International Financial Management 10

 Calculating VaR. Assume that Rt+1 ~ N(, 2), where the mean and variance are constant (in general, not a realistic assumption, since volatility changes over time). Then, VaRt+1(1 – ) =  + zα *  where zα represents the quantile from the standard normal distribution at level . Example: Suppose an FX trader, trading the CHF/USD, assumes ef,t ~ N(=-0.000886, 2 = (0.03151)2). The calculation of  and  was done using monthly data. Then, the FX trader calculates the maximum expected loss next month (T = 1) with a 99% confidence level as (zα .01 = -2.33): VaRt+1(.99) = -0.000886 – 2.33 * 0.03151 = -0.0743043. If the value of the portfolio traded is CHF 1.45 M, then the CHF VaRt+1(.99) for the portfolio is: CHF VaR(mean, .99) = CHF 1.45 M * (-0.0743043) = CHF -0.1077412 M. ¶ If the mean and the variance are time-varying, the VaRt+1 becomes: VaRt+1(1 – ) = t+1 + zα * t+1 Example: Suppose the FX trader, based on time-varying model of the variance, believes the variance next month will increase to 5%. Then, VaRt+1(.99) = -0.000886 – 2.33 * 0.05 = -0.117386. The CHF VaRt+1(.99) for the portfolio will be: CHF VaR(mean, .99) = CHF 1.45 M * (-0.117386) = CHF -0.1702097 M. ¶  Extending VaR to T periods: √T Rule So far we have calculated the VaR at 1 period (above, 1 month) horizon. Suppose, we want to calculate for T periods, say T=10 (as required by bank regulators). Following the logarithmic approximations discussed in the text, the risk is scaled by √T and the mean is scaled by T. Example: Suppose an FX trader, trading the CHF/USD, assumes monthly ef,t ~ N(=-0.000886, 2 = (0.03151)2). Now, the FX trader wants to calculate the maximum expected loss in the next 3 months (T = 3) with a 99% confidence level as (zα .01 = -2.33): VaRt+1(.99) = 3 (-0.000886) – 2.33 *√3 * 0.03151] = -0.12982. ¶ This rule is very simple, but it relies on the assumption that (logarithmic) returns are independent. For example, in the previous example, we implicitly assumed monthly changes in the CHF/USD are independent. But, if the independence assumption is not true, the √T rule is not precise. If the correlation is positive, as it tends to be for many assets, the rule tends to underestimate the risk in the portfolio. IFM-LN.137 CHAPTER 10 - BONUS COVERAGE III: The Normal Distribution Suppose the random variable X has a probability distribution function (pdf) given by: fX(x) = [1/(22)1/2] exp{-(x – )2/(22)} - < x < , where  and 2 are any number such that -<<, and 0<2<. Then X is said to follow a normal distribution. We will use the following notation: X ~ N(,2). The pdf has the following bell shape: If Z follows a standard normal distribution then Z ~ N(0,1), that is the green function in the above graph. Using the formula of the pdf normal distribution, tables for the cdf of the standard normal distribution have been tabulated. 1.B.1 Useful Results of the Normal Distribution Let X ~ N(,2). Then, (i) (ii) (iii) (iv) (v) E[X] =  (the mean, , is also the mode and the median) Var(X) = E[(X - )2] = 2 The shape of the pdf is a symmetric bell-shaped curve centered on the mean. Let Z = (X - )/. Then, Z  N(0,1). If  and ß are any numbers with  < ß, then P[< X < ß] = P[( - )/ < Z < (ß - )/] = FZ[(ß - )/] - FZ[(-)/], where FZ represents the cdf of Z. IFM-LN.138 Example: Let X be the annual stock returns (in percentage points) in the U.S. Assume that X ~ N(11.44,16.222). (The mean and variance of X have been obtained from annualizing the 1980-1990 U.S. weekly mean return and variance.) Suppose you are the manager of a portfolio that tracks the U.S. Index. You want to find the probability that your portfolio's return (X) is lower than -30% next year (i.e., a market crash). P[X < -30]= P[Z < (-30 - 11.44)/16.22]= P[Z < -2.55]= FZ[-2.55]= 1 - FZ[2.55]= 1 - .9878 = .0122. That is, the probability that next year stock return is lower than 30% is 1.22%. ¶ Example: Go back to the previous example. Now, suppose you want to determine a minimum return, say %, with probability .95. That is, you want to find the probability that your portfolio's return exceeds a level  with probability .05. That is, .95 = P[X > ] = P[Z > ( - 11.44)/16.22] = 1 - FZ[( - 11.44)/16.22]. From the Normal Table, we obtain that if FZ(z) = .05, z = -1.645. Then, ( - 11.44)/16.22 = -1.645   = 11.44 - 1.645 (16.22) = -15.323. That is, there is a 95% probability that next year's portfolio return will be bigger than -15.323%. ¶ Using the above properties, it is very easy to construct confidence intervals for the random variable X, which is normally distributed with mean  and variance 2. The key to construct confidence intervals is to select an appropriate z value, such that X [  zα/2 ] with a probability (1 – ). For a 99% confidence interval (i.e, =.01) z is equal to 2.58. For a 98% confidence interval (i.e, =.02) z is equal to 2.33. For a 95% confidence interval (i.e, =.05) z is equal to 1.96 (2). For a 90% confidence interval (i.e, =.10) z is equal to 1.645. IFM-LN.139 Chapter 10 - Measuring FX Exposure – Part 2 Three areas of FX exposure - Transaction exposure: associated with specific transactions (in FC). - Economic exposure: associated with futures cash flows –true exposure for owners. - Translation exposure: associated with a firm's consolidated statements. TE is simply to calculate: Value in DC of a specific transaction denominated in FC. We can measure TE, and analyze the sensitivity of TE to changes in St. Use a statistical distribution or a simulation. The less sensitive TE is to St, the lower the need to pay attention to changes in St. MNCs have measures for NTE for: - A single transaction - All transactions (Netting + taking into account comovements of transactions. A portfolio approach) The portfolio approach incorporates correlations. Recall that the co-movement between two random variables can be measured by the correlation coefficient. The correlation between the random variables X and Y is given by: Corr(X,Y) = XY = XY/(YY). Interpretation of the correlation coefficient (xy [-1, 1]): - If xy = 1, X changes by 10%, Y also changes by 10%. - If xy = 0, X changes by 10%, Y is not affected --(linearly) independent. - If xy = -1, X changes by 10%, Y also changes by -10%. Figure 10.1: Co-movement by Major Currencies (1999-2012) IFM-LN.140 Note: As shown in Figure 10.1, currencies from developed countries tend to move together -i.e., positive correlations. But, there are periods where the correlations can be quite negative. • MNC take into account the correlations among the major currencies to calculate NTE  Portfolio Approach. A U.S. MNC: Subsidiary A with CF(in EUR)>0 Subsidiary A with CF(in GBP)<0 GBP,EUR is very high and positive. Net TE might be very low for this MNC. Hedging decisions: Not made transaction by transaction. Rather, they are made based on the exposure of the portfolio. Example: Swiss Cruises. Net TE (in USD): USD 1 million. Payment: 30 days. Loan repayment: CAD 1.50 million. Payment: 30 days. St = 1.47 CAD/USD. CAD,USD = .924 (1990-2001) Swiss Cruises considers the Net TE (overall) to be close to zero. ¶ Note: As seen in the previous graphs, currencies tend to move together, but not always. Correlations vary a lot across currencies. In general, regional currencies are highly correlated. From 2000-2007, the GBP and EUR had an average correlation of .71, while the GBP and the MXN had an average correlation of -.01. Correlations also vary over time. Correlation: GBP/US D and MXN/US D 4-year Correlation 1 0.5 0 1985 1990 1995 2000 2005 -0.5 -1 • Sensitivity Analysis for Portfolio Approach Do a simulation. That is, assume different scenarios (pay attention to the correlations!) Example: IBM has the following CFs in the next 90 days Outflows Inflows St GBP 100,000 25,000 1.60 USD/GBP EUR 80,000 200,000 1.05 USD/EUR IFM-LN.141 Net Inflows (75,000) 120,000 NTE (in USD) = EUR 120,000 * 1.05 USD/EUR + (GBP 75,000) * 1.60 USD/GBP = = USD 6,000 (this is our baseline case) Situation 1: Assume GBP,EUR = 1. (The correlation between the EUR and the GBP is high.) Scenario (i): EUR appreciates by 10% against the USD (ef,EUR=.10) Since GBP,EUR = 1, St = 1.05 USD/EUR * (1 + .10) = 1.155 USD/EUR St = 1.60 USD/GBP * (1 + .10) = 1.76 USD/GBP NTE (in USD) = EUR 120,000*1.155 USD/EUR+(GBP 75,000)*1.76 USD/GBP = = USD 6,600. (10% change = USD 600) Scenario (ii): EUR depreciates by 10% against the USD (ef,EUR=-.10) Since GBP,EUR = 1, St = 1.05 USD/EUR * (1 – .10) = 0.945 USD/EUR St = 1.60 USD/GBP * (1 – .10) = 1.44 USD/GBP NTE (in USD) = EUR 120,000*1.155 USD/EUR + (GBP 75,000)*1.76 USD/GBP = = USD 5,400. (-10% change = USD -600) Now, we can specify a range for NTE  NTE  [USD 5,400, USD 6,600] Note: The NTE change is exactly the same as the change in St. If a firm has matching inflows and outflows in different currencies –i.e., the NTE is equal to zero-, then changes in St do not affect NTE. That’s very good. Of course, we will draw more than 2 scenarios, say 10,000 draws for ef,EUR and then draw a histogram with the 10,000 NTEs. Finally, we can draw a (1-α)% Confidence interval. Situation 2: Suppose the GBP,EUR = -1 (NOT a realistic assumption!) Scenario (i): EUR appreciates by 10% against the USD (ef,EUR=.10) Since GBP,EUR = -1, St = 1.05 USD/EUR * (1+.10) = 1.155 USD/EUR St = 1.60 USD/GBP * (1-.10) = 1.44 USD/GBP NTE (in USD) = EUR 120,000*1.155 USD/EUR+(GBP 75,000)*1.44 USD/GBP = = USD 30,600. (410% change = USD 24,600) Scenario (ii): EUR depreciates by 10% against the USD (ef,EUR=-.10) Since GBP,EUR = -1, St = 1.05 USD/EUR * (1-.10) = 0.945 USD/EUR St = 1.60 USD/GBP * (1+.10) = 1.76 USD/GBP NTE (in USD) = EUR 120,000*0.945 USD/EUR+(GBP 75,000)*1.76 USD/GBP = = (USD 18,600). (-410% change = USD -24,600) IFM-LN.142 Now, we have a range for NTE  NTE  [(USD 18,600), USD 30,600] Note: The NTE has ballooned. A 10% change in exchange rates produces a dramatic increase in the NTE range. Having non-matching exposures in different currencies with negative correlation is very dangerous. Again, we will draw more than 2 scenarios, say 10,000 and then draw a histogram with the 10,000 NTEs. Finally, we can draw a (1-α)% Confidence interval. In both situations, given the high correlations, IBM only draws one variable (ef,EUR). For the other situations, where the correlation is not very high, IBM will draw from the empirical distribution (ED). In this case, IBM will randomly draw pairs together –say, ef,EUR & ef,GBP- and then calculate NTE’s for each draw. ¶ Alternatively, IBM can assume a distribution (say, bivariate normal) with a given correlation (estimated from the data) and, then, draw many scenarios for the St’s to generate an empirical distribution for the NTE. From this simulated distribution, IBM will get a range –and a VaR- for the NTE. Real World: Walt Disney Company’s VALUE AT RISK (VaR) According to Disney’s 2006 Annual Report: The Company utilizes a VaR model to estimate the maximum potential one-day loss in the fair value of its interest rate, foreign exchange and market sensitive equity financial instruments. The VaR model estimates were made assuming normal market conditions and a 95% confidence level. Various modeling techniques can be used in a VaR computation. The Company’s computations are based on the interrelationships between movements in various interest rates, currencies and equity prices (a variance/co-variance technique) These interrelationships were determined by observing interest rate, foreign currency, and equity market changes over the preceding quarter for the calculation of VaR amounts at fiscal 2006 year end. The estimated maximum potential one-day loss in fair value, calculated using the VAR model, is as follows (unaudited ,in millions): Interest Rate Sensitive Financial (in millions) Instruments VaR (year end 05) USD 22 Average VaR USD 19 Highest VaR USD 22 Lowest VaR USD 18 Currency Sensitive Financial Instruments USD 10 USD 13 USD 15 USD 10 Equity Sensitive Financial Instruments USD 1 USD 0 USD 1 USD 0 Combined Portfolio USD 21 USD 22 USD 24 USD 18 10.2 Measuring Economic Exposure EE: Risk associated with a change in the NPV of a firm's expected cash flows, due to an unexpected change in St. IFM-LN.143 Very general definition: It can be applied to any firm (domestic, foreign, MNC, exporting, importing, purely domestic, etc.). Q: How can we measure the degree to which CFs are affected by unexpected ef,t? A: Remember Random Walk. All changes in St are unexpected. Example: On February 2, 2015, Owens-Illinois (OI), the giant U.S. manufacturer of glass containers, reported its fourth-quarter results. OI reported that sales declined 9% year over year to USD 1.6 billion due to a stronger USD that adversely impacted sales by 6%. OI forecasted that, in 2015, earnings will be negatively impacted by the strong USD. The strong USD is expected to reduce translated sales by nearly 10%. This is economic exposure. ¶ • The degree to which a firm is subject to EE depends on: - The type and structure of the firm - The industry structure in which the firm operates. In general, importing and exporting firms face a higher EE than purely domestic firms do. Industry structure is also very important. In general, monopolistic firms face lower EE than firms that operate in competitive markets. Example: Suppose a U.S. firm face almost no competition in the domestic market. This U.S. firm is able to transfer to its prices almost any increase of its costs due to changes in St. Thus, this firm faces no EE, since its CFs are unaffected by changes in St. ¶ But, the degree of EE for a firm is an empirical question. • Economic exposure is: - Subjective. - Difficult to measure. Idea: To measure EE we need to relate future cash flows to changes in St. 1. A Measure Based on Accounting Data It requires to estimate the net cash flows of the firm (EAT or EBT) under several FX scenarios. (Easy with an excel spreadsheet.) Example: IBM HK provides the following info: Sales and cost of goods are dependent on St IFM-LN.144 Sales (in HKD) Cost of goods (in HKD) Gross profits (in HKD) Interest expense (in HKD) EBT (in HKD) St = 7 HKD/USD 300M 150M 150M St = 7.70 HKD/USD 400M 200M 200M 20M 130M 20M 180M EBT (in USD) at St = 7 HKD/USD: USD 130M/7 HKD/USD = USD 18.57M EBT (in USD) at St = 7.7 HKD/USD: USD 180M/7.70 HKD/USD = USD 23.38M A 10% depreciation of the HKD, increases the HKD cash flows from HKD 130M to HKD 180M, and the USD cash flows from USD 18.57M to USD 23.38 or a 25.9%. Q: Is EE significant? A: We can calculate the elasticity of CF to changes in St. For example, in USD, a 10% depreciation of the HKD produces a change of 25.9% in EBT, for a 2.59 elasticity. It looks quite significant. But you should note that the change in exposure is USD 4.81M. This amount might not be very significant for IBM! A judgment call maybe needed here. ¶ 2. An Easy Measure of EE Based on Financial Data Sometimes, accounting data are not very relevant, since it measure the past. EE deals with futures cash flows. If available, changes in stock prices –i.e., returns- should be used –recall that stock prices measure discounted future cash flows. • We want to measure the correlation between CF and ef,t. ⇒ we can use the correlation coefficient between CF and ef,t. Example: Kellogg’s and AT&T’s EE. Using monthly stock returns for Kellogg’s (Kret) and monthly changes in St (USD/TWC) from 1/19847/2019, we estimate ρK,e (correlation between Krett and ef,t) = 0.102. TWC represents a Trade Weighted Basket of Major Currencies. It looks small, but away from zero. We do the same exercise for AT&T (T), obtaining ρT,e = 0.026, small and close to zero. ¶ Interesting result: Correlations between returns and changes in exchange rates are time-varying. Recessions and crises affect the relation. Below, in Figure 10.2, we calculate the 12-month rolling correlation between the S&P returns and percentage changes in the USD/TWC, ef,t, from 1991:Jan to 2019:Jan: IFM-LN.145 Figure 10.2: Correlation between S&P returns & changes in the USD/TWC 1984-2019 1 2‐yr Rolling Correlations: USD/TWC & SP500 0.8 0.6 0.4 0.2 0 ‐0.2 1/1/2019 10/1/2017 7/1/2016 4/1/2015 1/1/2014 10/1/2012 4/1/2010 7/1/2011 1/1/2009 7/1/2006 10/1/2007 4/1/2005 1/1/2004 10/1/2002 7/1/2001 4/1/2000 1/1/1999 7/1/1996 10/1/1997 4/1/1995 1/1/1994 10/1/1992 7/1/1991 4/1/1990 1/1/1989 7/1/1986 10/1/1987 4/1/1985 1/1/1984 ‐0.4 After the financial crisis of 2007-2008, there is a higher correlation between stock returns and changes in exchanges rates. The average correlation is 0.173, which does not seem to be representative of the whole sample. It is better to run a regression on CF against unexpected ef,t, it gives us a test. Steps: (1) Collect data on CF and St (available from the firm's past). (2) Estimate the regression: CFt =  + ß ef,t + t,  ß measures the sensitivity of CF to changes in ef,t.  the higher ß, the greater the impact of ef,t on CF.  the higher R2, the greater the explanatory power of ef,t. (3) Test for EE  H0 (no EE): β = 0 H1 (no EE): β ≠ 0 (That is, evaluation of regression: t-statistic of ß and R2.) Note: One thing to do: Replace CFt by stock returns. A better measure. Stock returns measure changes in discounted future cash flows. Example: Kellogg’s EE. Now, using the data from the previous example, we run the regression: Krett =  + ß ef,t + t, R2 = 0.0104 Standard Error = 2.575 Observations = 424 Intercept (α) ef,t (β) Coefficients Standard Error t Stat P-value 0.3043 0.1263 0.0164 2.409 0.4787 0.2266 0.0353 2.112 IFM-LN.146 We reject H0, since |tβ = 2.11| > 1.96 (significantly different than zero). Note, however, that the R2 is low! (The variability of ef explains 1% of the variability of Kellogg’s returns.) ¶ Example: AT&T’s EE. Now, using AT&T data, we run the regression: Trett =  + ß ef,t + t, R2 = 0.0007 Standard Error = 2.714 Observations = 424 Intercept (α) ef,t (β) Coefficients Standard Error t Stat P-value 0.2005 0.1331 1.506 0.133 0.1299 0.2388 0.587 0.544 We cannot reject H0, since |tβ =-0.54| < 1.96 (not significantly different than zero). The R2 is extremely low: the variability of ef explains less than 0.1% of the variability of AT&T’s returns. ¶ • Sometimes the impact of St is not felt immediately by a firm.  contracts and short-run costs (short-term adjustment difficult). Example: For an exporting U.S. company a sudden appreciation of the USD increases CF in the short term. But, later, the export contract will be renegotiated. Run a modified regression: CFt =  + β0 ef,t + ß1 ef,t -1 + ß2 ef,t -2 + ß3 ef,t -3 + …+ ßQ ef,t-Q + t.  Sum of the β’s measures the sensitivity of CF to changes in St (ef,t). ¶ Practical issue: number of lags (Q in the modified regression)? Usual practice: include at most two years of information. Example: Kellogg runs the following regression to estimate EE with lags (t-stats in parenthesis): CFt = .006 + .478 ef,t + .264 ef,t -1 + .180 ef,t -2. R2= .045. (1.90) (2.87) (1.97) (1.08) K's CF (in USD) sensitivity to ef,t is 0.742 (= .478 +. 264).  a 1% depreciation of the USD increases CF (in USD) by 0.742%. ¶ • Note on regressions to measure EE Changes in St (et,t) is not the only variable affecting a company’s stock returns. A company grows, adds assets, then higher sales and EPS are expected. Also, the economy and the stock market grow over time. We need to be careful and “control” for these other variables, to isolate the effect of ef,t. A multivariate regression will probably be more informative, where we can include other independent (“control”) variables (income growth, inflation, sales growth, assets growth, etc.), not just ef,t as determinants of the change in CFs (or stock returns). IFM-LN.147 We can also borrow from the investments literature and use the three popular Fama-French factors (Market, Size (SMB), Book-to-Market (HML)) as controls. Then, we can run a regression to check if a company faces EE: Stock Returnt = α + β ef,t + δ1 Market Returnt + δ2 HMLt + δ3 SMBt + εt Example: Using the Fama-French Factors to compute Kellogg’s EE. Now, using the data from the previous example, we run the regression: Krett =  + β ef,t + δ1 Market Returnt + δ2 HMLt + δ3 SMBt + εt R2 = 0.1291 Standard Error = 2.424 Observations = 424 Intercept Market (Rm-Rf) Size (SMB) B-M (HML) ef,t (β) Coefficients Standard Error 0.12321 0.21486 -0.08947 0.03758 0.12455 0.12172 0.02864 0.04086 0.04338 0.21855 t Stat 1.012 7.501 -2.190 0.866 0.570 P-value 0.3120 3.79E-13 0.0291 0.3868 0.5690 Note: You can find this, the previous (K and IBM) and other (DIS) examples in my homepage: www.bauer.uh.edu/rsusmel/4386/kellogg-euro.xls A higher R2 is mainly due to the market factor. But, looking at EE, once we control for other factors (the FF factors), we cannot reject H0, since |tβ = 0.57| < 1.96 (not significantly different than zero); that is, at the 5%, we cannot reject the null hypothesis of no economic exposure. ¶ Evidence: The above regressions have been done repeatedly for firms around the world. (Without the FF factors, we have already done it for AT&T and with the FF factors, we have already done it for Kellogg.) On average, for large firms (MNCs) EE is small –i.e., β is small– and not significant at the 5% level. See Ivanova (2014).  Real World Example: Economic Exposure - The Case of Ericsson Ericsson is very dependent on the behavior of the SEK and on economic conditions in Sweden. Around 40% of all employees and 25% of total production is located in Sweden, but Sweden accounts for just 3% of all sales. With this substantial cost base in SEK, for example, an appreciation of the SEK against the major currencies will have a negative impact on Ericsson’s cash flows. As a matter of fact, during the year 2000, the depreciation of the EUR against the SEK had a negative impact on Ericsson compared to Ericsson’s competitors with costs denominated in EUR. Usually, Ericsson does not hedge economic exposure. Source: Ericsson Annual Report 2000.  IFM-LN.148 Chapter 10 - Measuring FX Exposure – Part 3 Review from Chapter 10 – Parts 1 & 2: - TE is easy to calculate: Value in USD of specific transaction or portfolio of transactions. ⋄ Correlations are very important. - EE more difficult to measure. More subjective. ⋄ Accounting-based measures need to simulate EAT under different St. ⋄ Finance-based measures need CFt & ef,t. Use returns, not CFt. Run a regression. - Testing for EE: Use a linear regression model. ⋄ Stock Returnst =  + β ef,t + t H0 (No EE):  = 0 H1 (EE):   0. Evidence: For large companies (MNCs, Fortune 500),  is not significantly different than zero. We cannot reject Ho: No EE.  In this class, we will cover the last FX exposure: Translation exposure. 10. 3 Translation Exposure Translation exposure: Risk from consolidating assets and liabilities measured in foreign currencies with those in the reporting currency. Assets and liabilities in a FC must be restated in terms of a DC. This translation follows rules set up by a parent firm's government or an accounting association –in the U.S., FASB. Problem: The translation involves complex rules that sometimes reflect a compromise between historical and current exchange rates. - Historical rates may be used for some equity accounts, fixed assets, inventories. - Current exchange rates are used for current assets, liabilities, expenses and income. Every item translated at historical rates is not exposed to changes in St. Note: Different exchange rates are used, imbalances will occur. Key issue: What to do with the resulting imbalance? It is taken to either current income or equity reserves. Measuring Translation Exposure There are several methods to translate foreign currency accounts into the reporting currency. Two methods that predominate: ⋄ Temporal method (monetary/nonmonetary method) IFM-LN.149 ⋄ Current rate method Terminology: Monetary: there is a date attached to the asset or liability. Three exchange rates can be used: S0: Historical exchange rate. St: Current exchange rate at the date of balance. SAVERAGE: Average exchange rate for the period. FASB #8 - Temporal Method (1976-1982) - Translate nonmonetary assets at S0, assets and liabilities use St - Translate most income statements items at the SAVERAGE - Translate shareholder equity at S0 - Bookkeeping exchange gains or losses are passed to the Income statement FASB #52 - Current Rate Method (since 12/15/1982) - Translate most amounts at St - Income statement items are translated at S0 or SAVERAGE - Translate shareholder equity at S0 - Exchange gains or losses are not reflected in income statement rather accumulate in an adjustment account in stockholders' equity: Cumulative translation adjustment (CTA). - Distinguished between functional currency (usually local currency) and reporting currency (currency the parent firm uses to prepare own financial statements) - Exceptions to FASB #52 1. Subsidiaries who mostly deal with parent, functional currency: USD  translate using temporal method. 2. Subsidiaries in hyperinflationary countries (100%+ over 3 years), functional currency: USD  translate using temporal method. IFM-LN.150 Example: IBM Hong Kong has the following balance sheet. IBM Hong Kong Balance Sheet in millions of HKD Balance CR Temporal accounts exposure exposure Assets Cash Accounts receivable Inventory Net fixed plant and equip. Total assets Total exposed assets 300 850 400 1,000 2,550 Liabilities and Capital Accounts payable Notes payable Long-term debt Shareholder's equity Total liabilities and capital Total exposed liabilities 200 300 900 1,150 2,550 Net exposed assets 300 850 400 1,000 300 850 N.ex. N.ex. 2,550 1,150 200 300 900 N.ex. 200 300 900 N.ex. 1,400 1,400 1,150 -250 At St = .128 USD/HKD, IBM’s exposure (in USD): Current rate method: HKD 1,150,000,000 x .128 USD/HKD = USD 147,200,000 Temporal method: HKD -250,000,000 x .128 USD/HKD = USD -32,000,000 ¶ Real World Example: Translation Exposure - The Case of Ericsson Ericsson has many subsidiaries outside Sweden. For most of the subsidiaries, the local currency is the currency in which the companies operate  local currency is the functional (business) currency. Financial statements are translated to SEK using the current rate method. For some subsidiaries (“integrated companies”), having very close relations with the Swedish operations  SEK is the functional currency Financial statements are translated using the temporal method. Translation exposure in foreign subsidiaries is hedged:  Monetary net in companies translated using the temporal method (translation effects in investment affecting the income statement) is hedged to 100%.  Equity in companies translated using the current rate method (translation effects reported directly in stockholders’ equity in the balance sheet) is hedged selectively up to 20% of the total equity. The translation differences reported in equity, during the year 2000, were SEK 2.0 billion, mainly due to a weaker SEK. Source: Ericsson Annual Report 2000. IFM-LN.151