1. Forecasting Exchange Rates9
Chapter
Forecasting Exchange Rates
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2. Chapter Objectives
To explain how firms can benefit from forecasting exchange rates;
To describe the common techniques used for forecasting; and
To explain how forecasting performance can be evaluated.
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3. Why Firms Forecast Exchange Rates
MNCs need exchange rate forecasts for their:
hedging decisions,
short-term financing decisions,
short-term investment decisions,
capital budgeting decisions,
earnings assessments, and
long-term financing decisions.
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4. Corporate Motives for Forecasting Exchange Rates
Decide whether to obtain financing in foreign currencies
Decide whether to hedge foreign currency cash flows
Decide whether to invest in foreign projects
Decide whether foreign subsidiaries should remit earnings
1QA\
Home cash flows
Cost of capital
Forecasting exchange rates
1QA\
Value of the firm
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5. Forecasting Techniques
The numerous methods available for forecasting exchange rates can be categorized into four general groups:
technical,
fundamental,
market-based, and
mixed.
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6. Forecasting Techniques
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7. Forecasting Techniques
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8. Can it be done?
Madura/Fox (p. 313)..”There are as yet no models that convincingly explain the movements in the exchange rate”
Alan Greenspan: “To my knowledge no model projecting movements in exchange rates is superior to tossing a coin”
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9. People still do it…. DnbNor February 23, 2011
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10. Lots of money waiting to be earned (or lost?)
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11. Technical Forecasting
Technical forecasting involves the use of historical data to predict future values.
E.g. time series models.
Speculators may find the models useful for predicting day-to-day movements.
However, since the models typically focus on the near future and rarely provide point or range estimates, they are of limited use to MNCs.
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13. Fundamental Forecasting
Fundamental forecasting is based on the fundamental relationships between economic variables and exchange rates.
E.g. subjective assessments, quantitative measurements based on regression models and sensitivity analyses.
Note that the use of PPP to forecast future exchange rates is inadequate since PPP may not hold and future inflation rates are also uncertain.
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14. Fundamental Approach
Involves econometrics to develop models that use a variety of explanatory variables. This involves three steps:
step 1: Estimate the structural model.
step 2: Estimate future parameter values.
step 3: Use the model to develop forecasts.
A fundamental model could look like:
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15. Fundamental Approach
We will forecast change in the Mexican peso exchange rate
INT = Euro interest – Mexican peso interest
INF = Euro inflation – Mexican inflation (lagged)
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16. Estimating the MXP exchange rate
Assume INFt-1 = 1%
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17. Fundamental Forecasting
In general, fundamental forecasting is limited by:
the uncertain timing of the impact of the factors,
the need to forecast factors that have an immediate impact on exchange rates,
the omission of factors that are not easily quantifiable, and
changes in the sensitivity of currency movements to each factor over time.
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18. Market-Based Forecasting
Market-based forecasting uses market indicators to develop forecasts.
The current spot/forward rates are often used, since speculators will ensure that the current rates reflect the market expectation of the future exchange rate.
For long-term forecasting, the interest rates on risk-free instruments can be used under conditions of IRP.
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19. Mixed Forecasting
Mixed forecasting refers to the use of a combination of forecasting techniques.
The actual forecast is a weighted average of the various forecasts developed.
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20. Forecasting Services
The corporate need to forecast currency values has prompted some consulting firms and investment/commercial banks to offer forecasting services.
One way to determine the value of a forecasting service is to compare the accuracy of its forecasts to that of publicly available and free forecasts.
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21. Evaluation of Forecast Performance
An MNC that forecasts exchange rates should monitor its performance over time to determine whether its forecasting procedure is satisfactory.
One popular measure, the absolute forecast error as a percentage of the realized value, is defined as:
| forecasted value – realized value |
realized value
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22. Absolute Forecast Errors over Time Using the Forward Rate as a Forecast for the British Pound
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23. Evaluation of Forecast Performance
MNCs are likely to have more confidence in their forecasts as they measure their forecast error over time.
Forecast accuracy varies among currencies. A more stable currency can usually be more accurately predicted.
If the forecast errors are consistently positive or negative over time, then there is a bias in the forecasting procedure.
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24. Forecast Bias over Time for the British Pound
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25. Graphic Evaluation of Forecast Performance
Perfect forecast line x z
Realized Value
Predicted Value
Region of downward bias
(underestimation)
Region of
upward bias
(overestimation)
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26. Graphic Evaluation of Forecast Performance
If the points appear to be scattered evenly on both sides of the perfect forecast line, then the forecasts are said to be unbiased.
Note that a more thorough assessment can be conducted by separating the entire period into subperiods.
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27. Forecast Bias in Different Subperiods for the British Pound
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28. Forecasting Under Market Efficiency
If the foreign exchange market is weak-form efficient, then the current exchange rates already reflect historical information. So, technical analysis would not be useful.
If the market is semistrong-form efficient, then all the relevant public information is already reflected in the current exchange rates.
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29. Forecasting Under Market Efficiency
If the market is strong-form efficient, then all the relevant public and private information is already reflected in the current exchange rates.
Foreign exchange markets are generally found to be at least semistrong-form efficient.
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30. Efficient Markets Approach
If Financial Markets are efficient prices reflect all available and relevant information.
If this is so, exchange rates will only change when new information arrives, thus:
St = E[St+1]
and
Ft = E[St+1| It]
Predicting exchange rates using the efficient markets approach is affordable and is hard to beat.
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31. Krugman and Obstfeld
Paul Krugman and Maurice Obstfeld write in their book "International Economics: Theory and Policy” : If exchange rates are asset prices that respond immediately to changes in expectations and interest rates, they should have properties similar to those of other asset prices, for example, stock prices. Like stock prices, exchange rates should respond strongly to "news," that is, unexpected economic and political events; and, like stock prices, they therefore should be very hard to forecast
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32. Exchange Rate Volatility
A more volatile currency has a larger expected forecast error.
MNCs measure and forecast exchange rate volatility so that they can specify a range (confidence interval) around their point estimate forecasts.
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33. Exchange Rate Volatility
Exchange rate volatility can be forecasted using:
recent (historical) volatility,
a historical time series of volatilities (there may be a pattern in how the exchange rate volatility changes over time), and
the implied standard deviation derived from currency option prices.
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34. We find that no model consistently outperforms a random walk, by the conventionally adopted mean squared error measure. However, along a direction-of-change dimension, certain structural models do outperform a random walk with statistical significance. We also find that interest rates predict quite well, although only at the longest horizon.
These forecasts are tied to the actual values of exchange rates in the long run, although in a large number of cases the elasticity of the forecasts with respect to the actual values is different from unity. Overall, we find that model/specification/currency combinations that work well in one period do not necessarily work well in another period. (22)
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35. While the results are not very positive, they do suggest that along some dimensions, structural models have predictive power. And, it is important to recognize that we have stacked the deck against these models having good predictive power, in that half of our estimates do not rely upon contemporaneous information. So, while useful forecasting models remain elusive, the identification of key empirical factors remains a productive, albeit challenging, enterprise.
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