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Academy of Economic Studies Bucharest Doctoral School of Finance and Banking DOFIN. FORWARD DISCOUNT PUZZLE AN APPLICATION FOR THE USD/GBP EXCHANGE RATE. Supervisor: Professor Moisa ALTAR MSc student: Anca-Ioana SIRBU. Bucharest, June 2004. CONTENTS. THE UNCOVERED INTEREST RATE PARITY - PowerPoint PPT Presentation
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Academy of Economic Studies BucharestAcademy of Economic Studies BucharestDoctoral School of Finance and BankingDoctoral School of Finance and Banking
DOFINDOFIN
FORWARD DISCOUNT PUZZLEFORWARD DISCOUNT PUZZLEAN APPLICATION FOR THE USD/GBP EXCHANGE RATEAN APPLICATION FOR THE USD/GBP EXCHANGE RATE
Supervisor: Professor Moisa ALTARSupervisor: Professor Moisa ALTARMSc student: Anca-Ioana SIRBUMSc student: Anca-Ioana SIRBU
Bucharest, June 2004Bucharest, June 2004
CONTENTSCONTENTS
• THE UNCOVERED INTEREST RATE PARITY
• FORWARD RATE UNBIASEDNESS HYPOTHESIS
• AN EMPIRICAL ANALYSIS
UNCOVERED INTEREST RATE PARITY(UIP)UNCOVERED INTEREST RATE PARITY(UIP)
• UIP is the cornerstone of international finance (it appears as a key behavioral relationship in almost all models of exchange rate determination)
• UIP states that if risk-neutral market hypothesis holds, then the expected foreign exchange gain from holding one currency rather than another must be offset by the opportunity cost of holding funds in this currency rather than the other-the interest rate differential
• Since UIP reflects the market’s expectations of exchange rate changes, it represents the starting point for any analysis which depends on future exchange rate values.
• That is why, if there are reasons to believe UIP will not hold precisely, an investor must be able to identify the source of deviation and respond accordingly.
Notations usedNotations used• St – nominal spot exchange rate at time t expressed as the
price, in “home-country” monetary units, of foreign exchange (USD against GBP);
• Ste – expected nominal spot exchange rate at time t;
• Ft – forward rate at time t;
• it, it* , nominal interest rate at time t in home country,
respectively in the foreign country.
• E(.) - expectation conditional on information available at time t.
• Small letters denote the nominal logarithm of the variable
COVERED INTEREST RATE PARITYCOVERED INTEREST RATE PARITY
• In the absence of arbitrage barriers across international financial markets, the arbitrage should ensure that the interest rate differential on two assets, identical in any relevant aspect, except currency of denomination, adjust to cover the movement of currencies at the maturity of the underlying assets in the forward market
or, a logarithmic approximation
• If
then
*
,
1
1
t
t
t
kt
i
i
S
F
1)1( *, tt
ktt i
S
Fi
)1()1( *,
ttt
kt iiS
F
*, tttkt iisf
COVERED INTEREST RATE PARITYCOVERED INTEREST RATE PARITY
• Testing for CIP 1. Computing actual deviations from interest parity 2. Regression analysis
Assuming rationale expectations and risk neutrality, we
get
In logs, this relationship is approximately
FRUH:
ttttkt uiisf )( *,
*
*1
1
)(
t
tt
t
ttt
i
ii
S
SSE
*1)( ttttt iissE
ttt fsE )( 1
FORWARD RATE UNBIASEDNESS FORWARD RATE UNBIASEDNESS HYPOTHESISHYPOTHESIS
• FRUH stipulates that under the joint hypothesis of risk neutrality and rational expectations, the current forward rate is an unbiased predictor of the future spot rate
• Bilson(1981) and Fama(1984)
FRUH: α = 0, β = 1, and Et(εt+1) = 0
• Typical finding: forward discount anomaly
kttktkt sfs )( ,
10 and
FORWARD RATE UNBIASEDNESS FORWARD RATE UNBIASEDNESS HYPOTHESISHYPOTHESIS
ENGEL(1996)
• the existence of a foreign exchange risk premium; • a peso problem;• irrational expectations;• international financial market inefficiency from various
frictions.
Fama(1984): omitted variables
which leads to the following decomposition
111 tttt rpsf
111 ttttt rpssf
FORWARD RATE UNBIASEDNESS FORWARD RATE UNBIASEDNESS HYPOTHESISHYPOTHESIS
• Frankel and Froot (1987): excess returns are due to systematic forecast errors – participants form expectations in an irrational manner
* irrational agents earn higher expected returns because they bear higher risk; * rational agents, being more risk-averse, are not necessarily able to drive the first group out of the market by aggressively betting against them.
• Baillie and Bollerslev (1994,2000): time series statistical properties, that is the long memory behavior exhibited by the forward discount, which results in an unbalanced regression.
• Granger (1999): structural changes or regime switches can generate spurious long memory behavior in an observed series
Techniques for analyzing FRUHTechniques for analyzing FRUH
1.OLS regression
2.Cointegration techniques: *long-run relation (cointegration between st+1 and ft)
*short-run relation (cointegration between st and ft)
Zivot(2000), Guerra(2002)
3.Fractional integration Structural changes (Bai and Perron (2001))
kttktkt sfs )( ,
Long Memory ProcessesLong Memory Processes • time domain
{Yt} a covariance stationary process exhibits long memory in the time domain if its autocorrelations ( ρ(k)) exhibit slow decay and persistence
• frequencies’ domain {Yt} exhibits long memory properties if the spectral density
function f(w) has the following property
• In our analysis we use: GPH estimator, MLP estimator and HURST
exponent
naskn
nk
)(
0~)(2
waswcwfd
The Bai and Perron Methodology for The Bai and Perron Methodology for estimating structural breaksestimating structural breaks
• estimation of single and multiple structural breaks in dynamic linear regression models
• estimates the unknown break points given T observations by the least squares principle
• provide general consistency and asymptotic distribution results under fairly weak conditions
*serial correlation *heteroskedasticity• considers the simple structural change in mean model
• pure and partial structural change models
EXCHANGE RATE DATAEXCHANGE RATE DATA
.0
.1
.2
.3
.4
.5
.6
.7
82 84 86 88 90 92 94 96 98
SPOT_RATE
.0
.1
.2
.3
.4
.5
.6
.7
82 84 86 88 90 92 94 96 98
FORWARD_RATE
-15
-10
-5
0
5
10
15
82 84 86 88 90 92 94 96 98
SPOT_DIFFERENCE
-15
-10
-5
0
5
10
15
82 84 86 88 90 92 94 96 98
FORWARD_DIFFERENCE
-.8
-.6
-.4
-.2
.0
.2
.4
82 84 86 88 90 92 94 96 98
FORWARD_DISCOUNT
-.0015
-.0010
-.0005
.0000
.0005
.0010
.0015
82 84 86 88 90 92 94 96 98
PROFIT
EMPIRICAL ANALYSISEMPIRICAL ANALYSISSTATIONARITY TESTSSTATIONARITY TESTS
SPOT_DIFFERENCE
ADF Test -15.2345 1% level critical value -2.573652
5% level critical value -1.942017
10% level critical value -1.615906
PP Test -15.234 1% level critical value -2.573652
5% level critical value -1.942017
10% level critical value -1.615906
FORWARD_DIFFERENCE
ADF Test -15.19883 1% level critical value -2.573652
5% level critical value -1.942017
10% level critical value -1.615906
PP Test -15.19852 1% level critical value -2.573652
5% level critical value -1.942017
10% level critical value -1.615906
MODELS OF COINTEGRATION BETWEEN MODELS OF COINTEGRATION BETWEEN sst+1t+1 AND f AND ftt – EG Methodology: Step 1 – EG Methodology: Step 1
• st+1 = 0.02395420+ 0.951353*ft + et
(0.899071) (0.019174) [2.664328] [49.61727]
MacKinnon critical values for cointegration
Level 0,01 0,05 0,1
Critical values -2.5376 -1.9420 -1.6519
RESID_COINTEGRATION_1
ADF Test -14.6227 1% level critical value -2.573685
PP Test -14.66957 5% level critical value -1.942022
10% level critical value -1.615903
MODELS OF COINTEGRATION BETWEEN MODELS OF COINTEGRATION BETWEEN sst+1t+1 AND f AND ftt – EG Methodology: Step 2 – EG Methodology: Step 2
MODELS OF COINTEGRATION BETWEEN MODELS OF COINTEGRATION BETWEEN sst+1t+1 AND f AND ftt – EG Methodology: Step 2 – EG Methodology: Step 2
White Heteroskedasticity-Consistent Standard Errors & Covariance
Variable Coefficient Std. Error t-Statistic Prob.
D(SPOT) -0.026943 0.014258 -1.889652 0.0599
D(FORWARD(-1)) -0.029963 0.013787 -2.173368 0.0306
RESID_COINTEGRATION_1 0.00989 0.000158 62.40018 0
R-squared 0.970949 Mean dependent var -5.66E-05
Adjusted R-squared 0.970728 S.D. dependent var 0.030691
S.E. of regression 0.005251 Akaike info criterion -7.64961
Sum squared resid 0.007251 Schwarz criterion -7.6092
Log likelihood 1020.398 Durbin-Watson stat 0.07203
White Heteroskedasticity-Consistent Standard Errors & Covariance
Variable Coefficient Std. Error t-Statistic Prob.
D(FORWARD(-1)) 0.00204 0.001337 1.525582 0.12830
D(SPOT) 1.000032 0.001288 776.3671 0.00000
R-squared 0.99971 Mean dependent var -0.00015
Adjusted R-squared 0.999709 S.D. dependent var 0.03067
S.E. of regression 0.000523 Akaike info criterion -12.26529
Sum squared resid 7.26E-05 Schwarz criterion -12.23842
Log likelihood 1639.416 Durbin-Watson stat 2.21143
MODELS OF COINTEGRATION BETWEEN MODELS OF COINTEGRATION BETWEEN sst+1t+1 AND f AND ftt – Johansen Methodology – Johansen Methodology
Trend assumption: No deterministic trend (restricted constant)
Series: SPOT FORWARD
Unrestricted Cointegration Rank Test
Hypothesized Trace 5 Percent 1 Percent
No. of CE(s) Eigenvalue Statistic Critical Value Critical Value
None * 0.05951 21.91900 19.96 24.6
At most 1 0.02083 5.59983 9.24 12.97
Hypothesized Max-Eigen 5 Percent 1 Percent
No. of CE(s) Eigenvalue Statistic Critical Value Critical Value
None * 0.05951 16.31917 15.67 20.2
At most 1 0.02083 5.59983 9.24 12.97
*(**) denotes rejection of the hypothesis at the 5%(1%) level
Trace test indicates 1 cointegrating equation(s) at the 5% level
Max-eigenvalue test indicates 1 cointegrating equation(s) at the 5% level
st+1 =- 0.008327+1.022440ft + et+1
(0.00296) (0.00634)
MODELS OF COINTEGRATION BETWEEN MODELS OF COINTEGRATION BETWEEN sst+1t+1 AND f AND ftt – Johansen Methodology – Johansen Methodology
-.15
-.10
-.05
.00
.05
.10
.15
82 84 86 88 90 92 94 96 98 00 02
Cointegrating relation 1
The cointegrating relation (stationary residuals:ADF and PP)
MODELS OF COINTEGRATION BETWEEN MODELS OF COINTEGRATION BETWEEN sst+1t+1 AND f AND ftt – Johansen Methodology – Johansen Methodology
Vector Error Correction Estimates
Cointegration Restrictions:
B(1,1)=1,B(1,2)=-1
Restrictions identify all cointegrating vectors
LR test for binding restrictions (rank = 1):
Chi-square(1) 7.603624653
Probability 0.005825108
Cointegrating Eq: CointEq1
SPOT 1.0000
FORWARD(-1) -1.0000
C -0.2108
0.0641
MODELS OF COINTEGRATION BETWEEN MODELS OF COINTEGRATION BETWEEN sstt AND f AND ftt – Johansen Methodology – Johansen Methodology
Trend assumption: No deterministic trend (restricted constant)
Series: SPOT FORWARD
Lags interval (in first differences): No lags
Hypothesized Trace 5 Percent 1 Percent
No. of CE(s) Eigenvalue Statistic Critical Value Critical Value
None * 0.058059 23.09491 19.96 24.6
At most 1 0.026018 7.065169 9.24 12.97
Hypothesized Max-Eigen 5 Percent 1 Percent
No. of CE(s) Eigenvalue Statistic Critical Value Critical Value
None * 0.058059 16.02974 15.67 20.2
At most 1 0.026018 7.065169 9.24 12.97
*(**) denotes rejection of the hypothesis at the 5%(1%) level
Trace test indicates 1 cointegrating equation(s) at the 5% level
Max-eigenvalue test indicates 1 cointegrating equation(s) at the 5% level
st =- 0.821933+1.022254ft + et
(0.00637) (0.29862)
MODELS OF COINTEGRATION BETWEEN MODELS OF COINTEGRATION BETWEEN sstt AND f AND ftt – Johansen Methodology – Johansen Methodology
Vector Error Correction Estimates
Cointegration Restrictions:
B(1,1)=1,B(1,2)=-1
Convergence achieved after 3 iterations.
Restrictions identify all cointegrating vectors
LR test for binding restrictions (rank = 1):
Chi-square(1) 8.858151
Probability 0.002918
Cointegrating Eq: CointEq1
SPOT(-1) 1
FORWARD(-1) -1
C -0.00214
-0.00067
[-3.18061]
MODELS OF COINTEGRATION BETWEEN MODELS OF COINTEGRATION BETWEEN sstt AND f AND ftt – Johansen Methodology – Johansen Methodology
-.006
-.004
-.002
.000
.002
.004
.006
82 84 86 88 90 92 94 96 98 00 02
Cointegrating relation 1
The cointegrating relation (stationary residuals:ADF and PP)
FORWARD DISCOUNTFORWARD DISCOUNT
Classic testing of FRUH
Forward discount – AR(1) process
11 )( tttt sfs
α = -0.398245 and β = -1.96552 (0.262959) (0.977023)
FWD_DISC = -0.21546 + 0.96085*FWD_DISC(-1) + RESID(0.079487) (0.022346)
[-2.710614] [42.99797]
FORWARD DISCOUNTFORWARD DISCOUNT
Lag
AC
F
0 5 10 15 20
0.0
0.2
0.4
0.6
0.8
1.0
Series : COINTEGRARE[["forward.discount"]]
Long memory in FORWARD DISCOUNTLong memory in FORWARD DISCOUNT
• GPH estimator
• MLP estimator
• HURST exponent = 0.979207261
dMLPstandard deviation
95%confidence interval
0.96947784 0.074574254[0.8233123 1.1156434 ]
bandwidth dGPH p-value0.625 0.48677 0.045
0.675 0.37448 0.066
0.7 0.32776 0.079
Structural breaks in FORWARD DISCOUNTStructural breaks in FORWARD DISCOUNT-
Specification
Zt = {1} q=1 p=1 h=13 M=5
Tests
SupFT(1) SupFT(2) SupFT(3) SupFT(4) SupFT(5) UDmax WDmax
2.6081 6.2952 18.8677** 13.1897*** 18.8941*** 18.8941*** 32.6855***
SupFT(2/1) SupFT(3/2) SupFT(4/3) SupFT(5/4)
15.5539** 15.5539** 40.5748*** 40.5748***
Number of breaks selected
Sequential 0
BIC 4
Break Point Dates (with Confidence Interval)
C1 0.833018(0.023847) T1 1984:09:00 [21.0000 39.0000]
C2 0.014112(0.008896) T2 1986:03:00 [49.0000 73.0000]
C3 -0.065802(0.010645) T3 1988:06:00 [71.0000 83.0000]
C4 -0.006225(0.013110) T4 1992:10:00 [124.0000 132.0000]
C5 - 0.017911(0.004947)
Structural breaks in FORWARD DISCOUNTStructural breaks in FORWARD DISCOUNT
CONCLUSIONSCONCLUSIONS• We find evidence of a negative β for 1982/01:2004/05, which suggests that the risk
premium is negatively correlated with the expected depreciation, which may explain the negative slope coefficient and can therefore explain the puzzle.
• Using cointegration techniques, we find that in the long run there is mixed evidence regarding the FRUH, as we can accept the unbiasedness, finding that the coefficients are close to their theoretical values, even though by imposing a priori restrictions, we reject the unbiasedness assumption.
• The short-run investigation clearly rejects the FRUH
• A possible explanation for the FRUH not to hold may be that the forward discount is a fractionally integrated process, so it exhibits long memory, which makes the classical regression unbalanced
• Part of the long-memory behavior turns out to be due to structural breaks. We identify four such structural break points for the analyzed period.
• Further analysis should identify how much of the long memory behavior may be explained by the existence of structural breaks
BIBLIOGRAPHYBIBLIOGRAPHY
• Bai, J. and P. Perron (2001): “Computation and Analysis of Multiple Structural Change • Models”, Journal of Applied Econometrics• Baillie, T. R. and T. Bollerslev, (1994): “Cointegration, Fractional Cointegration and • Exchange Rate Dynamics”, The Journal of Finance,vol. 49, no 2, 737-745.• Baillie, T. R. and T. Bollerslev, (2000), “The Forward Premium Anomaly is not as Bad • as you Think,” Journal of International Money and Finance, 19, 471-488.• Bilson, John F.0. (1981): “The ‘speculative efficiency’ hypothesis,” Journal of Business,• 54, 435-51.• Enders,W. (2000), “Applied Econometric Time Series”, in John Wiley & Sons.• Engel, C., (1996): The forward discount anomaly and the risk premium: A survey of • Recent Evidence Journal of Empirical Finance 3, 123–192• Engle, R.F. and C.W Granger., (1987): Cointegration and error correction: representation,• estimation and testing Econometrica 55, 251–276.• Evans, Martin D.D. and Karen Lewis (1995): “Do long-term swings in the dollar affect• estimates of the risk premia?” Review of Financial Studies, Vol. 8, No. 3, • 709-742.• Fama, E., (1984): Forward and spot exchange rates. Journal of Monetary Economics 14, • estimation and testing Econometrica 55, 251–276 • Froot, Kenneth A. and Jeffrey A. Frankel(1987): “Using Survey Data to Test Standard • Propositions Regarding Exchange Rate Expectations”, The American Economic • Review 77, no. 1, 133-153.• Froot, Kenneth A. and Jeffrey A. Frankel (1989): “Forward discount bias: is it an • exchange risk premium?” Quarterly Journal of Economics, 104, 139-61
BIBLIOGRAPHYBIBLIOGRAPHY
• Guerra, Roger (2002): “Forward Premium Unbiasedness Hypothesis: Old Puzzle, • New Results”, University of Geneva, Department of Economics, working • paper no. 02.02. • Hamilton, J., 1993. Time Series Analysis. Princeton University Press, Princeton (NJ).• Maynard A., Phillips P.C.B. 2001. Rethinking an old empirical puzzle: econometric • evidence on the forward discount anomaly. Journal of Applied Econometrics • 16: 671-708.• Sakoulis G., Zivot E. 2001. Time variation and structural change in the forward discount:• Implications for the forward rate unbiasedness hypothesis. Working Paper, • Department of Economics, University of Washington• Sarno, L. and M.Taylor(2000): “The Economics of Exchange Rates”, Cambridge • University Press.• Zivot, E., 1999. The power of single equation tests for cointegration when the • cointegrating vector is prespecified. Econometric Theory (in press) Department of
• Economics, University of Washington, Seattle.• Zivot E. 2000. Cointegration and forward and spot exchange rates. Journal of• International Money and Finance 19: 785-812• Zivot E, J. Wang(2003), Modeling Financial Time Series with S-Plus, Springer