# Presentation: 186991

## Resolving the Exposure Puzzle:The Many Facets of Exchange Rate ExposureFDICOctober 27, 2006

- Söhnke M. Bartram
- Lancaster University
- Gregory W. Brown
- University of North Carolina
- Bernadette A. Minton
- Ohio State University

## Motivation

- FX risk is a major financial risk to nonfinancial firms
- “FX Exposure Puzzle”
- Theoretical models predict exposure
- Bodnar, Dumas and Marston 2002; Marston 2001; Adler and Dumas 1984; Shapiro 1975
- Empirical studies find weak evidence
- Allayannis and Ihrig 2001; Dominguez and Tesar 2001a,b; Griffin and Stulz 2001; Williamson 2001

**Notes:**
A group of over 100 major trade associations which lobbies the U.S. government for a weaker currency. 

## How to Resolve the Puzzle?

- General idea/hypothesis of this paper
- Firms _*do*_ have potentially large FX risk
- Pricing and financial policies reduce exposures
- Analysis
- Use structural models to analyze different facets of FX exposure and hedging
- Gross (or pre-hedging) exposure
- Net (or post-hedging) exposure

## What We Do

- Motivating Example: Global Automotive Industry
- Bodnar and Marston (2002) model
- Detailed firm-level data
- Enhanced Bodnar, Dumas and Marston  (BDM, 2002) model
- Firm selling and producing in local and foreign market
- Exposure is a function of:
- market share
- product substitutability,
- sales and cost in foreign currency
- Use model to analyze a large sample of global manufacturing firms around the world

## Contribution

- Resolving the Exposure Puzzle
- Model predicts that firms should have large gross FX exposures
- Firms reduce these exposures via three channels:
- Pass-through (10%-15%)
- Operational hedging (10%-15%)
- Financial hedging (45%-50%)
- Residual FX exposures (as estimated in the prior literature) are economically and statistically small

## Automotive Industry

- Industry study to motivate main analysis
- 16 auto manufacturers from 6 countries
- Mature and competitive industry
- Important FX risk (Williamson 2001)
- Bodnar and Marston (2002)
*h**1*: percent foreign sales
*h**2*: percent foreign cost
*r  *: profit margin

**Notes:**
Industry data
largest fractions of sales occur in local region
most firms produce more than they sell locally

Firms with lower profit margin will have larger exposures, because the less profitable firm will have the same nominal exchange rate exposure, but a lower firm value.  We also note that this analysis stretches the application of the Bodnar and Marston (2002) model, which is for a monopolist.  We explicitly consider exposure in a model with a competitive product market in the next section. 

## Automotive Companies

|  | Volkswagen |  |  |
| --- | --- | --- | --- |
| Foreign Sales | 0.52 |  |  |
| Margin | 0.16 |  |  |
| Gross Exposure (h2=0)  | 3.29 |  |  |

**Notes:**
Foreign Sales = (Total sales – domestic sales)/Total sales; Euro area for European manufacturers
Margin = GROSS PROFIT MARGIN = Gross Income / Net Sales or Revenues
Gross Income = The difference between sales or revenues and cost of goods sold and depreciation 
Foreign Production = (Total production – U.S. production)/Total Production; Euro area for European manufacturers

Data is from WARD's World Motor Vehicle Data Book 2004	
It is in units (for both production and sales) and is the total of all auto types (passenger, light trucks, medium/heavy trucks and busses)	

If local currency depreciates by 1%, stock price increases by 2%.

## Automotive Companies

|  | Volkswagen |  |  |
| --- | --- | --- | --- |
| Foreign Sales | 0.52 |  |  |
| Margin | 0.16 |  |  |
| Gross Exposure (h2=0)  | 3.29 |  |  |
| Foreign Production  | 0.42 |  |  |
| Model Exposure  | 1.02 |  |  |

## Automotive Companies

|  | Volkswagen |  |  |
| --- | --- | --- | --- |
| Foreign Sales | 0.52 |  |  |
| Margin | 0.16 |  |  |
| Gross Exposure (h2=0)  | 3.29 |  |  |
| Foreign Production  | 0.42 |  |  |
| Model Exposure  | 1.02 |  |  |
| FX-Derivatives  | 0.46 |  |  |
| Foreign Currency Debt  | 0.18 |  |  |
| Residual Exposure  | 0.38 |  |  |

**Notes:**
FX derivatives = Notional Amount of FX derivatives*(1-Tax_Rate) / MarketCapitalization
Foreign currency debt = (Coupon_Rate*Total_Debt/3 + Total_Debt/30) / MarketCapitalization

## Automotive Companies

|  | Volkswagen |  |  |
| --- | --- | --- | --- |
| Foreign Sales | 0.52 |  |  |
| Margin | 0.16 |  |  |
| Gross Exposure (h2=0)  | 3.29 |  |  |
| Foreign Production  | 0.42 |  |  |
| Model Exposure  | 1.02 |  |  |
| FX-Derivatives  | 0.46 |  |  |
| Foreign Currency Debt  | 0.18 |  |  |
| Residual Exposure  | 0.38 |  |  |
| Actual Exposure  | 0.16 |  |  |
| p-value  | [0.270] |  |  |

**Notes:**
Regression: excess stock return on excess market return and change in trade-weighted exchange rate index (weekly data, 2000-2004)

## Automotive Companies

|  | Volkswagen |  | Mean |
| --- | --- | --- | --- |
| Foreign Sales | 0.52 |  | 0.55 |
| Margin | 0.16 |  | 0.22 |
| Gross Exposure (h2=0)  | 3.29 |  | 2.70 |
| Foreign Production  | 0.42 |  | 0.38 |
| Model Exposure  | 1.02 |  | 1.26 |
| FX-Derivatives  | 0.46 |  | 0.41 |
| Foreign Currency Debt  | 0.18 |  | 0.09 |
| Residual Exposure  | 0.38 |  | 0.76 |
| Actual Exposure  | 0.16 |  | 0.18 |
| p-value  | [0.270] |  | [0.291] |

**Notes:**
16 auto companies

## BDM Model

- FX exposure of exporter
- Exposure depends on
- Product market competition => pass-through
**********: degree of product substitutability
**: market share of exporting firm in foreign market
- Operational Hedging
**********: fraction of marginal cost due to foreign currency-based inputs

**Notes:**
Exposure = Conversion effect + market share effect + profit-margin effect

Imperfect competition

U(X1, X2): Consumers (in the foreign market) have CES utility function (constant elasticity of substitution)

Demand functions
P1=D1(X1, X2) = ...
P2=D2(X1, X2) = ...

Market share lambda:

Lambda = P1X1 / Y  (expenditure on firm 1’s products relative to total expenditure on this industry’s products)

## Enhanced BDM Model

- Two extensions of BDM model
- Both firms can have cost in local _and_ foreign currency
- Firm sells globally
- Global firm is sales-weighted average of foreign and domestic operations ( is percent of foreign sales).
- Model is more broadly applicable
- Captures global firms in global markets
- Allows for broader set of exposures and pass-through
- BDM model is a special case of enhanced model

**Notes:**
We examine only quantity competition (firms choose their production/sales quantity), but results for price competition could be derived as well.

Pass-through refers to the effect of the exchange rate on the exporter’s price in foreign currency. Changes in exchange rates should lead to proportionate changes in the exporter’s foreign currency price except for two factors:
First, the exporting firm has foreign-currency based costs, so changes in exchange rates change marginal costs in its home currency. 
Second, changes in exchange rates should cause the exporting firm to change its mark-up.

For both reasons, the pass-through is likely to be less than proportionate.

Exposure and Pass-through are related:
Pass-through has an effect on profitability (margin, profits), profitability affects exposure (defined as the effect of FX risk on profits)

## Enhanced BDM Model

- Foreign Exchange Rate Exposure
- Foreign Exchange Rate Pass-Through

**Notes:**
In delta_f:
First term captures the proportional impact of the exchange rate on profits (simple conversion/translation effect).
Second term captures the impact of the exchange rate on the share of total foreign expenditures accruing to the exporting firm
Third term captures the impact of the exchange rate on the domestic currency profit of the exporter.

## Parameters

|  | Description | Empirical Counterpart |
| --- | --- | --- |
|  | Percent foreign sales  | Percent foreign sales |
|  | Percent domestic sales | 1-Percent foreign sales |
| Exporting firm |  |  |
| γf1 | Firm’s fraction of marginal costs in foreign currency  | Percentage of foreign assets of firm |
| f2 | Import competing firms’ fraction of marginal costs in the foreign market, also foreign currency costs of other exporting firms  | Weighted average of the percentage of domestic assets of foreign firms and percentage of foreign assets of other domestic firms |
| f | Market share of firm in foreign markets  | Rest-of-world GDP-weighted average of import penetration ratio |

**Notes:**
For exporting firm
(domestic = country of exporter, foreign= country of import-competing firms) 
f=foreign market (from perspective of the exporter)

Also: 
1=exporter
2=import-competing firm

gamma_f1 is due to foreign currency inputs or foreign production

Calculation of Gamma_f2:
gamma_d2_avg : Average of the percentage of domestic assets of foreign firms: Weights are the levels of domestic sales (in USD)
gamma_f1_avg: Average of the percentage of foreign assets of domestic firms: Weights are the levels of foreign sales (in USD)

Combine both averages into  gamma_f2 by weighing them with the Importpenetration ratios:
gamma_f2 = ForImportPenetration*gamma_f1_avg + (1-ForImportPenetration)*gamma_d2_avg

BDM use input-output tables to calculate gammas. Japan has disaggregated export and domestic wholesale price indices for many manufactured goods.

## Parameters

|  | Description | Empirical Counterpart |
| --- | --- | --- |
| Import-competing firm |  |  |
| γd1 | Exporters’ fraction of marginal costs in the domestic market, also domestic currency costs of other domestic, import-competing firms  | Weighted average of the percentage of foreign assets of foreign firms and percentage of domestic assets of other domestic firms |
| d2
(=1- γf1) | Firm’s fraction of marginal costs in the domestic currency | 1-Percentage of foreign assets of firm |
| d | Market share of import competing firm in domestic market  | 1-Domestic market import penetration ratio  |
| f, d | Degree of product substitutability in the foreign (f) and domestic (d) markets | Specified exogenously |

**Notes:**
For Import-Competing Firm (domestic=country of import-competitor, foreign=country of exporting firms) 
d=domestic market (from perspective of the import-competing firm)

Also:
1=exporter
2=import-competing firm

Calculation of Gamma_d1
Average of the percentage of domestic assets of domestic firms: Weights are the levels of domestic sales (in USD)
Average of the percentage of foreign assets of foreign firms: Weights are the levels of foreign sales (in USD)

Combine both averages into  gamma_d1 by weighing them with the Importpenetration ratios:
gamma_d1 = ImportPenetration*(1-gamma_d2_avg) + (1-ImportPenetration)*(1-gamma_f1_avg)

## BDM vs. Enhanced BDM

**Notes:**
Exposures in BDM model are between +1 and +3.
BDM model imposes important restrictions, since exposures tend to be small (smaller than +1)

## Automakers Again...

**Notes:**
Foreign Sales = 100%
Delta=3: mu_f=1 (i.e. gamma_f1=0, gamma_f2=1), rho_f=1, lambda_f=0
all sales is abroad, no production/cost abroad, other firms have all their production/cost in that market, products are perfect substitutes, no market share in foreign market, relative cost disadvantage
pass-through=-1 (firm can pass through completely, but other firms cannot)

Delta=-1: mu_f=-1 (i.e. gamma_f1=1, gamma_f2=0), rho_f=1, lambda_f=0
all sales is abroad, all production/cost abroad, other firms have no production/cost in that market, products are perfect substitutes, no market share in foreign market, relative cost advantage
pass-through=0 (no impact of pass-through)

Foreign Sales = 0%
Delta=2: mu_d=1 (i.e. gamma_d1=0, gamma_d2=1), rho_d=1, lambda_d=0
all sales is domestic, no production/cost abroad, other firms have all their production/cost in that market, products are perfect substitutes, no market share in domestic market, relative cost disadvantage
pass-through=0 (no impact of pass-through)

Delta=-2: mu_d=-1 (i.e. gamma_d1=1, gamma_d2=0), rho_d=1, lambda_d=0
all sales is domestic, all production/cost abroad, other firms have no production/cost in that market, products are perfect substitutes, no market share in domestic market, relative cost advantage
pass-through=+1 (firm cannot pass through, but other firms can)

## Sample and Data

- 1,161 manufacturing firms from 16 countries
- Accounting data (USD) (Thomson)
- Market data (LC) (Datastream)
- Import penetration (UNIDO, SSIS)
- Herfindahl indices to measure industry competition (complete Worldscope universe)
- Collect data from annual reports for FX derivatives and foreign currency debt

**Notes:**
Require 25 weekly return observations during the year of the annual report.
Importpenetration = Imports / (Imports + Production)
Importpenetration = Imports / (Imports + Production - Exports) has extreme values for countries with large exports in certain industries.
For competition and importpenetration, we use values the year before the firm observation (if possible).

Regressions of LC stock return on local market and trade-weighted FX index.

## Sample Statistics

|  | Mean | Stdev | 25th Perc. | 75th Perc. |
| --- | --- | --- | --- | --- |
| Actual FX Exposure | 0.071 | 1.945 | -0.806  | 1.058 |
| Foreign Sales | 34.5% | 27.2% | 11.6% | 53.5% |
| Foreign Assets | 19.1% | 21.7% | 2.2% | 27.7% |
| Foreign Debt Dummy | 0.871 |  |  |  |
| FX Derivatives Dummy | 0.659 |  |  |  |
| Import Penetration | 24.1% | 17.3% | 11.9% | 32.8% |

**Notes:**
Actual exposures are zero on average
BDM model imposes important restrictions, since exposures tend to be small (smaller than +1)

Unlever exposures by * Marketvalue of Equity/(Total Debt + Marketvalue of Equity)

## Model Exposures & Pass-Through

|  | Mean | Stdev |
| --- | --- | --- |
| Model Exposure |  |  |
| f =d=0.5 | 0.428 | 0.296 |
| f =d=0.7 | 0.477 | 0.316 |
| f =d=0.9 | 0.549 | 0.355 |
|  |  |  |
|  |  |  |
|  |  |  |
|  |  |  |
|  |  |  |

**Notes:**
Exposures are positive on average, somewhat larger than actual exposures
Pass-through is negative on average (as expected).

## Model Exposures & Pass-Through

|  | Mean | Stdev |
| --- | --- | --- |
| Model Exposure |  |  |
| f =d=0.5 | 0.428 | 0.296 |
| f =d=0.7 | 0.477 | 0.316 |
| f =d=0.9 | 0.549 | 0.355 |
|  |  |  |
| Model Pass-Through |  |  |
| f =d=0.5 | -0.088 | 0.215 |
| f =d=0.7 | -0.061 | 0.218 |
| f =d=0.9 | -0.035 | 0.223 |

**Notes:**
Exposures are positive on average, somewhat larger than actual exposures
Pass-through is negative on average (as expected).

## Level of Model Exposure

|  | High | Low | p-value |
| --- | --- | --- | --- |
| Actual FX Exposure
 | 0.128 | -0.091 |  0.033 |
|  |  |  |  |
|  |  |  |  |
|  |  |  |  |
|  |  |  |  |
|  |  |  |  |
|  |  |  |  |
|  |  |  |  |

**Notes:**
Import-Penetration
	- Firms with high model exposures actually face significantly higher global competition in their home market.  
	- In contrast, competition in the foreign market is similar for firms with high and low model exposures. 

Herfindahl-Indices
A common assumption in the literature is that less concentrated industries are more competitive.  
Interestingly, there is no significant difference in global industry concentration.  In fact, when we calculate Herfindahl indices for each country-industry combination, firms with high model exposure are in industries that are somewhat more concentrated than firms with low model exposure. 
These results indicate that local industry concentration is not (directly) related to our theoretical measure of foreign exchange rate exposure. 

## Level of Model Exposure

|  | High | Low | p-value |
| --- | --- | --- | --- |
| Actual FX Exposure
 | 0.128 | -0.091 |  0.033 |
| Foreign Debt | 0.978 | 0.764 | <0.001 |
| FX Derivatives
 | 0.799 | 0.519 | <0.001 |
|  |  |  |  |
|  |  |  |  |
|  |  |  |  |
|  |  |  |  |
|  |  |  |  |

**Notes:**
Import-Penetration
	- Firms with high model exposures actually face significantly higher global competition in their home market.  
	- In contrast, competition in the foreign market is similar for firms with high and low model exposures. 

Herfindahl-Indices
A common assumption in the literature is that less concentrated industries are more competitive.  
Interestingly, there is no significant difference in global industry concentration.  In fact, when we calculate Herfindahl indices for each country-industry combination, firms with high model exposure are in industries that are somewhat more concentrated than firms with low model exposure. 
These results indicate that local industry concentration is not (directly) related to our theoretical measure of foreign exchange rate exposure. 

## Level of Model Exposure

|  | High | Low | p-value |
| --- | --- | --- | --- |
| Actual FX Exposure
 | 0.128 | -0.091 |  0.033 |
| Foreign Debt | 0.978 | 0.764 | <0.001 |
| FX Derivatives
 | 0.799 | 0.519 | <0.001 |
| Import Penetration | 0.289 | 0.193 | <0.001 |
| Foreign Import Penetration | 0.295 | 0.293 |  0.474 |
| Industry Herfindahl | 0.152 | 0.140 |  0.151 |
| Country-Industry Herfindahl | 0.478 | 0.355 | <0.001 |

**Notes:**
Import-Penetration
	- Firms with high model exposures actually face significantly higher global competition in their home market.  
	- In contrast, competition in the foreign market is similar for firms with high and low model exposures. 

Herfindahl-Indices
A common assumption in the literature is that less concentrated industries are more competitive.  
Interestingly, there is no significant difference in global industry concentration.  In fact, when we calculate Herfindahl indices for each country-industry combination, firms with high model exposure are in industries that are somewhat more concentrated than firms with low model exposure. 
These results indicate that local industry concentration is not (directly) related to our theoretical measure of foreign exchange rate exposure. 

## Level of Model Exposure

|  | High | Low | p-value |
| --- | --- | --- | --- |
| Actual FX Exposure
 | 0.128 | -0.091 |  0.033 |
| Foreign Debt | 0.978 | 0.764 | <0.001 |
| FX Derivatives
 | 0.799 | 0.519 | <0.001 |
| Import Penetration | 0.289 | 0.193 | <0.001 |
| Foreign Import Penetration | 0.295 | 0.293 |  0.474 |
| Industry Herfindahl | 0.152 | 0.140 |  0.151 |
| Country-Industry Herfindahl | 0.478 | 0.355 | <0.001 |
| Model Pass-through | -0.211 | 0.088 | <0.001 |

**Notes:**
Import-Penetration
	- Firms with high model exposures actually face significantly higher global competition in their home market.  
	- In contrast, competition in the foreign market is similar for firms with high and low model exposures. 

Herfindahl-Indices
A common assumption in the literature is that less concentrated industries are more competitive.  
Interestingly, there is no significant difference in global industry concentration.  In fact, when we calculate Herfindahl indices for each country-industry combination, firms with high model exposure are in industries that are somewhat more concentrated than firms with low model exposure. 
These results indicate that local industry concentration is not (directly) related to our theoretical measure of foreign exchange rate exposure. 

## Hedging Effects

|  | (1) |  |  |  |
| --- | --- | --- | --- | --- |
| Intercept | -0.37*** |  |  |  |
| FC Debt |  |  |  |  |
| FX-Derivatives |  |  |  |  |

**Notes:**
LHS = difference between actual, unlevered exposure (estimated from regression) and model exposure (from enhanced BDM model)

Once controlling for FC Debt, the enhanced BDM model cannot be rejected.
FX derivatives use also leads to lower levels of exposure, but the effect is not significant (i.e. small).

We winzorize the estimated exposures at +3 and -2 to match the restrictions of the model.

We cannot perform a similar analysis for pass-through, since we do not have output price series per firm (or even many countries and industries) to regress this on the exchange rate. Price indices exist in low frequency for few industries, but not in high frequency, for all countries and industries we consider.

## Hedging Effects

|  | (1) | (2) |  |  |
| --- | --- | --- | --- | --- |
| Intercept | -0.37*** |      -0.10 |  |  |
| FC Debt |  | -0.31*** |  |  |
| FX-Derivatives |  |  |  |  |

**Notes:**
LHS = difference between actual, unlevered exposure (estimated from regression) and model exposure (from enhanced BDM model)

Once controlling for FC Debt, the enhanced BDM model cannot be rejected.
FX derivatives use also leads to lower levels of exposure, but the effect is not significant (i.e. small).

We winzorize the estimated exposures at +3 and -2 to match the restrictions of the model.

We cannot perform a similar analysis for pass-through, since we do not have output price series per firm (or even many countries and industries) to regress this on the exchange rate. Price indices exist in low frequency for few industries, but not in high frequency, for all countries and industries we consider.

## Hedging Effects

|  | (1) | (2) | (3) |  |
| --- | --- | --- | --- | --- |
| Intercept | -0.37*** |      -0.10 | -0.30*** |  |
| FC Debt |  | -0.31*** |  |  |
| FX-Derivatives |  |  |       -0.10 |  |

**Notes:**
LHS = difference between actual, unlevered exposure (estimated from regression) and model exposure (from enhanced BDM model)

Once controlling for FC Debt, the enhanced BDM model cannot be rejected.
FX derivatives use also leads to lower levels of exposure, but the effect is not significant (i.e. small).

We winzorize the estimated exposures at +3 and -2 to match the restrictions of the model.

We cannot perform a similar analysis for pass-through, since we do not have output price series per firm (or even many countries and industries) to regress this on the exchange rate. Price indices exist in low frequency for few industries, but not in high frequency, for all countries and industries we consider.

## Hedging Effects

|  | (1) | (2) | (3) | (4) |
| --- | --- | --- | --- | --- |
| Intercept | -0.37*** |        -0.10 | -0.30*** |        -0.10 |
| FC Debt |  | -0.31*** |  | -0.30*** |
| FX-Derivatives |  |  |       -0.10 |        -0.01 |

**Notes:**
LHS = difference between actual, unlevered exposure (estimated from regression) and model exposure (from enhanced BDM model)

Once controlling for FC Debt, the enhanced BDM model cannot be rejected.
FX derivatives use also leads to lower levels of exposure, but the effect is not significant (i.e. small).

We winzorize the estimated exposures at +3 and -2 to match the restrictions of the model.

We cannot perform a similar analysis for pass-through, since we do not have output price series per firm (or even many countries and industries) to regress this on the exchange rate. Price indices exist in low frequency for few industries, but not in high frequency, for all countries and industries we consider.

## Hedging Effects

|  | Coef. | p-value |
| --- | --- | --- |
| Intercept | -0.15 | [0.156] |
| FC Debt only | -0.22 | [0.086] |
| FX Derivatives only | 0.42 | [0.156] |
| Both | -0.27 | [0.019] |

**Notes:**
Coefficients for “Both” are most significant and largest.

## Importance of Hedging Channels

- Gross FX exposure = 0.674
- no market share, no foreign assets, no financial hedging
- values at sample means, *f*=*d* =0.7
- Firms reduce FX exposure via 3 channels
- (1) Pass-through (10%-16%)
- - Market shares at sample average
- (2) Operational hedging (9%-16%)
- - Foreign assets at sample average
- (3) Financial hedging (46% - 50%)
- - FC Debt (45%)
- - FX Derivatives (1%)
- Consistent with Guay and Kothari (2003), derivatives have limited impact on risk profile of the firm

**Notes:**
Gross FX exposure = 0.674
f=d=0, γf1=0, γd2=1, f=d =0.7 
other values at sample means
Firms reduce FX exposure via 3 channels
(1) Pass-through (14.5%)
f=0.759 and d=0.294 (sample averages) 
(2) Operational hedging (15.5%)
γf1=0.192 and γd2=0.808 (sample averages)
(3) Financial hedging
FC Debt (45%)
FX Derivatives (1%)

## Industry Portfolios

**Notes:**
Firms with large exposures also have higher (more negative) pass-through
Firms with higher foreign import penetration (GDP-weighted average of import penetration in foreign markets)
Firms with more foreign costs have higher exposures (still higher after accounting for operational hedge – operational hedge does not work entirely)
This contrasts with the results for an individual firm (e.g., Panel B of Figure 4) where foreign assets reduce exposure, ceteris paribus.  These results indicate that firms with more foreign assets are likely to have higher exposure (e.g., because they typically have more foreign sales) and that operational hedges provide only a partial hedge for exchange rate movements. 
Firms with larger model exposures are more likely to use financial hedging

## Summary

- Comprehensive analysis of FX exposure
- Resolving the “Exposure Puzzle”
- Firms have large gross FX exposures
- Firms reduce these exposures via pass-through, operating and financial hedging
- Residual FX exposures are economically and statistically small
- FX risk management is effective, and companies can stop whining about FX risk