FOREIGN EXCHANGE EXPOSURE AND THE TERM- STRUCTURE OF INDUSTRY COST OF EQUITY

Transcription

1 FOREIGN EXCHANGE EXPOSURE AND THE TERM- STRUCTURE OF INDUSTRY COST OF EQUITY Alain Krapl* Carmelo Giaccotto* January 2012 ABSTRACT Using a single-factor Global CAPM (GCAPM) and a two-factor International CAPM (InCAPM), we study the effect of foreign exchange (FX) exposure on the term structure of industry cost of equity of 39 U.S. industries. Following Ang and Liu (2004), we estimate the term structure of industry expected returns at the end of 2010 and find that: 1) Capturing FX exposure in the asset-pricing model changes the position and shape of the spot discount curves; 2) The average industry FX risk premium is around 3% for cash flows with short-term maturities (around 24% of the total industry cost of equity); 3) For most industries the FX risk premium declines with increasing cash flow maturities; 4) The pricing error from ignoring the term structure is substantially larger than the pricing error resulting from the omission of the FX risk component. Keywords: Cost of Capital Term Structure, Foreign Currency Risk Premium, Global Market Risk Premium, Industry Cost of Capital, Short-term and Long-term Foreign Currency Exposure, Global CAPM, International CAPM JEL Classifications: G12, G15; EFM Classification Code: 330 We thank Tom O Brien for his helpful comments. *Department of Finance, University of Connecticut, School of Business, 2100 Hillside Road, Storrs, CT ; akrapl@business.uconn.edu, ; cgiaccotto@business.uconn.edu,

2 FOREIGN EXCHANGE EXPOSURE AND THE TERM- STRUCTURE OF INDUSTRY COST OF EQUITY I. Introduction and Motivation Current advances in the international asset pricing literature indicate that foreign exchange (FX) risk is a priced factor at the industry level. Francis, Hasan and Hunter (2008), use a conditional version of the three-factor Fama and French model and find that all of the industries used in their sample (36 U.S. industries) have a significant currency risk premium that adds about 2.47% to the industry cost of equity. The authors argue that methodological weakness rather than FX hedging explains the paucity of results in previous industry-level studies. The empirical findings of FHH (2008) differ substantially from previous studies which generally fail to find significant pricing effects of FX shocks at the industry-level 1. Applying a multifactor APT setting, Jorion (1991 finds that the unconditional FX risk premium is small and statistically insignificant for U.S. Industries. Bodnar and Gentry (1993) study the FX exposures and characteristics of Canadian, Japanese and U.S. industries. Although their study finds that FX rates partially explain industry returns on the economy-wide level, only 20% to 35% of industries show significant FX exposures. Griffin and Stulz (2001) observe that FX rate shocks explain almost nothing of relative industry performance. Studying factors affecting the FX 1 Several studies find that FX risk is priced on the aggregate market level (see Bartram and Bodnar, 2007 for an inclusive summary of literature). Dumas and Solnik (1995) use unconditional and conditional asset pricing models to study equity and currency returns in Germany, U.S., U.K., and Japan. The use of the conditional asset pricing model indicates that FX exposure is priced on the aggregate stock market level. De Santis and Gérard (1998) utilize a conditional international CAPM with a GARCH parameterization and find evidence of time-varying global market and FX risk; however their findings suggest that for the U.S. market the FX risk premium is only a small fraction of the total risk premium. Carrieri, Errunza and Majerbi (2006) employ a conditional setting to study equity returns of 10 developed and 12 developing countries. CEM (2006) also find that FX risk is a significant component of equity returns and spillover effects are exist during emerging market crises. 2

3 exposure of U.S. manufacturing industries, Allayannis and Ihrig (2001) find that only 4 out of 18 industries are exposed to FX risk. Contrasting the multi-industry studies is Williamson s (2001) work on the U.S. and Japanese automotive industries. Williamson (2001) incorporates changes in the industry competitive environment and finds substantial time-varying FX exposure. A puzzling empirical result is reported by Choi and Prasad (1995), who find significant FX exposure on individual firm level but report a substantial loss in significance when firms are aggregated into industry portfolios (this has become part of the FX exposure puzzle 2 ). Our paper provides two contributions to the current literature. First, using the methodology of Ang and Liu (2004), we estimate cost of equity term structures for 39 U.S. industries. Estimating industry discount rate curves with and without an FX risk component allows us to study the FX risk premiums for expected cash flows with varying maturities. Thus we gain insight into the term structure of the FX risk premium itself. Second, the provided methodology can be applied to value projects assuming that the risk free rate, the price of global market risk, the price of FX risk, the global market exposure coefficient and the FX exposure coefficient of an industry change over time. This is particularly valuable since it is unlikely that over the long horizons of many capital budgeting problems valuation parameters remain unchanged. The dynamic nature of the market risk premium is documented by Jagannathan, McGratten and Scherbina (2000) who observe a decline in the risk premium after Similarly, Fama and French (2002) document substantial changes in the U.S. market risk premium between 1872 and Fama and French (1997) report substantial time-variation in factor loadings of asset pricing models for industry portfolios. This is consistent with the 2 See Bartram and Bodnar (2007) for an inclusive discussion of the FX Exposure Puzzle. 3

4 observation that risk profiles of industries change over time. Among others, Allayannis and Ihrig (2001) find time-variation in industry FX exposure coefficients, whereas De Santis and Gérard (1998) find that dynamic conditional moments are not sufficient to capture the pricing effects of FX risk in conditional models with GARCH parameterizations. The later study stresses the importance of including time-varying prices of global market and FX risk. Francis, Hasan and Hunter (2008) find similar results using industry returns and two currency factors (developed and developing country currencies). In addition to the time-varying nature of asset pricing parameters, several papers in the FX literature suggest that short-term FX exposure is economically different from long-term FX exposure. Chow, Lee and Solt (1997), find that using long-horizon data captures FX exposures more clearly 3. By studying earnings data of industries they find that interest rate and cash-flow effects are offsetting over short horizons but complementary over long horizons, which leads to negative short-run but positive long-run FX exposures. Bredin and Hyde (2011) decompose the FX exposures of industry portfolios into a cash flow and discount rate component and find that many U.S. and foreign industries are subjected to cash flow and discount rate FX exposures. The study also finds that for U.S. industries unexpected changes in FX rates mainly affect discount rate news, which indicates that such FX shocks are transitory in nature. Conversely FX shocks for industries in most of the other G7 countries primarily affect industry cash flows and are therefore permanent. Changing industry FX exposure coefficients, a dynamic price of FX risk and differences between short-run and long-run industry FX exposures are likely to result in a time-varying FX risk premium. 3 Chow, Lee and Solt (1997) observe that if FX changes contain information about future interest rates and future expected cash flows that are further than a one-period horizon, short-term FX exposure estimates will not capture the full picture. Bodnar and Wong (2003) attribute increase in statistical significance of FX exposures based on longer-horizon data mainly to reduced noise in the FX exposure estimates. 4

5 We use conditional versions of the one-factor Global CAPM (GCAPM) and the twofactor International CAPM (InCAPM) to estimate industry discount rate spot curves for the end of our sample period (December 2010). We find that, on average, the industry FX risk premium is around 3% (or roughly 24% of total industry cost of equity) for expected cash flows with maturities around 2 to 3 years. The FX risk premium then declines monotonically with increasing cash flow maturities and reaches zero around years 16 to 17. Consequently, we find that omitting the FX risk premium leads to mispricing that peaks for projects with 6 to 7-Year maturities (omitting the FX component results in about 7% overpricing), assuming a constant stream of expected cash flows. On the other hand including a constant FX risk premium leads to a substantial under-pricing error (20.31% for a 30-Year annuity). We expand our empirical analysis and estimate spot discount curves for a pooled industry portfolio using a rolling 10-year estimation window for the period 1988 to We find an average industry FX risk premium of 2.56% that monotonically declines for cash flows with longer maturities. The organization of this paper is as follows. Section two describes estimating industry cost of equity term structures. Section three describes the data and empirical specifications. In section four we presents empirical estimates of industry spot discount curves and discuss the results and implications, whereas in section five we conclude. II. Estimating the Term Structure of Expected Returns Using the methodology developed by Ang and Liu (2004), we estimate spot discount rates for 39 U.S. industries and a pooled industry portfolio. Assuming that the expected return of a security is given by: 5

6 where is the price and is the cash flow which follows a specified process then the price of a security can be expressed as: If expected returns and cash flow growth rates are time varying and correlated, i.e. the simplifying assumptions of a Gordon model are violated, equation (2) has to be evaluated directly. Whereas Ang and Liu (2004) use the conditional CAPM model, we use two alternative conditional model specifications: 1) a single-factor Global CAPM (GCAPM) and 2) a two-factor International CAPM (InCAPM). where is the log expected return, is the risk-free rate, is the time-varying beta, is the time-varying FX exposure coefficient, is the time-varying price of global market risk, and is the time-varying price of currency risk. To take the expectation in (2) we need to estimate the evolution of,,, and the cash flows of the security: InCAPM we define a state vector:. For our analysis using the two-factor. The corresponding state vector for the single-factor GCAPM does not include the conditional FX exposure coefficients and the time-varying price of currency risk. We assume that the state vector follows a VAR(1) process and expected log returns take the form: 6

7 where and is a symmetric matrix of the following form 4 : Then the spot expected return is given by: where is a scalar, is a vector 5 and is a symmetric matrix. The coefficients of equation (3) are given by:, and are based on the following recursions: 4 To obtain for the single-factor GCAPM, we reduce the dimensions of the matrix by dropping the last two columns and bottom two rows. 5 is a vector, is a and is a column vector for the single-factor GCAPM. 7

8 represents a column vector of zeros with a 1 in the first place and the following are the initial conditions for the recursions. III. Empirical Specification and Data We use a sample of Nasdaq-, AMEX- and NYSE-traded firms between January 1978 and December 2010 to construct our spot discount curves. Using monthly data from CRSP on stock returns with and without dividends, stock prices and number of shares outstanding, we assign firms to 39 value-weighted industry portfolios based on their two-digit SIC classifications. We 8

9 follow the convention of Francis, Hasan and Hunter (2008) and use the industry classifications suggested by Bodnar and Gentry (1993). To estimate dividend cash flow growth rates, we compute monthly industry portfolio dividends as the difference between the portfolio value-weighted monthly returns with, and without dividends from CRSP. Annual dividend growth is calculated as where is the 12-month rolling sum of monthly dividends. Table I provides selected summary statistics of monthly portfolio log returns and annualhorizon dividend growth of 39 value-weighted industry portfolios. Mean and standard deviations are reported in percentage terms. The sample period spans Jan 1978 to Dec 2010 and includes firms traded on Nasdaq, AMEX and the NYSE. The columns titled Auto show the AR(1) coefficients of industry returns and industry dividend growth. Average monthly industry returns are positive during the sample period and range from 0.6 % to 1.6%. The industries with the lowest average monthly log returns are Air Transport with 0.612% and a monthly return standard deviation of 8.507%. Similar performing industries are Stone, Clay and Concrete (0.633% and SD of 8.065%), Textile Mill Products (0.671% and SD of 7.294%) and Heavy Construction other than Buildings (0.683% and SD of 8.010%). The sample industries with the highest returns are Business Services (1.630% and SD of 8.257%), Motion Pictures (1.582% and SD of %) and Tobacco (1.528% and SD of 6.704). The average industry portfolio based on pooled data has an average monthly return of 0.999% with a standard deviation of 5.506%. Portfolio return autocorrelations range from for Petroleum Refining to for Building Construction. The average industry portfolio has positively autocorrelated earnings 9

10 (0.190). The average annualized dividend growth rate is % with a standard deviation of 2.293%. [Insert Table I approximately here] In order to estimate the time-varying betas and FX exposure coefficients for our analysis based on the two-factor InCAPM, we use the following Fama MacBeth (1973) 60-month rolling period regressions: where is the value-weighted portfolio log return, is the observed and noisy price of global market risk at time. is the log return of the global market that is measured by the MSCI World Index 6. Similarly, where are the log returns of a foreign currency basket. 7 All returns are continuously compounded. For our analysis based on the single-factor GCAPM we omit from the regressions, resulting in timevarying estimates of. Table II reports the averages and standard deviations of the univariate (single-factor GCAPM) and bivariate betas (two-factor InCAPM) as well as gamma estimates (FX exposure coefficients). The average industry univariate beta is with a standard deviation of Industries with the lowest time-varying betas include Public Utilities (0.612 with and SD of 0.097), Tobacco (0.756 with an SD of 0.089) and Communications (0.793 with an SD of 0.089). Conversely, Building Construction (1.714 with an SD of 0.322), Electronic Equipment except 6 Although the MSCI All Country Index captures a larger portion of all markets in the world and therefore is more closely a true global index, the data is only available after January We choose to proxy global market returns with the MSCI World index that mainly covers developed countries but is available from January 1970 forth. 7 are the log returns of a foreign currency basket (the U.S. Fed s Major Currency Basket (MCI)) expressed in USD terms. This specification is needed because, the risk-free rate that is being subtracted is expressed in USD terms. 10

11 Computers (1.480 with an SD of 0.160) and Apparel & Accessory Stores (1.433 with an SD of 0.162). Bivariate Beta estimates are higher than their univariate counterparts. The average industry has a bivariate beta of with a standard deviation of Bivariate beta estimates range from to Industries with high univariate betas also have high bivariate betas. The average industry has a FX exposure (gamma) of with a standard deviation of With the exception of Metal Mining (0.081 with an SD of 0.266), all average time-varying gamma coefficients are negative. Industries with the lowest-magnitude FX exposures include Metal Mining, Public Utilities ( with an SD of 0.104), Oil and Gas Extraction ( with an SD of 0.264), and Chemicals ( with an SD of 0.099). On the other end of the spectrum are Apparel and Accessory Stores ( with an SD of 0.276), Hotels ( with an SD of 0.272) and Air Transport ( with an SD of 0.216). [Insert Table II approximately here] We estimate the price of global market risk and the price of currency risk using a set of instrumental variables. There is a wide array of informational instruments that are commonly used in the asset pricing literature 8. Dumas and Solnik (1995) use lagged equity index returns, dividend yield, a January effect dummy, a U.S. bond yield, and a short Euro deposit rate. Similarly, De Santis and Gérard (1998) include the dividend of the world index (MSCI), the change in the U.S. term premium, the change in the Eurodollar deposit rate, and the U.S. default premium. We follow Francis, Hasan and Hunter (2008) and use the Federal Funds rate (FED); the term premium (TERM), which is the difference in yields of the U.S. Treasury constant- 8 Among others, see Bekaert and Hodrick (1992) for an investigation of equity and FX excess returns. Also, Ferson and Harvey (1993) find that much of the equity excess return predictability can be attributed to changing price of global market risk. 11

12 maturity 10-Year and the 1-Year notes; and the default premium (DEF), which is the yield spread between Moody s Baa and Aaa- rated bonds, as instruments for the price of global market risk. The set of instruments for the price of currency risk includes: the Federal Funds Rate (FED); the Export Ratio (EXP), which is the ratio of U.S. exports to U.S. GDP; and the Import Ratio (IMP), which is the ratio of U.S. exports to U.S. GDP. In addition to the set of informational instruments we take advantage of potential autocorrelations in the prices of global market and currency risk and add the lagged values of and to the information set. The estimated coefficients of the following two models are used to generate the fitted values of and : where is the observed and noisy price of global market risk for time. is the log return of the global market that is measured by the MSCI World Index. Similarly, where are the log returns of a foreign currency basket. Table III reports the regression results of the price of global market and currency risk regressions (23) and (24). The adjusted are 1.91% for the price of global market risk and 5.83% for the price of currency risk. P-values are based on Newey-West HAC standard errors. Robust Wald statistics reject the null hypothesis of no explanatory power in the predictive equations at the 95% confidence level. [Insert Table III approximately here] 12

13 IV. The Term Structure of Industry Expected Returns In this section we present term structure discount rates for our sample industries and a pooled industry portfolio. In the first subsection we present industry discount rate spot curves for the end of our sample period, December In the second subsection we estimate pricing errors due to: 1) omitting the FX risk component and 2) ignoring the term structure of the cost of equity estimates. In the last subsection we expand our analysis of the FX risk premium and use a rolling 10-year window to estimate the discount rate spot curves for the pooled industry portfolio for the end of each month between 1988 and December We find an average FX risk premium of 2.56% that is monotonically decreasing for cash flows with increasing maturities. A. Estimated Industry Spot Discount Rates December 2010 In Figure 1 we present the estimated spot discount rate curves for the average industry in our sample (pooled industry portfolio). We report the spot discount rate curves at the end of our data sample, December The solid line shows the term structure of the average industry cost of equity using the single-factor GCAPM. The dotted line is the average industry cost of equity including an FX component (two-factor InCAPM). The top Panel of Figure 1 shows that the average industry s cost of equity term structure is upward sloping based on the single-factor GCAPM model. The cost of equity term structure changes by adding an FX component to the asset pricing model. Still upward sloping at the beginning, it slowly becomes inverted with increasing maturities. Consistent with Francis, Hasan and Hunter (2008), we find that FX exposure adds an economically significant positive premium to the average industry s cost of equity. Comparing cost of equity term structures, rather than two constant cost-of-equity measures, we find that the 13

14 FX risk premium plays a more important role for expected cash flows with shorter maturities. Panel B in Figure 1 depicts the distribution of the FX risk premium for the average industry in our sample along a 30-year term structure. We find that the FX risk premium reaches a peak for expected cash flows with a 2- to 3-year maturity. The risk premium then declines and reaches zero for expected cash flows occurring with a 17-year maturity. The average industry has a zero to slightly negative FX risk premium for long-term cash flows expected to occur more than 17 years in the future. As Ang and Liu (2004) point out, spot discount rate curves can take on a variety of shapes similar to the term structures of interest rates. Despite being a snapshot, our initial results illustrate that the FX risk premium is economically significant and perhaps more complex than expected. The bottom panel of Figure 1 suggests that industry FX risk premiums also have term structures. This can lead to two different FX-risk related pricing errors: First, omitting the FX risk component altogether; and second, assuming a constant FX risk premium for cash flows across all maturities. [Insert Figure 1 approximately here] Using the two-factor InCAPM, we present spot discount rate term structures for all industries in our sample in Table IV. In contrast to the term structures of industry cost of equity based on the single-factor GCAPM, including the FX component creates term structures that are more upward sloping for short-term maturities but then slightly downward sloping for the longterm maturities. There are substantial differences in the levels of industry cost of equity but many of the industries display similarly shaped cost of equity term structures. Figure 2 shows the industry cost of equity based on the single-factor GCAPM and the two-factor InCAPM for a subsample of industries. Similar to the results in Figure 1, we observe 14

15 upward sloping single-factor GCAPM cost of equity term structures and more complex twofactor InCAPM discount rate curves. Figure 3 shows the FX risk premiums for out selected subsample. Similar to the results in Figure 1, many of the industries have a positive FX risk premium for short-term expected cash flows. However, the FX risk premium then declines substantially and in some cases becomes slightly negative. The Air Transport industry has a substantial FX risk premium, about 5% for short maturity expected cash flows. The FX risk premium then declines at a decreasing rate, reaching zero for expected cash flows with 13-year maturities and then tapering off to about -1% for long-term maturity expected cash flows. In contrast, the Hotel industry has a positive 4% FX risk premium that declines to zero for 23-year maturity cash flows and remains zero for the remaining horizon. B. Omitting the FX Component Differences in Pricing In this subsection we investigate the economic significance of two potential pricing errors: 1) omitting of the FX risk component; 2) ignoring the term-structure of the FX risk premium. We use our end-of-2010 cost of equity term structures to price a $1 end-of-year annual cash flow stream. We compare valuation results using estimates based on the single-factor GCAPM and the two-factor InCAPM models. In Table V, we compute the value of a 30-Year $1 annuity at the end-of-2010 using: 1) The discount rate term structure that captures the FX component (InCAPM_Term); 2) the discount rate term structure that ignores industry FX exposure (GCAPM_Term); 3) the constant cost of equity based including an FX exposure component (InCAPM_Con); and 4) the constant cost of equity ignoring the FX exposure (GCAPM_Con). We define mispricing as: 15

16 where wrong is the dollar value of the annuity that either omits the FX component or assumes a constant industry cost of equity. We define correct as the dollar value of the annuity using the discount rate term structure that also captures industry FX exposure (based on the two-factor InCAPM). Considering pricing errors (30-Year $1 annuities) for the pooled industry portfolio, we observe that the largest error results from ignoring the term structure characteristics of the industry cost of capital and the FX risk premium. Assuming that the cost of capital and FX risk premium are constant will result in substantial under pricing of the 30-year annuity (20.31%). On the other hand, ignoring the FX component of the cost of equity term structure will result in a more modest pricing error (4.83% overpricing due to the omission of the positive FX premium). As we will point out in the next paragraph, this picture is somewhat misleading, given our specific discount rate spot curves on December There are fairly large differences in pricing errors among our selected subsample of industries. Ignoring the time-variation of the FX risk premium in the Air Transportation industry would result in a 40.11% under pricing error. On the other hand ignoring the FX risk premium in the Hotel industry would result in a 8.56% over valuation. [Insert Table V approximately here] In Figure 4 we investigate the pricing error of the pooled industry portfolio that results from the omission of the FX component. The top part of Figure 4 shows the marginal pricing error or the pricing error that is a results for a particular cash flow that is expected a time ( is depicted on the horizontal axis). Similarly to our analysis above we assume that the correct model is based on using the discount rate spot curve that includes the FX component (using the two factor InCAPM). The wrong model ignores the FX risk premium altogether (using the one 16

17 factor GCAPM). The top graph shows that expected cash flows with maturities around 6 years are overpriced by about 9.5%, whereas longer-maturity cash flows (around 26 years) are under priced by about 4%. The bottom graph of Figure 4 depicts the total pricing error of a $1 annuity with maturity. As reported in Table V, the pricing error of a 30-year annuity is slightly below 5%. The largest mispricing error is for 7 to 8-year maturities, here ignoring the FX premium will result in a 7% overpricing error. [Insert Figure 4 approximately here] Figure 5 reports the pricing errors for a subgroup of our sample. Similar to the pooled industry portfolio, we can see that the pricing errors decline for longer-maturity annuities. Pricing errors are more substantial for shorter maturities, although there are substantial differences among the industries in our subsample. Most of the depicted industries have overpricing errors for short-to mid-term annuities (7% to 9% overpricing). Industrial Machinery and Real Estate have less substantial overpricing; around 3% for short-term annuities. Figure 5 shows that the pricing errors across industries differ substantially for longer-term maturities. [Insert Figure 5 approximately here] C. Average Industry Discount Spot Curves 1988 to 2010 In this subsection we expand our analysis and estimate discount rate term structures for the pooled industry portfolio. We use a rolling 10-year period to estimate the price of global market risk, the price of currency risk, and the VAR system in Equation (5). This allows us to obtain average industry cost of equity term structures for each month between 1988 and Our analysis focuses mainly on the shape and magnitude of the FX risk premium. To 17

18 limit the risk of distortions by outliers, we trim the spot discount rate estimates by 5% for each time-to-maturity. The top panel of Figure 6 shows the average industry term structures for the pooled industry portfolio for the period 1988 to The solid line is based on the single-factor GCAPM, whereas the dashed line includes the FX risk component (two-factor InCAPM). Although the average term structure is downward sloping, the shape and magnitude of the FX risk premium is consistent with our initial findings. We find a positive industry FX risk premium that is on average 2.56% and monotonically decreasing for cash flows with longer maturities. The FX risk premium is 3.67% for cash flows with a 1-Year maturity and then declines to 1.77% for 30-Year maturity cash flows. We consider shorter sub-periods in Figure 7 and estimate average industry term structures for five sub-periods. Consistent with Francis, Hasan and Hunter (2008) we find substantial timevariation in the FX risk premium. In addition we also find substantially different FX risk premium term structures, depending on the sub-period of interest. Consistent with our previous findings, we observe that during the late 1980s and early 1990 s (and between 2006 and 2010) the FX risk premium was decreasing for increasing maturities. Conversely, we observe an increasing FX risk premium term structure for most of the 1990s. [Insert Figure 6 and Figure 7 approximately here] V. Conclusions In this study we use conditional versions of the one-factor Global CAPM (GCAPM) and the two-factor International CAPM (InCAPM) to estimate industry discount rate spot curves. The main contributions of our paper are: 1) Study the effects of FX exposure on the industry cost 18

19 of equity term structure; 2) Provide a valuation framework that captures the industry s FX exposure and allows pricing parameters to change during the valuation horizon. Using an industry sample based on Bodnar and Gentry (1993), we find that, on average, the industry FX risk premium is around 3% (or roughly 24% of total industry cost of equity) for expected cash flows with maturities around 2 to 3 years. The FX risk premium then declines monotonically with increasing cash flow maturities and reaches zero around years 16 to 17. Some of our sample industries even display slightly negative FX risk premiums for long-term maturity cash flows. Consequently, we find that omitting the FX risk premium leads to mispricing that peaks for projects with 6 to 7-Year maturities (omitting the FX component results in about 7% overpricing), assuming a constant stream of expected cash flows. On the other hand including a constant FX risk premium leads to a substantial under-pricing error (20.31% for a 30- Year annuity). In addition to the snapshot of industry term structures at the end of 2010, we also use a rolling 10-Year window and estimate discount rate spot curves for our pooled industry portfolio for the period 1988 to We observe a positive industry FX risk premium that is on average 2.56% and monotonically declines for cash flows with increasing maturities. We find that the term structure of the FX risk premium changes substantially depending on the sub-period of choice (as a consequence of changing spot discount curves). Future research could study changes in industry cost of capital term structures and its potential determinants. In addition further insight into the FX risk premium itself, particularly the short-term and long-term nature of industry FX exposure, would be very valuable. 19

20 References Adrian, T. and F. Franzoni, 2002, Learning about Beta: An Explanation of the Value Premium, Working Paper MIT Allayannis, G. and J. Ihrig, 2001, Exposure and Markups, Review of Financial Studies 14, Ang, A. and J. Chen, 2002, CAPM over the Long-Run: , Journal of Empirical Finance 14, Ang, A. and J. Liu, 2004, How to Discount Cashflows with Time-Varying Expected Returns, Journal of Finance 59, Bartram, S. M., and G. M. Bodnar, 2007, The Exchange Rate Exposure Puzzle, Managerial Finance 33, Bekaert, G., and R.J. Hodrik, 1992, Characterizing Predictable Components in Excess Returns on Equity and Foreign Exchange Markets, Journal of Finance 47, Bodnar, G.M. and W.M. Gentry, 1993, Exchange Rate Exposure and Industry Characteristics: Evidence from Canada, Japan, and the USA, Journal of International Money and Finance 12, Bodnar, G.M. and F.M.H. Wong, 2003, Estimating Exchange Rate Exposures: Issues in Model Structure, Financial Management 32, Bredin, D. and S. Hyde, 2011, Investigating Sources of Unanticipated Exposure in Industry Stock Returns, Journal of Banking and Finance 35, Carrieri, F., V. Errunza and B. Majerbi, 2006, Does Emerging Market Exchange Risk affect Global Equity Prices? Journal of Financial and Quantitative Analysis 41, Choi, J.J. and A.M. Prasad, 1995, Exchange Risk Sensitivity and its Determinants: A Firm and Industry Analysis of U.S. Multinationals, Financial Management 24, Chow, E.H., W.Y. Lee, and M.E. Solt, 1997, The Exhange-Rate Risk Exposure of Asset Returns, Journal of Business 70, De Santis, G., and B. Gérard, 1998, How Big is the Premium for Currency Risk? Journal of Financial Economics 49, Dumas, D. and M. Solnik, 1995, The World Price of Foreign Exchange Risk, Journal of Finance 50,

21 Fama, E.F. and K.R. French, 1997, Industry Cost of Equity, Journal of Financial Economics 43, Fama, E.F. and K.R. French, 2002, The Equity Risk Premium, Journal of Finance 57, Fama, E.F. and J. MacBeth, 1973, Risk, Return, and Equilibrium: Empirical Tests, Journal of Political Economy 81, Ferson, W.E., and C.R. Harvey, 1993, The Risk and Predictability of International Equity Returns, Review of Financial Studies 6, Francis, B.B, I. Hasan, and D.M. Hunter, 2008, Can Hedging Tell the Full Story? Reconciling Differences in United States Aggregate- and Industry-Level Exchange Rate Risk Premium, Journal of Financial Economics 90, Griffin, J., and R. Stulz, 2001, International Competition and Exchange Rate Shocks; A Cross- Country Industry Analysis of Stock Returns, Review of Financial Studies 14, Jagannathan, R., E.R. McGrattan and A. Scherbina, 2000, The Declining U.S. Equity Premium, Quarterly Review, Federal Reserve Bank of Minneapolis 24, Jorion, P., 1991, The Pricing of Exchange Rate Risk in the Stock Market, Journal of Financial and Quantitative Analysis 64, Newey, W. and K. West, 1987, A Simple, Positive Semi-Definite, Heteroscedasticity and Autocorrelation Consistent Covariance Matrix, Econometrica 55, Williamson, R. 2001, Exchange Rate Exposure, Competitiveness, and Firm Valuation: Evidence from the World Automotive Industry, Journal of Financial Economics 59,

22 Table I Selected Industry Portfolio Summary Statistics Table I reports summary statistics mean, standard deviation (SD), and first-order autocorrelation coefficients (Auto) for portfolio log returns and dividend growth. Monthly log returns are reported for 39 value-weighted industry portfolios (following Bodnar and Gentry, 1993). Dividend growth is has a monthly frequency but an annual horizon and is calculated as where is the 12-month rolling sum of monthly industry dividends that are calculated as the difference between monthly CRSP returns with and without dividends (Ang and Liu, 2004). Average industry is based on a pooled all-industry portfolio. The data includes all firms traded on the NYSE, Nasdaq and Amex between January 1978 and December 2010 with a minimum of 36 consecutive observations per firm. Industry Returns Dividend Growth SIC Industry Name Mean SD Auto Mean SD Auto 10 Metal Mining Oil and Gas Extraction Building Construction Heavy Construction other than Buildings Food and Kindred Products Tobacco Textile Mill Products Apparel & Other Clothes Lumber and Wood Furniture and Fixtures Paper and Allied Products Printing and Publishing Chemicals Petroleum Refining Rubber and Misc. Plastics Leather and Leather Products Stone, Clay and Concrete Primary Metals Fabricated Metal Products except Machines Industrial Machinery & Computer Equipment Electronic Equipment except Computers Transport Equipment Instruments Railroad Transport Motor Freight Transportation Water Transport Air Transport Communication Public Utilities Wholesale Trade, Durable Goods General Merchandise Stores Food Stores Apparel & Accessory Stores Eating and Drinking Places Miscellaneous Retail Real Estate Hotels Business Services Motion Pictures Average Industry

23 Table II Time-Varying Univariate, Bivariate Betas and Gammas Table II reports the mean and standard deviation (SD) for the beta estimates of the single-factor Global CAPM (GCAPM): and the beta and gamma estimates of the two-factor International CAPM (InCAPM):, where are the excess returns of the value-weighted industry portfolio; is the excess return of the global market portfolio; and is the excess return of a trade-weighted foreign currency portfolio. The betas and gammas are estimated with 60-month rolling period regressions (Fama and MacBeth, 1973) with a monthly frequency. The data used for the estimation of our rolling-period regressions spans from February 1973 to December Univariate Beta Bivariate Beta Gamma SIC Industry Name Mean SD Mean SD Mean SD 10 Metal Mining Oil and Gas Extraction Building Construction Heavy Construction other than Buildings Food and Kindred Products Tobacco Textile Mill Products Apparel & Other Clothes Lumber and Wood Furniture and Fixtures Paper and Allied Products Printing and Publishing Chemicals Petroleum Refining Rubber and Misc. Plastics Leather and Leather Products Stone, Clay and Concrete Primary Metals Fabricated Metal Products except Machines Industrial Machinery & Computer Equipment Electronic Equipment except Computers Transport Equipment Instruments Railroad Transport Motor Freight Transportation Water Transport Air Transport Communication Public Utilities Wholesale Trade, Durable Goods General Merchandise Stores Food Stores Apparel & Accessory Stores Eating and Drinking Places Miscellaneous Retail Real Estate Hotels Business Services Motion Pictures Average Industry

24 Table III Predicting the Risk Premiums Table III reports coefficients (and robust p-values), robust Wald statistics and adjusted values for the predictive regressions for the global market risk premium (GRP) and the foreign currency risk premium (CRP). We use the following two regression equations to model the ex-ante risk premium expectations: and, where and are the monthly log excess returns of the global market portfolio and the foreign currency portfolio. Following Francis, Hasan and Hunter (2008) we use the one-period lagged predictive instruments: The U.S. Federal Funds rate (FED); the term premium (TERM), which is the yield spread between the constant-maturity 10-Year and 1-Year notes; the default premium (DEF), which is the yield spread between the Moody s Baa and Aaa bonds, the export ratio (EXP), which is the ratio of U.S. exports and U.S. GDP; and similarly the import ratio (IMP), computed as the ratio of imports and GDP. The estimation window spans monthly data from 1971 to 2010 for the GRP, and 1979 to 2010 for the CRP (due to data availability). Panel A: Predicting the Global Market Risk Premium Constant GRP FED TERM DEF Wald GRP p-values Panel B: Predicting the MCI Currency Risk Premium Constant CRP FED EXP IMP Wald CRP p-values

25 FX Risk Premium Cost of Equity Cost of Equity Term Structures Dec % % % 8.00% 7.00% 6.00% Tau Pooled_FX Pooled_No_FX Foreign Currency Exchange Premium Dec % 3.00% 2.50% 1.50% 1.00% 0.50% -0.50% Tau Figure 1. Discount Curves for the Average Industry Portfolio December The top of Figure 1 shows the discount curves, with years on the horizontal axis, computed at the end of our sample period (December 2010). The Pooled_FX discount curve includes the FX component (based on the twofactor InCAPM), whereas the Pooled_NoFX discount curve is based on the single-factor GCAPM. The bottom of Figure 1 shows the FX risk-premium (defined as the difference between the two discount curves). 25

26 Table IV Industry Cost of Equity Term Structures Dec 2010 Table IV reports industry discount rate curves. The cost of equity estimates are based on the two-factor InCAPM and therefore capture the FX risk component. The estimation window includes data from January 1978 to December Discount rate curves are calculated for the end of the sample period. The average estimates are based on an all-industry pooled portfolio. SIC Industry Name Metal Oil Construct Oth.Const Food Tobacco Textile Apparel Wood Furniture Paper Printing Chemicals Refining Rubber Leather Stone Prim.Met Met.Prod Mach.Com Elec.Equ Tran.Equ Instru Rail Motor Water Air Comm Utilities Wholesale Merchand Food.Sto Appar.Sto Rest Misc.Ret RealEst Hotels Bus.Serv Movies Average

27 Building and Construction Air Transportation % 11.00% 9.00% 7.00% 13.00% % % 8.00% 7.00% 6.00% 15_FX 15_NOFX 45_FX 45_NOFX Hotels Industrial Machinery & Computers % 11.00% 11.00% % 8.00% 9.00% 7.00% 7.00% 6.00% 70_FX 70_NOFX 35_FX 35_NOFX Transportation Equipment Real Estate 13.00% 11.00% 17.00% % 9.00% 11.00% 7.00% 9.00% 7.00% 37_FX 37_NOFX 65_FX 65_NOFX Figure 2. Selected Industry Discount Curves December Figure 2 shows the discount curves, with years on the horizontal axis, computed at the end of our sample period (December 2010), for a sub-sample of six industries: 1) SIC 15 Building and Construction; 2) SIC 45 Air Transport; 3) SIC 70 Hotels; 4) SIC 35 Industrial Machinery & Computers; 5) SIC 37 Transportation Equipment; and 6) SIC 65 Real Estate. The FX discount curve includes the FX component (based on the two-factor InCAPM), whereas the NOFX discount curve is based on the single-factor GCAPM. 27

28 Building and Construction Air Transport 3.00% 3.00% 1.00% 1.00% -1.00% -1.00% % -3.00% Hotels Industrial Machinery & Computers 3.00% 3.00% 1.00% 1.00% -1.00% -1.00% % -3.00% Transportation Equipment Real Estate 3.00% 3.00% 1.00% 1.00% -1.00% -1.00% % -3.00% Figure 3. Selected Industry FX Risk Premiums December Figure 3 shows FX Risk premiums at the end of our sample period (December 2010), for a sub-sample of six industries: 1) SIC 15 Building and Construction; 2) SIC 45 Air Transport; 3) SIC 70 Hotels; 4) SIC 35 Industrial Machinery & Computers; 5) SIC 37 Transportation Equipment; and 6) SIC 65 Real Estate. The FX risk-premium is defined as the difference between the two discount curves based on the GCAPN and InCAPM. 28

29 Table V Mispricing - Single-Factor GCAPM and Two-Factor InCAPM In Table V we value an end-of-year $1 annuity for a window of 30-Years using: 1) The discount curve including the FX component (InCAPM_Term); 2) the discount curve excluding the FX component (GCAPM_Term); 3) the constant cost of equity based on the InCAPM (InCAPM_Con); and 4) the constant cost of equity based on the GCAPM (GCAPM_Con). The valuation date is December The $ value of the 30-Year annuity is shown in bold numbers. The pricing error is defined as: where we assume that InCAPM_Term is the most complete model that captures the FX risk aspect and changing betas, gammas and risk premiums. The mispricing is reported in percentage terms. We report results for the average (pooled) industry and a selected industry sub-sample including: 1) SIC 15 Building and Construction; 2) SIC 45 Air Transport; 3) SIC 70 Hotels; 4) SIC 35 Industrial Machinery & Computers; 5) SIC 37 Transportation Equipment; and 6) SIC 65 Real Estate. InCAPM_Term GCAPM_Term InCAPM_Con GCAPM_Con Average Pooled % % 0.48% Building & Construction % % -2.61% Air Transportation % % % Hotels % % 14.10% Industrial Machinery & Computers % % % Transportation Equipment % % 5.37% Real Estate % 0.92% 24.06% 29