Interrelationship between Philippine Stock Exchange Index ... · between exchange rate and stock market, Ramasamy & Mathew (2001) noted that whether stock price movements cause exchange

Procedia - Social and Behavioral Sciences 40 ( 2012 ) 768 – 782

1877-0428 © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Asia Pacific Business Innovation and Technology Management Society doi: 10.1016/j.sbspro.2012.03.264

International Conference on Asia Pacific Business Innovation & Technology

Management

Interrelationship between Philippine Stock Exchange Index and USD Exchange Rate

Kuo-Jui Wua, Cheng-Cheng Lub, Haruhiro Jonoc and Irell Perezd

aMasterof Business Administration, De La Salle University, Philippines

bBachelor of science in Business Administration, De La Salle Araneta University, Philippines c,dMaster of Economic, De La Salle University, Philippines

Abstract

This study investigates the correlation between Philippine Stock Exchange Index and USD Exchange rate through Time Series Analysis in Philippine market. The correlation can be demonstrated in formulate to provide the trend for government or company to forecast the impact of Philippine Stock Exchange Index and USD Exchange Rate. This can offer the evidence to guide the government to establish the correct financial policy or provide the future break event point for company to set up.

© 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Asia Pacific Business Innovation and Technology Management Society (APBITM).”

Keywords: Time Series Analysis, Exchange Rate, USD, Stock.

1. Introduction

The literature that studies the relationship between stock prices and foreign exchange rates are far from conclusive. Two approaches have been developed in the literature to establish a relationship between exchange rate and stock prices. First approach, known as goods market approach, (e.g. Dornbusch & Fischer (1980) suggest that changes in exchange rates affect the competitiveness of a firm, which in turn influence the firm’s earnings or its cost of funds and hence its stock price. On a macro basis, then, the impact of exchange rate fluctuations on stock market would depend on both the degree of openness of domestic economy and the degree of the trade imbalance. Second approach, known as portfolio balance approach (e.g. Frankel (1993)), stresses the role of capital account transactions. A rising stock market would attract a capital flow which increases the demand for domestic currency and cause exchange rate to appreciate.

Alternatively, changes in stock prices may influence movements in exchange rates via portfolio adjustments. If there were persistent upward trend in stock prices, inflow of foreign capital can rise (Kadir et al., 2011). However a decrease in stock price would induce a reduction in domestic investor’s wealth leading to a fall in demand of money and lower interest rates causing capital outflow that could

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769 Kuo-Jui Wu et al. / Procedia - Social and Behavioral Sciences 40 ( 2012 ) 768 – 782

results in currency depreciation. Therefore in portfolio approach, stock price would lead exchange rates with negative correlation. (Tabak, B. 2006)

Although theories suggest causal relationship between exchange rate and stock prices, existing evidence indicates a weak link between them at a micro level. For US firms, Jorion (1990, 1991), Bodnar and Gentry (1993) were unable to find a significant relationship and for Japaneses firms, He & Ng (1998) found that only 25 percent of their sample of 171 Japaneses multinationals has significant exchange rate exposure on stock returns.

On the macro level, Ma & Kao (1990) found that a currency appreciation negatively affects the domestic stock market for an export-dominant country and positively affects the domestic stock market for an import-dominant country, which seems to be consistent with goods market theory. Ajayi and Mougoue (1996), using daily data for eight countries, show significant interactions between foreign exchange and stock markets, while Abdulla & Murinde (1997) document that a country’s monthly exchange rates tends to lead its stock prices but not the other way around. On the issue of causality between exchange rate and stock market, Ramasamy & Mathew (2001) noted that whether stock price movements cause exchange rate volatility or vice versa is country and time dependent. (Tahir, Ghani 2002).

In this paper, the objective is to provide additional evidence on the short-run dynamics between exchange rate and stock price in the Philippines. This paper will try to attempt to fill the gap in the literature by investigating how information is transmitted between these two economic variables through short term price interactions. This study contributes to the literature in two distinct aspects. First, the paper empirically explores the short-term dynamic relationships between price behaviours of stock price and exchange rate in the Philippines, which is useful since financial managers can have better understand about the short-term movements of stock price and exchange rate.

This paper attempts to examine how changes in exchange rates and stock prices are related to each other for Philippines over the period 1987-2010 using ARIMA modelling/ forecasting and VECM with the help of Gretl and Eviews as our statistical software. The paper is organized as follows: Section 2 contains a brief literature review. Methodology and empirical results are presented in Section 3 and 4. Concluding remarks take place in Section 5.

2. Literature review

Many economists have suggested a significant relationship between exchange rates and stock prices, but the results have been quite mixed for the sign and causal direction between exchange rates and stock prices. Mok (1993) found weak bi-directional causality between stock prices and exchange rates, while Bahmani-Oskooee and Sohrabian (1992) and Nieh and Lee (2001) argued for a bi-directional causality between stock prices and exchange rates in the short run, but not in the long run. In addition, there are some studies that have found very weak or zero association between stock prices and exchange rates (for instance, Franck and Young, 1972; Bartov and Bodnar, 1994; Fernandez, 2006; H.Y. Yau and C.C. Nieh, 2009).

Ajayi and Mougoue (1996) found bidirectional relations for eight advanced countries: increases in stock prices have a long-run positive effect on the domestic currency value while currency depreciation has a negative long-run effect on the stock market. Wu (2000) shows that Singapore dollar exchange rates Granger cause stock prices and the explanatory power of exchange rates on stock prices has increased over time (M.S. Pan and etc., 2007).

Other studies have found supportive evidence on the internationalisation of stock markets following the abolition of foreign exchange controls also (K. Phylaktis, F. Ravazzolo, 2005), for instance Taylor and Tonks (1989) for the case of UK based on a bivariate cointegration analysis, and Kasa (1992) for a multivariate framework on US, Japan, England, Germany and Canada. With regard to emerging capital markets, see e.g. Bekaert and Harvey (1995), who have used a regime switching framework to model changes in market integration based on one factor asset pricing model; Bekaert and Harvey (2000) who


conditioned mean and variance on both local and world information to capture changes in the degree of market integration; and Phylaktis and Ravazzolo (2002) who measured financial integration by analysing the covariance of excess returns of national stock markets.

3. Methodology

3.1 Data Description

Hua Zhao (2009) mentioned that with the complicated change of the international economy, it is difficult to describe the appreciation and depreciation of currency by bilateral exchange rate, nevertheless, the real effective exchange rate not only considers the currencies changes of main trade fellow countries, but also eliminate the inflation factors. Hence this paper mainly analyzes the dynamic relationship between the USD exchange rate and Philippines stock exchange index (PSEI). We used monthly data from July 1997 to July 2010 with 157 obesrvations, the PSEI’s samples used adjust close index.

Fig. 1. Stock Price Movement with USD Exchange Rate

3.2 Methodology

This paper utilizes various time-series models, conventional linear and advanced non-linear algorithms, to analyze the long-term and short-term interrelationships between PSEI and the USD exchange rate.

3.2.1 Unit root test

There are three situations in ADF test for every time series; random process includes intercept (c) and trend (t), random process includes intercept but no trend and random process includes no intercept and trend (Y. Wang et al., 2006). According to Schwert (1989), the ADF test with long lags is superior to the others. The three differencing AR models of ADF are expressed as the following forms:

tyyy it

p

i

itt

1

1

1 (1)

tyyy it

p

i

itt

1

1

1 (2)

tytyy it

p

i

itt

1

1

1 (3)

Model (1) is a pure random walk with the lag terms. Model (2) possesses a drift. Model (3) includes a drift and a time trend. The null hypothesis for ADF test is: H0: = 0, with the alternative H1: -2 < < 0 (H.Y. Yau and C.C. Nieh, 2005).


The results of the test show (Table 1) that the variables of the real effective exchange rate and PSEI are respectively stationary in first difference. In other words, these variables are I(1) or integrated processes at the level. The test rejects the null hypothesis that there is a unit root in first difference of every variable at 5% significant level and at 1% significant level.

Table 1 ADF Test

3.2.2 Johansen co-integration test

Given that all series in the model are I(1) processes, the next test if USD exchange rate and PSEI have co-integration relationship. First, considering sensitivity of VAR model to the number of lags of the variables, the optimal lags of the variables of the model must be determined. In this work, we use Akaike Information Criterion (AIC), Schwartz Bayesian criterion (BIC), Hannan-Quinn Criterion (HQC). Based on these statistics, lag 2 (Table 3) will be chose and apply in the VAR model.

H.Y. Yau and C.C. Nieh (2005) indicated that various methods of estimating co-integration have been applied to capture the long-run equilibrium relationship among the variables. Among those, Johansen methodology based on the likelihood ratio with non-standard asymptotic distributions involving integrals of Brownian motions is found to be the best method to proceed with co-integration estimation by Gonzalo (1994).

The elaborate works developed by Johansen (1988, 1990, and 1994) are summarized into five VAR models with ECM, which are presented in the following forms:

tttktktt DXXXXrH 1)1(1110 :)( (4)

tttktktt DXXXXrH )1)(,(:)( ,10)1(111*1 (5)

tttktktt DXXXXrH 01)1(1111 :)( (6)

tttktktt DXXXXrH 0,11)1(111*2 )1)(,(:)( (7)

tttktktt DtXXXXrH 101)1(1112 :)( (8)

To analyze the deterministic term, Johansen decomposed the parameters 0 and 1 in the directions of and as iii , thus we have ii 1)( and ii

1)( . The nested sub-models of the general model of null hypothesis are, therefore, defined as:

tYrH

tYrH

YrH

YrH

YrH

)(:)(

:)(

:)(

:)(

0:)(

11002

100*2

001

0*1

0

ADF Test without constant with constant with constant and trend

test statistic p-value test statistic p-value test statistic p-value

USD Exchange Rate 0.0322462 0.6931 -1.6579 0.4528 -1.23094 0.9033

PSEI 0.371786 0.7917 -0.887148 0.793 -1.8498 0.6803

First Difference test statistic p-value test statistic p-value test statistic p-value

USD Exchange Rate -11.2062 6.389e-058* -11.204 1.316e-017* -11.2419 1.022e-016*

PSEI -3.31274 0.0009047* -3.43512 0.009847* -3.43596 0.04668*


5

Johansen (1994) emphasized the role of the deterministic term,tY 10 , which includes constant

and linear terms in the Gaussian VAR. A decision procedure, following Nieh and Lee (2001) among the hypotheses H(r) and H*(r) of the above five different models, is presented in the following procedure:

)1()1()1()1()1()1(

)1()1()1()1()0()0()0()0()0(

2*21

*102

*21

*102

*21

*10

pHpHpHpHpHH

HHHHHHHHH

We diagnose models one by one until the model that cannot be rejected for the null.

3.2.3 Granger’s causality test (GC test)

If the variables are non-stationary and co-integrated, the adequate method to examine the causal relations is the VECM (Granger, 1988); otherwise a VAR model is used in the case of no co-integration found among the variables (Granger, 1969). The standard GC test then examines if there exists feedback (bi-directional) or one-way causality between variables. Considering two series, tX

and tY, the GC test is in the formats follows:

1

1

1

112111 )()(

n

i

m

jXtjtitt YjXiX (9)

2

1

2

122212 )()(

n

i

m

jYtjtitt YjXiY (10)

where Xt and Yt are stationary random processes intended to capture other pertinent information not contained in lagged values of tX and tY . The lag lengths, n and m, are decided by AIC in our study. The series tY fails to Granger cause tX if 1,,3,2,10)(12 mjj ; and the series

tX fails to cause tY if 1,,3,2,10)(21 nii .

3.2.4 Vector error-correction model (VECM)

As result of USD exchange rate has the co-integration relationship with PSEI, there exists a vector error-correction model (VECM) describing the adjustment mechanism of the exchange rate from short-term to long term. We can specify the vector error-correction model as follows:

Considering rank k VAR model:

tktkttt YYTY 2211 (11)

Where tY is an n-dimensional vector, and t are random terms (Campbell, Lo, and MacKinlay,

1997). Suppose )1(~ IYt . Then simple mathematical manipulation of (9) puts it in a more usable form,

that is:

ttktkttt YYYYY 1112211 (12)

where k

ijj

k

ii kiiI

11

)1,,2,1(, (13)

where , as a parameter matrix, and sometimes is called the long-run parameters, where each column in the matrix represents a co-integrating vector. ),,,( 21 r has r cointegrating


vector, , is called adjustment coefficient matrix, and I is the identity matrix. Each element in represents the speed of adjustment of the error correction term. )1(~1 IYt , contains r error correction term (ECT). The error correction representation of the co-integrated system (10) can be written as follows:

ttktkttt YYYYY 1112211 (14)

Equation (12) is called vector error correction model. It is a restricted VAR designed for use with non-stationary series that are known to be co-integrated. The VEC has co-integration relations built into the specification so that it restricts the long-run behavior of the endogenous variables to converge to their co-integrating relationships while allowing for short-run adjustment dynamics (Y. Wang et al., 2007).

3.2.5 Generalized impulse response functions and variance decompositions

(G-IRF and G-VDC)

H.-Y. Yau, C.C. Nieh (2006) mentioned that more recent researches have largely applied the impulse response functions (IRF) and variance decompositions (VDC) to conquer the difficulty of interpreting the estimated coefficients of a VAR model. An IRF traces the response of current and future values of the endogenous variables to a one standard deviation shock through the dynamic structure of the VAR. The IRF is in the following expression:

itiX

ijkt )(

0

(15)

where is a 2 1 vector of constants, it is a 2 1 error vector, jk(i) is a 2 2 matrix with and elements of are the “impact multipliers”, which examine the interaction among USD exchange rate and PSEI over the entire path.

VDC, on the other hand, decomposes forecast error variance (FEV) in an endogenous variable into percentage shocks to its own and other endogenous variables in the VAR, which in turn offers information about the relative importance (exogenous ordering) of each random innovation to the variables.

The corresponding variance–covariance matrix of the k-step ahead forecast error and its decomposition can be expressed as follows:

111100 )()()())(( kttktttttktttktt DEDDEDDEDXEXXEXE (16)

where ,,,| 2 aktktakttTkt XXXXDxE is VMA representation of

iitti iiitit CVVCDCX ,,, 10 is a 2 2 matrix with, and V is a 2 2 lower

triangular matrix of the Choleski decomposition.

Cooley and LeRoy (1985) criticize that orthogonalized IRF and VDC based on Choleski decomposition are, in general, not meaningful. King, Plosser, Stock, and Watson (1991) and Zhou (1996) further pointed out that as there is more than one common trend in a model, different ordering of variables may significantly affect the results of IRF and VDC if the common trends are not absolutely uncorrelated.


The newly developed generalized-VAR (the G-IRF and G-VDC) models by Pesaran and Shin (1998) overwhelmthe shortcoming of orthogonalized IRF and VDC, which have the advantage of being invariant to the ordering of the variables.

4. Empirical results

Based on our informal analysis, correlogram of USD exchange rate and PSEI (Fig. 2 & 3) shows that there is unit root. Unit root tests are summarized in Table 1, which show that the null of non-stationarity cannot be rejected for any confidence levels. After getting the first difference, the null is rejected at least at the 5% significance level for all series. Hence, we conclude that all the variables considered in this paper are the I(1) type series.

Fig. 2 Correlogram of USD exchange rate Fig. 3 Correlogram of PSEI

Secondly, the data need to test for its seasonality moving. If there has the seasonality moving effect, then we need to eliminate the seasonal affect. Table 2 shows that USD exchange rate and PSEI are both affected by seasonal moving present at the 1% level. Our next step is to deseasonalized our data.

Table 2 F-test for seasonality

Sum of squares Dgrs. freedom Mean square F-value

USD exchange rate Between Years 39.2282 11 3.566198 3.362**

Error 128.3434 121 1.060690

PSEI Between Years 555.5039 11 50.500358 3.711**

Between Years 1646.5967 121 13.608237

**Moving seasonality present at the one percent level

Before the Johansen co-integration test, Var lag selection is the prerequisite step in order to find the best lag for fallowing step. The Var lag table shows in Table 3, the lag is significant to use in the Johansen co-integration test.

Table 3 Var Lag Selection

lags loglik p(LR) AIC BIC HQC

1 -1074.20579 15.002858 15.126600* 15.053140

2 -1065.45083 0.00154 14.936817* 15.143054 15.020620*

3 -1064.87053 0.88454 14.984313 15.273045 15.101637

4 -1064.14435 0.83505 15.029783 15.401009 15.180628


The Gregory and Hansen (1996) test expands previous Johansen’s test to enable testing over periods incorporating structural breaks. Consistent with the finding from the above Johansen tests, the results shown in Table 4 reveal co-integration relationship among the two variables in the presence of a possible situation.

Johansen tests indicate that there is co-integration existed among the USD exchange rate and PSEI, we apply Granger (1969) VAR model to test the causal relationship between variables of USD exchange rate and PSEI. The results of the GC test are shown in Table 5. Causality was revealed between PSEI and USD exchange rate. Hence, the co-integration regression of USD exchange rate can be formulated as 9141.536(PSEI)-0.0030292_ EXUSD .

Table 4 Johansen co-integration test

Rank Eigenvalue Trace test p-value Lmax test p-value

0 0.10118 18.346 0.0166* 16.534 0.0195*

1 0.011624 1.8123 0.1782 1.8123 0.1782

beta (co-integrating vectors)

USD_Ex PSEI

USD_Ex -0.17390 -0.063745

PSEI -0.0011297 0.0011439

alpha (adjustment vectors)

USD_Ex 0.28832 -0.011280

PSEI -16.931 -14.739

renormalized beta

USD_Ex 1.0000 -55.726

PSEI 0.0064961 1.0000

renormalized alpha

USD_Ex -0.050138 -1.2904e-005

PSEI 2.9443 -0.016859

long-run matrix (alpha * beta')

USD_Ex -0.049419 -0.00033861

5 -1062.18739 0.41778 15.058158 15.511880 15.242525

6 -1060.95346 0.65040 15.096576 15.632792 15.314464

7 -1056.09045 0.04530 15.084590 15.703301 15.335999

8 -1053.25479 0.22508 15.100761 15.801967 15.385692

9 -1049.98438 0.16224 15.110894 15.894595 15.429346

10 -1046.04213 0.09590 15.111696 15.977892 15.463669

11 -1042.55491 0.13725 15.118818 16.067509 15.504312

12 -1041.23744 0.62065 15.156076 16.187261 15.575091

13 -1038.73727 0.28726 15.176906 16.290586 15.629443

*, ** denote significance at the 1% and 5% significant level


PSEI 3.8838 0.0022669

Table 5 Pairwise Granger Causality Tests

Null Hypothesis: Obs F-Statistic Probability

PSEI does not Granger Cause USD 155 8.76870 0.00025***

USD does not Granger Cause PSEI 1.96525 0.14371

Co-integrating regression

Dependent Variable coefficient std. error t-ratio p-value

const USD_EX

53.9141 1.42980 37.71 5.63e-080 ***

PSEI -0.00302926 0.000660424 -4.587 9.22e-06 ***

const PSEI

3926.68 413.992 9.485 4.11e-017 ***

USD_EX -39.4531 8.60137 -4.587 9.22e-06 ***

Note: (1) ***, ** denote significance at the 1% and 5% significant level. (2) The null hypothesis, H0 is for ‘‘no causal relation’’. (3) Lag length is 2 selected by Var lag selection.

CG test indicated that USD exchange rate does not Granger Cause PSEI, only PSEI cause USD exchange rate. It means that for every the one USD increase, PSEI decrease by 0.0064961. However, the PSEI has significant effect to the USD exchange rate, but USD exchange rate doesn’t have strong evidence to prove that will effect to the PSEI.

Table 6 Vector Error Correction Estimates

beta (co-integrating vectors, standard errors in parentheses)

USD_Ex 1.0000

PSEI 0.0064961

(0.00000) (0.0020403)

Log-likelihood AIC BIC HQC

-1185.4498 15.4252 15.6215 15.5049

Equation 1: d_USD_Ex_ds

coefficien std. error t-ratio p-value

const 3.16138 0.753178 4.197 4.59e-05***

d_USD_Ex_ds_1 0.123771 0.0772094 1.603 0.1110

d_PSEI_1 -0.00103980 0.000482538 -2.155 0.0328**

EC1 -0.0501380 0.0122509 -4.093 6.92e-05***

Mean dependent var 0.109725 S.D. dependent var 0.980672

Sum squared resid 116.1584 S.E. of regression 0.877075

R-squared 0.215700 Adjusted R-squared 0.200117

rho 0.000760 Durbin-Watson 1.997004

***, ** denote significance at the 1% and 5% significant level


The results of G-IRF and G-VDC are exhibited in Fig. 4 and Table 7, respectively. Through G-IRFs (Fig. 4), it shows that the exchange rate and PSEI interact with each other, an eternal effect of the response of PSEI to the shock of USD exchange rate, and the affect of response of PSEI to USD exchange rate will become slow after period 10 from -0.46% decline to -0.25% monthly. Whereas USD exchange rate provides a strong increase for the PSEI in the short term, USD exchange rate will provide the average 2.42% monthly growth to PSEI from period 1 to 20, and then the increasing power will tend to become slow.

Note: grey area presents the 95% confidence level

Fig. 4 Generalized-impulse response functions (G-IRF)

Table 7 The results of the forecast error variance decomposition

Period USD_Ex_ds PSEI period USD_Ex_ds PSEI

USD_Ex_ds

1 100.0000 0.0000

PSEI

1 5.3075 94.6925

2 97.8429 2.1571 2 3.1828 96.8172

3 95.5711 4.4289 3 2.2858 97.7142

4 93.4429 6.5571 4 1.7997 98.2003

5 91.3206 8.6794 5 1.4870 98.5130

10 79.4077 20.5923 10 0.7585 99.2415

20 53.9091 46.0909 20 0.3831 99.6169

30 35.2903 64.7097 30 0.3790 99.6210

40 24.1565 75.8435 40 0.4726 99.5274

50 17.6605 82.3395 50 0.5846 99.4154


60 13.7204 86.2796 60 0.6896 99.3104

Notes: (1) Each number is in a percentage value. (2) The values of the variance decomposition decomposes forecast error

variance (FEV) in an endogenous variable into percentage shocks to its own and other endogenous variables in the VAR, which

in turn offers information about the relative importance of each random innovation to the variables.

5. Concluding remarks

This paper examines the interrelationship between USD exchange and PSEI using monthly data from July 1997 to July 2010. The results show that there is a stable long-term relationship between USD exchange rate and PSEI based on the co-integration test. Vector error-correction model is used to identify the affect of each variable. Although the PSEI’s trend movement will increase intense in the short run, the movement will tend to become zero (present in the Fig. 5). Whereas the growth of USD exchange rate will keep dropping in some reasons. In addition, the GC test shows that the effect of USD exchange to PSEI is not significant; it means PSEI will effect movement of USD exchange rate directly. The forecasting of USD exchange rate and PSEI present in the Fig. 6. In order to increase the accuracy of forecast, the seasonal adjust needs to multiply the forecast value, it shows in Table 8. After seasonal adjust, the trend of USD exchange rate will keep declining until May 2011, and the trend of PSEI keep increasing to July 2011. The Mean Absolute Percentage Error of VECM is 1.2343% only; it is significant forecast model to measure the USD exchange rate and PSEI.

Philippine government must realize that the growth will tend to slow down up to the point it become zero. In another hand, the government needs to give incentive for the growth of USD exchange rate, if the USD exchange rate keeps going down, it will become a disadvantage for the Philippine export. The main issue is to investigate the policy which effect PSEI much, then via the interrelationship between USD exchange rate and PSEI to control the change rate in order to find a balance point to maintain a well situation for export and import industry.

Fig. 5 The forecast of increasing USD_Ex to PSEI and PSEI to USD_Ex


USD exchange rate PSEI

Fig. 7 Forecast of USD exchange rate and PSEI with seasonal adjust

Fig. 6 Forecast of USD exchange rate and PSEI

Table 8 Forecast of USD exchange rate and PSEI

USD Exchange Rate PSEI

Month AVGE Forecast 95% interval AVGE Forecast 95% interval

Aug 2010 1.01 46.05535 44.34167 47.76902 0.957 3310.11 3036.781 3583.429

Sep 2010 1.017 46.04287 43.41502 48.67071 0.972 3386.574 2965.29 3807.859

Oct 2010 1.016 45.68264 42.41613 48.94915 0.965 3385.384 2860.048 3910.73

Nov 2010 1.008 45.02157 41.28358 48.75955 0.991 3500.093 2868.687 4131.489

Dec 2010 1.003 44.50739 40.37586 48.63891 1.012 3597.964 2870.194 4325.743

Jan 2011 1.003 44.22486 39.73582 48.7139 1.034 3700.117 2879.256 4520.958

Feb 2011 0.992 43.46826 38.71288 48.22363 1.016 3658.941 2781.93 4535.942

Mar 2011 0.99 43.11705 38.08407 48.15003 1.006 3645.664 2711.794 4579.533

Apr 2011 0.984 42.60084 37.33347 47.86821 1.013 3693.672 2690.802 4696.542

May 2011 0.984 42.3527 36.83592 47.86948 1.015 3723.416 2658.813 4788.009

Jun 2011 0.997 42.66733 36.83682 48.49785 1.005 3708.721 2597.875 4819.568

Jul 2011 1.005 42.7691 36.65803 48.88017 1.004 3726.778 2562.439 4891.127


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