LongerTerm Exchange Rate and Price Movements - PowerPoint PPT Presentation

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LongerTerm Exchange Rate and Price Movements

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Basic forecasting model (in logs): lnRt = a b lnRt-1 ut, ut N(0, 2u) Long-Run Mean: lnR ... standard deviation of disturbance ut. VI. Testing for mean reversion ... – PowerPoint PPT presentation

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Title: LongerTerm Exchange Rate and Price Movements


1
Longer-Term Exchange Rate and Price Movements
  • International Finance
  • Dick Sweeney

2
Take Aways
  • Using PPP to help value projects, firms
  • Reduces uncertainty
  • PPP helps to estimate your uncertainty
  • PPP more useful at longer time horizons
  • But much uncertainty even at longer horizons
  • Technical analysis better for shorter horizons
  • PPPs usefulness, limitations in predicting
    longer-term exchange rates and prices
  • PPP helps but is no guarantee
  • Very useful for 100 projects with modest
    correlation

3
I. PPP and Trends
  • PPP relevant for high-inflation countries trends
  • Look at Brazil or Argentina in old days
  • But did exchange rate depreciate as much as
    inflation? More? Less?
  • Floating (Zambia) crawling peg (Brazil)
    adjustable peg (Indonesia)
  • What about low-inflation, low-depreciation
    countries Dont have dominant long-run trends
  • Conclusion Not exchange rate, but exchange rate
    versus prices, that mattersreal exchange rate

4
II. Real Rate and Competitiveness
  • A competitiveness approach to PPP
  • S is spot rate in European terms for DEM relative
    to USD (DEM for one USD)
  • Hey! What about the Euro? Come back in 3-4 years
  • PUS, price of basket of goods in U.S. in USD
  • PGer, price of same basket in Germany in DEM
  • Which basket? bit arbitrary
  • Look at CPIs common choice

5
II. Real Rate (cont.)
  • Compare US prices and German prices
  • Convert German price index to USD divide by S,
    so PGer,USD PGer /S
  • Or multiply by S' ( 1/S S' is rate in American
    terms)
  • Define real exchange rate R as the ratio
    PUS / PGer,USD R
  • Arbitrary Could look at R' 1/R
  • Often look at lnR, log of R
  • Often compare lnSt versus lnRt

6
III. Behavior of Real Rate
  • Two possible ways to think about the real
    exchange rate (see following graph)
  • The real exchange rate is a non-stationary,
    "random walk" variable
  • Changes are mostly not forecastable
  • Alternative PPP holds in long runmean-reverting
    real rate
  • May take a very long time
  • Actual path to long run fairly uncertain new
    shocks
  • Forecastable, but still substantial uncertainty

7
Mean Reversion? or Not?
Et lnRth lnRt, h gt 0
? ?
?
lnRt
a / (1 - b)
lnR
Talk about in few minutes
?
time
8
IV. ExampleWhy you care
  • DEM FCFs of German operation directly
    proportionate to German price level
  • Hold constant US price level in example
  • Keep it simple!
  • Receiving DEM 2m now S DEM 2/USD, RR 10
  • PV ECF/RR (2m/2)/.1 1m/.1
    USD 10m
  • DEM now depreciates to DEM 3/USD--what is new PV?
    With random-walk real rate, PV (2m/3)/.1
    USD 6.67m

9
IV. Example (cont.)
  • What if mean-reverting real exchange rate?
  • Suppose S doesnt adjust but PGer does
  • Pass through of depreciation to prices,
    familiar in countries with inflation
  • Might have both S and Pger adjust
  • Keep it simple!
  • In long run, S up by 50 means PGer up by 50,
    DEM FCFs up by 50 to DEM 3m
  • In long run, PV (3m/3)/.1 USD 10m, as before

10
IV. Example (cont.)
  • Short run effects suppose gap between initial
    and long-run real rate made up at 20 per year of
    initial gap
  • Future FCFs are then DEM 2.2m, 2.4m, 2.6m, 2.8m,
    3m, 3m
  • PV (2.2m/3)/(1.1) (2.4m/3)/(1.1)2
    (2.6m/3)/(1.1)3 (2.8m/3)/(1.1)4
    (3m/3)/(1.1)5 (3m/3)/.1/(1.1)5 9.447m
    versus 6.67m versus 10.0m

11
V. Basic Forecasting Model
  • Need to know Mean reversion? Random-walk real
    rate?
  • Basic forecasting model (in logs)
    lnRt a b lnRt-1
    ut, ut ? N(0, ?2u)
  • Long-Run Mean lnR
  • lnR a b lnR ? lnR a / (1-b)
  • Makes sense only if b lt 1
  • Imagine b ? 1 from below (1 - b) ? 0, a / (1 -
    b) ? ? ? as a gt,lt 0
  • Graphical interpretation of model (previous
    graph) lnR is (log of) long run real exchange
    rate
  • Disturbances move lnRt away from long run level

12
V. Forecasting Model (cont.)
  • How to use estimates to forecast real exchange
    rate
  • Need a, b for long-run equilibrium real rate
    a / (1 - b) lnR
  • Need (1 - b) for speed of adjustment (see below)
  • Need estimate of error variance, ?2u, to
    estimate the uncertainty of real-rate forecast
  • ?2u ? ?u, standard deviation of disturbance ut

13
VI. Testing for mean reversion
  • For single currencies by themselves, cannot
    reject null of b 1
  • Random walk, no mean reversion
  • But tests on a system of currencies,for G-10
    countries relative to US
  • Usually some econometric gain to pooling like
    this--Like adding more observations for any one
    country
  • Jorion and Sweeney (1996) Often reject null
    hypothesis of b 1 in favor of alternative that
    b lt 1 ? mean reversion!

14
VI. Testing Notes
  • For your information, in dealing with
    econometrics geeks
  • b lt 1? or b 1?
  • b 1 ? real rate non-stationary, mean reversion
    doesnt work
  • Econometric problems Mainstream statistics are
    for stationary, mean-reverting variables
  • But if b ? 1, lnRt is non-stationary cannot use
    standard t-statistic critical values (2.0, etc.)
  • Can still calculate t-statistics, of course, but
    have to find new critical values
  • Use simulation based on your data set to find
    critical values Jorion and Sweeney (1996) use
    simulation

15
VII. Forecasting Results
  • Acid test How do different approaches do out
    of sample?
  • This is what you need in decisions
  • On average, mean reversion models work better
    than random walk models
  • For any one MA deal like German firm, may lose
    if bet on mean reversion
  • For 100 MA deals like German firm,
  • very high probability that bets on mean
    reversion pay off if modest correlation

16
VII. Forecasting Results (cont.)
  • Using PPP model, benefits increase as the
    forecast horizon lengthens
  • Benefits increase with longer estimating period
    (Siddique and Sweeney 1998)
  • Even at one-year horizons, PPP calculations
    reduce uncertainty by only about 10 percent
    relative to random-walk forecasts
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