A C T A U N I V E R S I T A T I S L O D Z I E N S I S FOLIA OECONOMICA 192, 2005
J o a n n a B r u z d a *, T o m a s z K o ź li ń s k i * *
N O N -L IN E A R IT Y AND T H E P U R C H A S IN G P O W E R PA R ITY H Y P O T H E S IS F O R E X C H A N G E R A T E J P Y /U S D
Abstract. In the paper we verify the purchasing power parity (PPP) hypothesis for the exchange rate JPY/USD for different time periods and price indexes. We build several forecasting models for this exchange rate including some non-linear specifications, for example the univariate BL-GARCH model and fundamental VEC models based on a long run equilibrium relationship. We have found that under some specific conditions the PPP hypothesis does hold. An adjustment process towards a long run equilibrium turned out to be o f a non-linear nature. However, the impact o f this adjustment on short-run dynamics o f the exchange rate has a linear error correction form.
Keywords: purchasing power parity, forecasting exchange rates, non-linear adjustment, non-linear error correction models.
JEL Classification: C22, C53, E310, F310.
1. INTRODUCTION
F o rec astin g exchange rates is one o f the m o st im p o rta n t activities o f all financial in stitutions. V olum e o f all tran sa ctio n s on in tern a tio n al and dom estic m oney m ark e ts is considerably bigger th a n tu rn o v e r o n capital m arkets. In tern atio n al organizations, national banks, m u ltin atio n al com panies and individual investors are often m ore connected o r fam iliar with currencies th a n w ith stocks or derivative instrum ents. In case o f in tern a tio n al co r p o ra tio n s forecasting exchange rates is a crucial m a tte r in a budgetin g and risk m an ag e m en t process.
* Dr (Ph.D., Assistant Professor), Department o f Econometrics and Statistics, Nicolaus Copernicus University in Toruń. The author acknowledges the support of the Polish Foundation for Science under the scholarship for young scientists in 2004.
** Mgr (M.A., Ph.D. student), Department of Finance Management, Nicolaus Copernicus University in Toruń.
T im e series o f exchange rates have som e sho rt- an d long-term p ro p e r ties. S h ort-term d a ta depend on financial, econom ic and political info r m atio n . Investors can use technical analysis to try to predict its volatility in a d ay -to -d ay o r week-to-week q u o tatio n s. But th ere are also theories based on econom ics which indicate th a t exchange rates should be d eter m ined by som e significant processes and in fo rm atio n in a lo ng run. In ou r p ap e r we exam ine factors influencing long-term fluctuation s o f the exchange ra te JP Y /U S D . Jap an ese and U nited S tates’ econom ics have been in deep and long econom ic and political relations since the Second W orld W ar I. T h e exchange ra te was floating alm ost all tim e exccpt for som e interven tions by the N a tio n al Bank o f Ja p a n to co u n teract yen ap p reciatio n against US d o lla r.1
2. PURCHASING POWER PARTY HYPOTHESIS
In econom ics th ere is a law o f one pricc th a t states th a t price o f one com m odity o r basket o f p ro ducts should be eq ual, after exchange rate ad ju stm en t, to strictly the sam e goods or basket o f com m odities in an o th er coun try . T his can be m athem atically w ritten as:
(1) P Japan = ( J P Y / U S D ) P us,
where: P Japan is a price o f com m odity or basket o f goods in J a p a n , P us is a price o f com m odity or basket o f goods in the U nited S tates, J P Y / U S D m eans a n om inal exchange rate, a m o u n t o f Jap an ese yens p aid fo r one US dollar.
T his law is a foundation o f the purchasing pow er parity (PPP) hypothesis. T he hypothesis states th a t the exchange ra te is driven in a long run by prices m easured by different price indexes o r differences between them in tw o cou ntries. T his im plicates th a t there are, generally, tw o m odels o f PPP. T h e first one is an absolute version o f the P P P h ypothesis an d the next one is a relative ap p ro ach (cf. R osen berg 1996, p. 14):
(2) J P Y / U S D = PJapJ P us,
(3) % A J P Y / U S D = % A PJapan - % A P us.
T here are m any m odel representations o f the tw o versions o f PPP. F or exam ple m odel (4) is a m odification o f the abso lu te version by add in g an
1 In spite o f huge amount of some intervention by the National Bank o f Japan long-term appreciation could not be broken.
intercept and talcing logarithm s and an o th er one (5) is based on differences in this case an intercept does no t have to be significant (S trzała 2002, pp. 106-107):
(4) In ( J P Y / U S D ) = a 0lnC + a j l n P j a ^ - a 2ln P us, (5) ln(AJ P Y / U S D ) = a 0lnC + a i ln(A PJapan) - a 2ln(A P ÜS).
B urda and W ypłosz (2000, pp. 262-264) describe the relative P PP (6) under assu m p tio n o f the co n stan t real exchange rate ( r ( J P Y / U S D ) = С ф 1) and tran sfo rm this eq u a tio n to (7):
(6) ( J P Y / U S D ) = r ( J P Y / U S D ) ■ (PJapaJ P us), (7) A r ( J P Y /U SD ) A ( J P Y / U S D ) A P US A P Japan
r ( J P Y / U S D ) J P Y / U S D P vs P Japan ‘
I f price indexes are integrated o f o rd e r 1, em pirical testing o f the hypotheses (4)-{5) is based on cointegration. Sim ilar results should arise from testin g statio n arity o f the real exchange ra te series r ( J P Y / U S D ) . In test eq u a tio n s it does n o t ap p e ar a determ inistic tren d because it m eans th a t the real exchange rate is n o t constant.
(8) r ( J P Y / U S D ) = ( J P Y / U S D ) ■ (P us/PjaPan),
(9) \ n[r(JPY/ USD)] = a 0l n ( J P y / USD) + a i lnP vs - a 2lnP Japan.
where: r ( J P Y / U S D ) is the real exchange ra te calculated from a nom inal exchange ra te afte r adjustm ent to in flation indexes in J a p a n an d the U nited States.
3. VERIFICATION OF THE PURCHASING POWER PARITY HYPOTHESIS
I t is interesting to test the above relations d u rin g differen t periods o f tim e an d by applying different price indexes’ definitions. In case o f the Jap a n ese econom y the real exchange ra te calculated by the B ank o f Jap a n has been published. It is the m onthly average index o f w eighted average yen’s real exchange rates versus 15 m ajo r w orld currencies officially used in 26 countries. T esting this tim e series fo r statio n arity should indicate sim ilar results as looking for unit roots in r ( J P Y / U S D ) calculated according to the m odel (8), co m pare results in T ables 1 and 4.
Table 1. Results o f unit root tests for real effective exchange rate for yen weighted by 15 major currencies
Specification ADF PP KPSS
Include in test equation AIC HQ Bartlett kernel Bartlett kernel
Intercept 0.3903 0.3903 0.4092 1.569819
Trend and intercept 0.0314** 0.1137 0.2373 0.105905***
None 0.7936 0.7936 0.8538
-Note: *** denotes rejection of the null hypothesis o f a unit root at 1% significance level, ** and * at 5% and 10% accordingly. A D F - Augmented Dickey-Fuller, PP - Phillips-Perron, KPSS - Kwiatkowski, Phillips, Schmidt and Shin tests. AIC - Akaikc information criterion, HQ - Hannan-Quinn criterion.
T h ere are 6 m ain testing appro ach es to the verification o f the PPP hypothesis (e.g. an overview in the paper by S trzała 2002). In our paper we co n c en trate on tw o o f them , based on m odels (4) and (8). A fter G ran ger and N ew bold (1974) results, co intcgratio n theory has been m ainly applied to the verification o f the P P P hypothesis. If there is a coin teg ratin g equation in the m odel (4) w ith an intercept, a relative P P P holds, if there is n o t an intercept in this eq u a tio n , an ab solute P P P is verified. In th e second case after im posing r ( J P Y / U S D ) = 1, we get:
(10) J P Y / U S D = IXy ■ P j apan — d
2PvS-A fte r cho osing testing equ atio n s there is a t least o ne m o re problem concerning inflation index definition. T here are m an y price indexes, which have different base definitions, fo r exam ple consum er, p ro d u c tio n , wholesale, im p o rt and ex p o rt price indexes. F o r testing PPP hypothesis this index should be used, which describes price m ovem ents well and is th e m ost often used in tra d e agreem ents between decision m akers in Ja p a n an d the United S tates. Interesting results can be achieved with the help o f im p o rt and ex p o rt price indexes, th a t are w eighted average prices o f trad ed goods counted for the Jap an ese econom y. In ad d itio n US B ureau o f L ab or and S tatistics publishes one m ore im p o rt and exp o rt price index (IP IE P I), which m easures price volatility o f all goods by the location o f origin fro m /to Ja p a n .2 In the last index definition o f all com m odities are tak en into account, which logically can influence the exchange ra te JP Y /U S D . T his qu o tien t can be used for testing co in teg ratio n with sp ot exchange rates and reverse
2 IPIEPI denotes import/export price index for all commodities, which are exchanged between USA and Japan, location of origin is Japan (1993 January - 2003 December), taken from the US Department o f Labor.
Specifications 1975 1976 1977 1978 1979 1980 1981 1982 1983
Akaike VAR Lag intervals 1 4 1 4 1 4 1 4 1 4 1 4 1 2 1 2 1 2
Akaike Johansen coinl Lag intervals 1 3 1 3 1 3 1 3 1 3 1 3 1 1 1 1 1 1
Data trend Cointcgration Tracc Max-Eig Trace Max-Eig Trace Max-Eig Trace Max-Eig Trace Max-Eig Trace Max-Eig Trace Max-Eig Trace Max-Eig Trace Max-Eig
1 None No Intercept No Trend 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1
2 None Intercept No Trend 1** 1 1* 1 1* 1 1** 1 1** 2 1** 1 1* 1 1* 1 1* 1
3 Linear Intercept No Trend 0 0 0 0 0 0 0 1 1** I 1** 1 0 0 0 0 0 0
4 Linear Intercept Trend 1 1 1 1 1 1 1 2 2 2 1 2 1 1 1 1 1 1
5 Quadratic Intercept Trend 1 1 1 1 1 1 2 2 2 2 3 3 1 1 1 1 1 1
1984 1985 1986 1987 1988 1989 1990 1993
Akaike VAR Lag intervals 1 2 1 2 1 2 1 2 1 2 1 1 1 2 1 1
Akaike Johansen coint Lag intervals 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Data trend Cointcgration Tracc Max-Eig Tracc Max-Eig Tracc Max-Eig Trace Max-Eig Trace Max-Eig Trace Max-Eig Trace Max-Eig Trace Max-Eig
1 None No Intercept No Trend 1 1 2 2 2 2 1 1 1 1 1 1 1 1 2 2
2 None Intercept No Trend 1* 1 1** 2 1** 1 1* 1 1* 1 1* 0 1** 1 2** * 0
3 Linear Intercept No Trend 0 0 0 0 1* 1 0 0 0 0 0 0 0 0 0 0 SIC Rank (0) AIC (1)
4 Linear Intercept Trend 1 1 1 1 1 2 1 1 1 0 1 1 0 0 0 0 SIC Rank (0)
5 Quadratic Intercept Trend 1 1 1 1 2 2 1 1 1 0 3 0 0 0 0 0
Note: VAR (1 3) means one to three lags interval for endogenous; Johansen cointcgration (1 3) means one to three lags interval in first differences for endogenous; (1 1) means one cointegration equation according to trace test and one according to max-cigcnvalue test, only for 2nd and 3"“ options: ** trace test indicates 1 cointegrating equation at the 5% (1%) level, * trace test indicates 1 cointegrating equation at the 5% level; Max-Eig - max-eigenvaluc test; for 1975-2002 and 1993-2002 if trace test did not indicate cointcgration equation were written Schwarz and Akaike information criteria (SIC and AIC).
Table 3. Results of the Johansen cointegration test
Specifications SPOT C PIJA P SA C PIU SA SA SPOT JEPI SA JIPI SA SPOT IP IE P I SA
Akaike Akaike VAR Johansen coinl Lag intervals Lag intervals 1975-2002 1 2 1 1 1993-2002 1 1 1 1 1975-2002 1 2 1 1 1993-2002 1 2 1 1 1993-2002 1 4 1 3
Data trend Cointegralion Tracc Max-Eig Trace Max-Eig Trace Max-Eig Trace Max-Eig Trace Max-Eig
1 None No Intercept No Trend 2 2 1 1 0 0 SC rank (0) 0 0 AIC SC rank(0) 0 0 SC rank (0) 2 None Intercept No Trend 2** 2 1** 1 0 0 SC rank (0) 0 0 AIC SC rank(0) 0 0 SC rank (0)
3 Linear Intercept No Trend 3** * * 1 SC rank(l) 0 0 SC rank(0) AIC 0 0 0 0 0 0
rank(l)
4 Linear Intercept Trend 2 2 0 0 SC rank(O) 1* 0 0 0 1 1 AIC rank (1)
5 Quadratic Intercept Trend 1 1 AIC rank (1) 0 0 1* 1* AIC rank (1) 0 0 2 2
Note: As Table 2.
Table 4. Results of unit root tests for real exchange rates, for levels
SPOT W PI SA JW PI SA SPOT C PIU SA SA C PIJA P SA SPOT JIPI SA JEPI SA SPOT EPIIPISA Real exchange rate 1975-2002 1993-2002 1975-2002 1993-2002 1975-2002 1993-2002 1993-2002
A D F intercept 0.3863 0.7562 0.6703 0.6481 0.6703 0.6481 0.4134
o f it could be used for verifying statio n arity in real ex change ra te ac cord in g to e q u a tio n (8). T his price index should be the best ap p ro x im atio n o f the on e price theory, because it gets rid o f som e lim itations, e.g. th a t all goods arc no t trad ab le, trad a b le goods are n o t from applicable coun try .
All series were extracted from the In tern atio n al F inancial S tatistics and the B ank o f J a p a n d atab ases cover the period Ja n u a ry 1975 to M ay 2003. T h e last five observ atio n s have been left for forecasting accuracy evalu a tio n , so the in-sam ple period ends in D ecem ber 2002. T h e A D F test indicates th a t alm ost all series are /(1). Lags for Jo h an se n co in teg ratio n test and V EC m odels were determ ined using the m u ltiv ariate A kaike info r m a tio n criterion. In the Jo h an sen test we presum e five different com b in atio n o f assum ptions. T h e occurrence o f a co in tcg ratin g vector w ithout an intercept proves the absolute PPP , w hereas th e presence o f an intercept in a co in tcg ratin g e q u a tio n implies the relative P PP (cf. results in T ables 2 and 3).
In a b o u t h alf o f cases we succeeded in finding co in teg ratin g vectors, how ever, there arc periods, for which the P P P hypothesis d oes n o t hold. F o r longer tim e series cointegrating vectors occur m o re often. T h ere is no u nam biguous indication, which price index is the m o st useful in finding co in teg ratin g relations bu t when W PI (w holesale price index) is applied c o in teg ratio n ap p ears m ore often. Real exchange rates seem to be non- sta tio n a ry in levels, w hat rejects the PPP hypothesis (cf. T ab le 4).
T h e ex isten ce o f a c o in te g ra tin g v ec to r e n a b le s to b u ild v ec to r e rro r co rrec tio n m odels. In three o u t o f seven m odels an e rro r correction term has a m in us sign and there is no trend in a co in teg ratin g eq uation, w hich m a k e s it p o ssib le to in te rp re t th e m o d els in a p ro p e r way. T h e best five-m onths-ahead forecasts were obtained w ith V A R m odels based on IP IE P I and W PI for the period 1993-2002, w ith the forecasting accuracy m easured by T heil inequality coefficient. T h e best one-year-ahead forecasts were o btain ed w ith the V A R m odel for indexes JE P I - Jap a n ese ex po rt price index - and J IP I - Jap a n ese im p o rt price index - (1975-2002) and V EC m odel for W PI (1993-2002).3 T he results o f forecasting accuracy
3 Variables are described as and taken from: JWPI - wholesale prices index for Japan, period averages 1995 = 100, taken from the International Financial Statistics o f the International Monetary Fund (15863...ZF...); WPI - producer prices index for the USA, period averages 1995 = 100, extracted from the IFS IMF (11163...ZF). VAR WPI means vector autoregression model with seasonally adjusted indexes JWPI_SA and WPI_SA. VEC WPI is explained similar and generally means vector error correction model with use o f JWPI_SA and WPI_SA. JEPI - export price index for Japan - index on yen basis calculated on 222 items, 2000 year average = 100 - all commodities, taken from the Bank o f Japan; JIPI - import price index for Japan - index on yen basis calculated on 227 items, 2000 year average =100 - all commodities, extracted from the Bank o f Japan; IPIEPI - import/export price index for the USA for all
co m p ariso n as well as estim ation o u tp u ts are available u p o n request from the au th o rs.
4. NON-LINEARITY IN EXCHANGE RATE JPY /U SD - METHODOLOGY
T h ere are m any em pirical studies, which do cu m en t the failu re o f linear exchange ra te m odels. A lso theoretical extensions o f linear exchange rate behav io r to no n -lin ear fram ew ork have grow n in econ om etric literatu re (for exam ple the co ncept o f bubbles and self-fulfilling expectation s, targ e t zone m odels and o th er nonlin ear ad justm ent m odels - e.g. for d etails S am o and T a y lo r 2002). T here is a grow ing recognition th a t the in tro d u ctio n o f non- linearities in the m odeling fram ew ork enables to explain the slowness of the exchange rates adjustm ent process tow ard its lo n g-ru n equilibrium (e.g. M a and K anas 2000, Baum et al. 2001, D ufrenot 2002). N on-linear fram ework fo r m odeling exchange rates allows tak in g into acco u n t such phenom ena like m ultiple long-run equilibria, presence o f targ e t zones, a b ru p t changes in a d ju stm en t speed and different adjustm ents according to the sign and size o f the deviation from parity. All these factors im ply either a non-linear relatio n sh ip betw een exchange ra te and its fu n d am en tals o r a non-linear ad ju stm en t m echanism . Besides, it is interesting to exam ine the im pact o f the non-lin ear adju stm en t on sh o rt-ru n dynam ics o f exchange rates in a no n-lin ear e rro r correction form .
In o u r p ap e r we co n cen trate m uch m ore on forecasting p ro p erties o f considered m odels th a n on their explanato ry pow er. A t the beginning, we co nsider several univariate n on -linear specifications to m odel logarithm s and lo garithm ic re tu rn s o f the exchange ra te JP Y /U S D . T h e m odels are non -lin ear in th e conditio n al variance a n d /o r in the co n d itio n al m ean o f the process. T hey are as follows:
• ra n d o m w alk w ith drift and G A R C H errors
( 1 1 ) y t - f i + y t - i + c „ (T2(et \Clt - l ) = ht = a> + aLEf-1 + ß h , - 1,
• A R (1) fo r logarithm ic return s with G A R C H erro rs (12) Ay, = y A y , - i +E„ a 2(et \Clt -.l )ht = a> + a t ef - 1 + ß h t- u
• G A R C H -M .
(13) A y t = ц + Oh, + e„ a 2(Et \ i l t- l ) = ht = (o + a E ? -i + ß l i t- 1,
commodities originated from Japan, acquired from US Department o f Labor; SPOT - market exchange rate JPY/USD means yens per US dollar, end o f period, taken from the IFS IMF (158..AE.ZF...).
• autoregressive subdiagonal bilinear m odel w ith G A R C H errors
In th e above m odels we assum e t-S tudent co n d itio n al d istrib u tio n s for residual term s. T h e m odel (14) has been chosen from the w hole fam ily o f bilinear m odels w ith G A R C H disturbances as the m o st p ro m ising in the sense o f fitting and forecasting accuracy. Such specification m akes it possible to m erge the general form o f non-linearity in th e m ean value o f a process w ith non-lin earity in the conditional variance o f the process in to one m odel.
A s a basis for co n stru ctio n o f m u ltiv ariate fu n d am en tal m odels for exchange ra te d eterm in a tio n we consider a m odel based o n th e m o n etarist in te rp re ta tio n o f exchange ra te m ovem ents (e.g. S arn o and T a y lo r 2002). T h e specification includes such explanatory variables like m oney supplies m easu red as M l , o u tp u ts ap proxim ated by indu strial p ro d u c tio n , inflation rates and long-term interest rates. A p a rt from the ab ove variables we include o th e r th ree facto rs em erging from behavioral equilibrium exchange rate m odels (cf. for details R osenberg 1996; R ubaszek 2003): to tal reserves m inus gold, exp o rts and im ports. A dditio nally we con sid er sh are prices for industry.
All variables are taken in logarithm s and the ra tio s o f th e dom estic co u n try to th e foreign co u n try value o f the variables are calculated. O n the basis o f the transform ed variables several V A R (V E C ) m odels are estim ated. In the specification o f these m odels we use results o f G rang er causality tests and we perform a cointegration analysis fo r chosen collections o f variables with the Jo h an sen m ethodology.
D eviations from the exchange rate long-run relatio n sh ip represent the so-called ad ju stm en t process. As we have pointed o u t earlier in the text it is o ften o f a non-linear nature. T h e non-linear ad ju stm en t can be c h a rac terised in term s o f a sm ooth tran sition autoregressive (S T A R ) m odel o f G ra n g e r and T e rä sv irta (1993). We assum e th a t d e v ia tio n s from o u r long-run eq uilib ria can be described by an expon en tial S T A R (E S T A R ) m odel o f the form :
(14) A y, = fi + у Ay, _ t + OAy,- je, - 2 + e„ a 2(Et \ i l , - i ) = h , = ш + a£,2- i + ßh, _ j .
(15)
w here the tran sitio n function F ( - ) is U -shaped:
T h e E S T A R m odel can be viewed as a generalization o f a double-thresho ld T A R m odel o f T o n g (1990). W hen z , - d = c*, we get F( ) = 0 and (15) takes a form o f stan d ard a A R (p) representation . F o r extrem e deviations from long-run equilibrium (15) becomes a different A R (p) m odel with intercept k + k* and autoregressive param eters nj + n f . In o u r analysis a convenient re p ara m e trizatio n o f (15) is the following:
p- i / p - i \
(17) A z t = k + X z , - i + £ <PjAz,-j + l k* + X z , - t + £ <pfAz,-j ) F ( •) + e,.
J = i \ J = i /
T h e eq u atio n (18) corresponds with the usual testing eq u ation for augm ented D ickey-F uller test:
p- i
(18) Azt = k' + X z , - i + X ^ A z (_j + £(. )=i
In the E S T A R m odel the coefficient X governs the ad ju stm en t for small deviations from the equilibrium , w hen the coefficient X* corresp o n d s to large deviations. T o ensure the stability and m ean-reverting p ro p erties o f the ad ju stm en t process the q u an tity X + X* m ust be negative. T his m eans th a t the stable z t process m ay follow a unit ro o t or even explosive behavior for sm all deviations (X > 0), but it should be m ean-reverting for large errors, so th a t the co n d itio n А + Л * < 0 is fulfilled.
T o test fo r linearity o f the ad ju stm en t m echanism we apply T eräsv irta (1994) linearity test against E S T A R alternative. If th e delay p aram eter d is k n o w n , th e test consists o f estim atin g an artificial reg ressio n o f the form
p
(19) Zt — ß 00 + Y,(ßoj z t-J + ß l j zt-j Z,-d + ß2jZ,-jZ?-d) + e„ J= í
and testing the hypothesis
(20) H o : ß lJ = ß 2J = 0 (j = \, p).
T h e o rd e r o f au toregression p is chosen on the basis o f serial co rrelatio n Ljung-B ox test on residuals. T o determ ine the delay p a ram eter M ichael, et al. (1997) suggest repeating the linearity test fo r different values o f d and choosing th e delay p aram eter corresp onding to the sm allest /?-value o f the test statistic. W hen linearity is rejected, we proceed w ith estim atio n of E S T A R m odels. Since in o u r application th e process z t is zero-m ean, we can assum e th a t к = k* = c.
As we have noticed before, besides m odeling the n o n -lin ear ad ju stm en t process solely (this approach dom inates in the existing econom etric literature), it is interesting to examine an im pact o f the possibly non-linear m isalignm ents on th e sh o rt-ru n dynam ics o f exchange ra te process. It ca n be done in a n o n -lin e a r e rro r c o rrec tio n (N E C ) fram ew o rk . T h e re la tio n betw een co in teg ratio n and e rro r correction is well recognized in a linear context. T h e extension o f it to the non-linear case seems to be one o f th e m o st im p o rta n t challenges for econom etricians. E scriban o an d M ira (2002) start to fill this gap by partially extending the G ra n g er rep resen ta tio n Theorem to the non-lin ear fram ew ork. T hey sta rt co nsideration s with new concepts o f /(0 ) processes and linear co integration, w hich are based o n a definition o f so-called n ea r epoch d ependent (N E D ) process. T h e definitio n o f a N E D process is a relaxation o f the concept o f a m ixing process. H euristically, a process is m ixing, if it is sho rt-ran g e dependent, i.e. w hen th e dependence betw een p ast and fu tu re events becom es negligible with th e tim e span converging to infinity. E scribano and M ira found th a t, if variables are /(1 ) w ith a n o n -lin e a r e rro r co rrec tin g m ech anism , th en th ey are linearly co integ rated un der certain conditions o n the n on -lin ear adju stm en t. In p artic u la r, they give sufficient conditions for the N E C m odel to be well specified and balanced.
A non-lin ear e rro r correctio n (N E C ) m odel can be w ritten as follows:
(21) Ay l = £ ôjAXt- j + 'ZvjAyt- j + l 1zt- l + X2f ( z t- 1) + Et,
)= i i
(22) z, = y t — ß X t,
where X, is a vector o f explanatory variables and Sj are vectors o f param eters respectively, the tran sitio n function / ( •) satisfies som e reg ularity conditions and z, is N E D . In w hat follow s we consider tw o fo rm u latio n s o f the tra n sitio n fu nction (cf. for com parison C h aouachi et al. 2003):
• an expon ential sm oothing tran sitio n function: (23) / ( z t- i ) = 1 - e x p L - K z ? - i - c ) J ,
• a cubic polynom ial function
(24) / ( z t- i ) = ocl z , - i + a 2zt2_ i + a 3zt3_x.
T h e first tran sitio n function allows for discrim ination betw een corrections w ith regard to sm all and large deviations from th e equilibrium path. A dditio n ally , the correction changes in a sm o oth way. N E C m odels based on cubic polynom ials allow exam ining general form o f asym m etric dynam ics
between overvaluation and und ervaluation periods. T his type o f non-linearity is connected w ith varying strength o f a ttra ctio n to a linear a ttra c to r. F o r exam ple, the a ttra c to r m ay be stro n g er on one side th an o n th e other.
5. NON-LINEARITY IN EXCHANGE RATE JPY /U SD - EMPIRICAL RESULTS
As previously all series are extracted from the I M F ’s IF S an d the Bank o f Ja p a n d atab ases and cover the period Jan u a ry 1975 to D ecem ber 2002.4 E x p lan ato ry variables have been seasonally adjusted with the C ensus X-12 m eth o d . In the first step we conducted pairw ise G ra n g er causality tests with the n u m b er o f lags 4. Because o f a n o n -statio n arity o f the variables, results o f these tests are only approxim ately valid. O n th e basis o f these results several V A R m odel were specified. F o r fu rth e r analysis we chose IM P , T R E S and W P I as explanatory variables. R esults corresp o n d in g to o th er specifications are available u p o n request from th e au th o rs. A ugm ented D ickey-Fuller (A D F ) and Phillips-Perron (PP) tests indicate th a t all variables, which we chose after causality tests, arc /(1 ) processes. T he K PSS test (K w iatkow ski et al. 1992) gives o th er results indicating th a t T R E S is an 1(2) process. In T ables 5-8 below estim ation o u tp u ts fo r the univariate m odels (11)—(14) are presented.
Table 5. Estimation output for the random walk model with drift and i-distributed GARCH errors
Specification Coefficient Std. error z-Statislic Prob. MU OMEGA ALPHA BETA I D F -0.001326 2.25E-05 0.036637 0.950848 4.946433 0.001607 1.18E-05 0.021870 0.020699 1.605981 -0.824827 1.908074 1.675222 45.93696 3.080006 0.4095 0.0564 0.0939 0.0000 0.0021
Log likelihood 672.0810 Akaike info criterion -3.982573
Hannan -Quinn crit. -3.959878 Schwarz criterion -3.925646
Q(2) = 3.2830 (p = 0.194) 6(4) = 5.7905 (p = 0.215) 0 (8) = 11.6170 (p = 0.169)
4 We use the following notation: natural logarithms o f monthly exchange rate JPY/USD (end o f the month) are denoted by SPOT; the remaining variables are logarithms of the ratios o f the domestic value to the foreign value total reserves minus gold (TRES), money supply measure M l (M l), federal founds rate (INTREST), share prices for industry (SHARES), wholesale price index (WPI), hourly earnings (WAGES), industrial production (IP), exports (EXP), imports (IMP).
Table 6. Estimation output for the AR(1) model for logarithmic returns with t-distribulcd GARCH errors
Specification Coefficient Std. error 2-Statistic Prob.
GAMMA 0.106509 0.053719 1.982684 0.0474
OMEGA 3.28E-05 1.66E-05 1.978339 0.0479
ALPHA 0.037195 0.023327 1.594481 0.1108
BETA 0.940415 0.025469 36.92378 0.0000
TDF 5.544558 1.952147 2.840236 0.0045
Log likelihood 654.8985 Akaike info criterion -3.987108
Hannan Quinn crit. -3.963930 Schwarz criterion -3.929026
<2(2) = 0.5917 0> = 0.744) Q(4) - 2.9972 (p = 0.558) 0 (8 ) = 8.0296 (p = 0.431)
Table 7. Estimation output for the G A R CH -in-m ean model
Specification Coefficient Std. error z-Statistic Prob. MU THETA OMEGA ALPHA BETA TDF -0.677642 14.21781 0.000148 0.008084 0.930834 2.683520 0.144882 3.214230 6.41 E-05 0.003336 0.027708 0.533691 -4.677199 4.423394 2.303252 2.423005 33.59490 5.028229 0.0000 0.0000 0.0213 0.0154 0.0000 0.0000
Log likelihood 644.1574 Akaike info criterion -3.855936
Hannan-Quinn crit. -3.828448 Schwarz criterion -3.787016
Q(2) - 2.340 (p = 0.309) Q(4) = 4.4549 (p = 0.348) Q(8) = 10.900 (p = 0.207)
Table 8. Estimation output for the subdiagonal bilinear model
Specification Coefficient Std. error z-Statistic Prob. MU GAM M A THETA OMEGA ALPHA BETA TDF -0.001708 0.114806 1.988684 2.59E-05 0.039438 0.943596 5.821522 0.001655 0.055459 1.420782 1.28E-05 0.022882 0.023795 2.037318 -1.032021 2.070113 1.399711 2.027156 1.723516 39.65593 2.857443 0.3021 0.0384 0.1625 0.0426 0.0848 0.0000 0.0043
Log likelihood 660.6572 Akaike info criterion -3.985714
Hannan-Quinn crit. -3.953418 Schwarz criterion -3.904766
Q(2) = 1.2520 (p = 0.535) ß(4) = 4.2085 (p = 0.379) Q(8) = 8.9321 (p = 0.348)
T h e next step in o u r analysis was estim ation o f fu n d a m e n tal m odels for exchange ra te determ in atio n . In T able 9 we present results o f the Joh ansen co in teg ratio n test for SPO T, T R E S , W PI and IM P . T h e lag length o f the unrestricted V A R m odel in levels was determ ined by using the m u ltivariate A k aikc in fo rm atio n criterion allow ing fo r a m axim um lag length o f 10. T h e m u ltiv ariate Schw arz criterion was also em ployed but generally it underestim ated the V A R lag length, resulting in serially co rrelated residuals.
Table 9. Results o f Johansen cointegration test
Unrestricted Cointegration Rank Test
Hypothesized Trace 5% 1%
No. o f CE(s) Eigenvalue Statistic Critical Value Critical Value
None * 0.078949 52.76163 47.21 54.46
At most 1 0.049315 24.96461 29.68 35.65
At most 2 0.022689 7.871120 15.41 20.04
At most 3 0.000337 0.113845 3.76 6.65
Hypothesized Max-Eigen 5% 1%
No. o f CE(s) Eigenvalue Statistic Critical Value Critical Value
None * 0.078949 27.79701 27.07 32.24
At most 1 0.049315 17.09349 20.97 25.52
At most 2 0.022689 7.757275 14.07 18.63
At most 3 0.000337 0.113845 3.76 6.65
*(**) denotes rejection o f the hypothesis at the 5%(1%) level
R esiduals from the coin teg ratin g eq u a tio n were tested fo r linearity with T e ra sv irta linearity test against E S T A R alternative. R esults o f this test indicate the value o f the delay p aram eter d = 1 (cf. T ab le 10). F o r this value o f d an E S T A R m odel for residuals was estim ated (T able 11).
Table 10. Results of linearity tests
D P F-statistic p-value
1 5 2.4949 0.0599
2 7 2.0828 0.1499
3 5 0.8592 0.4244
T h e o u tp u t in T ab le 11 indicates th a t the analyzed ad ju stm en t process is o f a n on-linear n atu re and can be described by an E S T A R m odel. T he next questio n w as w hether the sh o rt-ru n dynam ic o f lo g arith m s o f the
exchange ra te JP Y /U S D can be described by a non -lin ear e rro r correction m odel. T ables 12 and 13 include results o f the estim atio n o f N E C m odels with exponential sm ooth tran sitio n and cubic tran sitio n functions.
Table 11. Results o f the estimation o f an ESTAR model for residuals
D(Z) - C( 1) • Z ( - l) + C(2) • D(Z(-1)) + C(3) • 0(Z (-2 )) + C(4) • D (Z (-3 » + C(5) • O (Z H )) + (C(6) ■ Z ( - l) + C(7) ■ D (Z (-1)) + C(8) • D (Z(-2)) + C(9)
• D (Z(-3)) + C(10) • D (Z (-4))) • (1-EA-P(-C(11) • Z (-l) л 2))
Coefficient Std. error t-Statistic Prob.
C (l) -0.448822 0.566375 -0.792448 0.4287 C(2) -0.703820 0.198410 -3.547310 0.0004 C( 3) -0.771434 0.241977 -3.188050 0.0016 C(4) 0.011856 0.166023 0.071413 0.9431 C(5) -0.254250 0.170146 -1.494309 0.1361 C( 6) 0.319664 0.570963 0.559869 0.5760 C(7) 0.593508 0.212359 2.794834 0.0055 C(8) 0.811723 0.249751 3.250134 0.0013 C(9) 0.011488 0.190713 0.060239 0.9520 C(10) 0.346184 0.188299 1.838478 0.0669 C (ll) 1824.641 939.3330 1.942486 0.0530
R-squared 0.150912 Akaike info criterion -3.490890
S.E. o f regression 0.041556 Schwarz criterion -3.364536
Log likelihood 588.7424 Durbin-Watson stat. 2.034494
Table 12. Estimation of the NEC model with an exponential smooth transition function
D(SPOT) = C (l) + C(2) ■ D (S P O T (-l))+ C (3 )ß (I M P ( -l) ) + C(4) • D(TRES(-1)) + C(5) ■ D(WPI(-1)) + C(6) • R l( - l) + C(7) • (1 + Е Х Р (С (8 )((Л 1 (-1 ))л 2 )))
Coefficient Std. error I-Statistic Prob.
C (l) -0.000845 0.010547 -0.080164 0.9362 C( 2) 0.103890 0.055262 1.879945 0.0610 C(3) -0.026565 0.031115 -0.853764 0.3939 C(4) -0.001947 0.032795 -0.059381 0.9527 C(5) -0.119628 0.317199 -0.377139 0.7063 C(6) -0.056916 0.016359 -3.479173 0.0006 C( 7) -0.001754 0.009983 -0.175705 0.8606 C(8) -A 134.328 53296.97 -0.077572 0.9382
R-squared 0.044027 Mean dependent var -0.002609
Adjusted R-squared 0.023500 S.D. dependent var 0.033715
S.E. of regression 0.033316 Akaike info criterion -3.941872
Sum squared resid 0.361855 Schwarz criterion -3.850587
Table 13. Estimation o f the NEC model with a cubic transition function
D(SPOT) = C (l) + C(2) D (SPO T(-l)) + C(3) D(IMP(-1)) + C(4) • D(TRES(-1)) + C(5) - 0(W PI(-1)) + C(6) • ЯЦ -1) + C(7) • R l( - 1)2 + C(8) • Л1(-1) л 3
Coefficient Std. error í -Statistic Prob.
C (l) -0.003480 0.002512 -1.385276 0.1669 C(2) 0.108562 0.055199 1.966726 0.0501 C(3) -0.026474 0.031043 -0.852828 0.3944 C(4) -0.003746 0.032799 -0.114203 0.9091 C( 5) -0.124487 0.313562 -0.397009 0.6916 C(6) -0.040895 0.028837 -1.418129 0.1571 C(7) 0.075367 0.121329 0.621182 0.5349 C(8) -0.582498 0.762447 -0.763984 0.4454
Л-squared 0.046451 Mean dependent var -0.002609
Adjusted R-squared 0.025976 S.D. dependent var 0.033715
S.E. of regression 0.033274 Akaike info criterion -3.944411
Sum squared resid 0.360937 Schwarz criterion -3.853126
Log likelihood 666.7166 Durbin-Watson stat. 2.008753
B ecause the non-lin ear e rro r correction is n o t significant, we conclude th a t o u r e rro r correctio n m odel has a linear form . T a b le 14 includes results o f fo recastin g accuracy com parison betw een the u nivariate m odels and VEC and V A R m odels fo r the exchange ra te JP Y /U S D (cf. also F ig u re 1).
Table 14. Forecasting accuracy comparison
Specification RW AR(1) M-GARCH BL VEC FU N D . VAR IPIEPI
Root mean square error 0.008576 0.009118 0.022635 0.007913 0.015518 0.00827 Mean absolute error 0.007463 0.007367 0.019323 0.006973 0.013767 0.00770 Mean absolute precentage error 0.001563 0.001543 0.004041 0.001459 0.002883 0.00161 Theil coefficient /1 /2 /3 0.000401 0.208838 0.480989 0.406371 0.000426 0.375955 0.757265 0.018233 0.001061 0.728753 0.000134 0.271140 0.00037 0.002787 0.424275 0.657793 0.000725 0.787029 0.143971 0.097795 0.00041
In SPOT --- BL — - VAR IPIEPI (1993-2002) ---- — RW - - - AR(T) --- M-GARCH
— - VEC FUND. 1975-2002
Fig. 1. Forecasting accuracy comparison
6. CONCLUSIONS
W e have found th a t in som e periods the exchange ra te JP Y /U S D is form ed by changes in price levels in Ja p a n and the U nited States and the p urchasing pow er p arity hypothesis holds under som e specific conditions for th e exchange rate. F o r longer tim e series th e coin teg ratin g eq u atio n s occur m o re often. T here is a problem in selection o f a p ro p e r price index, w hich should be used for m odeling, bu t W PI seems to be the best solution for it. T h e best forecasting m odel for the exchange ra te JP Y /U S D is a non-linear u n iv ariate B L -G A R C H m odel. It tu rn s ou t th a t th e adju stm ent process to w ard s a long-run equilibrium fo r this exchange ra te is o f a n o n linear n a tu re and can be effectively described by an E S T A R m odel. However, the im pact o f this adjustm ent process on short-ru n dynam ics o f the exchange ra te has a linear e rro r correction form . U sually V A R m odels, especially based o n im p o rt/e x p o rt price index for goods fro m /to J a p a n , give better a p p ro x im a tio n o f the spot exchange rate th a n V E C m odels.
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Joanna Bruzda, Tomasz Koźliński
NIELINIOWOŚĆ I HIPOTEZA PARYTETU SILY NABYWCZEJ PIENIĄDZA DLA
KIJRSU
WALUTOWEGOJPY/USD
(Streszczenie)
W artykule przedstawiamy wyniki weryfikacji hipotezy parytetu sity nabywczej pieniądza dla kursu walutowego JPY/USD dla różnych okresów i różnych indeksów cenowych. Prezen tujemy kilka modeli prognostycznych dla tego kursu, włączając pewne specyfikacje nieliniowe, jak np. jednowymiarowy model BL-GARCH oraz fundamentalne modele VEC, oparte na zależności w długookresowym położeniu równowagi. Wyniki empiryczne wskazują, że przy pewnych specyficznych warunkach hipoteza PPP zachodzi. Proces dostosowania do długo okresowego położenia równowagi okazuje się mieć postać nieliniową. Jednakże wpływ tego dostosowania na krótkookresową dynamikę kursu walutowego JPY/USD ma formę liniowej korekty błędem.
