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Yield curve shapes a management research project

YIELD CURVE SHAPES:
A MANAGEMENT RESEARCH PROJECT
2016
DECLARATION
2
TABLE OF CONTENTS
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
LIST OF TABLES viii
LIST OF FIGURES ix
LIST OF ABBREVIATIONS 1
ABSTRACT xi
CHAPTER ONE: INTRODUCTION 1
1.1 Background 1
1.2 Movement of Yield Curve 2
1.2.1 The Kenyan Scenario 4
1.3 Overview of Yield Curve Shapes 5
1.4 Patterns of Yield Curves and their Implications 6
1.4.1 Flat 7
1.4.2 Upward Sloping/Normal 8
1.4.3 Downward Sloping/Inverted 9
1.5 Problem Statement 11
1.6 Objectives of the Study 11
1.7 Significance of the Study 12
CHAPTER TWO: LITERATURE REVIEW 14
2.1 Introduction to Theoretical Review 14
2.2 Theories of the Term Structure of Interest Rates 14
3
2.2.1 Expectations Hypothesis 14
2.2.2 The Segmented Market Theory 17
2.2.3 The Liquidity Premium Theory 18
2.3 Theoretical Behavior of the Term Structure and Economic Activity 19
2.4 Empirical Review 20
2.5 Conclusion 23
CHAPTER THREE: RESEARCH METHODOLOGY 24
3.1 Introduction 24
3.2 Research Design 24
3.3 Target Population 24
3.4 Data Collection 25
3.5Data Analysis 25
CHAPTER FOUR: DATA ANALYSIS 29
4.1 Introduction 29
4.2 Descriptive Analysis 29
4.2.1 Descriptive Statistics 29
4.2.2 Yield Curve 31
4.2.3 Comparison Yield Curve and Real GDP Growth Rate 32
4.3 Reliability Test 33
4.4 Correlation Analysis 33
4.5 Regression Analysis 34
4.5 Discussion of the Findings 34
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS 39
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5.1 Introduction 39
5.2 Summary and Discussions of the Study 39
5.3 Conclusion 40
5.4 Recommendation 41
5.5 Implications of the Study 41
5.6 Limitation of the Study 42
5.7 Suggestion for Further Research 42
APPENDICES 48
Appendix One: Data 48
5
LIST OF TABLES
Table 4.1: Data Description 29
Table 4.2: Descriptive Statistics 30
Table 4.3: Reliability Statistics 33
Table 4.4: Correlation Analysis 33
Table 4.5: Model Summary 34
Table 4.6: ANOVA 35
Table 4.7: Coefficients of the Regression Model 36
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LIST OF FIGURES
Figure 1: Flat Yield Curve, (David, 2013) 7
Figure 2: Upward-sloping/Normal Yield Curve, (David, 2013) 9
Figure 3: Downward-sloping/Inverted Yield Curve (David, 2013) 10
Figure 4.4: Lagged and Un-lagged Yield Curve 31
Figure 4.5: Real GDP Growth Rate 32
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LIST OF ABBREVIATIONS
ANOVA Analysis of variance.
CBK Central bank of Kenya.
CBR - Central bank rate.
GDP - Gross domestic product.
REHTS - Rational expectation hypothesis of term structure.
SPSS - Statistical package for social sciences.
T.BILL - Treasury bill.
U.S. - United states.
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ABSTRACT
The idea of interest rates (yields) is the defining indicator of the global debt capital market and
an understanding of it is vital to the smooth running of an economy as a whole. All participants
in the market be it the issuers of capital, borrowers, investors or bank intermediaries will have a
need to estimate, interpret and understand interest rate yields phenomenon. Yield curve explains
the difference between short term and long term interest rates. Normally because short term rates
are lower than longer term ones such that a graph plotting interest rates as a function of maturity
distinct rises as maturity lengthens. Factors that play a role in causing long term yields to move
up and down include inflation, economic growth, supply and demand factors and investor
attitude toward risk. The focus of this research was on the phenomenon of the term spread in
forecasting economic activity (recession/boom) in Kenya. It looked at two objectives which were
to establish the various patterns of the yield curve over time in Kenya and to determine the
ability of yield curve to predict economic growth and recessions. Causal research design was
adopted for this study. The study targeted real GDP growth rate and yield spread for 8 years
ranging 2006 to 2013. Secondary data was used for the purpose of this study. Data was collected
from World Bank data base. Descriptive statistics such as mean and standard deviation was used
to describe the variables. Regression analysis was used to describe the relationship between
variables and determine the ability of yield curve to predict economic growth and recessions.
The study found that the shape of yield curve indicates the cumulative priorities of all lenders
relative to a particular borrower. Lenders usually are concerned about a potential default so they
offer long term loans for higher interest rates than they offer for short term loans. It found that
Lagged Yield Spread was seen having a weak positive association (0.097) with Real GDP
growth rate which means that lagged yield spread cannot effectively predict real GDP growth
rate for the next period. It found that real GDP growth has a significant relationship with Lagged
Yield Spread. The study recommends that the government should take the findings of the study
more seriously so as to minimize the impacts of recessions through policy decisions. It finally
recommended that studies be done on other models based on Nelson & Siegel.
9
CHAPTER ONE: INTRODUCTION
1.1 Background
The idea of interest rates (yields) is the defining indicator of the global debt capital market and
an understanding of it is vital to the smooth running of an economy as a whole. All participants
in the market be it the issuers of capital, borrowers, investors or bank intermediaries will have a
need to estimate, interpret and understand interest rate yields phenomenon. Fund managers that
accurately predict the shape, nature and directions of yields will consistently outperform those
that do not. Mishkin (2010) defined yield curve as a plot of yields on bonds with differing terms
to maturity but the same risk, liquidity and the tax consideration. The term yield refers to the
annualized percentage increase in the value of a debt instrument in the context of debt market
(Yield Curve, 2011, June 12). The Kenya government securities yield curve is derived from the
relation between interest rates of treasury bill/bonds and the time to maturity of bonds of
different tenors.
Yield curve explains the difference between short term and long term interest rates. Normally
because short term rates are lower than longer term ones such that a graph plotting interest rates
as a function of maturity distinct rises as maturity lengthens. But now and then, the difference
begins to flatten and eventually, short term rates will rise above long term rates at which point
the yield curve is said to be inverted (Hulbert, 2014). The Kenyan yield curve is currently the
flattest it’s been since 2009 when it was emerging from a recession in the same year (Hulbert,
2014). A flattening yield curve doesn’t appear to be that worrisome from a macroeconomic point
of view, but it will undoubtedly have a bigger impact on certain industries like in financial sector
where flattening yield curve signals disappointing net interest incomes and bank margins.
10
The shape of yield curve indicates the cumulative priorities of all lenders relative to a particular
borrower (Rjwilmsi, 2002). Lenders usually are concerned about a potential default so they offer
long term loans for higher interest rates than they offer for short term loans. Occasionally when
lenders are seeking long term debt contracts more aggressively than short term debt contract,
yield curve inverts with interest rates (yields) being lower for the longer periods of repayment so
that lenders can attract long term borrowing. This would be the case in the event that the
suppliers of funds expected that future rates would be even lower. In that case, offering low rates
for the long term bonds is seen as a way of cashing out and securing better rates now than is
expected in the long term.
Yield curves are usually upward sloping asymptotically (Rjwilmsi, 2002). Two common
explanations for upward sloping yield curve are; markets are anticipating a rise in interest rates.
Investors who are willing to lock their money in now need to be compensated for the anticipated
rise in the rate thus the higher interest rate on long term investments. Another explanation is that
longer maturities entail greater risk for the lender. A risk premium is needed by the market since
at longer durations there is more uncertainty and a greater chance of catastrophic events that
impact the investment (Rjwilmsi, 2002). Explanations depend on the notion that economy faces
more uncertainties in the distant future than in the near term. If the market expects more
volatility in the future even if interest rates are anticipated to decline, the increase in the risk
premium can influence the spread and cause an increasing yield.
1.2 Movement of Yield Curve
Factors that play a role in causing long term yields to move up and down include inflation,
economic growth, supply and demand factors and investor attitude toward risk. Generally,
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slower growth, low inflation and depressed risk attitude will help the performance of shorter
term bonds and cause yields to fall and vice versa (Thomas, 2007).Changes in investor
expectation can also change the slope of yield curve. Signals provided by yield curves maybe
very sensitive to changes in financial market conditions. The precise effect of these changes on
yield curve will depend on whether they stem from technical factors or economic fundamentals.
For example because different maturities of fixed income securities appeal to different clientele,
a permanent shift in the relative importance of clientele could produce permanent shifts in the
slope of yield curve (Arturo et al, 2006).
Yield curves help describe the relationship among short term, medium and long term rates at a
given point in time because it is the starting point for pricing fixed income securities and other
financial assets. The normal yield curve depicts a line that ascends from lower interest rates on
shorter term bonds to higher interest rates on longer term bonds. Researchers have studied the
yield curve statistically and have found that shift or changes in the shape of yield curve are
attributable to a few unobservable factors (Dal et al, 2000).
Wu (2001) examined the relationship between Federal Reserve monetary policy surprises and the
movement of the slope factor of the yield curve in the U.S. after 1982. He found that the Fed
Reserve monetary policy actions exert a strong but short lived influence on the slope factor. They
explain 80%-90% of the movement of slope factor but such influence usually dissipates in one-
two months. Again monetary policy surprises don’t induce significant changes in the level factor
implying that during this period the Fed Reserve affected the yield curve primarily through
changing its slope.
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Ang et al (2001) examines the influence of inflation and real economic activity on the yield
curve in an asset pricing framework, in their model bond yield are determined not only by the
three unobservable factors, level, slope and curvature but also by an inflation measure and a real
activity measure. They find that incorporating inflation and real activity into the model is useful
in forecasting the yield curve movement. Inflation and real activity help explain the most or short
term bond yield and medium term bond yield up to maturity of one year but most of long term
bond yield are still accounted for by the unobservable factors. Therefore they conclude that
macroeconomic variables can’t substantially shift the level of yield curve.
A monetary policy tightening generates high nominal short term interest rates initially but
because of its anti-inflationary effects these rates quickly fall back since long term rates embed
expectation of this behavior of short term rates they increase by only a small amount. As a result,
the slope of yield curve declines when contractionary monetary policy shocks occur. After all it
is difficult to believe that the structure of macroeconomic has little effect on long term interest
rates or on the level of the yield curve since long term nominal interest rates are the sum of
expected long run inflation and long term real interest rates (David, 2013).
1.2.1 The Kenyan Scenario
Kenya government securities yield curve is derived from the relation between interest rates of
treasury bills/bonds and the time to maturity of bonds of different tenors. Interest rates of
Treasury bills are the prevailing weighted averages rates for 91 day, 182 day and 364 day papers
while interest rates for Treasury bond are the average prevailing secondary market yield for
bonds based on years to maturity. CBK has attributed the recent sharp fall in Kenya shilling to
speculative activity. However, our opinion is that the recent moves on both the interest rates
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front and the currency are reflective of the monetary policy followed by the CBK specifically of
keeping interest rates too low for too long. The reason for the CBK’s cautious stance is to
promote economic growth as well as to guard against any macroeconomic instability (Irungu,
2014, July 8).
Whereas, market interest rates (T-bill and bond yields) have now corrected to the right levels, the
policy rates (CBR) remain too low at 6.25%. The last time inflation was at these levels in early
2009 when the CBR was at 8.25%, 200 basis points higher than the current levels. The only
interest rate directly linked to the CBR was the rate at which commercial banks borrow from the
CBK on overnight basis. Since overnight borrowing rates have been rising on account of this, the
CBK de-linked the overnight borrowing rate of commercial banks from the CBR on 29
th
June
2011 and set a rate of 8% for the overnight window. It led to a temporary cap on the exchange
rate of the shilling but more importantly limited the usefulness of the CBR.
1.3 Overview of Yield Curve Shapes
The term structure of interest rates refers to the relationship between the yields of bonds with
different terms to maturity. When interest rates of such bonds are plotted against their terms, it
represents the yield curve. The yield curve is also defined as the plot of yields on bonds with
different terms to maturity with the same risk profile, liquidity and tax considerations and it
describes the term structure of interest rates for particular types of bonds, such as long-term
government bonds of 10 years and over (Mishkin, 2010). Economists and investors believe that
the shape of the yield curve reflects the market’s future expectation for interest rates and
conditions for monetary policy. The yield curve can be classified as upward sloping, flat or
downward sloping (Mishkin, 2010).
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When the yield curve is upward-sloping, the short-term interest rates, such as 91-day Treasury
bill rate, are below the long-term rates, such as long-term government bond 10 years and over.
When yield curves are flat, the short-term interest rates and long-term interest rates are the same.
When the yield curves are downward-sloping, the long-term rates are below the short-term rates.
Mishkin (2010) states that, besides explaining different shapes of the yield curves, a good theory
of the term structure of interest rates must explain the following three important empirical facts:
i. Interest rates on bonds of different maturities move together over time.
ii. When short-term interest rates are low, yield curves are more likely to have an upward
slope; when short-term interest rates are high, yield curves are likely to slope forward and
be inverted.
iii. Yield curves usually slope upward.
Economists including Hicks (1946) and Mishkin (1999) have developed theories to explain the
empirical observations about the shape of the yield curve; the three main theories being the
expectations hypothesis, the segmented market theory and the liquidity premium theory. The
fourth theory, the preferred habitat theory, is closely linked to the segmented market theory.
These theories are explained further in chapter two.
1.4 Patterns of Yield Curves and their Implications
Markin et al (1986) argued that the persistence of change in the federal funds rate engineered by
the Federal Reserve helps explain why the yield curve from 3-6 months has had negligible
forecasting power. It is crucial that only bonds carrying the same risk are plotted on the same
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yield curve. The most commonly used are treasury securities due to their risk-free nature and are
as such a benchmark for working out yield on other types of debt.
1.4.1 Flat
The flat yield curve is observed when all maturities have similar yields and sends signal of
uncertainty in the economy. To become inverted yield curves must pass through a period where
long term yields are the same as short term rates and when that happens, the shape will appear to
be flat or more commonly a little raised in the middle (smartmoney.com). It could also imply that
interest rates are expected to remain constant and gives a consensus that future yields will remain
the same as current yields. Thus there is no difference between short-term and long-term yield
rates.
Figure 1: Flat Yield Curve, (David, 2013)
A flat yield curve is a sign that investors are not sure about the economic growth and inflation in
future.
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1.4.2 Upward Sloping/Normal
The normal yield curve depicts a curve whereby the yield rises as maturity lengthens and reflects
investor expectation for the economy to grow in the future and importantly for the growth to be
associated with a greater expectation that inflation will rise in the future rather than fall (Hulbert,
2014). This expectation that inflation will rise makes the CBK tighten monetary policy by raising
short term interest rates in future to slow economic growth and dampen inflationary pressure. It
also creates a risk premium associated with the uncertainty about the future rate of inflation and
the risk this possesses to the future value of cash flows. Since the rate of inflation is expected to
increase the risk on the value of money becomes such that a shilling today may be worth much
less tomorrow. Lenders price these risks into the yield curve by demanding higher yields for
maturities further into the future while borrowers profit from passage of time since yields
decrease as bonds get closer to maturity.
Ordinarily, short term bonds carry lower yields to reflect the fact that investor money is under
less risk. The longer you tie up your cash the theory goes, the more you should be rewarded for
the risk you are taking (Hulbert, 2014). Normal yield curve therefore slopes gently upwards as
maturities lengthen and yields rise from time to time. However the curve twists itself into a few
recognizable shapes each of which signals a crucial but different turning point in the economy.
When those shapes appear it’s often time to alter your assumptions about economic activity by
using yield curves. Investors who risk their money for longer expect to get a bigger reward in
form of higher interest than those who risk their money for shorter time periods and therefore as
maturities lengthen, interest rates get progressively higher and curve goes up.
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According to Shapiro (2005), a normal yield curve implies that forward rate is higher than
current spot rate. Generally, an upward sloping yield curve is an indicator that investors expect
firm future economic growth accompanied by high inflation and therefore high interest rates
Figure 2: Upward-sloping/Normal Yield Curve, (David, 2013)
1.4.3 Downward Sloping/Inverted
The inverted yield curve occurs when long term yields fall below short term yields and under
unusual circumstances, long term investors will settle for lower yields now if they think the
economy will slow/decline in the future. Campbell (1986) showed that an inverted yield curve
accurately forecast U.S. recession. New York Federal Reserve regards it as a valuable
forecasting tool in predicting recession two to six quarters ahead (Joseph et al, 1996). It also
implies that the market believes inflation will remain lower than current levels because even if
there is a recession, a low bond yield will still be offset by low inflation.
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It is like a paradox because why would long-term investors settle for lower yields while short
term investors take so much less risk? The reason to this is because long term investors will
settle for lower yields now if they think rates and the economy are going even lower in the
future. They are rare to occur and if they occur they are followed by economic slowdown or
recession and lower interest rates.
This curve implies that interest rates are expected to fall. Shapiro (2005) noted that a downward
sloping yield curve could also signal that forward rates are lower than current spot rates. This
downward sloping yield curve is a fairly accurate indicator of an expectation of slow future
economic growth by investors which is accompanied by low inflation rates and therefore low
interest rates.
Figure 3: Downward-sloping/Inverted Yield Curve (David, 2013)
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1.5 Problem Statement
Predicative capabilities are valued skills in any financial market. The yield curve, as represented
by the spread between long-term and short-term interest rates has gained prominence in recent
years as a useful tool for forecasting future economic activity and the likelihood of recession or
boom (Joseph, 2006). Several studies like Moolman (2002) and Khomo et al (2007) have
demonstrated that the slope of the yield curve or the term spread, as represented by the difference
between yields on long-term and short-term treasury securities, is a useful indicator for future
economic activity and thus a good leading economic indicator. The inversion of the yield curve,
where short-term interest rates rise above long-term interest rates, has in the past provided a
positive statistical relationship with the odds of a recession ahead and is thus widely regarded as
a harbinger for an economic downturn. Such evidence is extensively documented in the United
States and other developed countries. Inverted curves have been among the most reliable
predictor of recessions in the modern economic era (Arturo et al, 1996).
Unlike the South African scenario where Moolman (2002) and Khomo et al (2007) had
foundations for their research laid down by the pioneer Nel (1996), there is a considerably
distinct lack of similar research for the Kenyan scenario. Our paper aims to fill that crucial gap.
Less attention has been paid to the theoretical reasons explaining the yield curve’s predictive
power. The focus of this research was on the phenomenon of the term spread in forecasting
economic activity (recession/boom) in Kenya.
1.6 Objectives of the Study
i. To establish the various patterns of the yield curve over time in Kenya.
ii. To determine the ability of yield curve to predict economic growth and recessions.
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1.7 Significance of the Study
This research will help indicate the current tradeoff between maturity and yield confronting the
investor. If investor wishes to alter the maturity of a portfolio, the yield curve indicate what gain
or loss in rate of return maybe expected for each change in portfolio average maturity. With an
upward sloping curve, investor may be able to increase a bond portfolio expected annual yield by
extending the portfolio average maturity. However the prices of long term bond are more volatile
creating greater risk of capital loss. Moreover long term instruments tend to be less liquid and
less marketable and therefore investors must weigh the gain in yield from extending its maturity
against added price, liquidity and risk.
The study will also help show if a security’s rate of return lies above or below the yield curve.
The former indicates to investors that a particular security is temporarily underpriced relative to
other securities of the same maturity. This is a buy signal which investors will take advantage of
hence driving the price of the purchased security upward and its yield back down toward the
yield curve. If rate of return is below the yield curve, this indicates an overpriced security and
investors will sell it, pushing its price down and its yield back up toward the curve.
To banks, this study will help indicate what the shapes of yield curve reflect to these financial
intermediaries. A rising curve is generally favorable for the institution because they borrow most
of their funds by selling short term deposits and lend a major portion of these funds long term.
The more steeply the curve slopes upward the wider the spread between borrowing and lending
rates the greater the potential profit for financial intermediary earnings.
However if the curve starts to flatten out or slope downward, this should serve as a warning
signal to portfolio managers of these institutions because it squeezes the earnings of banks. If
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upward sloping yield curve start to flatten out, managers try to lock in relatively cheap sources of
funds by getting long term commitment from depositors and other funds supplying customers.
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CHAPTER TWO: LITERATURE REVIEW
2.1 Introduction to Theoretical Review
The aim of this chapter is to provide an explanation of the theories underpinning the term
structure of interest rates. By offering a complete schedule of interest rates across time, the term
structure embodies the market’s anticipation of future events. An explanation of term structure
gives us a way to extract this information and to predict how changes in the underlying variables
affect the yield curve. This chapter also addresses the empirical findings of the previous
literature on the term structure with regard to its predictive ability of economic activities. This is
important since such a review provides the background information about the topic, potential
problems that may arise in the calculation of the yield curve, and how the yield curve can be
used to determine the economy’s future direction.
2.2 Theories of the Term Structure of Interest Rates
2.2.1 Expectations Hypothesis
The expectations hypothesis of the term structure states that the interest rate on a long-term bond
will equal an average of the short-term interest rates that is expected to occur over the life of the
long-term bond (Mishkin, 1999). For example, if people expect that short-term interest rates will
be 10% on average over the coming five years, the prediction is that the interest rate on bonds
with five years yield to maturity will also be 10%. The key assumptions behind this hypothesis
are that short-term and long-term securities can be treated as perfect substitutes, investors are
risk neutral and the shape of the yield curve is determined by investors’ expectations of future
interest rates and future inflation (Michaelsen, 1965).
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To see how the assumption that securities with different maturities are perfect substitutes leads to
the expectations hypothesis, the following two investment strategies are considered: buy a one-
year bond, hold it for one year, and reinvest the proceeds in another one-year bond, one year
from now. The alternative would be to buy a two year bond and hold it for two years. According
to the expectations hypothesis, both strategies should yield exactly the same result, since
investors are indifferent to bonds of different maturities, and bonds are perfect substitutes. The
interest rate on the two-year bond must equal the average of the two one-year interest rates. For
example, assume the current interest rate on the one-year bond is 7% and an investor’s
expectation is that the interest rate on the one-year bond next year will be 10%. If the investor
pursues the strategy of buying the two one-year bonds, the expected return over the two years
will equal 8.5%, which is (7%+10%)/2. The investor will be willing to hold the two-year bond
only if the expected return per year of the two-year bond is equal to or greater than 8.5%. In
other words, the interest rate on the two-year bond must equal 8.5%, the average interest on the
two one-year bonds.
It is evident that the rising trend in expected short-term interest rates produces an upward-sloping
yield curve (positive yield curve) along which interest rates rise as maturity lengthens. It is also
clear from the numerical example that when the yield curve is upward-sloping, the short-term
interest rates are expected to rise in the future. In the event that long-term rates are currently
above the short-term rates, the average of future short-term rates is expected to rise.
When the yield curve slopes downward, the average of future short-term interest rates is
expected to be below the current short-term rates, implying that short-term interest rates are
expected to fall, on average, in the future (Mishkin, 1999). Only when the yield curve is flat does
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the expectations theory suggest that short-term interest rates are not expected to change, on
average, in the future (Howells et al, 2002).
The expectations hypothesis explains why interest rates of different maturities tend to move
together over time. Historically, an immediate increase in short-term interest rates tends to be
higher in the future. As such, a rise in short-term interest rates will raise people’s expectations of
future short-term rates. In this theory, long-term rates are the average of expected future short-
term rates; therefore a rise in short-term rates will also raise long-term rates, causing short-term
rates and long-term rates to move together over time (Howells et al, 2002). The expectations
theory also explains why, when interest rates are low, yield curves are usually upward sloping,
and when interest rates are high, yield curves are usually downward sloping. When short-term
interest rates are low, economists and market participants generally expect them to rise to some
normal level in the future. The average of future expected short-term rates will be above current
short-term rates, and as such the yield curve would have a positive slope. On the other hand, if
the short-term interest rates are high, the expectation will be that they will come down. Therefore
long-term rates would drop below short-term rates. This is because the average of expected
future short-term rates would be below current short-term rates and the yield curve would slope
downward and become inverted. The expectations hypothesis explains another important fact
about the relationship between the short-term and long-term interest rates: interest rates are
mean-reverting. They tend to return to their normal levels if they are unusually high or low, and
hover around that normal level (Howells et al, 2002).
This implies that short-term interest rates will have more volatility than long-term rates: short-
term rates represent an average of future short-term rates (Mishkin, 1999). The shortcoming of
the expectations hypothesis is that it cannot explain why long-term yields are normally higher
25
than short-term yields, in other words, why the yield curve is usually upward sloping. If the
short-term rates are low now, they are expected to go up in the future. In that case the yield curve
will be upward sloping. On the other hand, if the short rates are high now, they are expected to
go down and in that case the yield curve will be downward sloping. Now, at a given point in
time, short-term yields are as likely to be high as they are to be low. Therefore, they are as likely
to go up as they are to go down in the future. That means that the expectations theory predicts
that the yield curves are as likely to be upward sloping as they are to be downward sloping.
The critique of the expectations is that it cannot explain why the yield curve is usually upward
sloping. The key assumptions behind this hypothesis are that short-term and long-term securities
can be treated as perfect substitutes do not hold in all situations. The theory neglects the risks
inherent in investing in bonds.
2.2.2 The Segmented Market Theory
Howells et al, (1998) assert that individuals have strong maturity preferences and those bonds of
different maturities trade in separate and distinct markets. The demand and supply of bonds of a
particular maturity are supposedly minimally affected by bond prices of neighboring maturities.
It is clear that there is now a limit to how far one can go in maintaining that bonds of close
maturities will not be close substitutes. This theory holds that investors have specific investment
preferences that are ultimately dictated by the nature of their liabilities (Howells et al, 1998).
For example, commercial banks, whose liabilities are mainly short-term ones, tend to invest in
short-term securities, not to expose to the risk of interest increase, with a consequent fall in the
market value of the bonds when liabilities are due. By contrast, life insurance companies issue
many long-term contracts, and also tend to hold longer-term bonds. Thus, spot rates for any
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maturity should exclusively depend on the relative demand for and supply of securities with that
particular maturity. (Irturk, 2006)
This segmented market theory suggests that the segmented market behavior of lenders and
borrowers basically determine the shape of the yield curve. A market segmentation theory
implies that the rate of interest for a particular maturity is determined solely by demand and
supply factors for that maturity, with no reference to conditions for other maturities. Borrowers
and lenders have rigid maturity preferences and do not deviate from those preferences no matter
how attractive the yields are for other maturities. The inverted yield curve would signify a
greater demand and smaller supply for short-term securities or a greater supply of long-term debt
and lesser demand for long-term securities (Irturk, 2006). It does not, however, explain why
interest rates tend to move together over time. It also does not offer any insights into why yield
curves typically slope upward when interest rates are very low and slope downward when
interest rates are very high.
The segmentation theory has been criticized on the following grounds; it assumes that there is no
substitutability between markets i.e. it assumes that markets operate independently. This is not
true because the “market walls” are porous and participants can move freely.
2.2.3 The Liquidity Premium Theory
The Liquidity Premium theory advanced by Hicks (1946), states that the interest rate on a long-
term bond will equal an average of short-term interest rates expected to occur over the life of the
long-term bond, plus a term premium increasing with maturity. It concurs with expectations
theory in that it gives the same importance to the expected future spot rates but places more
emphasis on the effects of the risk preference of market participants. It asserts that risk aversion
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will cause long term rates to be systematically greater than short-term rates. This amount is
usually stated as an amount increasing with maturity. It can be said that longer term interest rates
are not only reflective of lenders future assumptions for the interest rates, but also include a
premium for holding these longer term bonds. The concept of compensation of investors for the
added risk of having their money tied up for a longer period is introduced (Irturk, 2006). That is,
investors must be paid an extra return in the form of an interest rate premium to encourage them
to invest in long-term securities and compensate them for the increased risk (Van Zyl et al 2003)
An increase in the riskiness of bonds reduces the demand for bonds. As a result, the bond prices
go down and the interest rates go up. Alternatively, according to the liquidity preference
framework, an increase in the riskiness of bonds raises the demand for money, resulting in an
increase in the interest rate (Wei, 2014). The liquidity premium theory’s main assumption is that
bonds of different maturities are substitutes, but not perfect substitutes, which means that the
expected return on one bond does influence the expected return on a bond of a different maturity.
The theory cannot explain flat and inverted yield curves and is only capable of explaining the
normal yield curve i.e. upward sloping yield curve. It cannot explain that yields of different
terms move together, because the supply and demand of the two markets are independent. In this
theory, term premiums do not need to be positive and an increasing function to maturity. (Irturk,
2006)
2.3 Theoretical Behavior of the Term Structure and Economic Activity
Interest rates are strongly affected by changes in the business cycle and often move in the same
direction that the business cycle is moving. According to Rose (2003), market interest rates tend
to rise when the economy is expanding toward its peak or highest point, and fall when the
28
economy is contracting toward the trough of a recession or its lowest point. Therefore an
upward-sloping yield curve suggests that short-term interest rates are expected to rise. An
inverted yield curve would imply short-term interest rates are expected to fall and a flat yield
curve suggests that the short-term and long-term interest rates are the same. This means that rates
are not expected to change in the future. If the term structure of interest rates reflects in part the
collective inflation expectations or recession, it is intuitive to believe that it must also reflect
market participants’ assessments of future real economic activity (Estrella et al, 1991).
In the US since the 1960s, yield curve inversions (as measured by the difference between the ten-
year and three-month Treasury rates) have preceded every recession on record (Estrella, 2005).
Since the interest rate cycles precede the business cycles, it is assumed that a positively sloped
yield curve is associated with economic expansion, hence growth in real economic activity.
2.4 Empirical Review
According to Joseph et al (1996), in reference to the Rational Expectation Hypothesis of the
Term Structure (REHTS), long term rates should reflect market expectation for the average level
of future short term rates. As such, the rates spreads that form the basis of forward rates should
predict future changes in the spot rate. In the purest interpretation of REHTS, there are no term
premiums in forward rates. Changes in the slope of yield curve are equivalent to what the market
expects interest rates to be at a particular point in the future. This term premium is the different
between the forward rate and the corresponding expected spot rate. Unfortunately, neither
expected interest rates changes nor term premiums are directly observable in the market.
The standard test of expectation theory uses a long term rate with a maturity equal to twice that
of the short rate. Studies by Shiller et al (1983) reported coefficients for the forward rate
29
premium that were not significantly different from zero indicating (using standard regression
method) that the yield curve from 3-6 month has had negligible power to forecast changes in 3
month rate. Two concepts central to the test of expectation theory reviewed below are the
forward and term premium rates. For example an investor can purchase a 6month T-bill now or
purchase a 3 month bill now and reinvest his funds 3 month from now in another 3 month bill.
The forward rate is the hypothetical rate on the 3 month bill 3 month in the future that equalizes
the rate of return from the two options given the current 3-6 month rates.
By using Campbell’s (1986) model, the slope of the term structure correctly predicted the five
cyclical turnings points over the last 40 years. It has to be said that many more expensive
econometric models cannot predict these turning points. This model is described in Campbell’s
(1989) dissertation. Basically, this interest rate based model is very simple. It has only two
components: slope of the term structure (the long term short term yield spread) and measure of
the average propensity to hedge in the economy.
Much closer to home, Khomo et al (2007) have done a study on similar properties of the yield
curve as a predictive tool. They sought to utilize Estrella et al’s (1991) probit and modified
probit models to assess the yield curve’s ability to predict recession in South Africa. The study
also sought to establish the source of the yield curve’s forecasting abilities. Their results
confirmed its ability to act as a simple tool for forecasting economic conditions of recession by
policymakers and investors in the South African market. It has left open the possibility of testing
for the same in the Kenyan market to assess whether or not the yield curve can be manipulated in
the same way to act as a predictor for both booms and recessions.
30
Similarly, Moolman (2002) used a probit model to evaluate the term structure or yield spread as
a predictor of turning points in the South African business cycle. The yield spread was defined as
the difference between the yield on 10-year government bonds and 3-month bankers’
acceptances. The results indicated that the term structure or yield spread successfully predicts
turning points of business cycle two quarters ahead. The negative empirical relationship between
the term structure of interest rates and the business cycle conforms to economic theory. If the
yield on 10-year government bonds is currently 4% or more below the yield on 3-month bankers’
acceptances, the probability that the economy will be in a recession two quarters ahead is 99%.
On the other hand, if the yield on 10-year government bonds is currently 4% above the yield on
3-month bankers’ acceptances, the probability that the economy will be in a recession two
quarters ahead is 15.4%. A yield spread greater than 1.5694% predicts that the economy is more
likely to be in a recession than in an expansion two quarters ahead.
First of all, we need to learn the link between the term structure and economic growth to
especially understand how to predict business cycle turning points with the term structure.
Basically, if we consider the basic intuition of the model, we can see that interest rates are ex
ante measures and represents expected future payoffs. Besides, if market rates are set, we can
assume that expectations of future economic growth influence this process.
This basic intuition is described using an example in the paper. If we assume that investors want
to insure their economic well being, thus most of the investors would prefer a reasonably stable
level of income rather than very high income in one stage of the business cycle and very low
income in another stage. Besides the investors side, assume that the economy is currently in a
growth stage and the general agreement is for a slowdown or recession during the next
year.Here, this desire to hedge will lead consumers to purchase a financial instrument which will
31
deliver payoffs in the slowdown, such as a one year discount bond. At this point, if many
consumers buy the one-year bond, then the price of the security will increase and the yield to
maturity will decrease. Besides, the consumers may sell their shorter term assets to finance the
purchase of the one year bonds. Thus, this selling pressure will drive down the price of the short
term instrument, besides raise its yield. (Irturk, 2006)
After understanding the assumptions, if a recession is expected under these circumstances, we
will see long rates decrease and short rates will increase. Thus, the yield curve or term structure
will become flat or inverted. As a result, we can say that the shape of the term structure of
interest rates provides a forecast of future economic growth.
2.5 Conclusion
Employing the term structure of interest rates is, indeed, useful for implementing monetary
policies. The Central Bank should also keep an eye on supply and demand for Treasury Bonds,
such as evenly distributed maturity in their issuance and government funds portfolios as the risk
premiums due to the continued imbalance of supply and demand increase the volatility of interest
rates. This in turn constraints the effectiveness of monetary policies (Lim, 2005).
Caution must be exercised as Fernandez et al (2011), point out that “no standard theoretical
approach exists to relate the yield curve to forecasts of future economic activity….and the
curve’s close association with subsequent changes in production, consumption, investment and
other components of real GDP remains purely empirical”. The ability of the yield curve to
predict recession is not necessarily policy invariant and there is no guarantee its performance will
continue unaffected in the current global economic phase and the near future.
32
33
CHAPTER THREE: RESEARCH METHODOLOGY
3.1 Introduction
This chapter identifies the procedure and techniques that will be used in the collection,
processing and analysis of data. Specifically the following subsections are included; Research
design, target population, sample design, data collection instruments, data collection procedures
and finally data analysis.
3.2 Research Design
Research design refers to the plan structure and strategy of investigation concerned so as to
obtain answers to research questions and control variance (Mugenda, 2003). Causal research
design was adopted for this study, since it seeks to establish the relationship between real GDP
growth and lagged yield spread. It explores the effect of one variable on another, measuring what
impact a specific change had on existing norms hence is useful in hypothesis testing (Kotler et
al., 2006). The design was preferred because it accommodates correlational studies that seek to
investigate the relationship between the research variables.
3.3 Target Population
According to Cooper and Schindler,(2003) a population element is the amount of quantitative
data on which measurements are being taken. The target population of this study is the interest
rates of the Central Bank of Kenya. The study targeted real GDP growth rate and yield spread for
8 years ranging 2006 to 2013.The study period was chosen because it contained the latest data
that will show how lagged yield spread determines real GDP growth rate today. The lagging was
34
done in order to determine the yield from the previous period. Data was lagged one year period
ahead.
3.4 Data Collection
Secondary data was used for the purpose of this study. Data was collected from World Bank data
base. Annual data obtained was from 2006 to 2013 which was the study period. Specifically, real
GDP figures were collected from the Kenya National Bureau of Statistics. This was to be used as
the dependent variable for our study. Interest rates were obtained from the CBKs annual reports
and were to be used in the calculation of the yield spreads.
3.5 Data Analysis
The collected data from World Bank was entered into the Statistical Package for Social Sciences
(SPSS) version 20 for analysis. Descriptive statistics such as mean and standard deviation was
used to describe the variables.Regression analysis was used to describe the relationship between
variables and determine the ability of yield curve to predict economic growth and recessions.
Regression analysis was done using the analytical model below;
Yi=𝛽
𝑜
+ 𝛽
1
𝑜
1
+ ε
Where;
Y = Real GDP growth
X
1
= Lagged Yield Spread
β
0
=
constant term of the model
β
1
=
Co-efficient of the model
35
є = error term.
Yi = Real GDP growth. GDP is the standardmeasure of aggregate economic activity. GDP
Growth-is the percentage change between the GDP amounts of two periods. A positive amount
indicate an economic growth while a negative amount indicate economic decline.Our research
model is designed to predictreal GDP growth into the futurebased on the current yield spread.
We achieve this by running a series of regressions using real GDPgrowth and the lagged interest
rate spread. We regressreal GDP growth against the index of leadingeconomic indicators like
bond and mortgage interest rates, stock futures markets and the yield curve. Thisenables us to
make a comparison with other simple forecasting techniques.
X
1
= Lagged Yield Spread one year ahead. It is the difference between the quoted rates of return
on two different investments usually of different credit quality. There are several measures of
yield spread like the z-spread and option adjusted spread but the former is mostly preferred
since it uses the entire yield curve to value individual cash flows of bonds and therefore provide
a more realistic valuation than an interpolated yield curve which is based on a single point of
curve.Increasing yield spreads are a leading indicator for expansions and decreasingyield spreads
are a leading indicator for recessions. According to the expectationshypothesis the yield spread is
equal to the expected future short rate and aterm premium. Falling yield spreads before
recessions are caused by both factors (fall in aggregate demand of products and services and rise
in interest rates), where the decreasing expectation to future short rates is more important
(Hamilton et al, 2002). Yield spreads predict future recessions based upon monetary policy.
Tight monetary policy is usedto stabilize output growth and causes the yield spread to decrease.
The powerof the yield spread as a leading indicator depends on the monetary authority’s
behavior (Estrella, 2005).
36
β
0 =
constant term of the model.
β
1 =
Co-efficient of the model. It measures the effect of the lagged yield spread on the real GDP
growth rate. It also allows us to answer the research question "is the predictor X
1
related to the
response Yi?" If the confidence interval for β
1
contains 0, then we conclude that there is no
evidence of a relationship between the predictorX
1
and the response Yi in the population. On the
other hand, if the confidence interval for β
1
does not contain 0, then we conclude that there is
evidence of a relationship between the predictor X
1
and the response Yi in the population.
є = error term. It is the amount at which the equation may differ during empirical analysis.The
error term is also known as the residual or the remainder term. When the actual GDP differs from
the GDP in the model during an empirical test, then the error term will not be equal to 0, which
means there are other factors that influence it.
We chose to conduct a reliability test so as to measure reliability of our data by administering the
same test twice over a period of time to each data set. The scores were then correlated in order
to evaluate the test for stability over time.Cronbach's alpha is a measure of internal consistency,
that is, how closely related a set of data is as a group. It is considered to be a measure of scale
reliability. A "high" value for alpha does not imply that the measure is uni-dimensional. The
Cronbach's alpha is therefore a coefficient of reliability or consistency
Correlation analysis contributed to our understanding of economic behavior by aiding in locating
the critically important variables on which others depend. The effect of correlation is to reduce
the range of uncertainty. The prediction based on correlation analysis is likely to be more
variable and near to reality.
37
Once sure that the two variables of real GDP growth and yield spread were closely related, we
estimated the value of one variable given the value of another using regression analysis.The
primary result of a regression analysis is a set of estimates of the regression coefficients.The
study used Ordinary Least Squares (OLS) regression model. This model was used to describe the
relationship between variables and determine the ability of yield curve to predict economic
growth and recessions.
We used OLS-regression as the method of choice since most international studies (Estrella and
Hardouvelis, 1991; Shiller et al, 1983; and Campbell’s, 1986) have used it, and thus making
comparisons between Kenyan and international results quite straightforward. The real change in
GDP tries to answer the question of how much we can expect the economy grow on a yearly
basis over the next, say, one or two years given the contemporaneous lagged yield spread.
Lagged yield spread was used over the un-lagged yield spread because it showed data for the
previous period as compared to current period.
38
CHAPTER FOUR: DATA ANALYSIS
4.1 Introduction
This chapter covers data analysis, interpretation and discussion of the research findings. The data
was analyzed using a regression analysis and presented in form of tables, graphs and regression
model. The chapter is organized in six sections; descriptive statistics, reliability test, correlation
analysis, yield curve and discussion of findings.
4.2 Descriptive Analysis
This section sought to determine a description of the data that was used to establish the various
patterns of the yield curve over time in Kenya and to determine the ability of yield curve to
predict economic growth and recessions.
4.2.1 Descriptive Statistics
Table 4.2 presents descriptive statistics
Table 4.1: Data Description
Years
Real GDP growth rate
(%)
Interest Rate Spread
(%)
Lagged Interest rate
Spread
2006
6.33
5.43
7.61
2007
6.99
7.31
5.43
2008
1.53
0.71
7.31
2009
2.74
4.62
0.71
2010
5.80
11.86
4.62
2011
4.42
1.33
11.86
2012
4.55
12.08
1.33
2013
4.69
10.06
12.08
Source: World Bank, 2014
39
GDP is the sum of consumer spending, Investment made by industry, Excess of Exports over
Imports and Government Spending. Due to inflation GDP increases and does not actually reflect
the true growth in economy. That is why inflation rate must be subtracted from the GDP to get
the real growth percentage called the real GDP. Real Gross Domestic Product (real GDP) is a
macroeconomic measure of the value of economic output adjusted for price changes. This
adjustment transforms the money-value measure, nominal GDP, into an index for quantity of
total output.Real GDP is calculated by nominal GDP divided by the deflator, or R = N/D. The
deflator is a measurement of inflation since a designated base year.
Moolman (2002) used a probit model to evaluate the term structure or yield spread as a predictor
of turning points in the South African business cycle. The yield spread was defined as the
difference between the yield on 10-year government bonds and 3-month bankers’ acceptances.
For this study, yield spread is the difference between the yield of 10 year government bonds and
3-month bankers’ acceptances.
Table 4.2: Descriptive Statistics
Mean
Std. Deviation
N
Real GDP growth rate (%)
4.6313
1.8152
8
Lagged Yield Spread one year
ahead
6.3687
4.25187
8
Source: Research Data, 2014
From the findings, a period of 8 years (2006 2013) was used in this study. Real GDP growth
rate had a mean score of 4.6313 and standard deviation of 1.8152 while Lagged Yield Spread
40
had a mean score of 6.3687 and a standard deviation of 4.2519. The findings indicate that over
the period under study, real GDP growth rate was 4.63% while lagged yield spread was 6.37%.
4.2.2 Yield Curve
The yield curve is estimated with the adjusted dataset. Figure 4.4 shows the results for the
adjusted dataset.
Yield Spread was lagged in order to determine the yield for the next period. In this study, yield
spread for 2006 was lagged to show the yield for the year 2007 until the year 2013. Figure 4.4
presents the findings for lagged and un-lagged spread sheets.
Figure 4.4: Lagged and Un-lagged Yield Curve
Source: Research Data, 2014
From the figure above, yield curve had no stable rate but keeps on changing period over period.
There were low yields reported in the year 2002008 while the highest was in years 2010 and
41
2012. The spread had an increase from the year 2008 to 2010 but decreased in the year 2011 but
increased again in the year 2012.
4.2.3 Comparison Yield Curve and Real GDP Growth Rate
The study sought a comparison between yield curve and real GDP growth rate. Figure 4.2
presents the findings.
Figure 4.5: Real GDP Growth Rate
Source: Research Data, 2014
From the figure above, real GDP growth rate showed an upward curve from the year 2006 to
2007 but decreased from 6.99% in the year 2007 to 1.53 in the year 2008. Growth rate increased
in year 2010 to 5.8%. From 2011, real GDP growth has shown an upward flow from 4.42% to
4.69% in the year 2013.
42
4.3 Reliability Test
Table 4.3: Reliability Statistics
Cronbach's Alpha
a
N of Items
.731
2
Source: Research Data, 2014
4.4 Correlation Analysis
The Pearson product-moment correlation coefficient (or Pearson correlation coefficient for short)
is a measure of the strength of a linear association between two variables and is denoted by r.
Basically, a Pearson product-moment correlation attempts to draw a line of best fit through the
data of two variables. Pearson correlation coefficient was conducted to examine the relationship
between variables.
Table 4.4: Correlation Analysis
Real GDP
growth
Lagged Yield
Spread
Pearson Correlation
Real GDP growth
1
0.097
Lagged Yield
Spread
0.097
1
Source: Research Data, 2014
The Pearson correlation coefficient, r, can take a range of values from +1 to -1. A value of 0
indicates that there is no association between the two variables. As cited in Wong &Hiew (2005)
43
the correlation coefficient value (r) range from 0.10 to 0.29 is considered weak, from 0.30 to
0.49 is considered medium and from 0.50 to 1.0 is considered strong. However, according to
Field (2005), correlation coefficient should not go beyond 0.8 to avoid multicollinearity. Since
the correlation coefficient is (0.097), which is less than 0.8, there is no multicollinearity problem
in this research.
From table 4.4, the findings indicate that real GDP growth rate and lagged yield spread were
shown to have a positive association between them but the association was weak which means
that lagged yield spread cannot effectively predict real GDP growth rate for the next period.
4.5 Regression Analysis
In addition, the group conducted a multiple regression analysis so as to test relationship between
Lagged Yield Spread (independent) on GDP (dependent). We used SPSS, a data analysis and
statistical software, to code, enter and compute the measurements of the multiple regressions for
the study. Researchers assumed 95% confidence interval. Table 4.4 presents regression model
summary
Table 4.5: Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
1
.097
a
.009
-.156
1.95147
Source: Research Data, 2014
44
Analysis in table 4.5 above shows that the coefficient of determination (the percentage variation
in the dependent variable being explained by the changes in the independent variables) R-
squared equals 0.009 that is, Lagged Yield Spread explains0.9 percent of real GDP growth rate.
The P-Value of 0.046 (Less than 0.05) implies that the model at the 5 percent was significant as
shown in table 4.5.
Table 4.6: ANOVA
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
.215
1
.215
.057
0.046
b
Residual
22.849
6
3.808
Total
23.065
7
a. Dependent Variable: GDP growth rate (annual %)
b. Predictors: (Constant), Lagged Interest rate Spread (%)
Source: Research Data, 2014
ANOVA findings (P- value of 0.046) in table 4.6 show that there is a weak correlation between
the predictor’s variables (Lagged Yield Spread) and response variable (Real GDP growth). An F
ratio is calculated which represents the variance between the groups, divided by the variance
within the groups. A large F ratio indicates that there is more variability between the groups
(caused by the independent variable) than there is within variables, referred to as the error term.
The P value is 0.046 which is less than 0.005 significance level.
45
Coefficient of determination explains the extent to which changes in the dependent variable can
be explained by the change in the independent variables or the percentage of variation in the
dependent variable (real GDP growth) that is explained by the independent variable (Lagged
Yield Spread). Table 4.6 presents the coefficients of the regression model.
Table 4.7: Coefficients of the Regression Model
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
4.369
1.303
3.354
.015
Lagged Interest rate
Spread (%)
.041
.173
.097
.238
.042
a. Dependent Variable: GDP growth rate (annual %)
Source: Research Data, 2014
From the coefficient of determination, the study model or equation:
Yi=𝛽
𝑜
+ 𝛽
1
𝑜
1
+ ε
𝛽
1
𝑜
1
+ 𝑜
𝑜
Where;
Y = Real GDP growth
X
1
= Lagged Yield Spread
β
0
=
constant term of the model
46
β
1
=
Co-efficient of the model
є = error term.
The study equation becomes:
Y=4.369 + 0.041X
1
According to the model, when Lagged Yield Spread is at zero, the dependent variable (real GDP
growth) will be 4.37%.
The determinant for Lagged Yield Spreadwas0.041.The findings show that Lagged Yield Spread
predicts 4.1% only of the change in real GDP growth rate.
4.6 Discussion of the Findings
The study found that yield curve is capable to predict economic growth and recessions but
cannot do it accurately because of a weak association it has with real GDP growth rate. Studies
by Shiller et al (1983) reported coefficients for the forward rate premium that were not
significantly different from zero indicating (using standard regression method) that the yield
curve from 3-6 month has had negligible power to forecast changes in 3 month rate. Studies by
Shiller et al (1983) reported coefficients for the forward rate premium that were not significantly
different from zero indicating (using standard regression method) that the yield curve from 3-6
month has had negligible power to forecast changes in 3 month rate. Much closer to home,
Khomo et al (2007) have done a study on similar properties of the yield curve as a predictive
tool. Their results confirmed its ability to act as a simple tool for forecasting economic
conditions of recession by policymakers and investors in the South African market.
47
Khomo et al (2007) have done a study on similar properties of the yield curve as a predictive
tool. Their results confirmed its ability to act as a simple tool for forecasting economic
conditions of recession by policymakers and investors in the South African market. According to
Joseph et al (1996), in reference to the Rational Expectation Hypothesis of the Term Structure
(REHTS), long term rates should reflect market expectation for the average level of future short
term rates. The results form a study done by Moolman (2002) indicated that the term structure or
yield spread successfully predicts turning points of business cycle two quarters ahead. The
negative empirical relationship between the term structure of interest rates and the business cycle
conforms to economic theory.
Campbell’s (1986) model, the slope of the term structure correctly predicted the five cyclical
turnings points over the last 40 years. Moolman (2002) used a probit model to evaluate the term
structure or yield spread as a predictor of turning points in the South African business cycle. The
findings showed that the term structure or yield spread successfully predicts turning points of
business cycle two quarters ahead.
48
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
This chapter presents the summary of the findings presented in chapter four in accordance to the
study objective. The study aimed at establishing the various patterns of the yield curve over time
in Kenya and to determine the ability of yield curve to predict economic growth and recessions.
It also presents conclusions and the recommendations to the study.
5.2 Summary and Discussions of the Study
The study found that the shape of yield curve indicates the cumulative priorities of all lenders
relative to a particular borrower. Lenders usually are concerned about a potential default so they
offer long term loans for higher interest rates than they offer for short term loans. Yield curves
are usually upward sloping asymptotically but this study found a varied trend in the yield curve.
The study found that the two common explanations for upward sloping yield curve are; markets
are anticipating a rise in interest rates and that longer maturities entail greater risk for the lender.
The study found that the factors that play a role in causing long term yields to move up and down
include inflation, economic growth, supply and demand factors and investor attitude toward risk.
Generally, slower growth, low inflation and depressed risk attitude will help the performance of
shorter term bonds and cause yields to fall and vice versa.
From the study findings, Lagged Yield Spread was seen having a weak positive
association(0.097) with Real GDP growth rate which means that lagged yield spread cannot
effectively predict real GDP growth rate for the next period. The study found that the coefficient
of determination (the percentage variation in the dependent variable being explained by the
changes in the independent variables) R-squared equals 0.009 that is, Lagged Yield Spread
49
explains 0.9 percent of real GDP growth. The study found that when Lagged Yield Spread is at
zero, the dependent variable (real GDP growth rate) will be 4.37%. It found that real GDP
growth has a significant relationship with Lagged Yield Spread.
The study found that yield curve is capable to predict economic growth and recessions but
cannot do it accurately because of a weak association it has with real GDP growth rate. Studies
by Shiller et al (1983) reported coefficients for the forward rate premium that were not
significantly different from zero indicating (using standard regression method) that the yield
curve from 3-6 month has had negligible power to forecast changes in 3 month rate. Much closer
to home, Khomo et al (2007) have done a study on similar properties of the yield curve as a
predictive tool. Their results confirmed its ability to act as a simple tool for forecasting economic
conditions of recession by policymakers and investors in the South African market.
The results form a study done by Moolman (2002) indicated that the term structure or yield
spread successfully predicts turning points of business cycle two quarters ahead. The negative
empirical relationship between the term structure of interest rates and the business cycle
conforms to economic theory. Campbell’s (1986) model, the slope of the term structure correctly
predicted the five cyclical turnings points over the last 40 years.
5.3 Conclusion
The study concludes that Lagged Yield Spread has a weak positive association with Real GDP
growth. It concludes that the coefficient of determination (the percentage variation in the
dependent variable being explained by the changes in the independent variables) R-squared
equals 0.009 that is, Lagged Yield Spread explains 0.9 percent of real GDP growth. The study
concludes that when Lagged Yield Spread is at zero, the dependent variable (real GDP
50
growthrate) will be 4.37%. It concludes that real GDP growth has a significant relationship with
Lagged Yield Spread.
The results of this study lend encouraging support for the ability of the yield curve to predict
changes in real GDP in Kenya. Including more explanatory variables, or more lagged values of
the GDP growth rate, to the regressions remains a subject for further research. The regressions of
this thesis also made use only of the spread between a long and a short yield, whereas more
information from the yield curve could be extracted by using more differences along the maturity
spectrum.
5.4 Recommendation
If the government can take the findings of the study more seriously, we can minimize the
impacts of recessions through policy decisions.
This research only tests commonly used models. It would be interesting to see a more extensive
review of available models and their performance against each other. It therefore recommends
that studies be done on other models based on Nelson & Siegel. The study also recommends to
change the data selection criteria from the one used in this study.
5.5 Implications of the Study
The study found that Lagged Yield Spread does not accurately predict real GDP growth rate. The
implication of this is that if a country depends on Lagged Yield Spread to predict the future real
GDP growth rate, policy makers can be misguided and formulate policies that will not be support
actual real GDP growth rate.
51
5.6 Limitation of the Study
The estimation of the model proposed by Estrella (2005) might prove difficult in the case of
Finland, due to the use of annual data, which leads to the problem of a relatively small sample.
However, it serves an explicit theoretical justification for investigating the predictive power of
the Kenyan yield curve to subsequent GDP.
5.7 Suggestion for Further Research
The coefficient of determination (the percentage variation in the dependent variable being
explained by the changes in the independent variables) R-squared equals 0.796 that is, Lagged
Yield Spread explains 79.6 percent of real GDP growth. It means there is 20.4 percent
unexplained. The study therefore suggests that other study be done to explain the 20.4 percent.
52
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