Measuring FX Exposure

Economic Exposure

Economic Exposure

Economic exposure (EE): EE measures how an unexpected change in St affect the future cash flows of the firm.

• Economic exposure is: Subjective.

Difficult to measure.

• Measuring CFs.

We can use accounting data (changes in EAT) or financial/economic data (stock returns) to measure EE. Economists tend to like more non-accounting data measures.

Note: Since St is very difficult to forecast, the actual change in St (ef,t) can be considered “unexpected.”

Measuring Economic Exposure

An Easy Measure of EE Based on Financial Data

We can easily measure how CF and St move together: correlation.

Example: Kellogg’s and IBM’s EE.

Using monthly stock returns for Kellogg’s (Krett) and monthly changes in St (USD/EUR) from 1/1994-2/2008, we estimate ρK,s (correlation between Krett and st) = 0.154.

It looks small, but away from zero. We do the same exercise for IBM, obtaining ρIBM,s=0.056, small and close to zero. ¶

• Better measure: 1) Run a regression on CF against (unexpected) St.

2) Check statistical significance of regression coeff’s.

• Testing and Evaluating EE with a regression

Steps:

(1) Collect data on CF and St (available from the firm's past)

(2) Estimate the regression: CFt = + ß St + t,

ß measures the sensitivity of CF to changes in St.

the higher ß, the greater the impact of St on CF.

(3) Test for EE H0 (no EE): β = 0

H1 (EE): β ≠ 0

(4) Evaluation of this regression: t-statistic of ß and R2.

Rule: |tβ= ß/SE(ß)| > 1.96 => ß is significantly different than

zero at 5% level.

• For companies listed on exchanges, we can use stock prices instead of CFs & stock returns instead of changes in CFs.

Example: Kellogg’s EE.

Now, using the data from the previous example, we run the regression: Krett = + ß ef,t + t

R2 = 0.023717

Standard Error = 0.05944

Observations = 169

Coefficients Standard Error t-Stat P-value

Intercept (α) 0.003991 0.004637 0.860756 0.390607

ef,t (β) 0.551059 0.273589 2.014185 0.045595

Analysis:

We reject H0, since |tβ = 2.01| > 1.96 (significantly different than zero).

Note, however, that the R2 is very low! (The variability of st explains less

than 2.4% of the variability of Kellogg’s returns.) ¶

Example: IBM’s EE.

Now, using the IBM data, we run the regression:

IBMrett = + ß ef,t + t

R2 = 0.003102

Standard Error = 0.09462

Observations = 169

  Coefficients Standard Error t-Stat P-value

Intercept (α) 0.016283 0.007297 2.231439 0.026983

ef,t (β) -0.20322 0.2819 -0.72089 0.471986

Analysis:

We cannot reject H0, since |tβ = -0.72| < 1.96 (not significantly different

than zero).

Again, the R2 is very low. (The variability of st explains less than 0.3% of

the variability of IBM’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.

Run a modified regression:

CFt = + ß0 St + ß1 St-1 + ß2 St-2 + ß3 St-3 + ... + t.

The sum of the ßs measures the sensitivity of CF to St.

• Practical issue: Number of lags?

Usual practice: Include at most two years of information.

Example: HAL runs the following regression.

CFt = .456 + .421 St + .251 St-1 +.052 St-2. R2= .168.

(.89) (2.79) (2.01) (0.77)

HAL's HKD CF (in USD) sensitivity to St is 0.672 (.421+.251). a 1% depreciation of the HKD will increase HKD CF

(translated into USD) by 0.672%. ¶

• Note on regressions to measure EE

et,t is not the only variable affecting CFs/returns of a company. 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 “control” for these other variables, to isolate the effect of ef,t.

A multivariate regression will work: Besides et,t include other “control” independent variables (income growth, inflation, sales growth, etc.

• 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 run a multivariate regression:

Stock Returnt = α + β ef,t + δ1 Market Returnt + δ2 HMLt + δ3 SMBt + εt

Evidence: The above regressions have been done repeatedly for firms around the world. (Without the FF factors, we have already done it for Kellogg and IBM.)

On average, for large firms (MNCs) EE is small –i.e., β is small- and not significant at the 5% level. See recent paper by Ivanova (2014).

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

St = 7 HKD/USD St = 7.70 HKD/USD

Sales (in HKD) 300M 400M

Cost of goods (in HKD) 150M 200M

Gross profits (in HKD) 150M 200M

Interest expense (in HKD) 20M 20M

EBT (in HKD) 130M 180M

EBT (in USD at St=7) : HKD 130M/7 HKD/USD = USD 18.57M

EBT (in USD at St = 7.7): HKD 180M/7.70 HKD/USD = USD 23.38M

Example (continuation):

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.

Q: Is EE significant?

A: We can calculate the (pseudo) 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. Quite significant. But you should note that the change in exposure is USD 4.81M.

This amount might not be significant for IBM! (Judgment call needed.) ¶

Note: Obviously, firms will simulate many scenarios to gauge the sensitivity of EBT to changes in exchange rates.