.298)" 0.093 ( - 3.555)" 0.054 ( - 3.433)"
0.465 ( - 0.964) 1.024 (0.096) 0.084 ( - 1.990) 1.204 (0.591) 1.234 (0.718) 0.621 (-0.914) 0.157 ( - 1.392) 0.109 ( - 2.179)* 0.597 ( - 0.787) 0.115 (-0.997) 0.146 (-1.117) 0.118 ( - 1.369) 1.379 (0.975) 0.359 ( - 1.167) 0.067 ( - 2.244)" 0.100 (-1.541) 0.901 ( - 0.243) 0.130 (-1.430) 1.230 (0.485) 2.009 (3.369)" 0.096 ( - 2.242)' 0.032 ( - 2.220)"
0.594 ( - 0.577) 0.766 ( - 0.730) 0.069 ( - 1.603) 0.946 (-0.124) 1.067 (0.164) 0.613 (-0.737) 0.165 ( - 1.089) 0.084 ( - 1.770) 0.647 ( - 0.543) 0.095 (-0.805) 0.134 (-0.894) 0.075 ( - 1.141) 1.219 (0.444) 0.376 (-0.897) 0.065 ( - 1.786) 0.103 (-1.262) 0.689 ( - 0.604) 0.137 (-1.119) 1.095 (0.159) 2.469 (3.870)* 0.095 ( - 1.774) 0.021 ( - 1.772)
0.710 ( - 0.351) 0.769 ( - 0.614) 0.065 ( - 1.378) 0.743 (-0.500) 0.969 (-0.064) 0.645 (-0.575) 0.154 ( - 0.941) 0.075 ( - 1.522) 0.690 ( - 0.407) 0.083 (-0.695) 0.115 (-0.778) 0.062 ( - 1.007) 1.134 (0.232) 0.349 (-0.796) 0.066 ( - 1.538) 0.113 (-1.118) 0.591 ( - 0.677) 0.146 (-0.944) 1.081 (0.115) 2.757 (3.943)" 0.101 ( - 1.508) 0.021 ( - 1.509)
S.H. Lee et aL /Journal of Banking & Finance 20 (19961 537-554
547
Table 2 (continued) Country
Number q of base observationsaggregated to form varianceratio 2
Uruguay Venezuela Yugoslavia Zaire
0.391 - 2.983)* 1.179 (0.858) 0.446 -3.552)* O.137 -4.794)*
4
8
12
16
0.408 ( - 1.550) 1.210 (0.539) 0.493 ( - 1.737) O.118 (-2.618)*
0.329 ( - 1.11O) 1.464 (0.753) 0.613 (-0.842) O.112 ( - 1.666)
0.247 ( - 0.984) 1.528 (0.676) 0.731 (-(I.475) 0.093 ( - 1,343)
0.170 ( - 0.924) 1.615 (0.671) 0.794 (-0.3211 (I.072 ( - 1.1711
The data consist of average weekly bid and offer secondary market prices of 26 developingcountry syndicated loans from July 7, 1988 to June 31, 1990. One week is taken as base observationinterval.The variance-ratio0-~-(q,1) is definedas 0-Z(q)/0- 2( 1), where 0-Z(q) is an unbiased estimator of 1/q of the variance of the qth difference of the natural logarithm of the syndicated loan price, and o-2(1) is an unbiased estimator of the variance of the first-difference of the natural logarithm of the syndicatedloan price. The variance-ratioo-2(q,11 is reported in the main row, and the heteroscedasticity-robustvariance-ratio test statistic Z" (q) is in parentheses. The null hypothesis is that o'2(q,1) is equal to one (prices follow a random walk). An asterisk indicates rejectionof the null at the 5 percent significancelevel. market prices of syndicated loans exhibit mean-reversion. The finding of mean-reversion agrees with Fama and French (1988), and French and Roll (1986), who reported negative serial correlation for American stocks, and with Poterba and Summers (1988), who have found mean-reversion for a sample of the European and Asian national stock index. For the second time period, July 1990 to May 1992, the random walk hypothesis is generally confirmed by the variance-ratio tests. Our findings of a random walk for secondary market prices of syndicated loans for LDCs for the second period, differ from Urrutia (1994), who reject the random walk for several Latin American equity markets, Lo and MacKinlay (1988), who reject the null of random walk for N Y S E - A M E X stock prices, and from Poterba and Summers (1988), who also reject the random walk hypothesis for stocks of several national equity markets, including European and Asian markets. On the other hand, our findings of random walk agree with Claessen et al. (1993), who do not reject the null of random walk for a sample of 20 emerging markets. These apparently contradictory results should not be surprising. In effect, Solnik (1974) in his investigation of the market structure of stock prices for several European markets concludes that prices are strongly affected by the specific characteristics of the individual markets. 5.2. Results o f tests o f market efficiency For the time period July 7, 1988 to June 31, 1990, the variance-ratio tests have partially rejected the random walk in favor of a mean-reversion process. However,
S,H. Lee et al. / J o u r n a l of Banking & Finance 20 (1996) 537-554
548
Table 3 Variance-ratio estimates and variance-ratio test statistics under heteroscedasticity of the random walk hypothesis for secondary market prices of LDCs syndicated loans. Time period: July 1, 1990-May 27, 1992 Country
Algeria Argentina Bolivia Brazil Chile Columbia Costa Rica Dora. Rep. Ecuador Honduras Ivory Coast Jamaica Mexico Morocco Nicaragua Nigeria Panama Peru Philippines Poland Senegal Sudan
Number q of base observations aggregated to form variance ratio 2
4
8
0.689 (-0.863) 1.009 (0.091) 0.962 ( - 0.607) 0.952 (-0.591) 1.046 (0.457) 1.014 (0.543) 1.084 (1.088) 0.978 (-0.153) 0.875 ( - 0.857) 0.799 ( - 1.463) 1.052 (0.707) 0.981 ( - 0.290) 1.027 (0.216) 0.953 (-0.353) 1.044 (0.339) 0.955 (-0.515) 1.125 (1.246) 1.026 (0.240) 0.896 ( - 0.534) 1.151 (0.991) 0.939 (-5.382)* 0.719 ( - 70.720) *
0.731 ( - 0.400) 1.156 (0.823) 0.982 (-0.154) 0.858 ( - 0.942) 1.357 (1.889) 1.030 (0.619) 1.109 (0.749) 0.589 ( - 1.507) 0.897 (-0.375) 0.713 ( - 1.117) 0.809 ( - 1.390) 1.024 (0.199) 0.942 0.246) 0.972 -0.111) 1.115 (0.472) 0.745 ( - 1.544) 1.238 (1.271) 1.049 (0.240) 1.025 (0.067) 1.351 (1.299) 1.059 (2.783) * O.582 ( - 56.180) *
0.582 ( - 0.393) 1.379 (1.268) 0.884 ( - 0.627) 0.738 ( - 1.096) 1.435 (1.460) 0.857 ( - 1.888) 1.089 (0.388) 0.458 ( - 1.258) 0.817 ( - 0.423) 0.716 ( - 0.700) 0.659 ( - 1.565) 1.191 (0.999) 0.797 ( - 0.544) 1.096 (0.241) 1.196 (0.509) 0.583 ( - 1.602) 0.882 ( - 0.400) 1.096 (0.299) 1.329 (0.570) 1.495 (1.097) 1.129 (3.907)* 0.522 (-42.551)"
-
12
16
0.423 ( - 0.428) 1.164 (0.432) 0.609 ( - 1.669) 0.595 ( - 1.341) 1.664 (1.773) 0.751 ( - 2.594)* 1.149 (0.512)
0.296 ( - 0.444) 0.816 (-0.414) 0.434 ( - 2.059)* 0.534 ( - 1.321) 1.783 (1.797) 0.882 ( - 1.049) 1.295 (0.863)
(.) 0.625 ( - 0.684) 0.692 ( - 0.599) 0.537 ( - 1.677) 1.141 (0.583) 0.866 ( - 0.284) 1.167 (0.337) 1.388 (0.793) 0.604 ( - 1.201) 0.843 ( - 0.420) 1.152 (0.374) 1.427 (0.584) 1.541 (0.946) 1.133 (3.201)* 0.467 (-39.126)*
(.) 0.487 ( - 0.797) 0.682 ( - 0.528) 0.406 ( - 1.835) 1.070 (0.248) 0.814 ( - 0.337) 1.241 (0.426) 1.432 (0.754) 0.680 ( - 0.825) 0.643 ( - 0.812) 1.143 (0,299) 1,316 (0.370) 1.638 (0.950) 1.147 (3.031)" 0.477 (-33.766)*
549
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
Table 3 (continued) Country Uruguay Venezuela Yugoslavia Zaire
Number q of base observations aggregated to form variance ratio 2
4
8
12
16
0.961 (-0.363) 0.884 (-0.895) 1.103 (0.887) 0.980 (-0.911)
0.759 ( - 1.197) 1.006 (0.024) 0.990 (-0.045) 0.965 (-0.865)
0.743 (-0.809) 1.057 (0.149) 0.667 (-0.972) 0.971 (-0.450)
0.765 (-/).583) 1.087 (0.180) 0.560 ( - 1.013) 1.014 (0.170)
0.774 (-0.478) 1.016 (0.029) 0.489 ( - 1.001) 1.086 (0.902)
The data consist of average weekly bid and offer secondary market prices of 26 developing country syndicated loans from July 1, 1990 to May 27, 1992. One week is taken as base observation interval. The variance-ratio cr 2(q, 1) is defined as o": (q)/o- z( 1), where o-2(q) is an unbiased estimator of 1 / q of the variance of the qth difference of the natural logarithm of the syndicated loan price, and o'2(1) is an unbiased estimator of the variance of the first-difference of the natural logarithm of the syndicated loan price. The variance-ratio o-2(q,1) is reported in the main row, and the heteroscedasticity-robust variance-ratio test statistic Z *(q) is in parentheses. The null hypothesis is that o'2(q,1) is equal to one (prices follow a random walk). An asterisk indicates rejection of the null at the 5 percent significance level.
the existence of autocorrelation in the secondary market prices of syndicated loans does not necessarily imply market inefficiency (Lucas, 1978; Levitch, 1979). In effect, spurious autocorrelation m a y also be due to infrequent or n o n s y n c h r o n o u s trading (Poterba and S u m m e r s , 1988; Scholes and W i l l i a m s , 1977). It has also been suggested that small securities trade less frequently than larger securities and that noise trading may also provide explanations for the presence of transitory c o m p o n e n t s of prices (Cutler et al., 1988). In this section we present specific tests of market efficiency. Tables 4 and 5 present the basic statistics o f w e e k l y returns of the secondary markets for L D C s syndicated loans for the first and second period, respectively. 9 It can be seen that the series exhibit kurtosis and skewness. Also, studentized ranges are larger than 6.0, i m p l y i n g that the sample does not c o m e f r o m a normal distribution. ~o A test for i n d e p e n d e n c e b e t w e e n successive returns w h i c h does not require normality is the filter rule test. E m p l o y i n g a 2.5% filter rule, the results of the filter tests are presented in Table 6.
'~For details about the formulas to compute mean, standard deviation, skewness and kurtosis (and their standard errors), and the studentized range, see Fama (1976). l0 See Fama (1976).
550
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
Table 4 Summary statistics of weekly returns of secondary markets of LDCs syndicated loans. Time period: July 7, 1988-June 31, 1990 Country
Mean
Standard deviation
Skewness
Kurtosis
Skewness std. error
Kurtosis std. error
Studentized range
Algeria Argentina Bolivia Brazil Chile Columbia Costa Rica Dom. Rep. Ecuador Honduras Ivory Coast Jamaica Mexico Morocco Nicaragua Nigeria Panama Peru Philippines Poland Senegal Sudan Uruguay Venezuela Yugoslavia Zaire
-0.13284 -0.50891 0.08747 -0.74656 0.08679 -0.02388 0.62221 -0.12840 -0.42279 -0.18371 - 1.40729 0.0358 -0.16379 -0.09548 0.49118 0.01702 -0.52166 -0.21902 -0.04769 -0.98301 -0.27010 -0.17531 -0.18570 -0.18359 0.28818 -0.04691
2.4579 6.2141 6.9730 5.5738 1.6468 3.1394 8.8119 13.3722 7.4553 20.1373 24.0219 10.5614 3.0267 3.7671 58.5701 10.2434 10.0490 16.5682 2.9693 4.2313 8.0005 75.0975 2.4034 3.4739 2.4583 5.8663
-0.25909 0.71279 -0.04935 -0.36488 -0.23175 0.32336 0.12728 -0.13136 0.31048 -0.20757 -0.57491 0.07646 -0.54528 -0.70849 0.03500 -0.14324 0.24264 0.18228 0.20236 - 1.18022 -0.06038 0.00857 0.10518 -0.45667 0.74939 -0.06997
2.65881 1.54597 -0.11169 0.97987 2.54396 0.96733 2.23686 -0.53571 1.61358 7.50094 6.98035 4.18026 4.23172 3.17969 -0.48723 1.74917 1.78341 2.20615 2.87139 5.15220 -0.61897 -0.57218 3.94672 4.36828 1.72737 1.14976
0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24744 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019 0.24019
0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.49487 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038 0.48038
6.80372 6.33801 4.20487 5.42603 6.81225 6.29514 6.86071 4.47792 5.72944 6.88422 8.31350 6.60133 7.52495 6.45555 4.43668 5.15740 5.81741 5.98123 6.58364 7.75535 4.74039 3.20643 6.69237 7.77407 6.40687 5.37402
Weekly returns are computed as R, = 1001n(Pt / P t - I), where the price is the average of the bid and offer prices. Standard errors are computed as: V/6/N for skewness and 2V/-~-/N for kurtosis. The studentized range is computed as: (maximum return- minimum return)/standard deviation. The number of observations N = 104, except for Costa Rica N = 98.
E m p i r i c a l e v i d e n c e o f m a r k e t i n e f f i c i e n c y is f o u n d for the first time p e r i o d , July 1988 to J u n e 1990. T h e s e results a g r e e w i t h t h o s e r e p o r t e d by H a r v e y (1993) and C l a e s s e n et al. (1993), w h o r e p o r t e d i n e f f i c i e n c y in e m e r g i n g markets. O n the o t h e r h a n d , the null o f m a r k e t e f f i c i e n c y g e n e r a l l y c a n n o t be r e j e c t e d for the m o r e r e c e n t t i m e p e r i o d , July 1990 to M a y 1992. T h e s e results agree w i t h Urrutia (1994) and c o n f i r m our h y p o t h e s i s o f an i n c r e a s e in e f f i c i e n c y o f the L D C s
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
551
Table 5 Summary statistics of weekly returns of secondary markets of LDCs syndicated loans. Time period: July 1, 1990-May 27, 1992 Country
Mean
Standard deviation
Skewness
Kurtosis
Skewness std. error
Kurtosis std. error
Studentized range
Algeria Argentina Bolivia Brazil Chile Columbia Costa Rica Dom. Rep. Ecuador Honduras Ivory Coast Jamaica Mexico Morocco Nicaragua Nigeria Panama Peru Philippines Poland Senegal Sudan Uruguay Venezuela Yugoslavia Zaire
0.03121 1.29820 0.19392 0.51319 0.31814 0.23570 0.48098 - 0.26352 0.50724 0.40451 0.50355 0.48097 0.38350 0.08151 0.72311 0.36441 1.03610 1.21053 0.03764 0.47603 0.27508 0.52125 0.37660 (I.29225 - 1.01433 -0.66829
4.7812 4.7315 4.0943 6.4050 1.0143 1.9271 2.6703 0.8740 4.6081 2.6574 12.4937 3.1211 1.8655 3.4807 8.3080 3.0925 6.1904 8.3364 3.4965 6.0057 3.8101 16.0689 2.7932 2.2367 5.4089 4.4487
-2.68785 0.00902 2.16818 - 1.13675 1.45396 6.72884 3.47697 - 3.31662 0.44446 1.87302 2.66697 3.28568 0.77163 0.20635 0.15657 1.25969 2.32351 0.74020 0.10264 1.18163 5.70011 4.53549 0.94325 0.00613 0.02475 -5.76321
31.7167 1.9929 28.0341 5.4739 4.5939 50.5629 19.6094 11.0000 2.6990 16.1513 29.2533 24.3699 3.4717 2.1805 5.4535 4.5752 10.8477 4.2676 5.1572 4.1563 41.7395 36.4601 12.5542 2.5283 4.1682 42.1855
0.24744 0.24744 0.24744 0.24744 0.24744 0.24744 0.24871 0.73855 0.24744 0.24744 (}.24744 0.24744 (I.24744 0.24744 0.24744 0.24744 (I.24744 0.24744 (/.24744 (I.24744 0.27386 (/.24744 0.24744 0.24744 0.24744 0.24744
0.49487 0.49487 0.49487 0.49487 0.49487 0.49487 0.49742 1.47710 (I.49487 (I.49487 (/.49487 0.49487 0.49487 0.49487 0.49487 0.49487 0.49487 0.49487 (I.49487 (I.49487 0.54772 0.49487 (/.49487 (I.49487 (I.49487 (I.49487
11.9315 6.5082 11.3747 7.3254 6.4432 9.8031 9.(/708 3.3166 6.2787 9.9862 11.5236 10.7103 7.3120 5.9894 7.(175(I 6.7831 7.6245 6.6301 7.440 6.9992 9.5550 1(t.4175 9.4585 7.1251 6.6332 10.4613
Weekly returns are computed as Rt = 1001n(P~ ~P t-1 )' where the price is the average of the bid and offer prices. Standard errors are computed as: v ~ / N for skewness and 2 ~ / N for kurtosis. Thc studentized range is computed as: (maximum r e t u r n - m i n i m u m return)/standard deviation. The number of observations N = 98, except for Costa Rica N 97, for Dom. Rep. N - l 1, and for Senegal N = 80.
syndicated ciency
loans market over time. A plausible explanation
is a l a r g e r t r a d i n g v o l u m e
and increase
in m a r k e t
for the higher effimaturity
during
the
s e c o n d p e r i o d . A s it w a s s t a t e d e a r l i e r , t h e t r a d i n g v o l u m e in t h e s e c o n d a r y m a r k e t f o r L D C s y n d i c a t e d l o a n s i n c r e a s e d f r o m $ 2 b i l l i o n in 1 9 8 4 to a r o u n d $ 5 0 0 b i l l i o n in 1 9 9 2 .
S.H. Lee et al. /Journal of Banking & Finance 20 (1996) 537-554
552
Table 6 Nominal annual returns for in-weeks and out-weeks for 26 countries based on the 2.5% filter rule Countries
July 1 9 8 8 - J u n e 1990
Ri.(%) Algeria Argentina
July 1 9 9 0 - M a y 1992
Ro.t(%)
T
Rin(%)
Rout(%)
T
- 39.38 27.14
26.84 -57.32
- 2.73 * 1.40
2.87 73.72
- 3.25 51.98
0.05 0.41
- 188.06 - 2.63
353.32 - 61.44
- 10.96 * 0.96
16.69 48.77
4.70 - 16.82
0.25 0.74
Chile Columbia Costa Rica Dom. Rep. Ecuador Honduras Ivory Coast Jamaica Mexico Morocco
- 0.20 - 38.40 - 200.59 - 346.71 -184.24 - 529.02 - 1183.26 - 177.65 - 7.03 - 47.77
7.22 47.77 295.68 560.05 117.09 435.71 398.23 489.12 - 12.36 164.20
- 0.42 - 2.65 * - 6.33 * - 8.16 * -4.33* - 5.22 * -6.21" - 5.78 * 0.14 - 4.08 *
16.54 24.75 28.52 - 15.07 16.57 13.79 -39.03 25.01 13.25 23.10
- 3.72 19.57 0.0 50.85 368.95 71.16 144.50 - 32.19
1.58 0.34 -0.51 - 26.77 * -0.82 - 2.83 * 1.43
Nicaragua Nigeria Panama Peru Philippines Poland Senegal
- 2757.90 - 193.82 - 180.57 - 594.06 - 10.75 - 26.61 - 240.56
1836.99 384.73 94.57 399.35 46.57 76.58 348.37
*
29.21 5.75 86.19 52.06 15.76 66.57 6.77
52.06 28.05 - 6.95 93.08 - 30.89 - 35.88 18.59
- 0.25 -0.66 1.31 - 0.43 1.02 1.67 - 0.28
Sudan Uruguay Venezuela Yugoslavia Zaire Average
- 2866.22 - 38.63 22.72 2.12 - 133.07 -382.12
2536.31 14.23 - 26.63 41.48 175.69 328.42
- 9.73 * - 2.21 * 1.37 - 1.37 - 5.87 * -4.17"
105.80 11.23 8.90 - 73.27 - 15.96 21.10
- 22.74 93.07 38.28 - 46.80 - 45.19 32.90
0.66 - 1.75 - 0.84 - 0.36 0.74 -1.10
Bolivia Brazil
- 11.20 -6.12 - 2.82 - 6.98 - 0.80 1.16 - 9.56
* * * *
Mean returns on in-weeks (Rin) and out-weeks (Ro.t) selected by 2.5% filter rule. In-week and out-week two-sample t-statistics are denoted by T. An asterisk indicates significance at the 5% level.
6. Summary and conclusions This
paper
secondary time
series
increasing enhance
empirically
market
properties market
investigates
for LDCs
syndicated
of the LDCs
integration
our understanding
and
the sample
is d i v i d e d
1990, and from July
loans. The
syndicated globalization
of this fast-growing
In order to facilitate the analysis time,
the randomness
and to evaluate
in t w o
1990 through
time
May
loans
the efficiency
is i m p o r t a n t
of security
the evolution from
July
of the
efficiency
and
in a w o r l d
markets.
and increasingly
periods:
1992.
and
study of market
It w i l l
important
of also
market.
of this market
over
1988
June
through
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