China-USA Business Review, ISSN 1537-1514
August 2012, Vol. 11, No. 8, 1031-1050
Emerging Markets Portfolio Creating a Latin
American Portfolio Peruvian Case Study
Edmundo R. Lizarzaburu, Julio Quispe Salguero
ESAN University, Lima, Peru
Renzo Berrocal
Thunderbird University, Phoenix, US
The case study seeks to identify the most important issues encountered in developing a new portfolio in a Latin
America country, exploring several alternatives which include not only stock and sovereign bonds but also more
sophisticate products such as American Depositary Receipt (ADR) or Exchange Traded Fund (ETF) from emerging
countries, and determine what are the risks involved in the process following not only Basil III standards, but also
the local best practice recommend by the local regulators. The study at the beginning used historical information
(normal distribution formulas) of several equities and bonds (n = 142) and then selected five Peruvian instruments
(one of this involved at least 25 equities, N = 5, n = 30) and then other 30 (one of this include an ETF, N = 30, n =
55) in order to determine the best return and risk combination for an emerging market portfolio. Besides, the
additional objective is to examine and introduce the reader in some statistics formulas used in finance and risk
management. Senior management must evaluate the issues associated with the new portfolio and strategy
developed.
Keywords: portfolio theory, sovereign bonds, shares, ADRs, emerging markets, financial instruments
Introduction
In the summer of 2011, Gabriela and Rosa were looking for a summer internship in New York City, after
completing their first master degree study year in the United States. These were difficult times (June and July
of 2011) because of the subprime mortgage crisis, while investment banks Lehman Brothers (in September,
2008) and Bear Stearns (in March 2008) had ceased operating in the international financial market. Regulatory
changes and the economic stimulus from the United States government to its own financial system created
sub-optimal conditions in the New York, Chicago, Tokyo, and London contract and investment financial
markets. Mishkin (1996) defined a financial crisis as an alteration of financial markets where adverse selection
and moral risks appear significantly, and financial markets are no longer to be the efficient funds channel
towards the loci offering the best productive investment opportunities.
Also, these were times when emerging economies started to emerge as a clear specific option in the global
Edmundo R. Lizarzaburu Bolaños, Eng., GMBA Department, ESAN University, GFI del Peru SAC.
Julio Quispe Salguero, MBA, Finance Department, ESAN University.
Renzo Berrocal, student, GMBA Department, Thunderbird University.
Correspondence concerning this article should be addressed to Edmundo R. Lizarzaburu Bolaños, Alonso de Molina1652,
Monterrico Surco, Lima 33, Peru. E-mail: elizarzaburu@esan.edu.pe.
DAVID PUBLISHING
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mailto:elizarzaburu@esan.edu.pe�
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economy. Not just the BRICS (Brazil, Russia, India, China, and South Africa), but also countries including
Colombia, Chile, Turkey, and Peru became interesting destinations for investing in financial assets, both for the
short term (exchange rate or interest rate) and for the long term (bonds or stock). Stock exchanges in those
countries, their “stable” exchange rate and so-called “sovereign” government debt provided attractive options
for investors, in view of their credit ratings’ likely improvement.
A Literature Review on Portfolio Processes
Lane (1993) identified four necessary conditions for financial markets to enforce effective discipline. The
first is an open and free market. Governments should follow the same market rules as any other public or
private agent. Interest rates and insurance premiums paid by governments should reflect their solvency, to
prevent situations of privilege characterized by artificially low interest rates or unlimited access to credits.
The second condition concerns transparency. It requires high quality budget financial data regarding
government transactions and the involved agents, whether borrowers, lenders, or intermediaries.
Thirdly, market discipline forbids governments from accepting sub-central government debt (“no-bail-out”
rule).
Finally, borrowers should be able to react to increases in interest rates or risk premiums (spreads) by
reducing their demand for credit or debt issuances.
Kopits (2001, p. 15) hold that “transparency leads to successful fiscal policy, for the design of both
rule-based and discretional policies”.
Liquidity has been defined as “the ability to rapidly and cheaply transact large amounts of a given asset at
any time” (Harris, 2003, p. 394), or the possibility of buying and selling an unlimited amount of a given stock
(Lee, Petroni, & Shen, 2006).
In 1952, Harry M. Markowitz published a seminal research paper on Modern Portfolio Theory. He started
by describing an individual or institutional investor with a given amount of money to invest at a given time for
a certain period (the holding period). At the end of the period, the investor sells its holdings and then consumes
or reinvests the proceeds or both (Sharpe, 1990).
Markowtz’ model assumes that the investors’ rational behavior when making decisions and choosing
among investment instruments. Consequently, they will try to identify the optimum risk and performance
relationship among the instruments comprised in the portfolio, as well as the amount to invest on each such
component, so as to maximize returns without accepting an excessively high risk, thus meeting their specific
interests and needs.
The Efficient Markets Theory is based on the Random Walk Theory. A random walk is that where future
steps or directions cannot be forecast on the basis of past actions. When applying the term to capital markets, it
means asset movements may not be predicted.
Minimum Variance Portfolio, in this Portfolio, the change in risk for an investment equals zero. Its main
feature is that it “offers investors the minimum available risk for a set of assets that may make up an investment
portfolio. To reach that portfolio, diversification is maximized and, consequently, a combination is achieved
with the lowest risk level” (Rodríguez, 2005).
Optimum Portfolio, any of the portfolios found along the portfolio efficiency frontier, on the tangent point
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with the Capital Markets Curve (CMC)1
Presently in Latin America, the stock exchanges of Peru (Bolsa de Valores de Lima—BVL), Chile (Bolsa
de Comercio de Santiago—BCS), and Colombia (Bolsa de Valores de Colombia—BVC) are coming together
in the so-called Integrated Latin American Market (Mercado Integrado Latinoamericano—MILA) to transact
stocks in a single market. Operations started in May 2011. These stock exchanges, according to the World
Federation of Exchanges, rank among the most profitable worldwide. The purpose of the merger project,
following integration and merger trends as in London and Toronto, or New York and Frankfurt, is to provide
better conditions for investors in each market.
This integration will create Latin America’s first largest stock market by the number of issuers, the second
largest by the stock capitalization and the third by volume of trade. Remarkably, integrating the three stock
exchanges will allow investors to create more diversified portfolios in more liquid markets. Issuers will benefit
from better conditions for raising capital while commission takers and intermediaries will be able to create new
products and expand the frontiers of their business, all under the oversight of local regulators.
. The process proposed by Black (1972) and Merton (1973) concerned
about an optimum portfolio to be identified by maximizing the slope of the curve linking the risk-free returns
point and the efficiency frontier. When this maximum value is reached, the curve becomes the Capital Markets
Curve.
Capital Markets in Latin America and Peru
Diversification will be geographic and by industry. The Peruvian, Colombian, and Chilean stock markets
are present in various industries that have a potential for complementing each other. Peru’s market is mainly
focused on metals, including precious gold and silver, as well as zinc, and features major mining companies
like Buenaventura, Hoschild, Volcan, and Milpo; the energy and oil industries are strong players in the
Colombian market, including Ecopetrol, Pacific Rubiales, ISA, and Colinversiones, as well as financial
organizations such as Bancolombia and Corficolombiana. The retail and financial industries have a strong
presence in the Chilean market, including companies such as Falabella and Cencosud.
The Designed Portfolio
This portfolio aims at creating a framework for reviewing the present potential of the Peruvian market to
act efficiently in a developed capital market. The rationale behind the creation of this investment portfolio
includes participation in traditional asset classes: fixed income and equity, in addition to the Exchange Traded
Fund (ETF), which the researchers considered that, a stock market enhancer because of its liquidity
peculiarities.
Instrument Selection
We describe below the five instruments used in the simulation used to create a proxy for a simplified
efficient investment curve.
The first instrument reviewed for this investment portfolio is Peru’s sovereign fund with maturity May 5,
2015 (see Figure 1). This bond carries an outstanding debt stock worth PEN 1.586 billion Nuevos Soles and
was issued on May 5, 2005, at an original 9.91% coupon rate.
At the end of the second half of 2011, holders (see Figure 2) of the 2015 sovereign bond were distributed
1 Vélez Pareja, I. Decisiones empresariales bajo riesgo e incertidumbre [Business Decisions Under Risk and Uncertain
Conditions] Editorial Norma, 2003.
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as follows, pursuant to the data published by the Ministry of Economy and Finance of Peru.
Figure 1. Local currency denominated sovereign bond (2015); Source: Bloomberg.
Figure 2. Sovereign bond holders (May 5, 2015)-as of June 2011.
The pie chart shows non-residents are the main creditors of the Peruvian government’s bonds due in 2005.
Their share reaches 50.8%. Peruvian banks follow at 40.6%. The rationality underlying the breakdown of bond
holdings is the need for foreign agents to hold paper bearing returns in local currency (Nuevo Sol) to achieve a
return on funds held by non-domiciled agents in Peru with two main characteristics: a market with acceptably
liquid trading and the absence of withdrawals on capital earnings’ income tax.
Bancos
40.56%
AFPs
4.50%
Seguros
0.64%
Otros
1.85%
No Residentes
50.80%
Fondos Públicos
1.47%
Personas Naturales
0.01%
Fondos Privados
0.17%
Tenencia Bonos Soberanos 05MAY2015
1 686 627 Unidades
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Figure 3. Local currency-denominated sovereign bonds (2037); Source: Bloomberg.
The second instrument under consideration for our portfolio model for Peru is the sovereign bond with
maturity date August 12, 2037 (see Figure 3).
Compared with the previous bond, this is one of the longest-term available holdings in the Peruvian yield
curve. This characteristic allows adopting more aggressive strategies for portfolio investments.
The bond was issued on July 26, 2007 and to date 4.75 billion Nuevos Soles have still been outstanding.
The original coupon rate was 6.9%.
At the end of the second half of 2011, sovereign 2037 (see Figure 4) bond holdings were distributed as
follows, as shown by data from the Ministry of Economy and Finance.
Figure 4. Sovereign bond holdings, August 12, 2037-as of June 2011.
The main holders of the Peruvian government 2037 bond are Peruvian Private Pension Funds with 55.86%
Bancos
5.34%
AFPs
55.86%
Seguros
0.08%
Otros
0.52%
No Residentes
30.21%
Fondos Públicos
7.20%
Fondos Privados
0.79%
Tenencia Bonos Soberanos 12AGO2037
4 750 000 Unidades
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share. Non-resident investors rank next, with 30.21% share. The rest of the issue is split among other Peruvian
institutional investors.
The investment rationale behind the 2037 bonds is not directly linked to bond face yields because of the
carry trade. Rather, the rationale for holding these bonds combines the above factor and a bet on an
improvement in Peru’s credit rating, and a more aggressive investment strategy focusing on the bond’s
duration.
The third financial instrument under review is Peru’s Exchange Traded Fund (ETF)—EPU (see Figure 5).
Its nature, creation, redemption, administrator, the reasons why it was created, and the advantages it provides to
the investors are explained below.
Figure 5. ISHARES MCSI details.
Peru’s EPU (Peru’s ETF2
The first significant characteristic of EPU is its objective to replicate exclusively Peru’s equity market.
The chart below (see Figure 6) shows a 99.96% concentration on stocks.
) is a fund aiming at replicating the behavior of the Peruvian equity market. Its
outstanding USD 446 million meets kind-in creation and kind-in redemption criteria. In other words, any
sophisticated, IT-capable brokerage can create and liquidate EPUs after registering with the corresponding
administration fund, making EPU more liquid efficient than any other instrument reviewed for this portfolio.
By sectors, mining accounts for 52.98% of holdings, reflecting the importance of the mining industry in
Peruvian exports and the Growth Domestic Product. Other significant sectors are banking (16%) and food (7%).
As regards concentration of stocks, Buenaventura and Banco de Crédito (Credicorp) fill significant EPU
positions, at 17.37% and 13.567%, respectively.
The fourth selected instrument is Banco de Crédito del Perú’s ADRs (see Figure 7), one of the most
2 Retrieved from http://www.us.ishares.com/product_info/fund/distributions/EPU.htm.
http://us.ishares.com/product_info/fund/distributions/EPU.htm�
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significant stocks in Peru’s exchange market, where they are highly traded.
These ADRs represent a wide range of financial products and account for a relatively large share of the
local market’s total capitalization.
Figure 6. ISHARES MCSI breakdown; Source: Bloomberg.
Figure 7. Banco de Crédito del Perú’s ADRs.
The authors chose to review Banco de Crédito del Peru’s ADRs for this portfolio to include a market
A LATIN AMERICAN PORTFOLIO PERUVIAN CASE STUDY
1038
efficiency criterion as a characteristic of this portfolio’s instrument. They are traded in US dollars. A
subsequent section examines the implications of exchange rate risk, and the need to ensure a uniform and
simplified yield in local currency. As this analysis was being prepared (August 23, 2011), Banco de Crédito’s
market value reached 7.366 billion, and its floating stock (ADRs) was worth 79.76 million.
The fifth and last instrument under review is Compañía de Minas Buenaventura (see Figure 8), also traded
as New York market ADRs. For the same reasons as BAP, it is a major instrument in Peru’s stock market.
Buenaventura is a Peruvian mining holding corporation with shares in other major local mining
conglomerates, though the opposite is not true. Thus, it is a fully locally-owned company focusing on gold
exploitation.
Figure 8. Compañía de Minas Buenaventura’s ADRs.
As of the date for this analysis (August 23, 2011), Buenaventura’s ADRs market value reached
USD12.323 billion distributed among 274.9 million shares.
Having profiled the five instruments chosen for modeling, the authors now describe the individual and
collective statistical peculiarities of these stocks. This choice was made for reasons of convenience and
designed by both Rosa and Gabriela, who sought to create highly diversified portfolio comprised of innovative
instruments from emerging markets.
Variance and Co-variance Matrix
The price sampling of the selected instruments starts on July 1, 2010 and ends on August 23, 2011. Time
series data include daily percent price changes for each of the financial assets so as to meet the two following
conditions of cancelling possible spurious relationships and root units within each analysis variable, and
secondly, providing a more intuitive content to daily variations in the form of capital gains/losses.
As is already mentioned, the selected instruments include:
• Cía Minera Buenaventura ADR (code BVN);
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• Credicorp (code BAP);
• ISHARES Instrument (EPU);
• Peruvian Government Sovereign Bonds (code S2015);
• Peruvian Government Sovereign Bonds (code S2037).
The authors present the below graphs showing the evolution over time of the daily percent (see figure 9)
fluctuations of the financial instruments under review.
Figure 9. VAR evolution of the instruments selected.
Although the conclusions that may be drawn from this graph are limited, the authors can still hold BVN
and EPU showed the widest daily price fluctuations.
The authors analyze below the variances and co-variances (see Table 1) of the financial instruments under
review.
Table 1
Variance and Co Variance Matrix of the Instruments Selected
BVN BAP EPU S 2015 S 2037
BVN 0.0410%
BAP 0.0179% 0.0328%
EPU 0.0218% 0.0212% 0.0229%
S 2015 0.0005% 0.0005% 0.0006% 0.0004%
S 2037 0.0020% 0.0034% 0.0029% 0.0006% 0.0037%
For the period under review, stocks and EPU show the greatest fluctuations and risk, when compared with
fixed income assets. Only BVN and EPU recorded significant co-variance levels, since BVN appears as the
asset with the greatest weight in the ETF.
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Total Return and Risk Stimulation
The following chart shows the total return from capital gains for all selected financial assets for the period
under review, as well as their respective standard deviations.
EXPECTED RETURN: An average is used i.e., the arithmetic summation of data divided by the number of
data extracted from a historical database.
( ) ∑
=
=
n
n
i
ri
rE
1
where:
n: number of data;
ri: retures of rates in period n.
VARIANCE: The difference between a value from the database and the expected or average value for that
same set of data.
𝝈𝝈𝟐𝟐 = �(𝒓𝒓𝒊𝒊 − 𝑬𝑬(𝒓𝒓))𝟐𝟐
𝒏𝒏
𝒊𝒊=𝟏𝟏
𝒏𝒏
STANDARD DEVIATION: SD is the square root of the variance and is regarded as the basic risk
measurement because it assesses both positive and negative deviations similarly. We assume a normal
distribution on yields (percentages).
𝝈𝝈 = �𝝈𝝈𝟐𝟐
COVARIANCE: A statistical value measuring the relationship between two random variables, or how the
yields of two financial instruments or assets c and d, “move together”. A positive value reveals both moves in
the same direction, while a negative value reflects movements in opposite directions. A value close to zero
reveals little or no relationship at all.
𝑪𝑪𝑪𝑪𝑪𝑪𝑨𝑨,𝑩𝑩 = �(𝒓𝒓𝑨𝑨 − 𝑬𝑬(𝒓𝒓𝑨𝑨)). (𝒓𝒓𝑩𝑩 − 𝑬𝑬(𝒓𝒓𝑩𝑩))
𝒏𝒏
𝒊𝒊=𝟏𝟏
𝒏𝒏
CORRELATION: This statistical measure reflects the degree to which two variables are related on a linear
basis. The correlation coefficient measures the association between two variables, and the positive or negative
direction is shown. A correlation coefficient has values between -1 and 1.
−𝟏𝟏 < 𝝆𝝆𝑨𝑨,𝑩𝑩 < 1 𝝆𝝆𝑨𝑨,𝑩𝑩 = 𝑪𝑪𝑪𝑪𝑪𝑪𝑨𝑨,𝑩𝑩
𝝈𝝈𝑨𝑨.𝝈𝝈𝑩𝑩
After reviewing the main statistical tools, we present below the values of these statistics for the time
period under review (see Table 2).
Table 2
Variance and Average Matrix of the Instruments Selected
BVN BAP EPU S 2015 S 2037
Var 0.041% 0.033% 0.023% 0.000% 0.004%
Average 0.065% 0.023% 0.054% -0.006% 0.022%
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As may be drawn from the above, capital gain returns are not high. In fact, carry trade resulted in larger
yields from fixed income instruments during the period under examination. Assuming investors bought both
types of bonds, the total return from the 2015 sovereign bond reached -0.006% + 9.91% in annual terms.
Likewise, the 2037 sovereign bond yielded 0.022% + 6.90%. With these total returns including capital gains
and carry trade gains we can create alternative optimum portfolios.
We may start with the following expected corrected returns:
BVN 0.065%; BAP 0.023%; EPU 0.054%; S2015 9.90%; S2037 6.922%
To estimate the behavior of the chosen fixed income and equity instrument allocation, the authors assume
that from a 1% fixed income allocation, 50% would be invested in the 2015 sovereign bonds, and the remaining
50% in the 2037 bond. Alternatively, a 1% equity allocation would be distributed among BVN, BAP, and EPU
in equal 33% shares. Based on this assumption, the following graph shows the likely scenarios:
Figure 10. Capital market line.
This graph (see Figure 10) shows the greatest risk is found in stock and ETF volatility which is not
compensated by the instrument’s yield. The reason is that an overall decline in short term yields from equity
assets. Each additional percent point in return rate free contributes significantly to the total portfolio’s yield.
The assumption of the portfolio efficiency curve does not hold for Peru.
The following graph showed (Figure 11) the information of the prices per instrument:
Gaby and Rosa have a hypothesis and it is that a stock’s short term performance does not result in the
possibility of creating optimum investment portfolios. Because of poor and even negative yields, it is better to
hold a fixed income portfolio, rather than accept equity portfolio volatility and low expected yields.
Consequently, the approach of possible yield scenarios does not hold for markets showing persistent stock
volatility. Otherwise, when the expectation of a trend to stock price revaluation disappears in a low growth
scenario, it is always preferable to hold a portfolio of fixed income instruments, even if globally stock indexes
have historically yielded higher returns in the long term.
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Figure 11. Prices per instrument.
Conclusion
(1) Based on the portfolio analyzed, diversifying the portfolio reduces the standard deviation (risk measure)
and invest in ETF is a positive alternative because its equity combination is more valuable and has less
volatility if we compare it with a simple equity.
(2) The Stock exchange integration among Chile, Colombia, and Peru will be a very powerful tool in order
to diversity portfolios and if this effort includes more countries such as Mexico, the net contribution to the
investor will be positive because it could increase the turn over and liquidity of the markets. Besides, the
exchange rate should be analyzed in order to guarantee the settlement of the transactions.
(3) Identifying the most important risks became a significant process that all the investors have to follow,
because it allows them to consider the impact of the rate and price (market risk), bid and ask tendency (liquidity
risk), settlement and internal process (operational risk), credit rating and credit line with counterparts (credit
risk), and one of the most important risks, reputational risk.
(4) The information becomes one of the most significant assets when an investor decides to invest,
because the information reduces several risks and it is used to calculate or try to calculate some tendencies
using GARCH or order methods. But it is important to indicate that predicting a market is so difficult that it is
better to have a clear investment policy in order to reduce negative impact (using stop loss aspects for instance)
and obtain gains with positive tendencies (selling and reinvesting with the portfolio have positive return).
Drafting the Report
Gabriela and Rosa spent some time thinking about the portfolio, the instruments that they had analyzed did
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not provided a significant return, so they are interested in evaluating other alternatives and focus on short term
instruments instead of long term instruments. They consider that the return in Latam currencies (an important
factor to consider in the MILA process in order to assure the settlement and, because usually the exchange rate,
it is part of the monetary policy and each country has different “currency regulation” so, including the
exchange rate will be a path to develop in the future, considering the local standards for MILA) and interest
rate could be higher than the return investing in G-7 countries (currencies or interest rates), according to the
studies done by several economists in past three years and it could generate “carry trade”. In fact, Gabriela and
Rosa thought that there were assets that their portfolio should have in order to mitigate the market risks.
(1) Present the major risks that they have to consider evaluating each new instrument showed in the
Appendixes A1, A2, and A3.
(2) Identify Gabriela and Rosa’s key priorities (short- and/or long term) and discuss the strategy to attain
them.
(3) Determine the returns and standard deviation of each asset showed in Appendixes A1, A2, and A4.
What do you recommend and why?
Gabriela and Rosa feel confident that the new portfolio could open a new opportunities in the New York
labor market to them, so they are going to prepare several alternatives to show in the next corporate meeting.
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Appendix A
Table A1
Information of Peruvian Market
Date Exchange rate peru
Overnight rate (%)
Libor rate (%)
Term Term
1 1 3 6 9 12
1-Jul-11 2.7470 3.50 0.185 0.246 0.397 0.565 0.735
4-Jul-11 2.7490 3.50 0.185 0.246 0.397 0.564 0.735
5-Jul-11 2.7500 3.50 0.185 0.246 0.397 0.564 0.735
6-Jul-11 2.7490 3.50 0.185 0.246 0.399 0.565 0.735
7-Jul-11 2.7440 3.50 0.186 0.246 0.399 0.565 0.735
8-Jul-11 2.7440 3.50 0.186 0.246 0.399 0.567 0.736
11-Jul-11 2.7450 3.50 0.186 0.246 0.403 0.567 0.735
12-Jul-11 2.7420 3.50 0.187 0.249 0.410 0.575 0.741
13-Jul-11 2.7420 3.50 0.187 0.249 0.413 0.577 0.743
14-Jul-11 2.7410 3.50 0.186 0.250 0.416 0.578 0.743
15-Jul-11 2.7420 3.50 0.187 0.250 0.417 0.578 0.744
18-Jul-11 2.7380 3.40 0.186 0.251 0.420 0.583 0.749
19-Jul-11 2.7370 3.40 0.186 0.252 0.423 0.585 0.750
20-Jul-11 2.7380 3.40 0.187 0.253 0.423 0.585 0.751
21-Jul-11 2.7360 3.40 0.187 0.253 0.424 0.585 0.752
22-Jul-11 2.7370 3.40 0.187 0.253 0.423 0.584 0.752
25-Jul-11 2.7370 3.40 0.187 0.252 0.425 0.584 0.754
26-Jul-11 2.7370 3.40 0.187 0.253 0.425 0.583 0.755
27-Jul-11 2.7380 3.45 0.187 0.253 0.426 0.585 0.756
28-Jul-11 Holiday Holiday Holiday Holiday Holiday Holiday Holiday
29-Jul-11 Holiday Holiday Holiday Holiday Holiday Holiday Holiday
1-Aug-11 2.7430 3.45 0.192 0.257 0.432 0.593 0.759
2-Aug-11 2.7440 3.45 0.201 0.264 0.438 0.598 0.763
3-Aug-11 2.7430 3.45 0.206 0.268 0.440 0.602 0.765
4-Aug-11 2.7420 3.45 0.205 0.269 0.441 0.603 0.766
5-Aug-11 2.7420 3.45 0.206 0.272 0.443 0.604 0.765
8-Aug-11 2.7530 3.45 0.206 0.275 0.443 0.605 0.767
9-Aug-11 2.7480 3.45 0.208 0.278 0.448 0.608 0.770
10-Aug-11 2.7520 3.45 0.207 0.281 0.448 0.607 0.767
11-Aug-11 2.7450 3.45 0.207 0.286 0.452 0.609 0.770
12-Aug-11 2.7410 3.45 0.208 0.290 0.457 0.613 0.773
15-Aug-11 2.7420 3.45 0.210 0.292 0.459 0.617 0.775
16-Aug-11 2.7410 3.45 0.210 0.293 0.460 0.618 0.776
17-Aug-11 2.7380 3.45 0.212 0.296 0.460 0.618 0.778
18-Aug-11 2.7390 3.30 0.213 0.298 0.463 0.621 0.780
19-Aug-11 2.7350 3.30 0.215 0.303 0.467 0.624 0.784
22-Aug-11 2.7320 3.30 0.217 0.308 0.471 0.628 0.788
23-Aug-11 2.7320 3.30 0.218 0.312 0.475 0.633 0.792
24-Aug-11 2.7320 3.30 0.219 0.314 0.476 0.635 0.795
25-Aug-11 2.7320 3.30 0.221 0.319 0.480 0.638 0.798
A LATIN AMERICAN PORTFOLIO PERUVIAN CASE STUDY
1045
(Table A1 continued)
Date Exchange rate peru
Overnight rate (%)
Libor rate (%)
Term Term
1 1 3 6 9 12
26-Aug-11 2.7310 3.30 0.221 0.323 0.480 0.636 0.796
29-Aug-11 Holiday Holiday Holiday Holiday Holiday Holiday Holiday
30-Aug-11 Holiday Holiday Holiday Holiday Holiday Holiday Holiday
31-Aug-11 2.7260 3.30 0.222 0.327 0.486 0.640 0.800
1-Sep-11 2.7270 3.30 0.222 0.329 0.489 0.643 0.802
2-Sep-11 2.7300 3.30 0.222 0.331 0.490 0.644 0.803
5-Sep-11 2.7310 3.30 0.224 0.333 0.496 0.648 0.807
6-Sep-11 2.7300 3.30 0.226 0.336 0.502 0.653 0.814
7-Sep-11 2.7260 3.25 0.226 0.337 0.503 0.656 0.815
8-Sep-11 2.7250 3.25 0.225 0.337 0.504 0.658 0.818
9-Sep-11 2.7270 3.45 0.226 0.338 0.504 0.651 0.821
12-Sep-11 2.7280 3.25 0.229 0.343 0.513 0.667 0.828
13-Sep-11 2.7350 3.25 0.229 0.347 0.517 0.670 0.831
14-Sep-11 2.7310 3.25 0.229 0.349 0.521 0.673 0.834
15-Sep-11 2.7310 3.25 0.230 0.350 0.522 0.674 0.836
16-Sep-11 2.7320 3.45 0.231 0.351 0.523 0.673 0.835
19-Sep-11 2.7380 3.45 0.231 0.353 0.525 0.675 0.836
20-Sep-11 2.7410 3.40 0.232 0.355 0.527 0.676 0.837
21-Sep-11 2.7480 3.40 0.234 0.356 0.529 0.678 0.839
22-Sep-11 2.7770 3.40 0.235 0.358 0.537 0.685 0.845
Note. Source: SBS, BCRP, Scotiabank, Bloomber, Reuters.
Table A2
Information of MILA and Latin America Countries
DATE
CLOSE PRICE
Colombia Chile Brasil México Perú
1-Jul-11 1762.500 465.2500 1.5599 11.7230 2.750
4-Jul-11 Holiday 465.5000 1.5580 11.6368 2.750
5-Jul-11 1770.000 464.5000 1.5637 11.5925 2.746
6-Jul-11 1766.000 463.7500 1.5662 11.6194 2.744
7-Jul-11 1760.900 460.9000 1.5581 11.6544 2.743
8-Jul-11 1760.450 462.8500 1.5634 11.5738 2.743
11-Jul-11 1770.000 467.0000 1.5796 11.6337 2.742
12-Jul-11 1766.200 466.8500 1.5773 11.7274 2.742
13-Jul-11 1757.300 462.6500 1.5762 11.7867 2.742
14-Jul-11 1747.650 462.1500 1.5729 11.7170 2.740
15-Jul-11 1759.850 462.4000 1.5743 11.7010 2.738
18-Jul-11 1757.500 463.3500 1.5828 11.7178 2.738
19-Jul-11 1757.250 461.9000 1.5691 11.7877 2.736
20-Jul-11 Holiday 463.0000 1.5651 11.6964 2.737
21-Jul-11 1754.000 460.9000 1.5567 11.6638 2.736
A LATIN AMERICAN PORTFOLIO PERUVIAN CASE STUDY
1046
(Table A2 continued)
DATE CLOSE PRICE
Colombia Chile Brasil México Perú
22-Jul-11 1755.800 461.4700 1.5547 11.6174 2.736
25-Jul-11 1763.500 462.7000 1.5449 11.6393 2.741
26-Jul-11 1758.700 457.5000 1.5345 11.6641 2.736
27-Jul-11 1767.500 456.8000 1.5639 11.6172 2.741
28-Jul-11 1773.000 455.7600 1.5651 11.6527 Holiday
29-Jul-11 1779.000 458.7000 1.5563 11.6821 Holiday
1-Aug-11 1767.100 457.0000 1.5551 11.7425 2.743
2-Aug-11 1771.150 458.9000 1.5656 11.7514 2.742
3-Aug-11 1771.250 458.5000 1.5651 11.7657 2.742
4-Aug-11 1786.000 462.1500 1.5752 11.8473 2.742
5-Aug-11 1789.300 465.2000 1.5895 11.9523 2.743
8-Aug-11 1815.500 472.6000 1.5999 11.9794 2.754
9-Aug-11 1812.500
473.0000 1.6334 12.1845 2.749
10-Aug-11 1796.000 473.3000 1.6183 12.3710 2.750
11-Aug-11 1788.800 471.1000 1.6306 12.3221 2.745
12-Aug-11 1785.050 470.5000 1.6157 12.3899 2.743
15-Aug-11 Holiday Holiday 1.5956 12.2992 2.740
16-Aug-11 1775.100 472.0000 1.5918 12.2424 2.741
17-Aug-11 1766.350 466.4500 1.5830 12.2631 2.736
18-Aug-11 1777.500 470.5000 1.6062 12.1651 2.738
19-Aug-11 1783.000 468.6000 1.5960 12.3685 2.733
22-Aug-11 1780.000 468.4000 1.6009 12.2386 2.733
23-Aug-11 1789.000 467.2500 1.6036 12.2948 2.732
24-Aug-11 1786.010 467.0000 1.6039 12.3357 2.733
25-Aug-11 1790.900 466.9500 1.6154 12.3952 2.731
26-Aug-11 1794.350 465.8500 1.6114 12.4259 2.730
29-Aug-11 1790.300 464.9000 1.5974 12.4953 Holiday
30-Aug-11 1789.000 465.0500 1.5904 12.4148 Holiday
31-Aug-11 1782.500 461.0500 1.5872 12.4838 2.727
1-Sep-11 1779.500 459.6100 1.6040 12.3480 2.729
2-Sep-11 1783.000 459.8000 1.6343 12.2616 2.729
5-Sep-11 1782.800 462.8000 1.6522 12.3735 2.735
6-Sep-11 1791.200 464.2500 1.6583 12.5353 2.727
7-Sep-11 1790.000 462.8000 Holiday 12.5102 2.725
8-Sep-11 1788.500 462.6600 1.6566 12.4661 2.724
9-Sep-11 1798.000 469.4000 1.6774 12.4956 2.728
12-Sep-11 1821.000 476.0000 1.6899 12.6324 2.734
13-Sep-11 1813.480 475.1000 1.7127 12.7687 2.730
14-Sep-11 1826.500 478.8000 1.7288 12.8994 2.732
15-Sep-11 1820.100 478.1500 1.7106 12.9646 2.729
A LATIN AMERICAN PORTFOLIO PERUVIAN CASE STUDY
1047
(Table A2 continued)
DATE CLOSE PRICE
Colombia Chile Brasil México Perú
16-Sep-11 1821.450 480.5000 1.7122 Holiday 2.734
19-Sep-11 1857.000 Holiday 1.7763 12.9127 2.740
20-Sep-11 1860.500 489.8000 1.7870 13.1860 2.735
21-Sep-11 1874.000 499.9000 1.8280 13.1669 2.758
22-Sep-11 1915.000 523.1000 1.9016 13.4045 2.771
Note. Source: BVC, BEC, BCB, DOF.
Table A3
G 7 Currencies Open Prices
DATE
G7 CURRENCY
OPEN PRICE
EUR/USD EUR/GBP EUR/JPY EUR/CAD GBP/USD USD/CAD USD/JPY GBP/JPY
1-Jul-11 1.4509/1.4510 0.9034/0.9035 117.38/117.40 1.3962/1.3964 1.6061/1.6062 0.9604/0.9605 80.90/80.91 129.92/129.95
4-Jul-11 1.4510/1.4510 0.9031/0.9032 117.30/117.32 1.3917/1.3919 1.6065/1.6067 0.9611/0.9613 80.84/80.85 129.88/129.90
5-Jul-11 1.4476/1.4476 0.8985/0.8987 117.33/117.35 1.3929/1.3932 1.6109/1.6110 0.9621/0.9622 81.05/81.06 130.57/130.59
6-Jul-11 1.4333/1.4333 0.8945/0.8946 115.92/115.93 1.3830/1.3833 1.6021/1.6022 0.9658/0.9660 80.87/80.88 129.58/129.60
7-Jul-11 1.4337/1.4338 0.8971/0.8972 116.48/116.49 1.3738/1.3738 1.5980/1.5982 0.9583/0.9584 81.24/81.25 129.83/129.85
8-Jul-11 1.4318/1.4319 0.8931/0.8932 115.44/115.46 1.3645/1.3645 1.6031/1.6032 0.9627/0.9629 80.63/80.64 129.25/129.27
11-Jul-11 1.4073/1.4076 0.8832/0.8835 113.13/113.16 1.3625/1.3627 1.5933/1.5933 0.9659/0.9662 80.39/80.42 128.05/128.11
12-Jul-11 1.3977/1.3980 0.8811/0.8813 111.33/111.33 1.3584/1.3584 1.5863/1.5866 0.9681/0.9684 79.62/79.65 126.33/126.36
13-Jul-11 1.4137/1.4139 0.8783/0.8785 117.65/117.68 1.3520/1.3522 1.6102/1.6105 0.9592/0.9592 78.81/78.85 127.19/127.21
14-Jul-11 1.4244/1.4247 0.8818/0.8821 112.63/112.66 1.3597/1.3600 1.6151/1.6154 0.9571/0.9573 79.06/79.09 127.69/127.75
15-Jul-11 1.4130/1.4150 0.8731/0.8751 111.89/112.01 1.3538/1.3540 1.608/1.614 0.9651/0.9655 78.15/78.17 125.18/125.22
18-Jul-11 1.4029/1.4050 0.8730/0.8740 110.66/111.67 1.3461/1.3465 1.6015/1.6020 0.9543/0.9550 77.25/77.32 120.35/120.38
19-Jul-11 1.4164/1.4167 0.8772/0.8775 111.81/111.83 1.3532/1.3534 1.6145/1.6148 0.9505/0.9508 78.91/78.94 127.43/127.46
20-Jul-11 1.4181/1.4184 0.8799/0.8802 111.89/111.92 1.3449/1.3451 1.6114/1.6117 0.9484/0.9487 78.89/78.92 127.12/127.18
21-Jul-11 1.4323/1.4326 0.8812/0.8815 112.57/112.60 1.3445/1.3447 1.6252/1.6255 0.9451/0.9454 78.58/78.61 127.71/127.77
22-Jul-11 1.4333/1.4336 0.8807/0.8810 112.45/112.48 1.3655/1.3658 1.6272/1.6275 0.9512/0.9515 78.44/78.47 127.64/127.70
25-Jul-11 1.4351/1.4354 0.8811/0.8814 112.17/112.20 1.3597/1.3599 1.6285/1.6288 0.9462/0.9465 78.15/78.18 127.26/127.32
26-Jul-11 1.4461/1.4464 0.8823/0.8826 112.86/112.89 1.3619/1.3623 1.6388/1.6391 0.9433/0.9436 78.03/78.06 127.88/128.94
27-Jul-11 1.4431/1.4434 0.8813/0.8816 112.33/112.36 1.3621/1.3625 1.6372/1.6375 0.9427/0.9431 77.83/77.86 127.42/127.48
28-Jul-11 Holiday Holiday Holiday Holiday Holiday Holiday Holiday Holiday
29-Jul-11 Holiday Holiday Holiday Holiday Holiday Holiday Holiday Holiday
1-Aug-11 1.4235/1.4238 0.8732/0.8738 109.87/109.90 1.3706/1.3709 1.6278/1.6280 0.9560/0.9563 77.17/77.19 125.65/125.69
2-Aug-11 1.4241/1.4244 0.8753/0.8756 109.99/110.02 1.3618/1.3620 1.6267/1.6271 0.9575/0.9578 77.22/77.25 125.61/125.67
3-Aug-11 1.4299/1.4302 0.8728/0.8731 110.02/110.05 1.3720/1.3724 1.6381/1.6384 0.9602/0.9605 76.98/76.96 126.01/126.07
4-Aug-11 1.4169/1.4172 0.8671/0.8674 112.11/112.14 1.3825/1.3829 1.6339/1.6342 0.9702/0.9705 79.11/79.14 129.26/129.32
5-Aug-11 1.4193/1.4196 0.8681/0.8683 111.36/111.39 1.3891/1.3899 1.6349/1.6352 0.9802/0.9805 78.45/78.48 128.25/128.31
8-Aug-11 1.4230/1.4233 0.8699/0.8702 110.31/110.33 1.4025/1.4029 1.6355/1.6358 0.9888/0.9891 77.51/77.54 126.76/126.82
9-Aug-11 1.4250/1.4253 0.8728/0.8731 110.11/110.14 1.4139/1.4143 1.6323/1.6326 0.9935/0.9938 77.26/77.29 126.11/126.17
10-Aug-11 1.4229/1.4232 0.8796/0.8799 108.76/108.79 1.4131/1.4133 1.6174/1.6177 0.9876/0.9879 76.42/76.45 123.62/123.66
11-Aug-11 1.4219/1.4222 0.8773/0.8776 109.12/109.15 1.4087/1.4091 1.6205/1.6208 0.9930/0.9933 76.73/76.76 124.34/124.40
A LATIN AMERICAN PORTFOLIO PERUVIAN CASE STUDY
1048
(Table A3 continued)
DATE
G7 CURRENCY
OPEN PRICE
EUR/USD EUR/GBP EUR/JPY EUR/CAD GBP/USD USD/CAD USD/JPY GBP/JPY
12-Aug-11 1.4271/1.4274 0.8759/0.8762 109.39/109.41 1.4059/1.4063 1.6290/1.6293 0.9856/0.9859 76.64/76.67 124.85/124.91
15-Aug-11 1.4404/1.4407 0.8807/0.8810 110.40/110.43 1.4110/1.4112 1.6354/1.6357 0.9853/0.9856 76.63/76.66 125.32/125.38
16-Aug-11 1.4391/1.4394 0.8779/0.8782 110.60/110.63 1.4162/1.4165 1.6388/1.6391 0.9828/0.9831 76.85/76.88 125.94/126.00
17-Aug-11 1.4486/1.4489 0.8781/0.8784 110.84/110.87 1.4165/1.4167 1.6494/1.6497 0.9789/0.9792 76.50/76.53 126.18/126.24
18-Aug-11 1.4336/1.4339 0.8687/0.8690 109.82/109.85 1.4170/1.4175 1.6502/1.6506 0.9909/0.9912 76.59/76.62 126.39/126.45
19-Aug-11 1.4414/1.4417 0.8697/0.8701 110.14/110.17 1.4185/1.4189 1.6570/1.6573 0.9871/0.9874 76.40/76.43 126.61/126.66
22-Aug-11 1.4419/1.4422 0.8739/0.8742 110.67/110.69 1.4190/1.4194 1.6498/1.6501 0.9856/0.9860 76.73/76.76 126.59/126.65
23-Aug-11 1.4390/1.4393 0.8711/0.8714 110.16/110.19 1.4257/1.4260 1.6517/1.6520 0.9883/0.9886 76.54/76.57 126.42/126.48
24-Aug-11 1.4468/1.4471 0.8794/0.8797 110.71/110.74 1.4262/1.4266 1.6450/1.6453 0.9855/0.9858 76.51/76.54 125.86/125.92
25-Aug-11 1.4432/1.4435 0.8824/0.8827 111.24/111.27 1.4215/1.4219 1.6353/1.6356 0.9807/0.9810 77.07/77.11 126.04/126.10
26-Aug-11 1.4408/1.4411 0.8861/0.8864 110.41/110.44 1.4239/1.4241 1.6259/1.6262 0.9902/0.9905 76.62/76.65 124.56/124.62
29-Aug-11 1.4536/1.4539 0.8852/0.8855 111.61/111.65 1.4154/1.4157 1.6418/1.6421 0.9760/0.9763 76.77/76.80 126.05/126.11
30-Aug-11 1.4222/1.4225 0.8805/0.8808 110.12/110.15 1.4098/1.4101 1.6637/1.6642 0.9540/0.9544 74.55/74.60 124.45/124.49
31-Aug-11 1.4438/1.4441 0.8845/0.8848 110.56/110.59 1.4138/1.4141 1.6323/1.6326 0.9756/0.9759 76.56/76.59 124.97/125.03
1-Sep-11 1.4238/1.4241 0.8815/0.8818 109.79/109.82 1.3950/1.3954 1.6151/1.6154 0.9757/0.9760 77.10/77.13 124.52/124.58
2-Sep-11 1.4198/1.4201 0.8772/0.8775 109.04/109.07 1.3941/1.3944 1.6182/1.6185 0.9819/0.9822 76.79/76.82 124.26/124.32
5-Sep-11 1.4105/1.4108 0.8751/0.8754 108.48/108.51 1.3964/1.3968 1.6116/1.6119 0.9891/0.9894 76.89/76.92 123.92/123.98
6-Sep-11 1.4041/1.4044 0.8759/0.8762 108.83/108.86 1.3989/1.3992 1.6028/1.6031 0.9946/0.9949 77.49/77.52 124.21/124.27
7-Sep-11 1.4013/1.4016 0.8774/0.8777 108.39/108.42 1.3874/1.3877 1.5968/1.5971 0.9893/0.9896 77.34/77.37 123.49/123.55
8-Sep-11 1.4003/1.4006 0.8713/0.8716 108.30/108.33 1.3821/1.3825 1.6068/1.6071 0.9836/0.9839 77.33/77.36 124.25/124.31
9-Sep-11 1.3691/1.3694 0.8620/0.8623 106.44/106.47 1.3715/1.3718 1.5880/1.5883 0.9957/0.9960 77.74/77.77 123.45/123.51
12-Sep-11 1.3611/1.3614 0.8606/0.8609 105.10/105.13 1.3665/1.3668 1.5814/1.5817 0.9960/0.9963 77.20/77.23 122.08/122.14
13-Sep-11 1.3686/1.3689 0.8648/0.8651 105.16/105.19 1.3541/1.3545 1.5824/1.5827 0.9910/0.9913 76.82/76.85 121.56/121.62
14-Sep-11 1.3695/1.3698 0.8675/0.8678 105.06/105.09 1.3579/1.3583 1.5785/1.5788 0.9910/0.9913 76.70/76.73 121.07/121.13
15-Sep-11 1.3880/1.3883 0.8759/0.8762 106.55/106.58 1.3630/1.3635 1.5843/1.5846 0.9847/0.9850 76.75/76.78 121.59/121.65
16-Sep-11 1.3800/1.3803 0.8727/0.8730 105.93/105.96 1.3559/1.3562 1.5810/1.5813 0.9834/0.9837 76.75/76.78 121.34/121.40
19-Sep-11 1.3613/1.3616 0.8693/0.8696 104.52/104.55 1.3420/1.3423 1.5657/1.5660 0.9897/0.9900 76.77/76.80 120.20/120.26
20-Sep-11 1.3684/1.3687 0.8765/0.8768 104.48/104.51 1.3590/1.3594 1.5610/1.5613 0.9989/0.9992 76.34/76.37 119.17/119.23
21-Sep-11 1.3684/1.3687 0.8765/0.8768 104.48/104.51 1.3590/1.3594 1.5610/1.5613 0.9989/0.9992 76.34/76.37 119.17/119.23
22-Sep-11 1.3468/1.3471 0.8771/0.8774 102.73/102.76 1.3891/1.3894 1.5353/1.5356 1.0292/1.0295 76.27/76.30 117.11/117.17
Note. Source: Several platforms.
A LATIN AMERICAN PORTFOLIO PERUVIAN CASE STUDY
1049
Table A4
G 7 Currencies Close Prices
DATE
G7 CURRENCY
CLOSE PRICE
EUR/USD EUR/GBP EUR/JPY EUR/CAD GBP/USD USD/CAD USD/JPY GBP/JPY
1-Jul-11 1.4512/1.4515 0.9030/0.9032 117.43/117.46 1.3967/1.3969 1.6064/1.6065 0.9600/0.9602 80.93/80.94 129.97/129.99
4-Jul-11 1.4512/1.4512 0.9033/0.9034 117.28/117.29 1.3921/1.3923 1.6062/1.6064 0.9615/0.9617 80.87/80.89 129.79/129.84
5-Jul-11 1.4408/1.4409 0.8976/0.8978 116.76/116.78 1.3924/1.3927 1.6049/1.6051 0.9626/0.9627 81.04/81.05 130.06/130.08
6-Jul-11 1.4304/1.4305 0.8946/0.8948 115.74/115.76 1.3835/1.3837 1.5987/1.5989 0.9654/0.9656 80.92/80.92 129.36/129.39
7-Jul-11 1.4355/1.4356 0.8990/0.8991 116.60/116.62 1.3736/1.3736 1.5967/1.5968 0.9587/0.9588 81.22/81.23 129.69/129.72
8-Jul-11 1.4244/1.4076 0.8887/0.8888 114.87/114.88 1.3647/1.3647 1.6026/1.6027 0.9615/0.9616 80.64/80.65 129.23/129.26
11-Jul-11 1.4022/1.4023 0.8811/0.8814 112.36/112.39 1.3615/1.3618 1.5909/1.5912 0.9684/0.9687 80.13/80.16 127.48/127.54
12-Jul-11 1.3982/1.3985 0.8812/0.8814 111.29/111.31 1.3580/1.3582 1.5860/1.5862 0.9679/0.9680 79.59/79.61 126.27/126.30
13-Jul-11 1.4142/1.4145 0.8778/0.8781 117.71/117.73 1.3525/1.3527 1.6108/1.6111 0.9594/0.9597 78.97/79.00 127.24/127.26
14-Jul-11 1.4141/1.4144 0.8762/0.8765 111.81/111.84 1.3603/1.3605 1.6136/1.6139 0.9593/0.9596 79.06/79.09 127.57/127.63
15-Jul-11 1.4170/1.4190 0.8778/0.8780 112.35/112.35 1.3543/1.3549 1.614/1.618 0.9659/0.9662 78.08/78.11 125.25/125.32
18-Jul-11 1.4139/1.4142 0.8790/0.8800 111.69/111.72 1.3469/1.3472 1.6127/1.6130 0.9634/0.9637 77.35/77.39 120.44/120.49
19-Jul-11 1.4155/1.4158 0.8763/0.8765 111.85/111.85 1.3525/1.3528 1.6151/1.6154 0.9511/0.9514 78.77/78.81 127.49/127.53
20-Jul-11 1.4171/1.4175 0.8805/0.8807 111.95/111.97 1.3438/1.3440 1.6111/1.6115 0.9489/0.9491 78.91/78.94 127.20/127.25
21-Jul-11 1.4328/1.4330 0.8807/0.8809 112.62/112.65 1.3441/1.3443 1.6247/1.6249 0.9456/0.9458 78.63/78.66 127.80/127.82
22-Jul-11 1.4338/1.4341 0.8812/0.8815 112.39/112.42 1.3660/1.3662 1.6269/1.6270 0.9517/0.9518 78.50/78.52 127.71/127.76
25-Jul-11 1.4387/1.4391 0.8821/0.8824 112.55/112.58 1.3607/1.3612 1.6307/1.6310 0.9446/0.9449 78.22/78.25 127.55/127.61
26-Jul-11 1.4515/1.4518 0.8843/0.8846 113.08/113.11 1.3611/1.3615 1.6412/1.6415 0.9428/0.9431 77.90/77.93 127.84/128.90
27-Jul-11 1.4441/1.4444 0.8815/0.8818 112.12/112.18 1.3626/1.3629 1.6385/1.6388 0.9444/0.9447 77.25/77.30 127.51/127.56
28-Jul-11 Holiday Holiday Holiday Holiday Holiday Holiday Holiday Holiday
29-Jul-11 Holiday Holiday Holiday Holiday Holiday Holiday Holiday Holiday
1-Aug-11 1.4240/1.4243 0.8744/0.8747 109.95/109.98 1.3710/1.3713 1.6283/1.6286 0.9568/0.9571 77.21/77.23 125.72/125.76
2-Aug-11 1.4185/1.4188 0.8709/0.8712 109.42/109.45 1.3622/1.3625 1.6286/1.6289 0.9600/0.9603 77.12/77.15 125.62/125.66
3-Aug-11 1.4314/1.4317 0.8718/0.8721 110.06/110.09 1.3728/1.3730 1.6415/1.6418 0.9634/0.9637 76.88/76.91 126.21/126.26
4-Aug-11 1.4133/1.4136 0.8684/0.8687 110.06/110.09 1.3832/1.3835 1.6271/1.6274 0.9780/0.9783 78.92/78.95 128.41/128.47
5-Aug-11 1.4288/1.4291 0.8726/0.8729 112.08/112.11 1.3903/1.3905 1.6371/1.6374 0.9783/0.9786 78.43/78.46 128.39/128.45
8-Aug-11 1.4203/1.4206 0.8682/0.8685 110.39/110.42 1.4032/1.4035 1.6356/1.6359 0.9912/0.9915 77.71/77.74 127.07/127.13
9-Aug-11 1.4228/1.4231 0.8770/0.8773 109.33/109.36 1.4132/1.4136 1.6221/1.6224 0.9940/0.9943 76.83/76.86 124.63/124.69
10-Aug-11 1.4229/1.4232 0.8802/0.8805 109.23/109.26 1.4139/1.4142 1.6164/1.6167 0.9887/0.9891 76.75/76.78 124.06/124.12
11-Aug-11 1.4213/1.4216 0.8767/0.8770 109.22/109.25 1.4083/1.4085 1.6208/1.6211 0.9894/0.9897 76.84/76.87 124.54/124.60
12-Aug-11 1.4234/1.4237 0.8744/0.8747 109.32/109.35 1.4065/1.4067 1.6277/1.6280 0.9913/0.9916 76.79/76.82 124.99/125.05
15-Aug-11 1.4443/1.4446 0.8813/0.8816 110.87/110.90 1.4113/1.4115 1.6386/1.6389 0.9817/0.9821 76.75/76.78 125.76/125.82
16-Aug-11 1.4406/1.4409 0.8751/0.8754 110.59/110.62 1.4157/1.4160 1.6459/1.6462 0.9829/0.9832 76.75/76.78 126.32/126.38
17-Aug-11 1.4446/1.4449 0.8721/0.8724 110.46/110.49 1.4160/1.4163 1.6562/1.6565 0.9807/0.9811 76.46/76.49 126.64/126.70
18-Aug-11 1.4329/1.4332 0.8688/0.8691 109.66/109.69 1.4165/1.4168 1.6491/1.6494 0.9901/0.9904 76.52/76.55 126.19/126.25
A LATIN AMERICAN PORTFOLIO PERUVIAN CASE STUDY
1050
(Table A4 continued)
DATE
G7 CURRENCY
CLOSE PRICE
EUR/USD EUR/GBP EUR/JPY EUR/CAD GBP/USD USD/CAD USD/JPY GBP/JPY
19-Aug-11 1.4384/1.4387 0.8721/0.8724 110.08/110.11 1.4180/1.4183 1.6489/1.6492 0.9888/0.9891 76.51/76.54 126.17/126.23
22-Aug-11 1.4371/1.4374 0.8723/0.8726 110.29/110.32 1.4125/1.4129 1.6472/1.6475 0.9888/0.9891 76.73/76.76 126.39/126.45
23-Aug-11 1.4425/1.4428 0.8741/0.8744 110.61/110.64 1.4248/1.4252 1.6501/1.6504 0.9892/0.9895 76.67/76.70 126.51/126.57
24-Aug-11 1.4469/1.4472 0.8805/0.8808 111.00/111.03 1.4249/1.4254 1.6373/1.6376 0.9884/0.9887 76.97/77.01 126.03/126.09
25-Aug-11 1.4371/1.4374 0.8825/0.8828 111.52/111.55 1.4207/1.4210 1.6282/1.6285 0.9862/0.9865 77.59/77.62 126.33/126.39
26-Aug-11 1.4479/1.4482 0.8858/0.8861 111.05/111.08 1.4235/1.4239 1.6342/1.6345 0.9848/0.9851 76.69/76.72 125.33/125.39
29-Aug-11 1.4528/1.4530 0.8848/0.8850 111.47/111.52 1.4160/1.4162 1.6425/1.6429 0.9767/0.9769 76.48/76.50 126.18/126.21
30-Aug-11 1.4227/1.4229 0.8810/0.8813 110.04/110.08 1.4110/1.4114 1.6645/1.6649 0.9537/0.9539 74.64/74.68 124.53/124.57
31-Aug-11 1.4444/1.4447 0.8836/0.8839 110.62/110.65 1.4129/1.4134 1.6332/1.6335 0.9750/0.9754 76.62/76.65 125.10/125.13
1-Sep-11 1.4275/1.4278 0.8822/0.8825 109.64/109.67 1.3942/1.3946 1.6178/1.6181 0.9753/0.9756 76.80/76.83 124.24/124.30
2-Sep-11 1.4186/1.4189 0.8749/0.8752 108.85/108.88 1.3934/1.3939 1.6211/1.6214 0.9841/0.9844 76.72/76.75 124.37/124.43
5-Sep-11 1.4091/1.4094 0.8753/0.8756 108.34/109.37 1.3922/1.3925 1.6097/1.6100 0.9909/0.9912 76.87/76.90 123.74/123.80
6-Sep-11 1.3999/1.4002 0.8777/0.8780 108.60/108.63 1.3972/1.3975 1.5948/1.5951 0.9893/0.9896 77.57/77.60 123.71/123.77
7-Sep-11 1.4084/1.4087 0.8814/0.8817 108.92/108.95 1.3865/1.3869 1.5976/1.5979 0.9861/0.9864 77.32/77.35 123.53/123.59
8-Sep-11 1.3901/1.3904 0.8702/0.8705 107.67/107.70 1.3814/1.3819 1.5973/1.5976 0.9885/0.9888 77.44/77.47 123.69/123.75
9-Sep-11 1.3659/1.3662 0.8606/0.8609 105.73/105.76 1.3706/1.3710 1.5869/1.5872 0.9972/0.9975 77.40/77.43 122.82/122.88
12-Sep-11 1.3617/1.3619 0.8611/0.8615 105.05/105.08 1.3669/1.3672 1.5810/1.5813 0.9965/0.9967 77.26/77.30 122.18/122.22
13-Sep-11 1.3729/1.3732 0.8678/0.8681 105.49/105.52 1.3532/1.3538 1.5819/1.5822 0.9872/0.9875 76.83/76.86 121.53/121.59
14-Sep-11 1.3752/1.3755 0.8715/0.8718 105.43/105.46 1.3580/1.3584 1.5777/1.5780 0.9898/0.9901 76.65/76.68 120.94/121.00
15-Sep-11 1.3872/1.3875 0.8766/0.8770 106.65/106.69 1.3618/1.3624 1.5851/1.5855 0.9856/0.9860 76.63/76.66 121.42/121.46
16-Sep-11 1.3808/1.3811 0.8733/0.8736 105.78/105.79 1.3542/1.3546 1.5818/1.5820 0.9842/0.9845 76.69/76.72 121.29/121.32
19-Sep-11 1.3681/1.3684 0.8713/0.8716 104.62/104.65 1.3140/1.3417 1.5697/1.5700 0.9897/0.9900 76.47/76.50 120.03/120.09
20-Sep-11 1.3673/1.3676 0.8773/0.8776 104.78/104.81 1.3582/1.3586 1.5583/1.5586 1.0034/1.0037 76.62/76.65 119.40/119.46
21-Sep-11 1.3673/1.3676 0.8773/0.8776 104.78/104.81 1.3582/1.3586 1.5583/1.5586 1.0034/1.0037 76.62/76.65 119.40/119.46
22-Sep-11 1.3453/1.3456 0.8767/0.8770 102.75/102.78 1.3883/1.3886 1.5343/1.5346 1.0298/1.0301 76.36/76.39 117.15/117.21
Note. Source: Several platorms.