Finance of International Business: Transaction Exposure Analysis

This case study analyzes the finance of international business, focusing on transaction exposure and the value-at-risk (VAR) method. It examines how exchange rate movements impact a firm's contractual transactions in foreign currencies, emphasizing the importance of assessing and managing currency exposure. The study includes a computation of the maximum one-day loss for a company receiving Mexican pesos, considering factors like standard deviation and confidence levels. Furthermore, it explores the VAR method using a currency portfolio, calculating maximum one-month losses for different currencies and analyzing the benefits of diversification. The analysis covers various hedging methods and the drawbacks of the VAR method, providing a comprehensive overview of international finance risk management strategies. The assignment covers the concepts of transaction exposure, VAR, and their application in managing currency risk.

Running head: FINANCE OF INTERNATIONAL BUSINESS
FINANCE OF INTERNATIONAL BUSINESS
Name of the Student:
Name of the University:
Author Note:

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Table of Contents
Introduction................................................................................................................................2
Transaction Exposure.................................................................................................................2
Assessment of Exposure via VAR.............................................................................................4
Maximum One-Day Loss.......................................................................................................4
Factors Affecting Maximum One-Day Loss..........................................................................5
VAR Method of Currency Portfolio..........................................................................................6
Drawbacks of VAR............................................................................................................8
Conclusion..................................................................................................................................8
References..................................................................................................................................9
Appendix..................................................................................................................................11

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Introduction
International finance plays a vital role in every business and more importantly there
are various factors like changes observed in exchange rate, economic change and changes in
the interest rate levels that can materially affects the business of a company. Changes on the
currency exchange rate can be one of a key and an important concern for the company, but it
becomes important for the company to well take important steps and actions for the purpose
of well hedging the forex risk. Transaction exposure is one of the key risk exposure that is
well faced by the company and this has been particularly due to the operations of the
company based on a global scale whereby it receives and pays an amount in a foreign
currency. Now changes in the value of the foreign currency can well change the receivable or
payable value and ultimately the value for the company could be affected due to the same.
Thus, it becomes important for the company to hedge its foreign currency with the help of
various derivative contracts that the company can well use in order to well hedge its
exposure. Application of various risk management strategies like VAR has been well applied
in order to well calculate or measure the loss that would be well incurring due to the
investment that has been done. Now this well shows or estimates how much a set of
investment that it might loose with a certain probability, under given set of normal market
conditions and in a specific period of time.
Transaction Exposure
The term transaction exposure is related to the uncertainty level in which the business
is involved in international trade face. The risk that will be faced by a firm after undertaking
the financial obligation concerning the fluctuation of currency exchanges is known as
translation exposure. International business can face capital losses if there is a high level of
vulnerability to shifting exchange rates (Khindanova 2015). The firm can save themselves

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from a high level of exposure related to exchange rate by implementing a hedging strategy.
Hedging strategy consists of currency swaps or currency future. By implementing a hedging
strategy, the firms can use forward rates that will allow the firm to choose a profitable rate of
currency exchange and will be beneficial for avoiding exposure to risk. Generally, the risk of
translation exposure is one-sided, that is business that completes a transaction in foreign
currency is only subject to vulnerability. The business that is dealing with home currency is
not subject to transaction exposure risk. In general, view the risk that are well associated in
association with fluctuation of exchange rate increases if time is more between agreement
and the contract settlement. In short, transaction exposure is the level of risk that is faced by
companies that are dealing in international trade. If the exposure is high related to exchange
rate fluctuation, then the company is going to bear a major loss in international trade.
Comparing the short-term and long-term impact on cash flow changes because of
forex fluctuation in the market will be equivalent in comparing the transaction and economic
exposure. Specific driving factors of transaction risk are future receivables or payables
related to foreign currency, whereas economic exposure is driven by future currency cash
flow or outflows (Arize et al. 2018). Often companies are exposed to cash flow variability
depending on the exchange rate fluctuations. Cash flow risk might be short-term as well as
long-term depending on their nature. Cash flow management is taken into consideration for
managing the transaction exposure (Disatnik, Duchin and Schmidt 2014). The transaction
that has contracted for drives the transaction exposure and is short-term in nature. In short,
the company has already undertaken a risk on cash flow. Risk of the exposure is limited to
the contract or as per the transaction as discussed earlier. The more easily identifiable risk
associated with foreign exchange is the transaction risk. A company faces transaction risk
only when it undertakes or enter into a contract that involves future receivables or payables in
terms of foreign currency. It is assumed that the scope of transaction risk is narrow. The

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nature of transaction exposure is tactical and technical in nature. Most company usually
prefers to hedge transaction exposure to experience less risk.
Companies trading in international investment would be well creating an exposure in
terms of currency volatility. The movement in the exchange rate affect the returns when there
is a change in the value of one currency when compared against another currency, and
ultimately, this leads to an increase or decrease in the value of an asset. Dealing in the
domestic market will only need to evaluate only the increase or decrease in the asset value.
However, while dealing in international trade, the company needs to analyze the impact of
exchange rate on the contract. In short, we can say that if domestic currency depreciates we
can purchase less of foreign currency and it ultimately reduces the purchasing power (Dong,
Kouvelis and Su 2014). If the domestic currency appreciates, we can buy more foreign
currency, thus, increasing purchasing power. Hedging can be done by various methods such
as forward contracts, options, exchange-traded fund, and contract for difference. The
companies try to hedge the currency risks as it can rapidly dissolve profits and mainly in
times of high volatility.
Assessment of Exposure via VAR
Maximum One-Day Loss
The Celia Company has provided consulting services to a Mexican firm and for this;
the company will receive 10 million Mexican pesos (MXP). The company is interested to
evaluate the maximum one-day loss that is because of the decline in the peso value and the
analysis will be done on 95% confidence level. The level of standard deviation that is related
to the daily percentage of Mexican is expected to be 1.2% during the last 100 days. The
maximum one-day loss can be estimated by left tail of the probability distribution if the daily
level of percentage changes are distributed on a normality basis. The standard deviation is

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approximately 1.65 away from the estimated percentage change in the value of peso. In this
case, we are assuming an expected percentage change of 0% for the next day and thus
calculating the maximum one-day loss.
Maximum one day loss = E (et ) - ( 1.65 X σ MXP)
= 0% - (1.65 X 1.2%)
= - 0.0198 or – 1.98%
The spot rate of peso is assumed $.09. Therefore, the maximum one-day loss of –
1.98% evaluates a peso value of-
Value of Peso based on maximum one-day loss = S X [1 + E (et )]
= $ 0.09 X [1 + (- 0.0198)]
= $ 0.088218
The maximum one-day loss of the peso value decrease to $ 0.088218. Celia’s position
in Mexican pesos will be responsible for the maximum one-day loss of the dollar value. If we
assume that Celia is having MXP 10 million then the value of dollar 900,000 (at dollar 0.09
per peso) will be obtained. Therefore, a decrease in the value of peso – 1.98% will give a loss
of dollar 900,000 * - 1.98% = - $ 17,820.
Factors Affecting Maximum One-Day Loss
For evaluating a currency maximum one-day loss, three factors can be taken into
consideration. First, the maximum one-day loss mainly depend on the expected percentage
change in the currency value. In this case, we are assuming expected outcome is -2% rather
than 0%. Hence, based on this expected outcome we are calculating maximum loss during the
one-day time.

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Maximum one-day loss = E (et ) - ( 1.65 X σ MXP)
= - 2% - (1.65 X 1.2%)
= - 0.0218 or – 2.18%
Second, the confidence level used is one of the significant factor on which the
maximum one-day loss is dependent. If there is a high confidence level, this will result in
more amount of maximum one-day loss by keeping the other factors constant. In this case if,
we use 97.5-confidence level instead of 95% confidence level. The standard deviation from
the expected percentage change in peso assumed to 1.96. Thus, evaluating maximum one-day
loss.
Maximum one day loss = E (et ) - ( 1.96 X σ MXP)
= 0% - (1.96 X 1.2%)
= - 0.02352 or – 2.352%
Lastly, standard deviation related to daily percentage fluctuation in the currency rate
will determine the maximum one-day loss. The standard deviation of the peso is assumed 1%
rather than 1.2 % and by taking this data, we are evaluating the maximum one-day loss.
Maximum one day loss = E (et ) - ( 1.65 X σ MXP)
= 0% - (1.65 X 1%)
= - 0.0165 or – 1.65%
VAR Method of Currency Portfolio
Benou is a US based exporting firm and is expecting to receive payment in Indonesian
rupiah and Thai baht in one month. By taking into consideration, the present day spot rate the

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value of the dollar of funds that is to be received by the company is assumed to be $600,000
for the rupiah and $400,000 for the baht. Benou Company uses a currency portfolio of
weighted 60 percent in rupiah and weighted 40 percent in baht (Fang and Miller 2014). The
company is well aware about the fact that there will be decline in the value of these
currencies and thus it wants to identify the maximum one-day month loss by taking into
consideration 95 percent confidence level (Mumtaz and Theodoridis 2015). By analyzing the
past 20 months data, the company estimates that, the standard deviation of monthly
percentage fluctuation of the rupiah is 7 percent and 8 percent for bath. It is estimated that a
correlation coefficient of 0.50 exist between the rupiah and baht. Therefore, calculating the
portfolio standard deviation by taking into account all the relevant information.
Σt = √ (0.36) (0.0049) + (0.16) (0.0064) + 2(0.60) (0.40) (0.07) (0.08) (0.50)
= about 0.0643, or about 6.43%
It is well-known fact is there is normal distribution of the percentage change in the
currency value then the percentage change of the portfolio must also be normally distributed.
A lower boundary profitability distribution is used to calculate the maximum one-month loss
of the currency portfolio. A standard deviation of 1.65 is estimated to be away from the
expected percentage change in the portfolio of the currency. In this case, we are assuming
that 0 percent expected change for each currency over the next month and calculating the
maximum one-month loss.
Maximum one-month loss of currency portfolio = E (et ) - ( 1.65 X σP)
= 0% - (1.65 X 6.43%)
= about – 0.1061, or about -10.61%
Comparison of maximum one-month loss

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Maximum one-month loss of rupiah = 0% - (1.65 X 7%)
= - 0.1155, or -11.55%
Maximum one-month loss of baht = 0% - (1.65 X 8%)
= - 0.132, or -13.2%
By evaluating the above data, we conclude that the maximum one-month loss for the
portfolio is comparatively less than the maximum one-month loss calculated for individual
currency this is because of the diversification effects (Goddard, Kita and Wang 2015). It is
estimated that if one currency bears maximum loss in a given month the other currency will
not likely will bear the maximum loss in that same month (Krol 2014). The diversification
benefits will be higher if there is less correlation between the movements in the two currency
(Tsen 2014). Based on the above calculation Benou.Inc might go for a hedging option related
to its rupiah position, baht position, neither position, or both position.
Drawbacks of VAR
VAR method assumes that exchange rate movement’s distribution is normal. The
methods claims that if the distribution of the exchange rate fluctuation is abnormal the
findings of the maximum expected loss will be subject to some error factors (Mittnik 2014).
Even this concept states that standard deviation of the exchange rate is stable overtime (Lwin,
Qu and MacCarthy 2017). It is assumed that if in case the exchange rate movement reflects
less deviation in the past as compared to the future the approximated expected loss calculated
by using the VAR method will be underestimated (Changqing, Yanlin and Mengzhen 2015).
Conclusion
The analysis has been well conducted with the help of the various risk management
strategies that can be well implemented by companies for the purpose of well mitigating the

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forex risk that the company faces in their operation. VAR as a key risk management tool can
be well applied for the purpose of well managing or estimating a loss given a set of
probability.
References
Arize, A.C., Andreopoulos, G.C., Kallianiotis, I.N. and Malindretos, J., 2018. MNC
transactions foreign exchange exposure: An application. International Journal of Economics
& Business Administration (IJEBA), 6(1), pp.54-60.
Changqing, L., Yanlin, L. and Mengzhen, L., 2015. Credit portfolio risk evaluation based on
the pair copula VaR models. Journal of Finance and Economics, 3(1), pp.15-30.
Disatnik, D., Duchin, R. and Schmidt, B., 2014. Cash flow hedging and liquidity
choices. Review of Finance, 18(2), pp.715-748.
Dong, L., Kouvelis, P. and Su, P., 2014. Operational hedging strategies and competitive
exposure to exchange rates. International Journal of Production Economics, 153, pp.215-
229.
Fang, W. and Miller, S.M., 2014. Does financial development volatility affect industrial
growth volatility?. International Review of Economics & Finance, 29, pp.307-320.
Goddard, J., Kita, A. and Wang, Q., 2015. Investor attention and FX market
volatility. Journal of International Financial Markets, Institutions and Money, 38, pp.79-96.
Khindanova, I., 2015. Analysis of Foreign Currency Transaction Exposure Using
Simulations. The Journal of Applied Business and Economics, 17(4), p.46.
Krol, R., 2014. Economic policy uncertainty and exchange rate volatility. International
Finance, 17(2), pp.241-256.

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Lwin, K.T., Qu, R. and MacCarthy, B.L., 2017. Mean-VaR portfolio optimization: A
nonparametric approach. European Journal of Operational Research, 260(2), pp.751-766.
Mittnik, S., 2014. VaR-implied tail-correlation matrices. Economics Letters, 122(1), pp.69-
73.
Mumtaz, H. and Theodoridis, K., 2015. The international transmission of volatility shocks:
An empirical analysis. Journal of the European Economic Association, 13(3), pp.512-533.
Tsen, W.H., 2014. Exchange rate volatility and international trade. J Stock Forex Trad, 3(2).

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Appendix
Assessment of Exposure via VAR
Particulars Formuala
s
Maximum one day loss:
E (et ) -
( 1.65 X σ
MXP)
0% - (1.65 X 1.2%)
-0.0198
Peso value based on maximum one-day loss
S X [1 + E
(et )]
$ 0.09 X [1 + (- 0.0198)]
$ 0.08821800
Factors Affecting Maximum One-Day Loss
Maximum one-day loss at 95% Level
E (et ) -
( 1.65 X σ
MXP)
Whereby E(et) is assumed to be -2%
- 2% - (1.65 X 1.2%)
-2.18%
Maximum one-day loss at 97.5% Level
E (et ) -
( 1.96 X σ
MXP)
0% - (1.96 X 1.2%)
-2.352%
Change of SD to 1% from 1.2%
Maximum one day loss
E (et ) -
( 1.65 X σ
MXP)
0% - (1.65 X 1%)
-1.65%

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Assessment of Exposure via VAR
Particulars Formualas
Standard Deviation of Portfolio
Σt = √ (0.36)
(0.0049) +
(0.16)
(0.0064) +
2(0.60)
(0.40) (0.07)
(0.08) (0.50)
Standard Deviation of Portfolio 6.428%
Maximum one-month loss of currency portfolio
E (et ) -
( 1.65 X
σP)
0% - (1.65
X 6.43%)
-10.61%
Maximum one-month loss of Rupiah
E (et ) -
( 1.65 X
σP)
0% - (1.65
X 7%)
-11.55%
Maximum one-month loss of Baht
E (et ) -
( 1.65 X
σP)
0% - (1.65
X 8%)
-13.20%