assessing exchange rate risk: part i
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Assessing Exchange Rate Risk: Part I. Forecasting Exchange Rates. The Truth is Out There. “There. Look at this. See? See? I'm right again. Nobody could've predicted that Dr. Grant would suddenly, suddenly jump out of a moving vehicle. ”. - PowerPoint PPT PresentationTRANSCRIPT
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Assessing Exchange Rate Assessing Exchange Rate Risk: Part IRisk: Part I
Forecasting Exchange Forecasting Exchange RatesRates
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Three econometricians went turkey hunting. The first took a shot and missed to the left. The second missed to the right. The third shouted “We got it!!”
“There. Look at this. See? See? I'm right again. Nobody could've predicted that Dr. Grant would suddenly, suddenly jump out
of a moving vehicle.”
See, here I'm now by myself, uh, er, talking to myself. That's chaos theory.
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Econometricians believe that there is “true” relationship between all things on our planet. If we run enough tests, we can eventually figure is out!
More importantly, this relationship is stable and can be used for prediction
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Probability
EventMean
Probability distributions identify the chance of each possible event occurring
1 SD
2 SD
3 SD
-1 SD
-2 SD
-3 SD
65%
95%
99%
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Mean = 1
Variance = 4
Std. Dev. = 2
Probability distributions are scaleable
22
2
σ,kkNy
kxy
μ,σNx
3 X =
Mean = 3
Variance = 36 (3*3*4)
Std. Dev. = 6
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Mean = 1
Variance = 1
Std. Dev. = 1
Probability distributions are additive
xyyxyx
yy
xx
σ,σNyx
,σNy
,σμNx
cov222
2
2
+Mean = 2
Variance = 9
Std. Dev. = 3
Cov = 2
=Mean = 3
Variance = 14 (1 + 9 + 2*2)
Std. Dev. = 3.7
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Mean = 6
Variance = 4
Std. Dev. = 2
Mean = $ 32,000
Variance = 16,000,000
Std. Dev. = $ 4,000
Suppose we know that your salary is based on your shoe size:
Salary = $20,000 +$2,000 (Shoe Size)
Shoe Size Salary
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We could also use this to forecast:
Salary = $20,000 +$2,000 (Shoe Size)
If Bigfoot had a job…how much would he make?
Size 50!!!
Salary = $20,000 +$2,000 (50) = $120,000
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Searching for the truth….
You believe that there is a relationship between shoe size and salary, but you don’t know what it is….
1. Collect data on salaries and shoe sizes
2. Estimate the relationship between them
Note that while the true distribution of shoe size is N(6,2), our collected sample will not be N(6,2). This sampling error will create errors in our estimates!!
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0
10000
20000
30000
40000
50000
60000
70000
0 2 4 6 8 10 12 14
Shoe Size
Sala
ry
Salary = a +b * (Shoe Size) + error
a
20,σNerror
Slope = b
We want to choose ‘a’ and ‘b’ to minimize the error!
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Regression Results
Variable Coefficients Standard Error t Stat
Intercept 45415.65 1650.76 27.51
Shoe 1014.75 257.21 3.94
Salary = $45,415 + $1,014 * (Shoe Size) + error
We have our estimate of “the truth”
Intercept (a)
Mean = $45,415
Std. Dev. = $1,650
Shoe (b)
Mean = $1,014
Std. Dev. = $257
T-Stats bigger than 2 are considered statistically significant!
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Regression Results
Variable P-value Lower 95% Upper 95%
Intercept 5.2E-102 42172.33 48658.97
Shoe 9.12E-05 509.40 1520.10
Intercept (a) Shoe (b)
$42,172 - $48,658 $509 - $1,520
The P-value tells you the probability that the coefficient is equal to zero
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Regression Statistics
Multiple R 0.17
Standard Error 11673.01
Observations 500
Error Term
Mean = 0
Std, Dev = $11,673
Percentage of income variance explained by shoe size
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Regression Results
Variable Coefficients Standard Error t Stat
Intercept 20,000 0 Infinite
Shoe 2,000 0 Infinite
Regression Results
Variable P-value Lower 95% Upper 95%
Intercept 0 20,000 20,000
Shoe 0 2,000 2,000
Regression Statistics
Multiple R 0
Standard Error 0
Observations 500
If we ever found “the truth”, it would look something like this!
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Using regressions to forecast….
Salary = $45,415 + $1,014 * (Shoe Size) + error
50
Mean = $45,415
Std. Dev. = $1,650
Mean = $1,014
Std. Dev. = $ 257
Mean = $0
Std. Dev. = $11,673
Salary Forecast
Mean = $96,115
Std. Dev. = $17,438
438,17$)673,11()257()50()650,1( 2222 StdDev
Given his shoe size, you are 95% sure Bigfoot will earn between $61,239 and $130,991
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We’ve looked at several currency pricing models that have potential for being “the truth”
Uncovered Interest Parity
% Change in e = Inflation – Inflation*
Purchasing Power Parity
% Change in e = Interest Rate – Interest Rate*Covered Interest Parity
% Change in e = Forward Premium/Discount
Currency Fundamentals
% Change in e = (%M - %M*) + (%Y - %Y*) + (i - i*)
Technical Analysis
% Change in e = Past Behavior of exchange rate
Any combination of these could be “the truth”!!
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-10
-8
-6
-4
-2
0
2
4
6
8
10
-10.0 -5.0 0.0 5.0 10.0 15.0
Inflation Differential
% C
han
ge in
Exch
an
ge R
ate
tttt bae *% Note: PPP implies that a = 0 and b = 1
PPP and the Swiss Franc
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Regression Results
Variable Coefficients Standard Error t Stat
Intercept .027 .231 .12
Inflation 1.40 .742 1.89
Regression Results
Variable P-value Lower 95% Upper 95%
Intercept .910 -.49 .43
Inflation .06 -.065 2.86
Regression Statistics
R Squared .02
Standard Error 2.69
Observations 155
For every 1% increase in US inflation over Swiss inflation, the dollar depreciates by 1.40%
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-10
-8
-6
-4
-2
0
2
4
6
8
10
-0.7
-0.1
-0.6
-0.4
-0.2 -0
-0.9
-0.2
0.02
-0.2 -0
0.01
-0.2
-0.3
-0.5
-0.4
0.03
0.34
0.48
0.83
0.13
0.41 0.6
0.1
0.4
-0.5
Predicted
Actual
Obviously, we have not explained very much of the volatility in the CHF/USD exchange rate
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tttt iibae *% Note: UIP implies that a = 0 and b = 1
UIP and the Swiss Franc
-10
-8
-6
-4
-2
0
2
4
6
8
10
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5
Interest Differential
% C
han
ge in
e
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Regression Results
Variable Coefficients Standard Error t Stat
Intercept .55 .31 1.77
Interest Rate -2.87 1.53 -1.87
Regression Results
Variable P-value Lower 95% Upper 95%
Intercept .07 -.06 1.18
Interest Rate .06 -5.89 .15
Regression Statistics
R Squared .02
Standard Error 2.69
Observations 155
For every 1% increase in US interest rates over Swiss interest rates, the dollar appreciates by 2.87%
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We still have not explained very much of the volatility in the CHF/USD exchange rate
-10
-8
-6
-4
-2
0
2
4
6
8
10
-0.1
-0.2 -0
0.03
0.13
0.24
0.21
0.29
0.33
0.39
0.32
0.28
0.28
0.26
0.22
0.19
0.08
0.04
0.06
0.07
0.07
0.11
0.16
Exchange Rate
Predicted Exchange Rate
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Using regressions to forecast….
= .55 – 2.87 * (i-i*) + error
(3 – 1.5) = 1.5
Mean = .55
Std. Dev. = .31
Mean = -2.87
Std. Dev. = 1.53
Mean = $0
Std. Dev. = 2.69
Salary Forecast
Mean = -3.755%
Std. Dev. = 3.58%
%58.3)69.2()53.1()5.1()31(. 2222 StdDev
Given current interest rates, you are 95% sure that the % change in the exchange rate will be between -10.91% and 3.40%!!
% Change in e
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Technical Analysis Uses prior movements in the exchange rate to predict the future
-10
-8
-6
-4
-2
0
2
4
6
8
10
-10 -8 -6 -4 -2 0 2 4 6 8 10
%Change (t-1)
% C
han
ge (t)
ttt ebae 1%%
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Regression Results
Variable Coefficients Standard Error t Stat
Intercept .12 .21 .57
Prior Change .29 .07 3.86
Regression Results
Variable P-value Lower 95% Upper 95%
Intercept .56 -.29 .53
Prior Change .0001 .14 .45
Regression Statistics
R Squared .09
Standard Error 2.59
Observations 154
A 1% depreciation of the dollar is typically followed by a .29% depreciation