2014 年 1 月 17 日提出

論文題目 DETERMINANTS OF INTEREST RATESIN HIGHLY DOLLARIZED CAMBODIA

地主 敏樹研究室
学籍番号 1062280e
氏名 TANN HOKLOING

CONTENTS

INTRODUCTION…………………………………………………………………… 1
CHAPTER 1: LITERATURE REVIEW…………………………………………….. 5
CHAPTER 2: RESEARCH METHODOLOGY AND MODELS ………………….. 10
2.1- Method for Assessing Expected Inflation and Inflation Risk …………….. 10
2.2- Estimation of Nominal Interest Rates …………………………………….. 12
2.3- Granger Causality …………………………………………………………. 13
CHAPTER 3: DATA AND UNIT ROOT TEST…………………………………….. 15
3.1- Data………………………………………………………………………... 15
3.2- Unit Root Tests ……………………………………………………………. 18
CHAPTER 4: EMPIRICAL RESULTS……………………………………………... 22
4.1- Estimation of Inflation with ARMA Model ………………………………. 22
4.2- Estimation of Inflation and Inflation Risk with ARMA-GARCH….. 25

4.3- Examination on Friedman-Ball Hypothesis Validity …………….………. . 27
4.4- Examination on Fisher Effect ……………………………………………... 29
4.5- Determinants of Interest Rates in Cambodia …………………….……….. 33
CONCLUSION……………………………………………………………………… 41
Acknowledgements………………………………………………………………….. 45
References…………………………………………………………………………… 46

INTRODUCTION

Determination of interest rates has been one of the most interesting themes thatmany economists and policymakers have been investigating both theoretically andempirically. The studies have been strikingly conducted, particularly after a well-known
economist Irving Fisher hypothesized the so-called Fisher effect in his 1930’s The
Theory of Interest. Fisher posits that expected inflation is the main factor determining
nominal interest rate. Fisher equation, the relation that nominal interest is equal to realinterest rate plus expected inflation, suggests that when the inflation is expected toincrease by one percent, the nominal interest rate will increase by one percent, leaving
the real interest rate independent of the expected inflation. The one-for-one relationshipbetween the nominal interest rate and the expected inflation is called the Fisher
hypothesis or the Fisher effect. Thus far, the Fisher effect has been extensively
examined in various periods in many economies, specifically in developed countries
and emerging economies. However, there have been relatively few researches on the
Fisher effect in developing countries and least developed countries (LDCs). Cambodia,
in particular, is a least developed country in which the Fisher effect and/or thedetermination of the interest rates have not been studied and published so far. For thisreason, this paper seeks to shed some light on the determinants of the (nominal) interest
rates in Cambodia, incorporating other tentative determinants into the conventional
Fisher equation.
The Fisher equation well explains the situation of the United States during thelast 50 years. Nonetheless, it does not work well for the period of late 19th and early 20th
centuries. Recent research suggests that this happens because the Fisher effect relates
the nominal interest rate to expected inflation, and the inflation during the early period
was quite unexpected (Mankiw, 2010, pp.94-97). Every economic agent cares about real
return on assets they are holding. However, since actual inflation is unknown when thenominal interest rate is set at a given time, the real return is also unknown. Therefore,
the economic agent has to assess the ex ante real interest rate by forming expectation of
the inflation. If the inflation expectation is uncertain, the real interest rate will also1

become unpredictable. This uncertainty of the inflation can be interpreted as the risk the
economic agent takes into account when making a portfolio decision. A risk-averse
investor prefers a higher return for a given level of risk, or a lower risk for a given levelof return. Therefore, it is reasonable to say that the higher uncertainty of the inflation
should be associated with the higher return (equivalently, the higher nominal interestrate). See, for example, Berument and Malatyli (2001). In this regard, the uncertainty of
the inflation—inflation risk—feasibly becomes another determinant of the nominalinterest rate.
When contemplating Cambodia’s economy, particularly banking and financial
sector, dollarization is an inevitable phenomenon to be taken into account as Cambodia
has been one of the most highly dollarized countries in the world 1. The causes of thedollarization in the country are as follows. The National Bank of Cambodia (NBC), thecentral bank of the country, was established in 1954 after the country gained the
independence from France in 1953. The riel, the national currency, was the legal tenderuntil 1975. Unfortunately, under the radical Khmer Rouge regime from 1975 to 1979,
the central bank was closed, the whole banking system was completely destroyed, andthe riel banknotes were no longer used (NBC’s website). After the so-called Khmer
Rouge regime collapsed in 1979, the riel was reintroduced in 1980, and Cambodia
became a centrally planned economy. Thanks to Paris Peace Agreements in 1991,
unsettled political conflicts ended, and the country shifted from the centrally planned
economy to a market-oriented one. During 1988-1991, NBC printed a massive amountof money to finance the recurrent budget deficits, resulting in high inflation in the rangeof 90-177 percent a year, and in an erosion of public confidence in the riel (Zamaróczyand Sa, 2002). The exchange rates also jumped from around 700 riels per dollar in 1991
to around 2,700 riels per dollar in 1992 (Samreth, 2010). From 1991 to 1993, United
Nations Transitional Authority in Cambodia (UNTAC) took over the country’s
administration in the purpose of peacekeeping, and then organized the first generalelections in 1993. The cost of this two-year operation was estimated to be close to US$2
billion, almost 75% of Cambodia’s GDP in 1993 (Zamaróczy and Sa, 2002). Since then,foreign currency deposits have increased, and the dollarization has gradually risen. The
2

dollarization, measured as the ratio of foreign currency deposits to broad money, hasrisen from about 60% in late 1990s to about 80% in 2010 (Duma, 2011). This increasing
dollarization is happening simultaneously with impressive macroeconomic and politicalachievements; Menon (2008) calls this simultaneous phenomenon a paradox. It is worth
noting that during 2000s, economic growth has averaged over 9%, income per capita
has doubled, and annual inflation has also been maintained low at a single digit during
this period, except in 2008.
Given the fact that Cambodia is a highly dollarized economy, the U.S. Federal
Reserve’s monetary policy is highly likely to substantially affect Cambodia’s economy,
particularly financial sector. It is exceedingly convincing that the U.S. (nominal) interest
rate influences the (nominal) interest rates in Cambodia 2. The U.S. interest rate,
therefore, is also included in the estimation of the determinants of the interest rates in
Cambodia.
As mentioned earlier, the main objective of this paper is to investigate thedeterminants of the interest rates in Cambodia. In the investigation, four questions are ofinterest. Firstly, does the Fisher hypothesis, suggesting that there is a positive
relationship between the expected inflation and the interest rates, hold in Cambodia?
And if the hypothesis is rejected, what factors could lead to the rejection? Secondly,
does the inflation risk (i.e., the inflation uncertainty) affect the interest rates? Thirdly, isthe interest rate in the U.S. positively correlated with the interest rates in Cambodia? If
the relationships exist, does the interest rate in the U.S. Granger-cause the interest rates
in Cambodia? Additionally, does Friedman-Ball hypothesis, which states that a higher
level of inflation leads to a higher inflation uncertainty, hold in Cambodia? The finalquestion is seemingly irrelevant to the determination of the interest rates, but it could be
easily addressed since the inflation uncertainty will have been already calculated.
This thesis is structured as follows. Chapter one reviews existing literature on
interest rate determination. Chapter two introduces research methodology including
econometric models such as ARMA, GARCH, and Granger causality test. Datadescription and unit root tests are explained in chapter three. In chapter four, all of theempirical results are reported and discussed. Finally, the last part is conclusion.
3

ENDNOTES:

1. Thus far, there have been several studies on the dollarization in Cambodia. They
include Viseth (2001), (Zamaróczy and Sa, 2002), Kang (2005), Menon (2008),
Samreth (2010), and Duma (2011).
2. In this thesis, when “interest rate” is used, it is simply referred to “nominalinterest rate” unless otherwise specified.

4

CHAPTER 1: LITERATURE REVIEWThe literature on determination of interest rates is predominantly centered on
the Fisher hypothesis. Cooray (2003) provides a very great review on the previous
empirical studies on the Fisher effect, and describes the methodologies employed inthose studies. Most of the researches done so far have focused on developed countries,of which the United States is one of the most observed economies. By contrast, thestudies on the developing countries have been sparse.
In his pioneering work, Fisher (1930) used annual data during 1890-1927 for
the United States and during 1820-1924 for the United Kingdom to investigate therelationship between the nominal interest rate and the price changes. He found that there
were no apparent short-run relationships between the nominal interest rates and pricechanges in the two countries. However, when many lagged price changes were included,
the correlation he obtained was 0.86 for the U.S. and 0.98 for the U.K. He therefore
concluded as on page 451 that: “We have found evidence general and specific, fromcorrelating P’ with both bond yields and short term interest rates, that price changes do,
generally and perceptibly affect the interest rate in the direction indicated by a priori
theory. But since forethought is imperfect, the effects are smaller than the theory
requires and lag behind price movements, in some periods, very greatly. When theeffects of price changes upon interest rates are distributed over several years, we havefound remarkably high coefficients of correlation, thus indicating that interest rates
follow price changes closely in degree, though rather distantly in time”.
Fama (1975) examines the market for one- to six-month U.S. Treasury bills to
explore whether the market is efficient. An efficient market, according to him,
accurately uses all relevant information in setting prices. He concludes that, during
1953-1971, there exist relationships between nominal interest rates and subsequent
inflation rates and that “the bill market seems to be efficient in the sense that nominalinterest rates summarize all the information about future inflation rates that is intime-series of past inflation rates.” Also, he finds some evidence to support the
hypothesis that real returns on the treasury bills are constant during the period studied 1.
5

Recognizing that in the United States the strong Fisher effect exists duringcertain periods but not for others 2, Mishkin (1992) attempts to explain why this is thecase. Utilizing monthly data between January 1953 and December 1990, he finds noevidence for a short-run Fisher effect in which changes in expected inflation and ininterest rates are correlated, but support for a long-run Fisher effect in which bothinflation and interest rates trend together. He interestingly argues that the Fisher effectwill exist only during samples in which both expected inflation and interest rates exhibit
stochastic trends. The reason is that if these two series have trends, their trends will be
the same. Fahmy and Kandil (2003), by using the U.S. monthly data in 1980s and early
1990s, provide additional support to Mishkin (1992), finding that the short-run Fisher
effect is insignificant, while long-run Fisher effect exists since the cointegration
relationship between nominal interest rates and inflation cannot be rejected.
In the case of Australia, Mishkin and Simon (1995) find that there is a long-run
Fisher effect, but no short-run Fisher effect. They suggest that short-run changes ininterest rates reflect changes in monetary policy, while longer-term levels are primarily
driven by inflation expectations.
Following Mishkin’s (1992) work, Crowder (1997) finds that nominal interest
rate and inflation together contain unit roots over the period from the first quarter of1960 to the fourth quarter of 1991, and there is cointegration between the two series andthus long-run Fisher effect in Canada. However, the relationship (between the interest
rate and the inflation) for the last three decades is not so stable due to two structural
breaks—one in 1971, and the other in 1982.
Koustas and Serletis (1999), by using post-war quarterly data and employing
nonstructural bivariate autoregressive methodology, reject the strong Fisher effect, but
provide little support to the weak Fisher effect in eleven advanced economies—Belgium,
Canada, Demark, France, Germany, Greece, Ireland, Japan, the Netherlands, the United
Kingdom and the United States. In their conclusion on page 124, they write: “Ourresults are consistent with most of the existing literature on the Fisher effect, whichmostly shows (by tests other than the one that we have conducted) that fully anticipatedinflation has less than a unit effect on nominal interest rates, and thus reduces real
6

interest rates even in the longest of runs.” Applying the concept of fractional
cointegration to monthly series of the three-month interest rates and the inflation rates in
G7 countries over the period between January 1970 to March 2001, Lardic and Mignon(2003) find that, except for the case of Germany, there is a fractional cointegration
relationship between the interest rate and the inflation rate, and thus a Fisher effect.
Berument et al. (2007), using data in G7 countries and 45 developing countries to findwhether the Fisher effect is universal, provide additional evidence that in all the G7
countries the traditional Fisher effect holds in the weak form (in which the coefficient of
expected inflation is positive and less than one) and the augmented Fisher effect (bywhich they refer to the new estimation of the Fisher effect, incorporating inflation
uncertainty into the conventional Fisher equation) also exists.
Overall, various studies on the Fisher effect in developed countries do not
produce unanimous results. However, most of them give some evidences to support the
weak form of the Fisher effect; that is, the less-than-unity Fisher effect. Empiricalresearches on the Fisher effect in developing countries are presented below.
Payne and Ewing (1997) employ the Johansen-Juselius cointegration approachto evaluate the Fisher effect in nine lesser-developed countries, namely, Argentina, Fiji,
India, Malaysia, Niger, Pakistan, Singapore, Sri Lanka, and Thailand. The cointegration
method suggests that there is a long-run relationship between nominal interest rates andinflation in four countries—Malaysia, Pakistan, Singapore, and Sri Lanka. Additional
investigation indicates that among these four countries, the full Fisher effect (theone-for-one relationship between nominal interest rates and inflation) is present in three
countries (i.e., Malaysia, Pakistan, and Sri Lanka). Considering various nominal interest
rates, Ahmad (2010) utilizes relatively new approach, that is, autoregressive distributedlag (ARDL)-bond testing, to examine the validity of the Fisher effect in India,
Bangladesh, Pakistan, Sri Lanka, Kuwait, and Saudi Arabia. From the empiricalevidence, he concludes there is a weak form, rather than strong form of the Fisher effectin India, Pakistan, Kuwait, and Saudi Arabia. Nonetheless, he cannot find enoughevidence to support the Fisher effect in Bangladesh, and find no clear-cut results in thecase of Sri Lanka.
7

Almost all the studies in the literature, in the exception of Berument andMalatyali (2001), Berument et al. (2007), and Lorde et al. (2008), focus only on the
Fisher effect. In other words, they aim at examining whether or not inflation (orexpected inflation) affects interest rates.
Berument and Malatyali (2001) incorporate inflation risk (i.e., conditional
standard deviation of inflation, deriving from GARCH specification) and maturity into
the traditional Fisher equation to explore the determinants of nominal interest rates in
Turkey. Their empirical results show that both the expected inflation and the inflation
risk have positive effects on the nominal interest rates. Surprisingly, the maturity factor,
by contrast, is negatively correlated with the interest rates. As a recommendation forpolicymaker, the authors suggest that the government uses both auction interest rates
and maturity as an instrument for lowering the debt burden because the creditors in the
country tend to desire shorter maturity while requiring high risk premiums.
Augmenting the conventional Fisher effect by including inflation uncertainty
into the equation, Berument et al. (2007) find that, in contrast to the G7 countries inwhich both the inflation and the inflation uncertainty have positive correlation with theinterest rates, among the 45 developing countries under consideration the positive
relationship between the inflation and the interest rates exists in only 23 countries andthe relationship between the inflation uncertainty and the interest rates is positive in 18
countries and negative in the other 7 countries.
In an attempt to find out the determination of nominal interest rates in fivesmall developing Caribbean countries—the Bahamas, Barbados, Guyana, Jamaica, andTrinidad and Tobago, Lorde et al. (2008) also employ an augmented Fisher equation.
Unlike the works of Berument and Malatyali (2001) and Berument et al. (2007) inwhich the traditional Fisher equation is augmented with the inflation risk, Lorde et al(2008) augment the Fisher equation with the US nominal interest rate since thoseeconomies greatly depend on the U.S. economy, particularly imports. Their results ofthe cointegration examination suggest the existence of a long-run relationship for The
Bahamas, Jamaica (when the country adopts flexible exchange rate), and Trinidad andTobago. The Fisherian link between the nominal interest rate and expected inflation is8

not present in Barbados and Guyana, while the link is negative for Trinidad and Tobago.
The nominal interest rate in The Bahamas moves one-for-one with the US interest rate,
whereas the movement is more than one-for-one in the other countries.

ENDNOTES:

1. However, the work of Fama (1975) has been challenged by some researchers suchas Carlson (1977) and Nelson and Schwert (1977).
2. Mishkin (1992, p.196) documents: “Although the Fisher effect is widely accepted
for the period after the Fed-Treasury Accord in 1951 until October 1979 in the
United States, this relationship between the level of short-term interest rates andfuture inflation is not at all robust. The level of short-term interest rates has no
ability to predict future inflation in the United States prior to World War II or
after October 1979.”

9

CHAPTER 2: RESEARCH METHODOLOGY AND MODELSIn this chapter, research methodology and models are presented. The previous
chapter reviews the literature on determination of nominal interest rates. As discussed inliterature review, most of the studies center on the validity of the Fisher hypothesis inwhich expected inflation is the main determinant of nominal interest rates. Among thosestudies, only Berument and Malatyali (2001), Berument et al. (2007), and Lorde et al.
(2008) extend the Fisher equation, incorporating more factors to detect what determine
the nominal interest rates in the countries under their consideration. In the augmentationof the Fisher equation, Berument and Malatyali (2001) add inflation risk, whereas Lordeet al. (2008) include nominal interest rate of the United States. Unlike these studies, mythesis incorporates both inflation risk and the U.S. nominal interest rate in addition to
the expected inflation in order to explore how nominal interest rates in Cambodia reflect
to these factors.
Section 2.1 provides a description of how the expected inflation and theinflation risk are assessed. Next, estimation models in which the expected inflation, theinflation risk, and the U.S. nominal interest rate are incorporated are the tasks of section
2.2. Finally, section 2.3 introduces Granger causality test approach.

2.1- Method for Assessing Expected Inflation and Inflation Risk
Before introducing how to assess the inflation risk, it is necessary to discuss themethod from which the expected inflation is formed. In this thesis, the inflationisassumed to follow an autoregressive moving-average (ARMA) process with the orderof. Formally,.1

follows ARMA

(1)

Here

and

are the coefficient of the pth lag of the inflation and thecoefficient of the qth lag of the residualis, where. The assumption for the residual

denotes information set at time
10

and

is

conditional variance of the inflation. From equation (1), the conditional expected valueof the inflation at timewith the given information set available at timeis asfollows:(2)

The inflation risk is the uncertainty or the volatility of the inflation. When the
inflation is volatile and uncertain, an economic agent will face uncertainty of their
future real return. The uncertainty of inflation affects the agent’s welfare and his
portfolio decision. In this sense, inflation uncertainty or volatility is called inflation risk.
Conditional variance and its square root; that is, conditional standard deviation of theinflation are commonly used as inflation uncertainty or inflation risk. The conditionalvariance of the inflation can be assessed using autoregressive conditional
heteroskedasticity (ARCH) estimation. The ARCH approach was developed by Engle(1982). In his groundbreaking work, Engle (1982) uses the ARCH models in estimating
the variance of the United Kingdom inflation. The ARCH models assume thatconditional variance of the inflation model can be explained by the lagged values of themodel’s squared residuals. With

lagged values of the squared residuals, theconditional variance is defined as follows:

Where,
When

are the squared residuals andare the ARCH parameters.

, there is no ARCH effect and the residual

simply becomes white noise.

To generalize the ARCH model, Bollerslev (1986) includes lagged values of
the conditional variance into (3). It is therefore called generalized autoregressive
conditional heteroskedasticity (GARCH) model 2. With
conditional variance, the GARCH

lagged values of thecan be written as follows:11Where,If

are the ARCH parameters and, the GARCH

model becomes ARCH

are the GARCH parameters.
, and if

, thensimply is white noise. The conditions for the conditional variance to be non-negativeand non-explosive are: all the estimated coefficients in (4) must be positive; and the sumof allandmust be less than unity.

2.2- Estimation of Nominal Interest Rates
Fisher (1930) posits that expected inflation is the main factor that determines
nominal interest rate. Therefore, the estimation of the Fisher equation is written as:(5)

Where,

andrespectively denote the nominal interest rate and the

expected inflation at time . Next,

is the coefficient of the expected inflation andis constant term, reflecting real interest rate. Finally,is the error term.

As explained in the introduction part, investors are concerned about real return
rather than nominal return. However, when the nominal interest rate is set at a given
time , the inflation is not yet known at that time. Hence, the investors have to form
expectation of the inflation to assess the real interest rate. Since the expected inflationand the realized inflation are not always the same due to some uncertainty of theinflation, the investors face some risk that the realized real return might deviate fromtheir expected real return. To compensate for this, the inflation risk, measured as theconditional standard deviation of the inflation, must be reflected in the nominal interest
rate. In other words, the inflation risk influences the interest rate:(6)

Where,

is the conditional standard deviation of the inflation at timeandis its coefficient. The others are the same as mentioned above (see their descriptions
below the equation (5)).
12Given the fact that Cambodia’s economy is highly dollarized, it is plausible thatthe economy is influenced by the U.S. monetary policy. Thus, the interest rate in
Cambodia is highly likely to be influenced by the interest rate in the United States.
Incorporating the U.S. interest rate, the model of the interest rate in Cambodia becomes:(7)

Where,

denotes the U.S. interest rate at timeand its coefficient is.

The other variables are the same as mentioned previously. OLS regression is used toestimate the models of interest rate. It should be noted that the error term

is assumed

to have zero mean and constant variance. Recent statistical software advance allows the
expected inflationand the conditional variancestandard deviation(and thus the conditional) to be calculated easily by fitting ARMA-GARCH models.

2.3- Granger Causality
Granger (1969) invents a marvelous method for testing causality between two
variables. One variable
the past values ofis said to Granger-cause another variable, the past values ofhelp in predicting

if, given. Granger causality test

is usually performed with a vector autoregressive (VAR) model. Consider a VAR(s)model of two stationary series below.

To find whether

Granger-causes, the null and alternative hypotheses areproduced as follows:

If the null hypothesis

is rejected, it implies that
13

Granger-causes.

ENDNOTES:

1. For more explanation on the ARMA models, see, for example, Enders (1995,
pp.63-128), Enders (2010, pp.49-118), Lütkepohl and Krätzig (2004, pp.8-85).
2. See, for instance, Enders (1995, pp.135-206), Enders (2010, pp.121-176),
Lütkepohl and Krätzig (2004, pp.197-220) for detailed explanation on ARCH andGARCH models.

14

CHAPTER 3: DATA AND UNIT ROOT TEST
3.1- Data
Monthly data series are used in this research. All the available data are used as
the sample period—from October 1994 to May 2012. Hereafter, the letter “m” denotes
month. In particular, it will be appearing along the x-axis, representing time—month, inalmost all of the graphs. For example, 1994m10 and 2012m5 are referred to October
1994 and May 2012, respectively.
Usually, inflation can be calculated from many price indices such as consumer
price index (CPI), wholesale price index (WPI), and GDP deflator. However, inCambodia only consumer price index is available for monthly data. Monthly andquarterly GDP deflators do not exist due to the lack of monthly and quarterly GDP data.
This thesis uses monthly consumer price index (CPI) series data from the International
Financial Statistics (IFS) CD-ROM, produced and published by the International
Monetary Fund (IMF). From the CPI data, inflation ( ) is calculated as follows 1:(9)Here we multiply the difference ofby 100 in order to make the unit of

the inflation to be in percent. Figure 3.1 shows the graph of the monthly CPI.
Figure 3.1: Monthly Consumer Price Index

15

Data on interest rates in Cambodia are so limited. The National Bank of
Cambodia (NBC), the central bank of the country, has published statistics of interest
rates on deposits and loans denominated in Khmer riel, and deposits and loans
denominated in the U.S. dollar on their homepage only from 2010. I have inquired thebank of how to get those interest rates series data, but there has been no any response
from the bank. Therefore, deposit rate and lending rate series data from the IFS are
utilized. It is worth noting that, according to the definitions by the IFS, the deposit rateis the simple average of rates on domestic-currency savings deposit reported by 10banks with largest deposits holding, and the lending rate is the simple average of rates
on foreign-currency loans to private enterprises by 10 banks with largest deposits
holding. The IMF’s IFS reports only these two interest rates for the case of Cambodia.
In most studies existing in the literature, authors have used interest rates on treasury
bills or bonds, for instance, 3-month T-bill. Unfortunately, in Cambodia thus far there
have been no these types of interest rates due to the absence of treasury bills andgovernment-backed bonds. Accordingly, there are no alternative but to use both thedeposit rate and the lending rate produced by the IMF’s IFS. Some researchers, such as
Berument and Jelassi (2002), Berument et al. (2007), Ahmad (2010), also use deposit
rate and/or lending rate in their analyses.
The federal funds rate series data from Board of Governors of the Federal
Reserve System is used as the interest rate of the U.S. Like the call rate in Japan, thefederal funds rate is the interest rate at which depository institutions lend balances at theFederal Reserve to other depository institutions overnight. The reason to choose thefederal funds rate here is that it is the shortest-term rate, thus the most influential
interest rate, affecting the level of all other interest rates in the U.S. economy. According
to the website of the Board of Governors of the Federal Reserve System, it documents
in Federal Open Market Committee (FOMC) section that “Changes in the federal funds
rate trigger a chain of events that affect other short-term interest rates, foreign exchange
rates, long-term interest rates, the amount of money and credit, and, ultimately, a rangeof economic variables, including employment, output, and prices of goods andservices.”
16

Figure 3.2: Deposit Rate, Lending Rate, Federal Funds Rate, and Inflation

From Figure 3.2, several remarkable inferences can be drawn. Firstly, theinflation from the beginning of the sample period until the end of 1998 is more volatile
than that after 1998. This happened because the political stability had been unfavorable
until the end of 1998. The high level of inflation in 1997m7 was the result of thepolitical conflict in July 1997 (see IMF, 1998) 2. Thanks to elections in 1998 resulting ina coalition government, and the surrendering of the last Khmer Rouges, the politicalsituation became stable at the end of 1998 (see Zamaróczy and Sa, 2002). Secondly, theinflation in early 2008 was very high, stemming from the global financial crisis and thehikes in global food and oil prices preceding the global financial crisis (see Socheth,2013 and Sovuthea, 2013). Thirdly, the deposit rate movement has been almost inparallel with the U.S. federal funds rate for the full sample period, except for mid-2000s.
Fourthly, the movement of the lending rate from the beginning of the sample period
until the end of 2001 had been in parallel with the federal funds rate, while themovement after 2001 has not been responding to the federal funds rate movement.
Particularly, there is a substantial rise in lending rate soon after the end of 2001. Finally,
the interest rate spread—lending rate minus deposit rate—is very large. Furthermore,
the spread between the lending rate and the federal funds rate is also great. These wide
spreads reflect the high country risk, the insufficient financial intermediation, and the17high costs of banking such as legal uncertainty, litigation costs, and default rates, due tofew secure lending opportunities (Zamaróczy and Sa, 2002, p.25). For summary of thedescriptive statistics of the series, see Table 3.1.

Table 3.1: Descriptive Statistics for Main Variables
Variable

Mean

Maximum

Minimum

S.D

Observations

DER

4.09589.60001.23002.9436212

LER

17.0029

19.1000

12.1600

1.3692212

FFR

3.28806.54000.07002.2448212

0.43267.95004.00661.4094211

Notes:
1.

DER, LER, FFR, anddenote Deposit Rate, Lending Rate, Federal Funds Rate, and Inflation,

respectively.
2.

S.D denotes Standard Deviation.

3.2- Unit Root Tests
In order to avoid a spurious regression, unit root tests for all variables ofinterest have to be conducted. As pointed out by Granger and Newbold (1974), a
spurious regression arises when time series appear related when they are actually not.
They appear to be related because the regression results come out with high degree of fit,measured byor, and high t-values. However, the Durbin-Watson statistic is

extremely low. Given the concern on such a spurious specification, time series analysts
have been testing for unit root to find out whether or not the considered series are
stationary, before running regressions. In this regard, this thesis employs two unit root
tests— Augmented Dickey–Fuller (ADF) test developed by Dickey and Fuller (1979),
and Phillips–Perron (PP) test developed by Phillips and Perron (1988).
Table 3.2 reports the results of the ADF unit root test for the four series—thedeposit rate, the lending rate, the federal funds rate, and the inflation—with both theirlevels and first differences. The results indicate that the inflation is stationary in its level
since the null hypothesis that the inflation has a unit root is rejected at 1% significance18

level. Thus, additional test for unit root in its first difference is not done. Interestingly,
for all of the interest rates (i.e., the deposit rate, the lending rate, and the federal fundsrate), the null hypotheses that the variables contain unit root are accepted, meaning thatthese interest rates are non-stationary in their levels. However, they become stationary
after they are first differenced, because the null hypotheses are rejected at 1%
significance level.

Table 3.2: Augmented Dickey–Fuller (ADF) Unit Root Test
Level

First Difference

VariableIntercept

Intercept & trend

Intercept

Intercept & trend

DER

0.867(0)1.541(0)6.390(5)***6.486(5)***LER

0.504(0)2.501(0)16.629(0)***16.737(0)***FFR

1.463(2)2.245(2)7.135(0)***7.100(0)***7.104(1)***7.085(1)***

N/A

N/A

Notes:
1.

DER, LER, FFR, anddenote Deposit Rate, Lending Rate, Federal Funds Rate, and Inflation,

respectively.
2.

ADF statistic is obtained by(in case of intercept), and by(in case, whereestimated,of intercept

is the difference operator,

andtrend),

forare the coefficients to beis the variable whose time series properties are examined, andis the white-noise

error term.
3.

The numbers in the parentheses indicate the optimal lag lengths selected by BIC by setting
maximum lag length to 12.

4.

The null hypothesis is(the series has a unit root or is non-stationary), while thealternative hypothesis is(the series has no unit root or is stationary).

5.

*** denotes the rejection of the null hypothesis at 1% significance level.

6.

N/A means that the unit root test for the first difference of inflationlevel variable is already stationary.

19

is not conducted since its

The results of the Phillips–Perron unit root test for the same variables arereported in Table 3.3. It is apparent that the Phillips–Perron test produces the same
results as the ADF test does. The results show that the inflation is stationary in its level,
while the deposit rate, the lending rate, the federal funds rate are non-stationary in theirlevels, but they become stationary after being first differenced. The level of significanceof the Phillips–Perron test statistics is 1%, like that of the ADF test statistics.
Based on the results in Table 3.2 and Table 3.3, it can be concluded that only
the inflation is stationary while all the interest rates are non-stationary, but their firstdifferences are stationary. In other words, the inflation is I(0) while the deposit rate, thelending rate, and the federal funds rate are I(1).

Table 3.3: Phillips–Perron (PP) Unit Root Test
Level
VariableIntercept

DER

1.047

LER
FFR

First Difference

Intercept & trend

Intercept

Intercept & trend

4.349

241.120***241.079***

0.523

16.459237.527***235.980***

1.739

6.156

79.229***79.289***158.401***

158.541***

N/A

N/A

Notes:
1.

DER, LER, FFR, anddenote Deposit Rate, Lending Rate, Federal Funds Rate, and Inflation,

respectively.
2.

3.

PP test suggests a non-parametric method of controlling for higher order autocorrelation in a series
and is based on the AR(1) process:, where

and

, and

are the coefficients,

is the first lag of theis the difference operator,
is white noise.

The lag truncation is set at five. The results of PP test are not sensitive to the choice of the lagtruncation.

4.

The null and the alternative hypotheses are the same as those of the ADF test.

5.

*** denotes the rejection of the null hypothesis at 1% significance level.

6.

N/A means that the unit root test for the first difference of inflationlevel variable is already stationary.

20

is not conducted since its

ENDNOTES:
1. Actually,

when

is approximately equal to()

is small. This means that the change of the logarithm of the CPI isapproximately equal to the proportional change of the CPI when the difference of
the CPI is small. Thus,is the percentage change in the

CPI; that is, inflation. See Stock and Watson (2011, pp.520-521) for more
explanation related to this.
2. The July-1997 political crisis substantially affected Cambodia’s economy. IMF(1998) documents that “During the first months of 1997, the macroeconomic
performance was broadly in line with the second-year ESAF (Enhanced Structural
Adjustment Facility) program, but the situation deteriorated markedly in theaftermath of Second Prime Minister Hun Sen’s seizure of power in July and theregional financial crisis. The effects of the political crisis seem to have been muchstronger than those of the regional financial crisis as they undermined growth andinvestors’ confidence.” The output growth in 1997 was estimated at 2%, down from
6.5% in 1996. In that troubled year, the instruction activities declined by 1% whileservice sector growth accounting for about 36% of the GDP slowed to around 1%.
Moreover, the monthly tourist arrivals in July plunged to around 6,000 from over
29,000 in March. In addition, Pum and Vanak (2010) mention that the domestic
political instability in 1997 led to postponement of aid activities by the international
assistance institutions, a halt in production by foreign firms, and a dramatic fall in
FDI.

21

CHAPTER 4: EMPIRICAL RESULTS

In this chapter, all of the empirical results are presented. The chapter proceeds
as follows. Firstly, in section 4.1 the inflation estimation with ARMA model isdiscussed. Section 4.2 explains the estimation of the inflation and inflation variancewith ARMA-GARCH

regression model. The validity of the Friedman-Ballhypothesis is examined in section 4.3. Afterwards, the Fisher effect, and determinants ofinterest rates in Cambodia are discussed in section 4.4, and section 4.5, respectively.

4.1- Estimation of Inflation with ARMA Model
As mentioned previously, the inflation is assumed to follow an autoregressive
moving-average (ARMA) model (see equation (1)).
Before fitting time-series models, a necessary condition is that the series inconsideration must be stationary. Since the stationarity of the inflation is confirmed by
the ADF unit root test and the Phillips–Perron unit root test presented in chapter 3, it ispossible to fit the inflation with ARMA models. The Box–Jenkins approach is utilized
in this section because it is a very vital method regarding ARMA modeling of timeseries (Becketti, 2013, pp.226-227; Enders, 2010, pp.78-81). The Box–Jenkins method
consists of three stages—identification stage, estimation stage, and diagnostic checking
stage. The main task in the identification stage is to visually examine the time plot of
the series, the autocorrelation function (ACF), and the partial autocorrelation function(PACF). From the visual ACF and PACF, an analyst may have some ideas on several
plausible models. In the second stage—estimation stage, the analyst fits all the tentativemodels chosen in the first stage, considers the coefficients estimated, and then chooses amodel that has a good fit. When comparing the goodness of fit in ARMA models, it isbelieved that using Akaike Information Criterion (AIC) and/or Bayesian Information
Criterion (BIC) is more appropriate than other measures of goodness of fit such as,or the average of the residual sum of squares. In the final stage, diagnostic checking
must be conducted to ensure that there is no autocorrelation in the residuals of themodel selected. A common way to do this is to use Ljung–Box test.
22

After considering the ACF and the PACF of the inflation, the tentative ARMA
models are likely to be

,

,

, and

. The 12th lag is also considered here because there is a spike at lag 12
and lag 24 in the ACF, and at lag 12 in the PACF. This implies that there is significant
seasonality in the inflation series since the monthly inflation data is seasonallyunadjusted. Therefore, including lag 12 in the model seems to be plausible and it also
helps control for the seasonality effect.
Table 4.1 presents the results of all the four tentative models. In model 1, all thecoefficients are statistically significant. Unfortunately, based on the Ljung–Box
Q-statistics for all cases of 12 lags, 24 lags, 36 lags, 48 lags, and 52 lags, the null
hypotheses that Q = 0 are rejected at 1% significance level, meaning that the lagged
residuals of this model have very high serial autocorrelation. Therefore, model 1 isapparently inadequate. Since the Ljung–Box test indicates no significant serial
autocorrelations in the lagged residuals, model 2 in which the 12th lag of the inflation isadded performs very much better, although the constant term

has a bit low t-value(=1.62) and is significant at 10.5% level. In the same fashion as model 1, model 3 has
acceptable coefficients, but the lagged residuals of the model exhibit high serial
autocorrelations. Hence, model 3 should be also eliminated from consideration. For the
final model—model 4, each coefficient is statistically significant. From the Ljung–Box
Q-statistic for 24 lags of the residuals, however, it is apparent that the null hypothesisthat Q = 0 is rejected at about 6% level, indicating considerable serial autocorrelation.
Furthermore, the AIC and BIC of model 4 are greater than those of model 2. Based onthese comparisons, model 2 has the best fit among the four tentative models considered.
The following is model 2 estimation of the inflation, with t-values in the parentheses.

(1.62)(4.26)(2.55)(4.31)(10)

Overall, model 2 is finally selected to be fitted with GARCH model, and theirestimation is presented in the next section.
23

Table 4.1: Estimates of Inflation with ARMA Models(Model 1)(Model 2)(Model 3)(Model 4)

p=1,2

p=1,2,12

p=1

p=1,2

q=0

q=0

q=2

q=12

0.427**

0.412

0.429***

0.421**(2.50)(1.62)(2.71)(2.07)

0.245***

0.217***

0.238***

0.250***(4.51)(4.26)(4.44)(4.45)

0.174**

0.172**

0.179**(2.46)(2.55)(2.49)

0.256***(4.31)
0.235***(3.56)
0.224***(3.58)
AIC

724.65710.90722.52712.56BIC

738.06727.66735.93729.32Q(12)36.21 [0.0003]

9.60 [0.6514]

30.89 [0.0021]

11.30 [0.5037]

Q(24)66.07 [0.0000]

28.98 [0.2209]

56.49 [0.0002]

35.42 [0.0625]

Q(36)82.91 [0.0000]

37.97 [0.3795]

71.25 [0.0004]

45.72 [0.1286]Q(48)97.22 [0.0000]

46.71 [0.5259]

83.28 [0.0012]

56.70 [0.1822]

Q(52)101.35 [0.0001]

50.46 [0.5348]

87.68 [0.0014]

60.38 [0.1987]

Notes:
1.

All the coefficientsandare reported with t-values in the parentheses ( ).

2.

** and *** denote 5% and 1% significance levels, respectively.

3.

Q( ) reports the Ljung–Box Q-statistic for the autocorrelations of theresiduals of the estimatedmodel. Significance levels are in the square brackets [ ]. According to Enders (2010, pp.69-70),
the maximum number of sample autocorrelations to use is typically set equal to T/4. Here, with211 observations, T/4 is approximately equal to 52.

24

4.2- Estimation of Inflation and Inflation Risk with ARMA-GARCH
So far, we have discussed the ARMA models, and after considering various
models, we have selected model 2 with
ARMA

becomes AR. This simply means the

because. In the model selected, Ljung–Box test

suggests that there is no significant residual autocorrelation. However, let us continue tocheck whether the residuals are homoskedastic or heteroskedastic 1. Homoskedastic isused to describe when a stochastic variable has a constant variance while
heteroskedastic is used when the variance changes, depending on time. To see if theresiduals are conditionally heteroskedastic, the Engle’s ARCH-LM test can be utilized.

Table 4.2: ARCH-LM Test for ARCH Effects
Lags

LM

Lags

LM

1

1.71 (0.1909)18

22.56 (0.2079)

32.38 (0.4148)

24

24.44 (0.4366)

6

6.37 (0.3831)

30

24.89 (0.7302)

919.57 (0.0208)**36

20.32 (0.9836)

12

19.88 (0.0694)*48

28.48 (0.9888)

1522.37 (0.0985)*

52

31.55 (0.9888)

Notes:
1.

The null hypothesis is that there are no ARCH effects in residuals as opposed to the alternative
hypothesis.

2.

LM represents the test statistic, following chi-squared distribution.

3.

* and ** denote the rejection of the null hypothesis at 10% and 5% significance levels,
respectively.

Table 4.2 reports the results of the ARCH-LM tests for presence of ARCH
effects. The null hypotheses that there are no ARCH effects are rejected at 5%
significance level for 9 lags, and at 10% significance level for 12 lags, and 15 lags. Thisimplies that there exist ARCH effects in residuals for 9, 12, and 15 lags. Enders (2010,
p.135) posits “as is typical of ARCH errors, the ACF and PACF of the squared residuals25(i.e.,) show significant correlation.” Following this, the Ljung–Box tests for

autocorrelation of the squared residuals are carried out, showing that for the lags of 9 to18 the autocorrelations are statistically significant at conventional levels.
It is believed that GARCH model performs better than ARCH model,
especially when the ARCH effects exist at long lags since estimating a totally free lag
distribution often leads to violation of the non-negativity constraints of the conditionalvariance (Bollerslev, 1986). One of the superior characteristics of the GARCH
specification is that it can be modeled with much shorter lag lengths. Moreover,empirically, a large number of time series with a conditionally heteroskedastic residual
have been adequately modeled with a GARCH
In this regard, we model the ARMA
last section, with GARCH

specification (STATA, 2013, p.25).
, which we have chosen in the. The specification results with t-values in theparentheses are as follows:(1.25)(2.60)(1.52)(3.16)(11)(3.93)

Here,and(3.81)(15.96)

are the inflation and its conditional variance, respectively.

Apparently, the inflation estimation with ARMA-GARCH

produces very similar

results as the inflation estimation with ARMA

. All coefficients are

significant at conventional levels, except the coefficients of the constant term and thesecond lag in the inflation equation. For the two coefficients of the constant term andthe second lagged inflation, their t-values are a bit low (1.25 and 1.52), implying thatthey are statistically significant at 21% and 12.9% levels. Although they are
insignificant at the conventional levels, they are collectively significant with the othercoefficients in the equation 2. Moreover, we cannot remove the second lag from theestimation as its removal leads to autocorrelation problem 3. In the conditional varianceequation, every coefficient is significant at 1% level. Furthermore, all the coefficients26

are positive, following the non-negativity condition of the variance. In addition, thecoefficients of the ARCH term (i.e.,) and the GARCH term (i.e.,) are 0.2500

and 0.7456 respectively, and the sum of the two is less than unity. This means that thevariance is stationary and it does not violate the non-explosive condition. We havechecked with other GARCH models, but the results tell that they are not superior to the
GARCH. Hence, we conclude that the ARMA-GARCH

is appropriate.

4.3- Examination on Friedman-Ball Hypothesis Validity
As mentioned previously, the Friedman-Ball hypothesis suggests that there is apositive correlation between inflation and inflation uncertainty, and the causality runs
from the inflation to its uncertainty 4. However, Cukierman and Meltzer (1986) argue
that increased inflation uncertainty leads to higher average inflation, meaning that thecausality runs from the inflation uncertainty to the inflation. This section attempts to
examine whether there is a positive relationship between the inflation and the inflation
uncertainty in Cambodia, and if the relationship exists, whether it supports theFriedman-Ball hypothesis or Cukierman and Meltzer (1986). In order to address thequestions, the Granger causality test is performed. Following the prevailing researches,
the conditional variance of the inflation obtained in the last section is used as the
inflation uncertainty 5.
Figure 4.1: Inflation and Its Conditional Variance27

From the visual graphs in Figure 4.1, it is obvious that there is a positive
relationship between the inflation and its uncertainty (i.e., conditional variance). The
periods with high inflation experience relatively high conditional variances, particularly
in mid 1997 and in early 2008. To understand the direction of the causality, the Granger
causality test is carried out, and the test results are reported in Table 4.3.

Table 4.3: Granger Causality Test for Inflation and Inflation Uncertainty: Inflation does not Granger-cause: Inflation uncertainty does not

inflation uncertainty

Granger-cause inflation

39.117*** (+)

0.594(0.000)(0.441)

Notes:
1.

*** indicates the rejection of the null hypothesis at 1% conventional level.

2.

The numbers in the parentheses below the test statistics are p-values.

3.

The lag length selection is determined by AIC, BIC, and HQIC. Here, the three information
criteria unanimously choose the lag length of 1. Other lag lengths are also tested, showing that theresults remain the same.

4.

(+) indicates that the coefficient is positive and significant.

In the results of the Granger causality test reported in Table 4.3, the null
hypothesis that the inflation does not Granger-cause the inflation uncertainty is rejected
at 1% significance level, indicating that the inflation Granger-causes the inflation
uncertainty. By contrast, the null hypothesis that the inflation uncertainty does not
Granger-cause the inflation is accepted. These results evince that there is a positive
relationship between the inflation and the inflation uncertainty, with the causality
running from the inflation to the inflation uncertainty 6. This suggests that in Cambodia
the Friedman-Ball hypothesis holds, while the argument of Cukierman and Meltzer(1986) is rejected by the test.

28

4.4- Examination on Fisher Effect
In this section, we examine whether the Fisher effect exists in Cambodia. In
other words, we attempt to investigate whether there is a positive relationship between
the deposit rate and the expected inflation, and between the lending rate and the
expected inflation. The ordinary least squares (OLS) method is employed to test for the
relationship between the expected inflation and the interest rates. Because both thedeposit rate and lending rate, as seen in the unit root tests section, are non-stationary,
their first differences whose stationarity was confirmed are used instead. Both ADF and
PP unit root tests suggest that the expected inflation derived from the
ARMA-GARCH

model discussed above is stationary. Here, the results of the unit

root tests for the expected inflation are not reported since they are not the main part ofthis section. Because all the series to be regressed are stationary, the regressions areadmissible.
By regressing the first difference of the deposit rate on the expected inflation,and the first difference of the lending rate on the expected inflation, the estimation
results are as follows.

(0.08)( 2.30)(12)( 1.03)

Here,

,

, and

(0.12)

are the first difference of the deposit rate, thefirst difference of the lending rate, and the expected inflation, respectively. The numbersin the parentheses below each coefficient are the t-values of those coefficients.
For the case of the deposit rate, the coefficient of the expected inflation isnegative and statistically significant at 5% level, with the t-value equal to2.30. Thisimplies that when the monthly inflation is expected to increase by 1 percent, the firstdifference of the deposit rate will decrease by 0.089 percent. Therefore, it is against the
Fisher hypothesis. The constant term is not significantly different from zero since it has
29an extremely low t-value of 0.08.
For the case of the lending rate, the coefficient of the expected inflation is0.006 with a positive sign, seemingly supporting the weak form of the Fisher effect, but
its t-value of 0.12 suggests that it is not significantly different from zero. Also, theconstant term is not significant at conventional levels.
These estimation results demonstrate that the Fisher effect does not hold for thecase of Cambodia during the full sample period (i.e., October 1994 to May 2012).
Actually, Cambodia is not the only country that the Fisher effect is absent. Some studies
also find no Fisher effect in many countries, particularly in developing economies. For
instance, using GARCH approach to estimate the expected inflation, Berument et al.
(2007) find that the Fisher hypothesis holds in all the G7 countries, but among 45
developing economies under their consideration, the relationship between interest rateand expected inflation is positive and significant in only 23 economies. For the other 22
developing countries, the Fisher effect is not supported. Among these 22 countries,
Egypt is the only country that the estimated coefficient of the expected inflation isnegative and statistically significant. The developing countries where the estimatedcoefficient of the expected inflation is statistically insignificant include Armenia,
Albania, Bahrain, Bulgaria, Dominica, Ghana, Grenada, Kuwait, Lao People, Lithuania,
Malawi, Namibia, Nepal, Sierra Leone, Solomon Islands, Sri Lanka, St Lucia,
Swaziland, Uganda, Zambia and Zimbabwe. The authors argue that there might be somereasons behind the absence of the Fisher effect in these economies. Firstly, the Fisher
hypothesis contains an implicit assumption that nominal interest rates can freely adjust
to changes in actual or expected inflation; so if interest rates are suppressed, thehypothesis may not hold. Secondly, interest rates may not or may only partially respondto the changes in inflation in those economies where the money markets are notsufficiently sophisticated. Thirdly, in some countries interest rate adjustment may bedifficult since it is believed to be a ‘sin’ for cultural reasons.
Lorde et al. (2008) employ cointegration method to investigate the long run
relationship between interest rate and expected inflation in five small developing
Caribbean countries—the Bahamas, Barbados, Guyana, Jamaica, and Trinidad and30

Tobago. Their finding is that the relationship holds in 3 countries (the Bahamas, Jamaica,
and Trinidad and Tobago), and the relationship is negative for Trinidad and Tobago. For
the other two countries, there is no long run relationship between the two variables. The
authors’ explanation on the reason why the nominal interest rate is inversely related to
expected inflation in Trinidad and Tobago is as follows: “It is possible that some
unknown influence may be driving the nominal interest rate and inflation rate in
opposite directions, a phenomenon that probably warrants further research to identify
other possible determinants of the nominal interest rate in Trinidad and Tobago.”
Given the findings in the existing studies, the nonexistence of the Fisher effectin Cambodia is neither unique nor bizarre. Most of the explanations on why the Fisher
effect does not hold in some countries by Berument et al. (2007) and Lorde et al. (2008)
are likely to be applicable as well for the case of Cambodia. The first and the second
reasons mentioned by Berument et al. (2007) seem to be true in Cambodia; their third
reason, however, is not convincing. As seen from Box 4.1 (General Functions andDuties of National Bank of Cambodia), the National Bank of Cambodia (NBC), thecentral bank of the country, has a duty to set interest rates (i.e., the 12th duty in Box 4.1).
Although by law the NBC has a duty to set interest rates, some economists argue that
this function is not operated effectively due to some monetary constraints such as highdollarization; and practically, each commercial bank can set interest rates on deposits
and loans by themselves based on market competition (see Pum and Vanak, 2010, p.69).
However, NBC might, to some extent, be able to affect the interest rates by both direct
regulations and indirect ways; e.g., through raising or lowering the reserve requirement
ratio (RRR) and the minimum capital requirement (MCR) 7. Secondly, the interest rates
in Cambodia might not respond to the inflation because the money and capital markets
in the country are underdeveloped and undiversified. Up to the present, there have been
no bill and bond markets, and interbank markets. Cambodia just opened its stock
exchange on July 11, 2011, becoming the last country in the Association of Southeast
Asian Nations (ASEAN) to introduce the securities market. Thus far, there has been
only one listed company, Phnom Penh Water Supply Authority (PPWSA), whose initialpublic offering (IPO) ushered on April 18, 2012.
31

Box 4.1: General Functions and Duties of National Bank of Cambodia
The National Bank of Cambodia shall have the following functions and duties:
1. To determine monetary policy objectives, in consultation with the Royal
Government and consideration of the framework of the economic and financial
policy of the Kingdom;
2. To formulate, implement and monitor monetary and exchange policies aimed at
the determined objectives;
3. To conduct regular economic and monetary analysis, make public the results, andsubmit proposals and measures to the Royal Government;
4. To license, delicense, regulate and supervise banks and financial institutions andother relevant establishments such as auditors and liquidators;
5. To oversee payments systems in the Kingdom, and to enhance interbank
payments;
6. To act as the sole issuer of national currency of the Kingdom;
7. To undertake and perform, in the name of the Kingdom, transactions resulting
from the participation of the Kingdom in public international institutions in thebanking, credit, and monetary spheres;
8. To establish the balance of payments;
9. To participate in the management of external debt and claims;
10. To participate in the formation and supervision of the money and financial
markets;
11. To license, delicense, regulate and supervise all those operating in the securities
and foreign exchange markets, the market for precious stones and precious
metals;12. To set interest rates.
Source: National Bank of Cambodia (NBC)

Finally, considering the reason explained by Lorde et al. (2008), there might beother factors leading the deposit rate and the expected inflation to move in opposite
directions. Further study on these factors should be conducted by later researchers. Here,32

one tentative factor; that is, money supply is considered. This might be able to be
explained by the liquidity preference theory 8. The liquidity preference theory assertsthat the nominal interest rate is determined by the supply and demand for money, where
the supply curve is vertical since the money supply is controlled by the central bank andit does not depend on the interest rate, while the demand curve is downward sloping
since the quantity of money demanded decreases when the interest rate rises, implying ahigher cost of holding money. When the central bank increases the money supply, thesupply curve shifts to the right, and the interest rate becomes lower. At the same time,
an increase in the money supply implies a rise in the price level because of the lower
purchasing power resulting from the more money injected in the economy. If this is true,
it means that the money supply drives the interest rate and the expected inflation tomove inversely. This might be true for the case of the deposit rate. But why it cannot
explain for the case of the lending rate? To answer this, two facts are noteworthy. First,
as explained in the data section in chapter 3, the deposit rate used in this research is thesimple average of the rates on savings deposits denominated in the national currency,
the riel, whereas the lending rate is the simple average of the rates on loans denominatedin the U.S. dollar. Secondly, the central bank of Cambodia cannot manipulate the supplyof the U.S. dollar; the dollar is freely and widely used in all forms of the three functionsof money (i.e., medium of exchange, unit of account, and store of value) throughout thecountry. That is why money supply (riels) might influence the deposit rate (on deposits
denominated in riel), but it might not affect the lending rate (on loans denominated in
the U.S. dollar).

4.5- Determinants of Interest Rates in Cambodia
In the last section, the validity of the Fisher effect in Cambodia is discussed.
The estimation results suggest that the Fisher effect in Cambodia, like some otherdeveloping countries, does not hold. In this section, the inflation risk and the interest
rate in the U.S. are incorporated in the estimation in order to see how these factors
might affect the interest rates in Cambodia. Following Berument and Malatyali (2001),
the conditional standard deviation of the inflation (i.e.,
33) is used as the inflation risk 9.

As mentioned previously, the U.S. federal funds rate is used as the nominal interest ratein the U.S. The estimation results are as follows 10:( 2.05)( 2.85)(2.46)(3.29)(13)( 1.21)

Here,

,

,

( 0.09),, and(0.86)(1.25)

are the first difference of thedeposit rate, the first difference of the lending rate, the expected inflation, theconditional standard deviation of the inflation, and the first difference of the U.S.
federal funds rate, respectively. The numbers in the parentheses below each coefficientare the t-values of those coefficients.
From the estimation results, all the coefficients in the first difference of thedeposit rate are statistically significant at conventional levels. Based on the t-values, thecoefficient of the expected inflation and the coefficient of the first difference of thefederal funds rate are significant at 1% level, while the coefficient of the inflation risk
and the constant term are significant at 5% level. The coefficient of the expected
inflation is equal to, meaning that when the expected inflation increases by 1%,

the first difference of the deposit rate will decrease by 0.113%. The coefficient of theinflation risk is 0.094, implying that when the inflation risk measured by the conditional
standard deviation of the inflation increases by 1%, the change in the deposit rate will
increase by 0.094%. Finally, the coefficient of the first difference of the federal funds
rate of 0.358 suggests that when the change in the federal funds rate rises by 1%, thechange in the deposit rate will rise by 0.358%. Overall, the expected inflation has anegative effect, while the inflation risk and the federal funds rate have positive effects
on the deposit rate in Cambodia.
The coefficients in the equation of the first difference of the lending rate for thefull sample period, on the contrary, are statistically not different from zero at all
conventional significance levels. To reveal why the lending rate specification is not
34

statically significant, it is worth going back to examine the lending rate movements. As
shown in Figure 3.2, there are some jumps in the lending rate after the end of 2001.
What happened then? Was there a structural change in late 2001?
Actually, there was a striking change in late 2001; that is, the introduction ofsome new regulations on banking and financial institutions in the country. The late-2001
implementation of the minimum capital requirement (MCR) and the reserve
requirement ratio (RRR) is the most influential factor driving the lending rate to jumpsoon after the end of 2001. According to Pum and Vanak (2010, p.67), article 1 of the
Prakas11 on banks’ minimum capital states that to “obtain a license, banks locallyincorporated as companies, must have a paid-up minimum capital equal to at least
50,000,000,000 riels.” Furthermore, another prakas on bank’s capital guarantee dated
October 15, 2001 stipulates that each bank has to deposit 10% of its minimum capitalrequirement as capital guarantee at the National Bank of Cambodia (NBC, 2004). In
addition to the MCR, the reserve requirement ratio was then set for its first time at 8%.
This means that every bank (commercial bank) has to keep at least 8% of its total
deposits with NBC. That is why the lending rate graph exhibits some jumps soon after
the end of 2001 12. Since then, many more regulations on banking and financial
institutions have been legislated.
Given such a dramatic change at the end of 2001, estimation of the lending ratewith a restriction of time

is conducted. The following is the estimation

result.

( 0.64)( 0.44)(0.50)(2.17)(14)

The result of this estimation is better than that of the estimation without
restriction. The coefficient of the first difference of the federal funds rate increases to0.389 and is now statistically significant at 5% level (with t-value equal to 2.17). Thisimplies that until the end of 2001, when the change in the U.S. federal funds rateincreases by 1%, the change in the lending rate in Cambodia will increase by 0.389%.
The other coefficients, however, are almost still the same as those of the estimation
35

without restriction; they are not statistically different from zero at all conventionalsignificance levels. This suggests that before 2002, the expected inflation and theinflation risk do not have significant influence on the lending rate (ondollar-denominated loans).
The same approach is applied to the estimation of the lending rate after the endof 2001, but it does not produce any significant results. The main reason behind thismight be because the lending rate has been substantially suppressed by the regulations
enforced more and more by NBC since after official launch of the Law on Banking and
Financial Institutions in 2000 and the implementation of the RRR and MCR in late
2001.
From the estimation results discussed above, it appears that the relations
between the federal funds rate and the deposit rate and between the federal funds rate
and the lending rate before 2001m12 are quite strong. As a robustness test for the
direction of the causal relations between the U.S. federal funds rate and the interest rates
in Cambodia, the Granger causality test is performed.

Table 4.4: Granger Causality Tests for Federal Funds Rate and Deposit Rate
: Change in the federal funds rate

: Change in the deposit rate does notdoes not Granger-cause change in

Granger-cause change in the federal

the deposit ratefunds rate

9.789* (+)

8.703(0.081)(0.121)

Notes:
1.

* indicates the rejection of the null hypothesis at 10% significance level.

2.

The numbers in the parentheses below the test statistics are p-values.

3.

The lag length selection is determined by AIC, BIC, and HQIC. Here, it is equal to 5.

4.

(+) indicates that the sum of the coefficients is positive and significant.

36

Table 4.5: Granger Causality Tests for Federal Funds Rate and Lending Rate()

: Change in federal funds rate does

: Change in lending rate does not

not Granger-cause change in

Granger-cause change in federal

lending ratefunds rate

23.234*** (+)

2.157(0.000)(0.707)

Notes:
1.

*** indicates the rejection of the null hypothesis at 1% significance level.

2.

The numbers in the parentheses below the test statistics are p-values.

3.

The lag length selection is determined by AIC, BIC, and HQIC. Here, it is equal to 4.

4.

(+) indicates that the sum of the coefficients is positive and significant.

Table 4.4 and Table 4.5 report the results of the Granger tests for the causality
between the federal funds rate and the deposit rate and between the federal funds rate
and the lending rate before 2001m12, respectively. As evident from the results in Table
4.4, the change in the federal funds rate Granger-causes the change in the deposit rate,
whereas the change in the deposit rate does not Granger-cause the change in the federal
funds rate. Similarly, Table 4.5 indicates that the change in the federal funds rate
Granger-causes the change in the lending rate, while the change in the lending rate does
not Granger-cause the change in the federal funds rate before 2001m12. These results
confirm that the interest rates in Cambodia are influenced by the interest rate in the
United States, but they cannot influence the interest rate in the United States.
The estimation results in this section can be concluded as follows. The
expected inflation has negative effect on the deposit rate, indicating the nonexistence of
the Fisher effect in Cambodia. There might be some other factors driving the expected
inflation and the deposit rate to move in opposite directions. One tentative factor ismoney supply manipulated by NBC, the central bank of Cambodia. It can happen
through the effect as explained by the liquidity preference theory. Nonetheless, the37

inflation risk measured by the conditional standard deviation of the inflation has
positive influence on the deposit rate. Besides, the deposit rate in Cambodia is directedby the U.S. federal funds rate; an increase in the federal funds rate leads to a rise in thedeposit rate. The lending rate, on the other hand, is not significantly influenced by the
expected inflation, the inflation risk, and the federal funds rate when the estimation is
performed, using the whole sample period. However, it is significantly affected by thefederal funds rate after the estimation is restricted with. It is restricted so

because of the fact that there was a substantial structural change in late 2001. Since then,the lending rate has been highly likely to be partially affected by various regulations
imposed by the central bank. As robustness tests, the Granger causality tests suggest thatthe federal funds rate in the U.S. does Granger-cause the deposit rate and the lending
rate in Cambodia, while the deposit rate and the lending rate do not Granger-cause thefederal funds rate.

ENDNOTES:

1. Some researchers have used “homoscedastic” and “heteroscedastic” instead of“homoskedastic” and “heteroskedastic”, respectively. For instance, Engle (1982)
uses the former in his pioneering research.
2. A test to see whether the coefficients of the first lag, the second lag, and the twelfth
lag of the inflation are collectively significant and different from zero is conducted.
The test statistic, which follows the chi-squared distribution with degree of freedomof 3, is 27.13, indicating that null hypothesis is rejected at 0% significance level.
Based on the test result, the coefficients are significant collectively.
3. The Ljung–Box Q-statistics of the residuals of the ARMA

, themodel from which the second lagged inflation is omitted, indicate that the
autocorrelations are significant at the conventional levels for almost all of the lags,
especially for the lags of 2, 8, and 18. The autocorrelations for the lags of 2, 8, and
12 are

,

, and
38

, respectively.

4. In his Nobel lecture, Friedman (1977) points out the potential of increased inflation
to create nominal uncertainty. He documents on page 465 that “the mostfundamental departure is that a high inflation rate is not likely to be steady during
the transition decades. Rather, the higher the rate, the more variable it is likely to be. ”
Ball (1992) uses game theory to justify Friedman’s argument. In Ball’s theoreticalanalysis, there are two kinds of monetary policymakers—one is conservative, andthe other is tough. When the inflation is low, both types of the policymakers will
choose to keep it low; thus the public feels certain that future inflation will be still
low. But when the inflation rises, there is a fear of recession if they choose
disinflation. Then, only the tough policymaker will decide to disinflate. However,the public does not know which type of the future policymaker will be, and whether
or not the disinflation will happen.
5. See, for example, Grier and Perry (1998) and Berument and Dincer (2005).
6. The results are consistent when we use conditional standard deviationinflation uncertainty instead of the conditional varianceas the.

7. Reserve requirement ratio (RRR) is the minimum reserve-deposit ratio that thecentral bank imposes on banks. Here, reserve-deposit ratio is the fraction of deposits
that banks keep in reserve. The higher RRR, the fewer loans banks can make, andthe less money banks create from every dollar of reserve. Therefore, an increase in
the RRR lowers the money multiplier and the money supply, thus raises interest
rates. Minimum capital requirements (MCR) are the central bank’s regulations that
require banks to keep sufficient capital so that the banks can pay off their depositors.
Like RRR, higher MCR leads to higher interest rates. See Mankiw (2010,
pp.550-556) for more explanation.
8. For detailed discussion on the liquidity preference theory, see Ball (2009,
pp.97-102) and Mankiw (2010, pp.301-304).
9. ADF and PP unit root tests indicate that the conditional standard deviation of theinflation is stationary.
10. Like the earlier section, since the deposit rate and the lending rate have unit root,
their first differences, whose stationarity is confirmed, are used instead. Similarly,
39the first difference of the federal funds rate is used instead of its level because thelevel series is non-stationary and the first difference is stationary.
11. Prakas is a kind of official announcement made by a ministry or equivalentinstitution to the public for use in a new law or regulation.
12. As also explained above in endnote number 7, higher RRR and/or MCR mean lessloans a bank can make, thus raise the lending rate. The graph of lending rate in
Figure 3.2 exhibits jumps soon after the end of 2001 because the RRR and MCR
were put into effect then.

40

CONCLUSION

This final part concludes the thesis, points out policy implication, raises somecautions in interpreting the empirical results, and finally suggests the subject of further
research.
In examination of determination of interest rates, most studies have been
extensively conducted by testing validity of the so-called Fisher hypothesis and most ofthem have focused on developed economies, leaving the researches on developing
economies, particularly least developed countries, relatively sparse. Thus far, there has
been no research regarding determination of interest rates, or even on the Fisher effect,
in Cambodia. Given this fact, this thesis seeks to throw some light on the determinantsof nominal interest rates in Cambodia, by incorporating inflation risk (i.e., conditional
standard deviation of inflation) and the U.S. interest rate into the conventional Fisher
equation. Data used in this thesis are monthly series with the sample period from
October 1994 to May 2012. Consumer price index (CPI), deposit rate, and lending rate
in Cambodia are taken from the IMF’s International Financial Statistics CD-ROM,
whereas the U.S. federal funds rate is from Board of Governors of the Federal Reserve
System. The main findings are as follows. Firstly, Augmented Dickey–Fuller (ADF)
unit root test and Phillips–Perron (PP) unit root test suggest that only the inflation is
stationary while all the interest rates—deposit rate, lending rate, and federal funds
rate—are non-stationary at their levels but their first differences are stationary. In otherwords, the inflation is I
rate are I

while the deposit rate, the lending rate, and the federal funds. Secondly, after considering various ARMA models of the inflation

following the Box–Jenkins approach, it is concluded that ARMAisthe best model. This implies that monthly inflation in Cambodia can be explained by its
past values at lag 1, 2, and 12. Thirdly, given the result that ARCH-LM test indicates thepresence of ARCH effects, we fit the ARMA model obtained above together withGARCH models and results suggest that ARMA-GARCH

is adequate. Therefore,

the expected inflation and the inflation risk can be computed by fitting this
ARMA-GARCH. Fourthly, the validity of the Friedman–Ball hypothesis, which
41

postulates that inflation and inflation risk (inflation uncertainty) are positively
correlated and the causality runs from inflation to inflation risk, for the case of
Cambodia is investigated by employing the Granger causality test. The Granger
causality test results suggest that the inflation Granger-causes the inflation risk, but the
inflation risk does not Granger-cause the inflation. These results support theFriedman–Ball hypothesis and reject what proposed by Cukierman and Meltzer (1986)
that argue that the causality runs from the inflation risk to the inflation. Fifthly,
empirical evidence indicates that the Fisher hypothesis does not hold in Cambodia. The
expected inflation has no significant effect on the lending rate, while it surprisingly has
a significantly negative effect on the deposit rate. Cambodia is not the only country
where the Fisher effect is absent; 22 countries among 45 developing countries understudy by Berument et al. (2007) and 3 countries among 5 developing Caribbean
countries under study by Lorde et al. (2008) as well as some more countries examinedby other researchers also experience the nonexistence of the Fisher effect. Also, for
Egypt in Berument et al. (2007) and Trinidad and Tobago in Lorde et al. (2008), therelationship between expected inflation and interest rate is negative. Exploring thereasons behind the absence of the Fisher effect in Cambodia might be the subject offurther study. A few tentative explanations are as follows. First, the interest rates,
especially the lending rate, are likely to be affected by regulations of the National Bankof Cambodia (NBC), at least to some extent. Second, the interest rates in Cambodia
might not respond to the inflation because the money and capital markets in the country
are underdeveloped and undiversified. Finally, there might be unknown factors driving
the deposit rate and the expected inflation to move in opposite directions. One factor
might be money supply. It can be explained by the liquidity preference theory, whichstates that nominal interest rate is determined by the supply and demand for money.
When NBC injects more riels (national currency) into the market given the other factors
unchanged, the vertical supply curve shifts to the right leaving the downward-sloping
demand curve unchanged. This shift results in a fall in interest rate for riels. At the sametime, an increase in money supply implies an increase in price level, and thus a rise in
expected inflation.
42

When the nominal interest rate is set at a given time, the inflation for that
period is unknown. Since investors are interested in real return when making a portfolio
decision, they therefore assess the ex ante real interest rate by forming expectation of
the inflation. In this regard, the inflation uncertainty is a risk to be taken into accountand it is reflected in the nominal interest rate. Empirical results show that the inflation
risk has positive influence on the interest rates in Cambodia, particularly the deposit rate.
However, this influence is seemingly insignificant for the lending rate. Given the resultsthat indicate a positive relationship between interest rate and inflation risk, this suggeststhat the government should adopt any policies that fight against the volatility of theinflation if it wants to keep interest rate low so that the economy is boosted byinvestment activities.
Additionally, the interest rate in the United States (i.e., the federal funds rate)
substantially directs the interest rates in Cambodia. The estimation results suggest thatwhen a change in the federal funds rate increases by 1%, a change in the deposit rate in
Cambodia will increase by 0.358%. The results also indicate that from October 1994 to
December 2001, when a change in the federal funds rate increases by 1%, a change inthe lending rate in Cambodia will increase by 0.389%. The relationship between thefederal funds rate and the lending rate seems to be insignificant after the end of 2001.
This is the case since there was a striking change in the banking and financial sector in
Cambodia since around the end of 2001. In late 2001, NBC introduced many new laws
among which the implementation of RRR (reserve requirement ratio) and MCR(minimum capital requirement) remarkably affected the lending rate, and since thenmany supplementary regulations have been enforced. Finally, as robustness tests, theGranger causality tests are carried out in order to confirm the causal relationships
between the U.S. federal funds rate and the interest rates in Cambodia. The results of theGranger causality tests confirm that the federal funds rate Granger-causes the interest
rates in Cambodia, but the interest rates in Cambodia do not Granger-cause the federal
funds rate. The empirical evidences presented above support our hypothesis proposed at
the beginning that the U.S. monetary policy influences Cambodia’s economy,
specifically through interest rate channels. Given this, it is recommended that the43

government and especially NBC should take into account the U.S. monetary policy
when considering any economic and monetary policies to be adopted and put into effectin the country.
When interpreting the empirical results obtained in this thesis, it is important tobear in mind that the interest rates—deposit rate and lending rate—used in this thesis
are subject to their definitions given by the IMF’s IFS, and thus do not reflect all of thetypes of interest rates in Cambodia. According to IFS, the deposit rate is the simple
average of rates on domestic-currency savings deposit reported by 10 banks with largest
deposits holding, and the lending rate is the simple average of rates on foreign-currency
loans to private enterprises by 10 banks with largest deposits holding. Using otherinterest rates provided by other institutions, if available, might produce different results.
It would be better or even best if various data on deposits and loans denominated in rieland on those denominated in the U.S dollar were provided by Cambodian authorities,
particularly NBC. This thesis suffers from the unavailability of those useful data.
This thesis utilizes the approach of ARMA-GARCH process from which the
expected inflation and the inflation risk are assessed, and then uses OLS method toestimate the interest rates. The analysis of the Fisher effect with these techniques,
therefore, can be viewed as a test for the short-run Fisher effect. Despite it is suggested
in this thesis that there exists no short-run Fisher effect in Cambodia, alternative
approaches such as cointegration relationship examination might give support to along-run Fisher effect. This is another subject of further research.

44

Acknowledgements
I gratefully acknowledge the full financial support provided by the Ministry of
Education, Culture, Sports, Science, and Technology (MEXT) of Japan, throughJapanese Government Scholarship program, for my 4-year undergraduate study. I am
grateful to Professor Jinushi Toshiki for his cheerful encouragement, valuable comments,
and useful document. Also, I would like to thank many friends and seniors for theiropinions, encouragement and generous helps. In particular, I am indebted to my goodfriend, Oum Chanmono, for his helps on some data. Finally, my family in Cambodia
contributed a lot to this thesis through their constant support and encouragement;
discussions with my eldest brother, Hoklun, were very helpful in writing this thesis.

45

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