Foreign Exchange Sensitivity-Analysis Disclosures and Market-Based Risk Measures*

Visarut Sribunnak

Faculty of Commerce and Accountancy Chulalongkorn University Bangkok, Thailand 10330 Visarut@acc.chula.ac.th

and

M.H. Franco Wong

Graduate School of Business University of Chicago

Chicago, Illinois 60637 franco.wong@gsb.uchicago.edu

Comments welcome

January 2004

* We thank Dan Bens, Gavin Cassar, Dan Collins, Bruce Johnson, Maria Nondorf, Mort Pincus, Ahmed Riahi-Belkaoui, Cathy Schrand, Dan Thornton, Weimin Wang, participants at the KPMG−UIUC Conference on Risk Reporting, especially the discussants, Shiva Rajgopal and Steve Ryan, and workshop participants at Tulane University, University of California at Berkeley, University of Illinois at Chicago, and University of Iowa for their comments. We gratefully acknowledge the financial support of the University of California at Berkeley, the University of Chicago Graduate School of Business, and the William Ladany Faculty Research Fund.

Foreign Exchange Sensitivity-Analysis Disclosures and Market-Based Risk Measures

Abstract

This paper examines foreign exchange (FX) sensitivity-analysis disclosures, which are provided

according to one of the three market-risk reporting formats allowed by the Securities and Exchange

Commission’s Financial Reporting Release No. 48 (FRR No. 48). We select a sample of FX

derivatives users from the 1997 Fortune 500 list and collect their market risk disclosures for the

three years 1997−1999. We estimate a Probit selection model to distinguish the sensitivity-analysis

reporters from the rest of the FX derivatives users, and use the Heckman two-stage procedure to

correct for potential sample selectivity bias, as well as the endogeneity of the market risk

disclosures. Our evaluation of the sensitivity-analysis disclosures indicates that the flexibility

allowed by FRR No. 48 makes it difficult to compare the disclosures across firms. Nonetheless, we

find that loss estimates are usually expressed in fair value when firms conduct the sensitivity

analysis at the derivatives-level, and in earnings or cash flows when the analysis is done at the

entity-level. We find that entity-level earnings sensitivity disclosure exhibits incremental

predictive power for the market-based exchange rate exposure and stock return volatility.

However, derivatives-level fair value sensitivity disclosure does not have explanatory power for

future market-based risk measures. These results are obtained after controlling for traditional risk

measures, the lagged market-based risk measures, and other derivatives-related disclosures.

Keywords: Derivative financial instruments; SEC market risk disclosures; sensitivity analysis;

foreign exchange risk; exchange rate exposure; stock return volatility.

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1. Introduction

This paper examines the sensitivity-analysis disclosures about foreign exchange (FX) risk.

The sensitivity-analysis format is one of the three risk reporting alternatives allowed by the

Securities and Exchange Commission (SEC). The 1997 SEC regulation, the Financial Reporting

Release No. 48 (FRR No. 48), mandated the disclosure of additional quantitative and qualitative

information about market risk exposures inherent in derivatives and other financial instruments.1

FRR No. 48 defines market risk as “the risk of loss arising from adverse changes in market rates

and prices.” The market risk information enables investors to assess current and/or future market

risk within four categories: interest rate risk, foreign exchange rate risk, commodity price risk, and

equity price risk. To enhance the comparability of disclosures across firms, the SEC limited the

choices of quantitative market risk disclosure formats to (1) tabular presentation, (2) sensitivity

analysis, and (3) Value-at-Risk.

The tabular presentation alternative provides fair values and related contract terms that are

sufficient to determine future cash flows by maturity dates for each of the next five years and an

aggregate amount thereafter. It is similar to the maturity gap disclosures provided by financial

institutions. The sensitivity-analysis reporting alternative presents the estimated loss from

derivatives and other financial instruments as a result of hypothetical changes in the underlying

financial rates or prices. The hypothetical market rate or price changes should be no less than 10%

of the end-of-period rate or price. The sensitivity analysis can be conducted at the financial

instrument, business exposure, or entity level. Further, the loss estimates can be expressed in fair

values, cash flows, or earnings. The sensitivity measure is similar to duration and delta measures,

both of which depict the change in market value given a unit change in underlying financial price

or rate. The Value-at-Risk alternative estimates the potential loss that a firm might suffer with a

given level of confidence over a specific period of time. It employs various assumptions to

generate scenarios of forward-looking changes in the underlying market risk factors and the 1 Other financial instruments, as defined by FAS 107, include among others, loans, mortgage-backed securities and investments. Therefore, companies may have to consider whether FRR No. 48 applies even if they do not use financial derivatives.

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resultant distribution of future profits and losses on the derivatives portfolio.2 Regardless of the

disclosure format chosen, firms must provide the information for trading and non-trading portfolios

separately.3

The SEC requires these enhanced disclosures about market risk after its review showed that

the existing disclosures filed by firms with the SEC prior to 1997 were confusing and misleading

(Linsmeier and Pearson, 1997). Although the mandated quantitative disclosures per FRR No. 48

are based on a more direct approach for assessing market risk exposure than other disclosures

existing at that time (e.g., SFAS No. 119 disclosures), the usefulness of these alternatives may be

restricted by their subjective nature. Specifically, the computation of these alternatives requires

historical data, models of cash flows and price processes, parameters (assumed or estimated), and

other information. There is no consensus on various assumptions used in calculating these

measures [see, e.g., Beder (1995) and Leong (1996)]. In fact, the Financial Accounting Standards

Board (FASB) decided to encourage, but not to require, the market risk disclosures in SFAS No.

119 because it believed that market risk measurement was subject to several shortcomings and that

the information might not be well understood or easy to furnish [FASB (1994, ¶69-75)].

Academics and practitioners also raised other concerns about the requirements of FRR No.

48. For example, in response to the proposed version of FRR No. 48, Culp and Miller (1996) argue

that the additional disclosures are costly and not feasible. There are also concerns over the

flexibility and comparability of the disclosure alternatives (Elmy, LeGuyader, and Linsmeier, 1998,

and Hodder, Koonce, and McAnally, 2000). According to FRR No. 48, the flexible disclosure

requirements aim to accommodate SEC registrants having different degrees of risk exposures and

different means of measuring market risk. Firms are allowed not only to choose among the three

reporting formats, but also to determine whether to express their estimates in fair values, earnings,

2 The sensitivity analysis method is, hence, simpler than the Value-at-Risk method, because it ignores the likelihood of the hypothetical changes in financial rates or prices, as well as the correlations among the rate and price changes. The idea behind these simplified assumptions is that the sensitivity analysis technique is used to assess the impact of extreme market conditions, in which past correlations among financial prices and rates would break down. 3 None of our sample firms reports using FX derivatives for trading purpose. See Linsmeier and Pearson (1997) for a detailed discussion of FRR No. 48 and the different methodologies that are used to estimate the sensitivity-analysis and VAR numbers.

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or cash flows. Furthermore, firms are not required to present the market risk information using the

same reporting format for different risk categories. As a result, whether the enhanced quantitative

disclosures per FRR No. 48 are useful for assessing exposures to financial risks is an open

question.

Based on a sample of 1997 Fortune 500 nonfinancial firms, we review their market risk

disclosures for the period from 1997 to 1999. Since firms do choose different reporting formats for

different risk categories, we focus exclusively on their disclosures about FX risk to better manage

data collection and analyses. We find that about two-thirds of these firms reported using FX

derivatives. We focus on the set of FX derivatives users that choose the sensitivity-analysis

reporting alternative, which is selected by 43% of the FX users. The focus on this subset of the FX

derivatives users potentially causes sample selectivity bias and limits the generalizability of our

findings. We deal with this issue by first estimating a Probit selection model to distinguish the

sensitivity-analysis reporters from the rest of the FX derivatives users, and use the resultant inverse

Mills ratio to correct for potential sample selectivity bias, as well as the endogeneity of the

sensitivity-analysis disclosures. The selection model correctly classifies approximately 55% of the

FX derivatives users.

Given the sensitivity-analysis subsample, we collect their disclosures and find that

comparability is an issue. In particular, firms conducted their sensitivity analyses at either the

derivatives- or entity-level, and they also used different performance measures (fair values,

earnings, cash flows) to express their loss estimates. Furthermore, the disclosures also lack

quantitative data about firms’ underlying business exposures. While not required by FRR No. 48,

quantitative information on underlying exposures is important for the interpretation of the

derivatives-level sensitivity-analysis disclosures. Nevertheless, we find that loss estimates are

usually expressed in fair value when firms conduct the sensitivity analysis at the derivatives-level,

and in earnings or cash flows when the analysis is done at the entity-level. We called these two

types of disclosures “derivatives-level fair value sensitivity” and “entity-level earnings sensitivity,”

respectively.

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Next, we test whether these sensitivity-analysis disclosures have power predicting the cross-

sectional variation in two equity market-based risk measures: exchange rate exposure and stock

return volatility. Prior studies have used these two measures in the investigation of disclosures

about financial risk exposures. Our research design takes into account the forward-looking nature

of the SEC-mandated disclosures by examining the predictive power of the disclosures for the

market-based risk measures. We conjecture that derivatives-level fair value sensitivity should be

negatively related to these future market-based risk measures if firms are using derivatives for

hedging. Moreover, entity-level earnings sensitivity should be positively related to the future risk

measures. The empirical results indicate that entity-level earnings sensitivity disclosure exhibits

incremental predictive power for both market-based risk measures, but derivatives-level fair value

sensitivity disclosure does not. We obtain these results after controlling for traditional risk

measures, the lagged market-based risk measures, and other derivatives-related disclosures.

Three other studies have examined the actual quantitative disclosures per the SEC

requirement. Roulstone (1999) reviews the SEC market risk disclosures made by 25 randomly-

selected firms and finds that market risk disclosures under FRR No. 48 have improved greatly from

those under SFAS No. 119, but that the disclosures vary in detail and clarity. Hodder (2001)

investigates the relevance and reliability of commercial banks' sensitivity disclosures. She finds

that fair value (or earnings) sensitivity exhibits little association with realized changes in fair value

(or earnings) and that such a relation is subsumed by extant regulatory disclosures. Liu, Ryan, and

Tan (2003) examine the VAR disclosures of 17 commercial banks under FRR No. 48. They find

not only that banks’ trading VAR predicts the variability of trading revenues, as documented by

Jorion (2002) using non-FRR No. 48 data, but also that the predictive power is positively related to

the banks’ technical sophistication. Moreover, they show that trading VAR has predictive power

for return variability, beta, and realized returns.

Three other lines of research also shed light on the SEC market risk disclosures, without

using the actual quantitative FRR No. 48 numbers. First, Schrand (1997), Ahmed, Beatty, and

Bettinghaus (2003), and Jorion (2002) use regulatory data for thrifts and banks to investigate the

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potential informativeness of tabular and VAR disclosures per FRR No. 48. Second, Linsmeier et

al. (2000) and Thornton and Welker (2004) test for changes in trading volume and stock price

exposure to oil price, respectively, around 10-K filing dates for first-time FRR No. 48 disclosers.

Third, Rajgopal (1999) and Rajgopal and Venkatachalam (1999) estimate the commodity price

sensitivities of oil producers and refiners, and then use the estimates to examine the potential

efficacy of the sensitivity-analysis disclosures per FRR No. 48.

We add to these studies in three ways. First, we examine the quantitative sensitivity-analysis

disclosures for a large sample of nonfinancial firms. Hence, our results are potentially more

generalizable because we use a larger sample and our sample firms represent a variety of industries.

The larger sample also allows us to provide a detailed description of the disclosure practices of

nonfinancial firms with respect to FX risk. Second, Hodder (2001) and Jorion (2002) use realized

financial performances to evaluate the market risk disclosures (Hodder also examines the sample

banks’ cost of capital). In contrast, we evaluate the sensitivity-analysis disclosures based on their

predictive power for future market-based risk measures. In that regard, we, like Liu et al. (2003),

focus on investors’ ex-ante assessment of the sensitivity disclosures under hypothetical FX market

conditions. Third, this is the first study to explicitly take into account potential sample-selection

bias and the possibility that the market-risk disclosures could be endogenous, both of which result

in biased coefficient estimates and thus inference problems. Given that FRR No. 48 allows firms to

choose among three reporting formats, the results from prior studies could not be generalized to

other firms that selected alternative disclosure formats.

The rest of the paper proceeds as follows. We present the sample and descriptive statistics

by reporting format in section 2. Section 3 discusses the implications of sample-selection bias for

our subsequent analysis. In section 4, we provide a detailed description of the data collection

process and the sensitivity-analysis data. Section 5 develops testable hypotheses and discusses the

research design. We report the main empirical findings in section 6 and conclude in section 7.

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2. Sample and Descriptive Statistics

This study examines the SEC-mandated FRR No. 48 disclosures (SEC 1997) for the three-

year period spanning 1997 through 1999. We begin the study in 1997 because FRR No. 48 is

effective for fiscal periods ending after June 15, 1997.4 We focus exclusively on the disclosures

about foreign exchange (FX) risk exposure to make the data collection manageable. Hence, our

sample selection procedure is designed to pick a set of large, nonfinancial firms that are likely to

have large exposure to FX risk, but with little or no exposure to other financial risks. To identify

such a sample, we start with the list of 1997 Fortune 500 companies. The firms on the list are

likely to be conglomerates or multinationals, because Fortune uses total sales as its selection

criterion. We exclude companies that are likely to be exposed to commodity or interest rate risk by

deleting firms that are in the oil and gas production (SIC code 1311), petroleum refining (SIC code

2911), utilities (SIC codes 4900-4999), or financial services (SIC codes 6000-6999) industries.

This selection procedure results in 342 firms in the 1997 sample.

We collect the SEC sensitivity disclosures about FX risk exposure for these 342 sample firms

from Item 7A of the 10-K reports and the derivatives-related disclosures from the footnotes to

financial statements for each of the three years from 1997 to 1999. Table 1, panel A shows that the

numbers of firms drop to 331 and 318 in 1998 and 1999, respectively, due to mergers and

acquisitions. The panel also indicates that about 65% of these firms reported using FX derivatives.

The percentage of FX derivatives users stays roughly the same during the three-year sample period.

On the whole, these large, nonfinancial, FX derivatives users are likely to be highly exposed to

foreign exchange risk.

Panel B in table 1 presents the number and percentage of FX derivatives users that chose

different SEC market risk reporting alternatives. We cannot identify the disclosure choice for

44.6% of the users in 1997, because they simply reported immaterial risk exposure. This figure

drops to 30.1% and 29.5%, respectively in 1998 and 1999, while the percentage of firms selected 4 For non-financial firms with market capitalization under $2.5 billion as of January 28, 1997, the new requirement is effective for fiscal periods ending after June 15, 1998. This exception has minimal impact on our analysis, as our sample firms are large in terms of market capitalization.

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the sensitivity analysis method increases by over ten percentage points. The sensitivity analyses

are the most common disclosure alternative choice and the tabular presentations are the least

popular among the available choices across all three years. Over 46% of the FX derivative users

presented their sensitivity figures in 1998 and 1999. Across the three sample years 1997−1999, a

total of 276 firm-year observations (42.8%) disclose sensitivity analysis, but only 6.8% present

tabular information and 15.5% provide Value-at-Risk figure. Overall, the disclosure choices of our

sample firms are similar to those examined in Roulstone (1999).

Table 2 compares and contrasts the characteristics of the FX derivative users by disclosure

format. In term of size, both the market value of equity and book value of assets indicate that the

firms that disclosed immaterial risk exposure (panel A) are the smallest on average, while those

that chose the Value-at-Risk format (panel D) are the largest. The median VAR firm has $11.6

billions of assets and $20.5 billions in market value. Because of the size difference, it is not

surprising to find that the amount of export and foreign sales (FC Sales), as well as the notional

amount of FX derivatives are also different across the four panels in a pattern reflecting the

difference in size. Specifically, the average notional amounts are $356, $1,075, $2,735, and $3,653

millions for the immaterial users, tabular presenters, sensitivity disclosers, and VAR firms,

respectively.5

To control for size differences, we scaled our variables of interest by the market value of

equity. The sensitivity-analysis disclosers (panel C) on average exhibit a slightly higher FC Sales-

to-MVE ratio than the immaterial users and tabular presenters (38.7% versus about 37% in panels

A and B). While the VAR firms have the lowest mean FC sales-to-MVE ratio (30.6%), they

actually have the second highest median FC Sales-to-MVE ratio. Regarding the notional amount

of FX derivatives (Notional) relative to MVE, we find that the sensitivity-analysis reporters and 5 Results not tabulated show that on average, the gross notional amount of FX derivative outstanding for all users are $2,021, $2,666, and $2,248 million for the three sample years, respectively. In contrast, the notional amount of FX derivative is only $395 million reported in Hentschel and Kothari (2001) for a sample of 425 nonfinancial firms, over the period 1990-1993. Moreover, Wong (2000) examines a selected sample of Fortune 500 manufacturing firms and finds that the mean notional amounts of FX derivatives are $1,230, $1,730, and $1,865 million, respectively, in 1994-1996. Taken together, these statistics indicate an increasing volume (in term of notional amount) of derivatives usage for large firms over the period 1990-1999.

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VAR firms have the highest median Notional-to-MVE ratios. On average, the immaterial users

(panel A) have the lowest Notional-to-MVE ratio, which is consistent with their claim that they

have immaterial exposure to derivatives-related FX risk. These four groups of FX users are also

different in their debt-to-equity and book-to-market ratios. In particular, both the mean and median

debt-to-equity and book-to-market ratios are lowest for the VAR firms, while the sensitivity

reporters have the second lowest debt-to-equity ratio.

In the main empirical tests, we focus on the sensitivity-analysis disclosures. We decide not

to examine the tabular and Value-at-Risk disclosures because it is difficult to specify the

underlying relation between these two forms of disclosures and the market-based risk measures.6

Table 1, panel B, indicates that about 43% of the firm-years selected the sensitivity analysis format,

but only 22% of the initial sample chose the tabular or VAR formats. The exclusion of these two

types of market risk disclosures limits the generalizability of the results and causes sample-

selectivity bias, to which we turn next.

3. Sample-Selectivity Bias

In this section, we discuss the implications of sample-selection bias for our subsequent

analysis. The statistics presented in table 2 indicate that the characteristics of the FX derivatives

users vary across reporting formats. Hence, the sensitivity-analysis subsample is not randomly

selected from the FX user population. If we narrowly define the population of interest as the set of

FX derivatives users that reported sensitivity-analysis data, the ordinary least squares (OLS)

method still gives unbiased estimations of our regression models (Wooldridge, 2002, chapter 17).

However, the narrow focus of such an analysis limits the generalizability of the empirical findings.

We are interested in making statistical inference on all FX derivatives users, using the

sensitivity-analysis subsample. Since the sample firms are not randomly selected from the

6 We note that Jorion (2002) derives a positive relation between the Value-at-Risk disclosures and the absolute value of future trading revenues under certain rather restrictive assumptions. Hodder and McAnally (2001) describe a procedure to estimate sensitivity and VAR measures from the tabular data.

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population of all FX derivatives users, the OLS estimation on the selected sample will be biased

and inconsistent. To make probable inference on all FX users, we correct for sample-selectivity

bias using the Heckman (1979) two-stage procedure (see Maddala, 1983, or Wooldridge, 2002).

The procedure involves two steps. In the first stage, we estimate a Probit selection equation to

model a FX derivatives user’s decision of choosing the sensitivity-analysis reporting alternative or

not. The inverse Mills ratio, also known as “Lambda” or sample-selection correction term, is

calculated using the predicted probability from the Probit selection model.7 In the second stage,

we include the inverse Mills ratio as an additional explanatory variable in the OLS estimation of

our regressions (to be discussed in section 5) on the sensitivity-analysis subsample. Heckman

(1979) shows that, with the inverse Mills ratio included, the second-stage OLS regression estimates

are consistent. This is the standard Heckman procedure, and we refer to it as the “Heckman OLS”

method in the rest of the paper.8

The Heckman OLS method assumes that the sensitivity-analysis variable is exogenous.

However, if firms set their risk management strategies as a function of their desired level of

market-based risk measure (the dependent variable in our main regression analysis), the market risk

disclosures will be endogenous. This endogeneity issue results in biased coefficient estimates and

thus inference problems. To take this into consideration, we use the instrumental variable (IV)

method in the second stage of the Heckman procedure. As instruments for the sensitivity-analysis

disclosures, we use the inverse Mills ratio and all exogenous variables in the Probit selection and

the second-stage regression equations. This will lead to consistent estimation of our regressions of

interest (Wooldridge, 2002, chapter 17). We refer to this procedure as the “Heckman IV” method.

3.1. Specification of the Probit selection model

7 The inverse Mills ratio, χ, is computed as φ(b′x) / Φ(b′x), where φ(.) and Φ(.) are the normal probability density function and the normal cumulative density function, respectively. b is the vector of estimated coefficients from the Probit selection model and, hence, b′x is the predicted probability of a FX derivatives user selecting the sensitivity-analysis disclosure alternative. 8 The Heckman approach assumes that the underlying variable that drives selection and variable of interest are jointly normally distributed and that the selection is based on factors that can be condensed into a single underlying variable using the Probit selection model.

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The purpose of the selection model is to distinguish the sensitivity-analysis reporters from

the others. Therefore, the dependent variable of the model is a binary variable that takes the value

of one for FX users that selected the sensitive-analysis disclosure alternative; and zero, otherwise.

As for the explanatory variables, we believe that a firm’s financial characteristics and level of

foreign exchange risk exposures might explain why a user chooses the sensitivity-analysis

reporting alternative over the other options. Based on the discussion in Roulstone (1999), firm

size, the magnitude of exposure, and the amount of derivatives uses are likely determinants of the

reporting choices. In particular, firms with an integrated risk management program are likely to be

large and choose sophisticated risk measures, such as sensitivity analysis or VAR, over the more

simple tabular presentation option. Moreover, firms with a larger amount and greater covariability

of exposures and hedging instruments are likely to employ more sophisticated risk measurement

and reporting alternatives. Therefore, we attempt to capture the above factors using the logarithm

of the market value of equity, the ratio of export and foreign sales to market value of equity, and

the notional amount of derivatives outstanding scaled by market value of equity. In addition, table

2 also shows that the sensitivity disclosers are different from the other groups in their debt-to-book

equity and book-to-market ratios. The summary statistics in table 2 also suggest that the values of

these variables for the sensitivity-analysis subsample fall somewhere in between those of the VAR

subsample (presumably more sophisticated users) and Tabular/Immaterial subsample (presumably

less sophisticated). We capture this feature by adding the quadratic terms of these variables into

the selection model.

Hence, the selection model is specified as follows:

Yjt = α + β1 Log(MVEjt) + β2 DTEjt + β3 BTMjt + β4 FC Salesjt

+ β5 Gross NAjt + β6 (Log(MVEjt))2 + β7 (DTEjt)2 + β8 (BTMjt)2

+ β9 (FC Salesjt)2 + β10 (Gross NAjt)2 + εjt , (1)

where Yjt = 1 for FX derivatives users that selected the sensitivity-analysis reporting

format; 0 otherwise.

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Log(MVEt) = Natural logarithm of the market value of equity. DTEt = The debt-to-market ratio. BTMt = The book-to-market ratio. FC Salest = Export and foreign sales, scaled by the market value of equity. Gross NAt = Gross notional amount of foreign exchange derivatives outstanding, scaled by

market value of equity.

The predicted value from the Probit model gives the probability that a FX user chose the

sensitivity-analysis alternative. We use this predicted probability to calculate the inverse Mills

ratio, which is then included as an additional variable in the second stage of the Heckman OLS and

Heckman IV procedures to correct for sample selectivity bias in section 5.

3.2. Estimation results of the Probit selection model

The Probit selection model (1) is estimated using pooled annual data, with fixed-year effect

dummy variables included. Table 3, column (1) first presents the estimation results for the

specification without the quadratic terms. Only the estimated coefficient on logarithm of market

value of equity is statistically different from zero at the five-percent level. The likelihood ratio test

is significant, suggesting that the explanatory variables as a group have power in explaining the

probability of a FX derivatives user selecting the sensitivity-analysis reporting alternative.

Furthermore, the estimated model correctly classifies 56.1% of the sensitivity-analysis reporters

and 50.7% of the non-sensitivity-analysis reporters.9

Column (2) in table 3 shows the results for the expanded model with the quadratic terms

added. We expect the estimated coefficients on the quadratic terms to have signs opposite to those

of the original variables. In this specification, the foreign and export sales ratio, its squared term,

and the DTE-squared terms exhibit significant explanatory power. Once again, the likelihood ratio

test for the null that the estimated coefficients on the explanatory variables are jointly zero is

9 The classification numbers are obtained using the Jackknifing method. Specifically, the Probit model is estimated with one observation omitted at a time and the estimated model is used to classify the omitted observation. This method avoids overstating the predictive ability of the model when in-sample data are used to evaluate the classification accuracy of the model.

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rejected at less than the 1% level. Finally, the percentage of overall correct classification of 54.7%

is slightly higher in this specification than that reported in column (1).

In passing, there is no theory to guide us in building the selection model. Instead, we specify

the model according to the discussions in Roulstone (1999) and the observed differences in firm

characteristics (table 2) among the different groups of FX derivatives users. Hence, we are not

directly modeling the behavior of the users, as usually done in similar settings. However, it is

possible that the explanatory variables in our selection model might capture (or correlated with) the

underlying reasons for the sample firms to choose the sensitivity-analysis alternative or not. The

main purpose of the selection model is to distinguish the sensitivity-analysis reporters from the other

three types of users. To a certain extent, it serves our purpose, as the correct classification

percentage of the model is higher than that by chance alone and the estimated selection equation is

significant.

4. Classification of the Sensitivity-Analysis Disclosures

Having identified the sensitivity-analysis subsample and discussed the implications of

sample-selectivity bias for our subsequent analysis, we turn to the construction of the sensitivity-

analysis disclosures database. In particular, we provide detailed descriptions and examples of the

FX sensitivity-analysis disclosures made by the sample firms and illustrate the scheme we used to

classify and code the data in the subsections that follow. We also present a descriptive evaluation

of the sensitivity disclosure data.

First, firms can choose to conduct and disclose their sensitivity analyses at the derivatives-

and/or entity-level.10 Table 4 presents descriptive statistics of these disclosures separately in panels

A and B, respectively. Second, firms also have a choice to report their sensitivity-analysis results

10 There are a few firms, such as Stone Container, that perform sensitivity analysis at the underlying business level. However, these firms do not use FX derivatives to manage their FX risk.

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in fair value, cash flows, earnings, or a combination of these performance measures. Thus, under

each panel in table 4, we also break down the descriptive statistics by performance measure.

4.1. Derivatives-level sensitivity disclosures

Table 4, panel A reports the derivatives-level sensitivity disclosures. Almost all of the

derivatives-level disclosures are provided in fair value. In particular, 123 firm-year observations

reported an average potential $73.8 million potential loss in fair value for FX derivatives under

adverse exchange rate conditions; the loss estimates range from 0 to $1,319 million, with an

interquartile range between $5.9 and $48.0 million. The following excerpt from Xerox provides an

example for this type of disclosures:

“Assuming a 10 percent appreciation or depreciation in foreign currency exchange rates as of December 31, 1998, the potential change in fair value of our net foreign currency portfolio would approximate $32 million. (Xerox, 1998 10-K, Item 7A)”

In this example, Xerox also voluntarily provides gain estimates for its FX derivatives positions.

The right-hand side of panel A shows that 64 of the 123 firm-years disclose gain estimates, with a

mean potential gain of $104.3 million and a median of $22.0 million. Since only a subset of the

firms voluntarily provides gain estimates, we do not incorporate such information into our

subsequent analysis.11 We label the potential loss estimates in fair value at the derivatives-level as

“derivatives-level fair value sensitivity,” and this type of disclosures will be used in the subsequent

regression analysis.

Besides the fair value measure, firms can choose to report potential loss in earnings, cash

flows, or a combination of them. Panel A indicates that none of the sample firms conducts

sensitivity analysis in cash flows, while eight firm-years provide derivatives-level analysis in

earnings. The mean estimated potential loss in earnings is $19.8 million, while the median is $10.5

11 In a control experiment, Koonce, Lipe, and McAnally (2002) find evidence consistent with investors distinguishing high-risk from low-risk positions based on the information about potential gains relative to potential losses (holding either one constant). Further, investors assess one-sided loss only disclosures as if potential losses are greater than potential gains in two-sided disclosures.

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million, with an interquartile range between $4.6 and $37.6 million. Below is an example from Eli

Lilly:

“Considering the company's derivative financial instruments outstanding at December 31, 1997, a hypothetical 10 percent weakening in the exchange rates (primarily the U.S. dollar) over a one-year period would decrease earnings by $51.0 million while a 10 percent strengthening in the exchange rates would increase earnings by $67.8 million. This calculation does not reflect the impact of exchange gains/losses on the underlying positions that would be offset, in part, by the results of the derivative instruments. (Eli Lilly, 1997 10-K, Item 7A)”

Eli Lilly explicitly emphasized, in the last sentence of the example, that the sensitivity figures

reported did not include the earnings effect of the underlying position (i.e., items being hedged).

This example also illustrates the fact that the estimated gain ($67.8 million) and potential loss

($51.0 million) can be asymmetric. Only a few firms disclose earnings or cash flows sensitivity at

the derivatives-level. Therefore, we exclude these observations from the subsequent analyses

because the reported numbers based on fair value is so different in nature from earnings/cash

flows-based disclosures that combining the two disclosure types is not meaningful.

Finally, there are 53 firm-year observations with immaterial exposure at the derivatives-level.

These firms usually state that FX rate fluctuations would not cause material loss in fair value,

earnings, or cash flows. In the regression analysis, we code the fair value sensitivity of these

observations as zero. An example is provided by Avon:

“Based on the Company's foreign exchange contracts at December 31, 1998, the impact of a 10% appreciation or 10% depreciation of the U.S. dollar against the Company's foreign exchange contracts would not represent a material potential loss in fair value, earnings or cash flows. This potential loss does not consider the underlying foreign currency transaction or translation exposures of the Company. (Avon, 1998 10-K, Item 7A)”

4.2. Entity-level sensitivity disclosures

As an alternative to disclosing market risk information at the derivatives level, firms can

report this information at the entity level. Panel B in table 4 presents summary statistics for the

entity-level sensitivity disclosures, by performance measure. Eleven firm-year observations used

15

the fair value measure to report their potential entity-level loss estimates. Since we do not have

enough observations to produce powerful large-sample analyses for this disclosure type, we opt to

exclude such observations from the analyses. Archer Daniels Midland is among one of these firms:

“The instruments used for hedging are readily marketable exchange traded futures contracts and forward contracts with banks. The changes in market value of such contracts have a high correlation to the price changes in the currency of the related hedged transactions. The potential loss in fair value for such net currency position resulting from 10% adverse change in foreign currency exchange rates is not material. (Archer Daniels Midland, 1999 10-K, Item 7A)”

Three and 68 firm-year observations conducted sensitivity analysis in cash flows and earnings,

respectively, at the entity level. The mean potential loss in earnings is $13.7 million, with a range

between $0 and $60.0 million (median of $4.0 and interquartile range between $0.0 and $20.0

million). The following example illustrates Continental Airlines’ estimated loss in entity-level

earnings from adverse FX condition:

“The result of a uniform 25% strengthening in the value of the U.S. dollar from December 31, 1998 levels relative to the yen would result in an estimated decrease in operating income of approximately $13 million for 1999, after the effect of hedging instruments in place… The Company estimates that at December 31, 1998, a 25% strengthening in the value of the U.S. dollar relative to the yen would have increased the fair value of the existing average rate options and forward contracts by $22 million.... (Continental Airlines, 1998 10-K, Item 7A)”

The potential loss in earnings was $13 million for Continental Airlines. We label these loss

disclosures in entity-level cash flows and earnings collectively as “entity-level earnings

sensitivity,” and they will be included in the subsequent regression analysis. Furthermore, the last

sentence in the above example indicates that Continental Airlines had a derivatives-level fair value

sensitivity of $22 million. Continental Airlines is one of a few firms that conducted and reported

sensitivity analysis at both the derivatives and entity levels. These firms are included in both the

fair value sensitivity and earnings sensitivity subsamples in our analysis.

Finally, there are also 76 firm-year observations with immaterial exposure at the entity-level

when the sensitivity analysis is conducted in fair value, earnings, or cash flows (see the Avon

16

disclosure above for an example). Hence, we set the earnings sensitivity of these observations to

zero.

Results not tabulated show that the sample firms use different assumptions on the extent of

hypothetical changes in underlying exchange rates. While most of the sample firms chose a

uniform 10% strengthening (or weakening) of the U.S. dollar to assess the hypothetical gain or loss

on their FX exposures, a few firms selected 1%, 5%, and 25% hypothetical changes. For example,

Continental Airlines assumed a 25% hypothetical change (see example above). We adjust for this

difference in the regression analysis by converting the hypothetical gain or loss figures to a

corresponding 10% change in the underlying financial prices using a linear adjustment.12

4.3. Implications of findings

In sum, we find that the flexibility allowed by FRR No. 48 makes it difficult to compare the

market risk disclosures across firms. Firms can choose not only the reporting formats, but also the

levels of analysis, performance measures, and estimation assumptions. Although Hodder and

McAnally (2001) offer a procedure to convert tabular information into sensitivity or VAR measures,

the process requires assumptions on rate changes over different maturity horizons. Furthermore,

loss estimates from the sensitivity analysis and VAR methods are not comparable.

The disclosures also lack quantitative data about firms’ underlying business exposures. While

not required by FRR No. 48, quantitative information on underlying exposures is important for the

interpretation of the derivatives-level sensitivity-analysis disclosures. Even if firms provide

quantitative information about the underlying exposure, it is not clear if financial statement users

can infer net exposure without knowing the effectiveness of the firms’ hedging program. This is

because simply aggregating or netting piecemeal disclosures about derivatives and underlying

exposures is not likely equal to the firms’ net FX exposures. Entity-level sensitivity disclosures do

not suffer from this problem and, hence, might be better understood by users. 12 Nine firm-year observations are subject to this adjustment. We acknowledge that such a linear approximation is not strictly correct, as the hypothetical gain or loss is unlikely to be a linear function of the change in the underlying exchange rates. However, we are not aware of other alternative adjustment methods. The empirical findings remain qualitatively unchanged if we delete these observations instead.

17

The SEC requires the disclosure of potential loss estimates only. Without the corresponding

gain estimates, financial report users might find it difficult to distinguish a high-risk position from a

low-risk one (Koonce et al., 2003). Furthermore, comparison of market risk information based on

different performance measures requires a subtle knowledge of how one performance measure maps

into another and of how the accounting is done. Finally, firms seldom discuss the models or

assumptions they used to estimate the sensitivity-analysis numbers. Given the above shortcomings,

we have made every effort to prepare the data in such a way that they are comparable for cross-

sectional analyses. To the extent that financial statement users face similar difficulties, the

usefulness of the market risk disclosures for assessing financial risk exposures would be limited.

5. Predictive Power of the Foreign Exchange Sensitivity-Analysis Disclosures

In section 5.1, we develop testable hypotheses for derivatives- and entity-level sensitivity

disclosures, respectively. We then specify the empirical models used to test the hypotheses in

section 5.2. Section 5.3 describes the estimation of the dependent variables: exchange rate

exposure and stock return volatility. Finally, section 5.4 presents summary statistics for the

regression variables.

5.1. Hypotheses development

We examine whether the FX sensitivity-analysis disclosures have power explaining firms’

equity market-based risk measures in future periods. The link between the sensitivity risk

disclosures and future market-determined risk measures is based on the premises that (i) exposures

to financial price risk, either through firms’ underlying business or derivatives activities, affect the

variability of cash flows, (ii) cash flows volatility reflects the underlying events that determine

security price risk, and (iii) the stock market is efficient in incorporating the information known by

18

investors in security prices.13 Consequently, if the sensitivity-analysis disclosures convey useful

information about firms’ financial risk exposures, the disclosures should have power predicting

market-based risk measures. We examine this conjecture on two types of sensitivity-analysis

disclosures in the following two hypotheses.

The first testable hypothesis deals with the sensitivity disclosures at the derivatives-level. In

particular, we consider the sensitivity of the fair value of the derivatives portfolio to change in the

underlying financial price (i.e., derivatives-level fair value sensitivity). The disclosures provide

information about the riskiness of the derivatives portfolio, with a larger sensitivity indicates a

higher risk (i.e., subject to bigger loss in adverse market conditions). However, if firms use

derivatives for the purpose of hedging, the gain/loss from the derivatives portfolio would offset the

loss/gain of the items being hedged. In such a case, net risk exposure (and total firm risk) should be

lower in the presence of derivatives use. We jointly test these two conjectures in the first hypothesis

(stated in alternative form):

Hypothesis 1: Firms use derivatives for hedging purposes and the sensitivity disclosure captures

the exposure-reducing aspect of derivatives use. Therefore, after controlling for the level of

underlying FX exposure, firms with a higher derivatives-level fair value sensitivity exhibit lower

future market-based risk measures.

The second hypothesis considers the sensitivity disclosures at the entity-level. We focus on

the sensitivity of entity-level earnings or cash flows to change in the underlying financial price (i.e.,

entity-level earnings sensitivity). In other words, the earnings sensitivity number measures the

effect of financial derivatives net of the hedged items on earnings or cash flows because of financial

price changes. Hence, all else equal, a high earnings sensitivity figure indicates high net exposure to

13 The last two assumptions are jointly examined in Beaver, Kettler, and Scholes (1970) and Minton and Schrand (1999).

19

financial price risk (regardless of whether firms are using derivatives for hedging or not). This lead

to the second hypothesis stated in alternative form:

Hypothesis 2: The sensitivity disclosure conveys useful information about net exposure of the firms

to financial risk. Therefore, firms with a higher entity-level earning sensitivity exhibit higher future

market-based risk measures.

5.2. Model specification

We use two market-based risk measures in our analysis of the sensitivity-analysis

disclosures: Exchange rate exposure and stock return volatility. The exchange rate exposure

measure isolates the stock price sensitivity to exchange rate fluctuations. Hence, it allows us to

focus on the disclosures for FX risk only. Moreover, under certain assumptions, the exchange rate

exposure measure can be directly linked to the SEC-mandated sensitivity risk disclosures.14 A

shortcoming of using exchange rate exposure as an evaluation benchmark is that it is difficult to be

estimated (Jorion, 1990 and Bodnar and Wong, 2003). However, prior studies are able to

document meaningful cross-sectional variation in exchange rate exposure (Jorion, 1990 and Bodnar

and Gentry, 1993), thereby allowing us to test if the sensitivity-analysis disclosures have

incremental power in explaining the cross-sectional variation in exchange rate exposure. We

predict that firms with high net foreign currency sensitivity exhibit a higher absolute exchange rate

exposure. Specifically, derivatives-level fair value sensitivity should be negatively related to the

absolute value of exchange rate exposure, if firms use derivatives to manage their exposures. On

the other hand, entity-level earnings sensitivity reflects a firm’s net FX exposure and should be

positively related to the absolute value of exchange rate exposure.

We supplement the exchange rate exposure analysis with a stock return volatility analysis.

Stock return volatility provides an alternative way for us to capture the “true” underlying exchange 14 Rajgopal and Venkatachalam (1999) formally link earnings sensitivity to market determined risk exposure using an earnings capitalization model. Our test is different from theirs in that we use actual SEC sensitivity disclosures (both fair value and earnings), while they used estimated earnings sensitivity using pre-FRR No. 48 earnings data.

20

rate exposure, if the true exchange rate exposure contributes a large amount of the variation in

stock return volatility. Moreover, regulators might find the results from the stock return volatility

analysis more important, because they are more concerned about the effect of derivatives use on

total firm risk.15 A major shortcoming of the stock return volatility is that other factors, including

exposures to non-FX financial risks, add to stock return volatility, making it a noisy measure.16 We

partly mitigate this problem by excluding from the final sample financial institutions, oil and gas

producers, petroleum refiners, and utility companies, which are highly exposed to interest rate and

commodity price risks. We predict that firms with large net foreign currency sensitivity show a

larger stock return volatility.17 In particular, derivatives-level fair value sensitivity should be

negatively related to stock return volatility, if firms use derivatives to manage their FX exposures.

On the other hand, we expect that stock return volatility to be positively related to entity-level

earnings sensitivity numbers because they reflect firms’ net exposures to FX changes.

Since the SEC market risk disclosures are forward-looking in nature, the empirical model is

specified to examine whether the sensitivity-analysis disclosures have incremental power

explaining the market-based risk measures in future periods. We use the following two regression

models to examine the fair value and earnings sensitivity disclosures separately:

Abs(FXbeta jt+τ) or Log(σjt+τ2)

= α + β1 Lambda jt + β2 LDVjt-1 + β3 Log(MVEjt) + β4 DTEjt + β5 BTMjt

+ βS FC Salesjt + βF Fair Value Sensitivity jt + εjt (2a)

Abs(FXbeta jt+τ) or Log(σjt+τ

2)

= α + β1 Lambda jt + β2 LDVjt-1 + β3 Log(MVEjt) + β4 DTEjt + β5 BTMjt

15 We note that regulators are also more interested in the downside (Ryan, 1997). Hence, one could argue for an asymmetric risk measure. 16 As Liu, Ryan, and Tan (2003) point out, the piecemeal disclosures about different classes of risk make it difficult for financial report users to assess total firm risk. This is because risk factors are intertwined in nature. Without knowledge of how one risk factor will affect others, financial report users may not be able to get a complete assessment of total risk. 17 Hentschel and Kothari (2001) examine whether derivatives use increase firm risk. They measure “derivatives use” using notional amount and “firm risk” using contemporaneous stock return volatility. Our research design differs from theirs in that we investigate whether sensitivity-analysis disclosures are related to future stock return volatility. We use future stock return volatility because the SEC market risk disclosures are designed to provide forward-looking information.

21

+ βE Earnings Sensitivity jt + εjt (2b)

where

Abs(FXbeta jt+τ) = Absolute value of market-based exchange rate exposure at month t+τ. Log(σjt+τ

2) = Natural logarithm of monthly stock return volatility at month t+τ. Lambdat = The inverse Mills ratio (from section 3). LDVjt-1 = The lagged dependent variable. Log(MVEt) = Natural logarithm of the market value of equity at month t. DTEt = The debt-to-market ratio of at month t. BTMt = The book-to-market ratio at month t. FC Salest = Export and foreign sales, scaled by total sales. Fair Value Sensitivityt = Estimated potential loss in fair value from a sensitivity analysis at the

derivative level, scaled by earnings. Earnings Sensitivityt = Estimated potential loss in earnings from a sensitivity analysis at the

entity level, scaled by earnings or cash flow measure.

The first explanatory variable, Lambda, is the inverse Mills ratio from the Probit selection

equation that models the disclosure format choice of the FX users (see section 3 for details). It is

included into (2a) and (2b) to correct for any sample-selection bias due to the fact that the

sensitivity-analysis subsample is not randomly selected from the FX users. The second variable is

the “lagged” dependent variable, measured at the same month when the sensitivity-analysis

information is released.

The next three explanatory variables (logarithmic market value of equity, the debt-to-equity

ratio, and the book-to-market ratio) are financial characteristics that have been shown to be

associated with contemporaneous exchange rate exposure in Wong (2000) and contemporaneous

stock return volatility in Christie (1982) and Hentschel and Kothari (2001). We include them as

control variables in (2a) and (2b), because they might also possess predictive power for future

exchange rate exposure or stock return volatility.

The “FC Sales” variable in equation (2a), captures the underlying exposures of the sample

firms to FX risk because of their involvements in foreign currency denominated transactions. All

else being equal, the higher the amount of FC Sales, the larger is the amount of currency exposure

(unless a firm manages to naturally hedge different foreign currency exposures). Hence, we expect

a positive coefficient on FC Sales. After controlling for the underlying FX risk exposure, (2a) tests

22

whether the fair value sensitivity disclosure conveys information about the exposure-reducing

aspect of derivatives use. Hypothesis 1 predicts a negative coefficient on Fair Value Sensitivity.

We examine earnings sensitivity disclosures using (2b). Note that we do not need to include

FC Sales in (2b), because earnings sensitivity is measured at the entity-level and it captures net FX

exposure. Based on the discussions leading to hypothesis 2, we predict a positive coefficient on

Earnings Sensitivity.18

We next address the incremental usefulness of the sensitivity-analysis disclosures over and

above the existing disclosures about derivative instruments. In particular, prior to the SEC market

risk disclosure regulation, firms are required to disclose notional amount and fair value information

on financial derivatives outstanding at year’s end (per SFAS No. 119 and its predecessors, SFAS

Nos. 105 and 107). Hence, we augment the regression models (2a) and (2b) by including notional

amount and fair value information as follows:

Abs(FXbeta jt+τ) or Log(σjt+τ2)

= α + β1 Lambda jt + β2 LDVjt-1 + β3 Log(MVEjt) + β4 DTEjt + β5 BTMjt

+ β6 Gross NAjt + β7 | ∆ Net FVjt |

+ βS FC Salesjt + βF Fair Value Sensitivity jt + εjt (3a)

Abs(FXbeta jt+τ) or Log(σjt+τ

2)

= α + β1 Lambda jt + β2 LDVjt-1 + β3 Log(MVEjt) + β4 DTEjt + β5 BTMjt

+ β6 Gross NAjt + β7 | ∆ Net FVjt |

+ βE Earnings Sensitivity jt + εjt (3b)

where

Gross NAt = Gross notional amount of foreign exchange derivatives outstanding, scaled by market value of equity.

|∆ Net FVt| = Absolute change in the net fair value of foreign exchange derivatives outstanding, scaled by market value of equity.

18 As discussed in section 3.3, the fair value and earnings sensitivity numbers reflect potential losses resulting from adverse exchange rate changes. To a certain extent, they might also reflect the potential gain from favorable changes in FX rates if one assumes the derivatives position / net position has a symmetric gain and loss function. On the other hand, behavioral research suggests that, in the absence of complete information, individuals may attempt to compensate for missing information by generating their own range of possible outcome and that they tend to believe that the distribution of outcomes is symmetric (see Hodder, Koonce, and McAnally, 2001, for details).

23

Gross notional amount, Gross NA, is merely a volume measure of derivatives use. Hence,

we do not have a prediction for its relation with future exchange rate exposure or stock return

volatility.19 As for the absolute change in fair value, |∆ Net FV|, we expect that a large annual

change (in either direction) in fair value indicates a large exposure to foreign exchange risk in the

past year. Hence, if this historical relation conveys information about the firm’s future derivatives-

related exposure to FX risk, we would expect a positive coefficient on |∆ Net FV|. On the other

hand, |∆ Net FV| is similar to the Fair Value Sensitivity measure, which is negatively related to the

market-based risk measures. In that case, the estimated coefficient on |∆ Net FV| would be

negative.

5.3. Estimation of market-based risk measures

We estimate exchange rate exposure using an augmented market model, for each firm j and

month t using daily stock and foreign exchange rate data:

Rj i = αj t + γj t XRi + βj t RM i + εj i , (4)

where Rj i is the stock return for firm j in day i (i = 1, ..., Nt), Nt is the number of days in month t,

XRi is the percentage change in the Federal Reserve System’s (1998) trade-weighted broad

exchange rate index in day i, RM i is the return on the S&P 500 stock index in day i, γjt is the

exchange rate exposure of firm j in month t, and βjt is the beta of the firm with respect to the S&P

500 index in month t. Equation (4) is widely used by researchers in studies of exchange rate

exposures (see, e.g., Jorion, 1990; Bodnar and Gentry, 1993; and Williamson, 2001).20 Since

19 Wong (2000) shows that foreign exchange exposure is positively related to the “net long” notional amount (i.e., long positions net of short positions) of derivatives outstanding. Unfortunately, less than half of our sample firms provide enough information for us to compute net long notional amount. In the interest of maintaining a large sample size, we use the gross notional amount measure in our tests. 20 Bodnar and Wong (2003) document a size effect in exchange rate exposure that makes the exposure estimate a noisy measure of a firm’s net cash flow exposure to exchange rate risk. They further show that using a size-matched market portfolio can mitigate the size effect. Therefore, we select the S&P 500 index as the market portfolio, since the sample firms are similar in size to the S&P 500 firms.

24

Scholes and Williams (1977) show that the use of daily data introduces biases into market

beta measures, we obtain unbiased beta and exchange rate exposure estimates using the

Dimson (1979) method with the Fowler and Rorke (1983) correction.21

We compute monthly stock return variance using the following equation:

σjt2 = ∑

i = 1

Nt Ri

2 + 2 ∑i = 1

Nt −1 Ri Ri+1 , (5)

where σjt2 is firm j’s monthly variance of daily stock returns in month t, computed using Nt daily

returns, Ri. We follow French, Schwert, and Stambaugh (1987) to include the second term in the

right hand side of the equation to take into account the first order autocorrelation in daily returns

induced by non-synchronous trading. We apply this formula to compute monthly stock return

variance for our sample firms over the period from April 1998 to March 2001. We define stock

return volatility as the square root of stock return variance.

5.4. Descriptive statistics on regression variables

The first row in Table 5 reports summary statistics on the distributions of the monthly

absolute exchange rate exposure. For the fair value sensitivity sample, the mean (median) absolute

exchange rate exposure is 2.152 (1.545). As for the earnings sensitivity sample, panel B shows that

the mean and median are 2.141 and 1.546, respectively. The interquartile ranges are similar across

the subsamples.

The second row in Table 5 reports summary statistics on the distributions of the monthly

logarithmic stock return volatility. The mean (median) logarithmic annualized volatility is 3.638

(3.645), with a standard deviation of 0.509, for the fair value sensitivity sample. The mean and

median figures for the earnings sensitivity subsample are, respectively, 3.636 and 3.646. We

choose to use the logarithmic of volatility instead of volatility itself because French et al. (1987)

21 Since the FRS trade-weighted broad exchange rate index is computed using the foreign exchange rates posted at noon in New York, therefore XR and Rj are measured over different interval. This biases the estimation of γjt , similar to the bias in the estimation of beta because of non-synchronous trading.

25

and Andersen et al. (2001) document that the distribution of the realized unconditional stock return

volatilities is approximately lognormal. Test results not tabulated indeed indicate that the

logarithmic volatility measures of our sample firms are close to normally distributed. In particular,

the sample skewness and kurtosis are within one standard error for the normal distribution and the

normality assumption cannot be rejected.

The rest of Table 5 presents descriptive statistics for the explanatory variables. All the

explanatory variables are winsorized at the 1- and 99-percentiles to mitigate the influence of

outliers. Panel A shows that the mean and standard deviation of Fair Value Sensitivity scaled by

the market value of equity are 3.3% and 6.5%, respectively. The corresponding median figure is

1.0%, with an interquartile range between 0.0% and 4.2%. Panel B reports that the Earnings

Sensitivity is on average 0.6% of the market value of equity. The numbers range from 0.0% to

7.4%. The median is 0.0%, with an interquartile range of 0.4%. These figures are lower than the

corresponding numbers for the fair value sensitivity subsample (in panel A). This reflects the fact

that the earnings sensitivity loss estimates are calculated at the entity level. If firms are using

derivatives for hedging, the gains/losses on derivatives tend to offset the losses/gains on the

underlying exposures.

The logarithm of market value of equity ranges from 6.133 to 12.20 for the fair value

sensitivity sample and from 6.133 to 12.52 for the earnings sensitivity sample. The average debt-

to-equity ratios are 0.393 and 0.403 for the two subsamples, respectively. On average, exports and

foreign sales is about 31.6% and 33.5% of total sales. The median gross notional amount and

absolute change in fair value, both scaled by market value of equity, are similar across the fair

value and earnings sensitivity subsamples.

6. Empirical Findings

We estimate the model using the same data structure as Fama and French (1992).

Specifically, for the period from April 1998 to March 2001, we run 36 monthly cross-sectional

26

regressions of the market-based risk measures on FRR No. 48 disclosures, pre-determined publicly

available financial information, and, if applicable, the inverse Mills ratio (i.e., the sample-selection

correction term). For example, if a firm has a December fiscal year-end, we use its 1997 disclosure

to explain monthly market-based risk measures from April 1998 to March 1999, and so on for their

1998 and 1999 disclosures.22 We construct the data similarly for firms with a non-December fiscal

year-end.23 The advantage of the Fama and French (1992) approach is that it allows us to use

monthly market data to assess annual disclosures, thereby increasing the power of the test.

Statistical inference is based on the time-series means and standard errors of the estimated

coefficients from the 36 monthly regressions (Fama and MacBeth, 1973). The Fama-MacBeth t-

statistics take into account cross-correlation among firms. As long as the estimated coefficients

from the cross-sectional regressions are consistent, the time-series standard errors obtained under

the Fama-MacBeth approach are correctly specified for statistical inference. Hence, we estimate

the cross-sectional regressions using one of three methods: OLS, Heckman OLS, and Heckman

IV.24 OLS is an unbiased estimator if (1) we are interested in only the set of FX derivatives users

that chose the sensitivity-analysis method, or (2) sample selection bias turns out to be not an issue

for the set of FX derivatives users in our sensitivity-analysis subsample. The Heckman OLS

method gives consistent estimation of the cross-sectional regression coefficients in the presence of

sample-selection bias. Finally, the Heckman IV method provides consistent estimation if the

sample is subject to selectivity bias and the sensitivity-analysis disclosures are endogenous.25

22 In other words, the dependent variable changes monthly. On the other hand, the explanatory variables are all based on annual accounting information and, hence, only vary on an annual basis. Fama and French (1992) use this data structure to examine the cross-section of expected monthly stock returns using market beta and annual accounting data. 23 We assume that annual 10-K reports are available to the public within three months after the fiscal year ended. The SEC requires its registrants to file 10-K reports within 90 days after the fiscal year ended. 24 Under the Heckman OLS and IV procedures, the estimated standard errors of the cross-sectional regressions are biased. However, we do not use these standard errors for statistical inference. Instead, the Fama-MacBeth t-statistics are computed using the standard errors of the time-series of coefficients from the 36 cross-sectional regressions. As long as the coefficient estimates from the cross-sectional regressions are consistent, the Fama-MacBeth t-statistics are well specified. 25 For the Heckman IV method, we use Lambda from the Probit selection model and all the explanatory variables in the selection model and second-stage regression as instruments for Fair Value Sensitivity and Earnings Sensitivity (Woolridge, 2002, Chapter 17).

27

6.1. Predictive power of SEC sensitivity disclosures

Table 6 presents the estimation results of the exchange rate exposure (columns (1) to (6)) and

stock return volatility (columns (7) to (12)) regression equations (2a) and (2b). As discussed in

section 5.2, both market-based risk measures have their advantages and disadvantages. Therefore,

we consider the findings together in evaluating the FX sensitivity-analysis disclosures.

First, Fair Value Sensitivity exhibits little explanatory power for future exchange rate

exposure and stock return volatility. However, FC Sales are positively related to both market-

based risk measures, except under the Heckman IV method in column (5).26 The positive estimated

coefficients on FC Sales are consistent with the conjecture that firms with more export and foreign

sales are perceived as more risky by the stock market. Since Fair Value Sensitivity is measured at

the derivatives-level, its lack of predictive power could be due to the failure of FC Sales to fully

control for firms’ underlying business exposure to FX risk. Therefore, we also consider alternative

controls for underlying exposures, such as foreign assets, foreign pre-tax income, the change in the

foreign currency translation adjustments, and foreign currency transaction gains and losses.

Regardless of which alternative control or set of control variables we used, the estimated

coefficients on Fair Value Sensitivity remained indistinguishable from zero. Hence, we find no

support for the joint hypotheses (hypothesis 1) that the sample firms are using FX derivatives to

reduce their exposure to FX risk and the derivatives-level fair value sensitivity disclosures capture

the exposure-reducing aspect of derivatives use.

Second, the estimated coefficients on Earning Sensitivity are statistically positive in all

specifications. This finding is consistent with our conjecture that the higher the net exposure, the

more the firms are exposed to FX risk (hypothesis 2). Since earnings sensitivity is measured at the

entity level, the disclosures capture the firms’ net exposure to FX risk. It, hence, gives a more

complete picture of the residual FX exposures that the firms have. As a result, it might explain

why the results here are stronger than those for derivatives-level fair value disclosures, which are

26 We use the Heckman IV method to check that the Heckman OLS results are not induced by the endogeneity of the sensitivity disclosures. It is not meant to imply that financial statement users might find the instruments for the sensitivity-analysis disclosures useful or not.

28

more difficult to interpret without the corresponding information pertaining to the items being

hedged.

Third, the estimated coefficients on Lambda, the sample-selection correction variable, are

statistically different from zero in columns (6), (9), and (11). While its lack of significance in other

regressions is consistent with the absence of sample-selection bias, it could also be due to the

correlation between Lambda and the rest of the explanatory variables. This is the case because

most of the explanatory variables are also included in the Probit selection model that used to

estimate Lambda (Wooldridge, 2002, chapter 17).

Finally, the above results are obtained after controlling for other determinants of exchange

rate exposure and stock return volatility. As expected, the lagged dependent variable has strong

predictive power for future periods’ market-based risk measures. It actually subsumes the

explanatory power of the market value of equity, the debt-to-equity (book leverage) ratio, and the

book-to-market ratio. Had we excluded the lagged dependent variable from the regressions, the

traditional risk measures would have become more significant.

6.2. Predictive power of SEC sensitivity disclosures, controlling for alternative disclosures

We examine whether the sensitivity-analysis disclosures have predictive power over and

beyond the information embedded in the notional amount and fair value of FX derivatives. The

disclosure of these two measures is required under the Statements of Financial Accounting

Standards (SFAS) Nos. 105 and 107, which are amended by SFAS No. 119 in 1994. Hence, if the

sensitivity-analysis disclosure per FRR No. 48 actually improves the information available to stock

market participants, it should exhibit predictive power incremental to those of alternative

derivatives disclosures.

Table 7 presents the estimation results of regression equations (3a) and (3b). The qualitative

findings for the exchange rate exposure regressions are similar to those reported in table 6. In

particular, the estimated coefficients on Fair Value Sensitivity are insignificant. Once again, we

obtain strong results for Earnings Sensitivity, even after correcting for sample-selection and

29

endogeneity biases. Furthermore, the gross notional amount and the absolute change in fair value

of FX derivatives exhibit no explanatory power, which are due to correlation with other

explanatory variables, especially FC Sales and Log(MVE).

Columns (7) to (12) summarize the findings for the expanded stock return volatility

regression. Relative to the corresponding results reported in table 6, the key findings remain

unchanged, except for those in column (11). In particular, Fair Value Sensitivity becomes

marginally significant, under the Heckman IV method. Moreover, the estimated coefficients on the

absolute change in net fair value are statistically positive in all earnings-sensitivity regressions.

In sum, the findings for the sensitivity-analysis disclosures stay qualitatively intact, even

with the inclusions of alternative disclosures about FX derivatives. In particular, we find strong

evidence that earnings sensitivity, but not fair value sensitivity, exhibits predictive power for future

market-based risk measures, above and beyond the traditional risk measures and alternative

derivatives disclosures. This finding is likely attributable to the fact that earnings sensitivity

provides entity-level net exposure information, while fair-value sensitivity only give derivatives-

level exposure. Hence, the fair-value sensitivity disclosures may not be adequate for financial

report users to assess net FX exposures, without subtle knowledge of the underlying business

exposures and the effectiveness of the firms’ risk management program.

7. Conclusion

Regulators and accounting standard setters have long attempted to enhance the disclosures

about derivatives and market risk after a string of catastrophic corporate losses involving derivative

financial instruments. The release of the Financial Reporting Release No. 48 (FRR No. 48) is a

major step by the Securities and Exchange Commission, which requires its registrants to provide

quantitative information about four market-risk categories in one of three reporting formats.

However, there are concerns about the costs and benefits of such enhanced disclosures, as well as

the comparability of these disclosures given the flexibility allowed under FRR No. 48.

30

We examine foreign exchange sensitivity-analysis risk disclosures, which are provided

according to one of the three market-risk reporting formats required by FRR No. 48. We select a

sample of large nonfinancial firms and review their sensitivity-analysis disclosures about foreign

exchange derivatives for the period 1997-1999. Our evaluation of the disclosures indicates that the

flexibility provided by FRR No. 48 creates comparability issue across firms. Next, we test if the

sensitivity-analysis disclosures have power predicting two market-based risk measures: Exchange

rate exposure and stock return volatility. We find that entity-level earnings sensitivity exhibits

incremental predictive power for both risk measures, but derivatives-level fair value sensitivity

does not. These findings are robust to the correction of sample-selectivity bias, as well as the

endogeneity of the disclosures. The lack of significant results for the fair value sensitivity

disclosures might be due to our failure to completely control for the underlying business exposure.

We do not have that difficulty with earnings sensitivity disclosures, because they are assessed at

the entity-level. To the extent that financial statement users also have problem understanding fair

value sensitivity disclosures without given the corresponding business exposure information, the

usefulness of the derivatives-level fair value sensitivity disclosures is limited.

Although we document a relation between earnings sensitivity-analysis disclosures and

future market-based risk measure in cross-section, we believe that the disclosure requirement can

be improved further. Throughout the paper, we mention the flexibility allowed by FRR No. 48

with respect to reporting format, level of analysis, performance measure, and assumption for the

analysis. These flexibilities make it difficult to compare the disclosures across firms and over time.

We feel that the market risk disclosures would become more understandable, if the disclosures

were standardized across firms.

31

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Table 1 Foreign exchange derivatives usage and SEC market risk disclosure choice a Panel A: Foreign exchange derivatives usage 1997 1998 1999 All FX derivatives N % N % N % N % Non-Users 120 35.1% 115 34.7% 111 34.9% 346 34.9% Users 222 64.9% 216 65.3% 207 65.1% 645 65.1% Total 342 100.0% 331 100.0% 318 100.0% 991 100.0% Panel B: SEC market risk disclosure choice of FX derivatives users 1997 1998 1999 All Disclosure Format N % N % N % N % Unknown – Risk Immaterial 99 44.6% 65 30.1% 61 29.5% 225 34.9% Tabular Presentation 14 6.3% 16 7.4% 14 6.8% 44 6.8% Sensitivity Analysis 77 34.7% 100 46.3% 99 47.8% 276 42.8% Value-at-Risk 32 14.4% 35 16.2% 33 15.9% 100 15.5% Total users 222 100.0% 216 100.0% 207 100.0% 645 100.0% a The initial sample consists of all firms on the Fortune 500 list in 1997. From this list, we delete firms that are in

the oil and gas production (SIC code 1311), petroleum refining (SIC code 2911), utilities (SIC codes 4900-4999), or financial services (SIC codes 6000-6999) industries. This results in 342, 331, and 318 companies in 1997, 1998, and 1999. The SEC-mandated risk disclosures are collected from Item 7A of the 10-K reports for each of the years from 1997 to 1999.

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Table 2 Descriptive statistics of sample firms that reported using foreign exchange derivative financial instruments by market-risk reporting format a Variable Mean S.D. Min 25% Med 75% Max Mean S.D. Min 25% Med 75% Max Panel A: Unknown – Risk Immaterial (N=225) Panel B: Tabular Presentation (N=44) MVE ($MM) 13,682 25,221 52 2,709 5,374 13,821 203,211 33,676 55,461 314 3,758 5,845 38,007 244,021Assets ($MM) 8,714 9,308 747 3,092 5,242 10,138 43,453 14,544 16,226 1,048 3,651 7,778 17,346 70,349FC Sales ($MM) 1,850 3,141 0 0 853 2,868 26,530 2,115 2,854 0 0 1,296 2,628 12,247Notional Amount ($MM) 356 887 0 0 51 391 7,556 1,075 2,111 0 37 406 1,092 10,607FC Sales /MVE 0.369 0.804 0.000 0.000 0.132 0.350 7.376 0.374 0.860 0.000 0.000 0.066 0.321 5.007Notional /MVE 0.105 0.346 0.000 0.000 0.008 0.058 4.029 0.256 0.919 0.000 0.008 0.023 0.103 5.587Debt-to-Equity 0.256 0.190 0.000 0.115 0.206 0.372 0.865 0.262 0.199 0.014 0.096 0.253 0.393 0.839Book-to-Market 0.272 0.158 0.011 0.164 0.269 0.334 0.921 0.228 0.124 0.042 0.112 0.235 0.296 0.550 Panel C: Sensitivity Analysis (N=276) Panel D: Value-at-Risk (N=100) MVE ($MM) 31,308 56,501 342 4,055 8,910 32,742 508,329 42,044 61,998 1,058 11,723 20,530 44,030 460,768Assets ($MM) 20,234 46,974 1,322 4,719 7,939 17,264 405,200 23,173 44,653 1,873 6,805 11,627 23,935 279,097FC Sales ($MM) 4,703 7,988 0 612 2,322 5,342 50,377 5,533 8,774 0 0 3,352 7,271 50,138Notional Amount ($MM) 2,735 8,326 0 78 351 1,278 66,995 3,653 8,438 0 367 1,351 3,260 56,999FC Sales /MVE 0.387 0.701 0.000 0.061 0.243 0.467 9.071 0.306 0.642 0.000 0.000 0.197 0.410 5.957Notional /MVE 0.130 0.370 0.000 0.012 0.041 0.108 3.580 0.124 0.196 0.000 0.022 0.050 0.140 1.375Debt-to-Equity 0.219 0.182 0.000 0.080 0.167 0.322 0.854 0.177 0.172 0.000 0.060 0.126 0.225 0.743Book-to-Market 0.237 0.138 0.008 0.126 0.211 0.339 0.652 0.176 0.111 0.011 0.088 0.160 0.232 0.607 a The sample consists of 645 firm-year observations that reported using foreign exchange derivatives during the sample years 1997−1999. The market-risk

reporting formats selected by the sample firms are identified from reading their disclosures made in the 1997, 1998, and 1999 10-K reports (Item 7A). All figures are in millions of dollars, except for ratios and percentages.

b Variable definitions (Data source):

MVE Market value of equity (Compustat). Assets Book value of assets (Compustat). FC sales Sum of exports and sales made by foreign subsidiaries (Compustat geographic segment). Notional amount Gross notional amount of foreign exchange derivatives outstanding (10-K). Debt-to-equity Ratio of book value of long- and short-term debts to market value of equity (Compustat). Book-to-market Ratio of book value of equity to market value of equity, winsorized from below at 0 (Compustat).

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Table 3 Summary of the estimation of the Probit selection equation (N=605) a

Explanatory Predicted

Dependent variable: Y = Binary variable indicating the choice of reporting sensitivity-analysis figures

variables b sign (1) (2) Intercept ? 1.108 2.978 (2.00)* (1.39) Year 1998 dummy ? 0.361 0.426 (2.79)** (3.22)** Year 1999 dummy ? 0.066 0.103 (0.51) (0.78) Log(MVE) + 0.103 0.466 (2.06)* (1.04) Debt-to-equity ratio ? 0.039 -0.283 (0.42) (1.41) Book-to-market ratio ? 0.520 1.707 (1.26) (1.62) FC Sales / MVE + 0.048 0.356 (0.62) (2.19)* Gross notional amount / MVE + 0.026 0.372 (0.17) (1.17) Log(MVE)2 − -0.018 (0.80) (Debt-to-equity) 2 ? 0.086 (2.05)* (Book-to-market) 2 ? -1.817 (1.14) (FC Sales / MVE) 2 − -0.045 (1.95)* (Gross notional amount / MVE)2 − -0.117 (1.42) Likelihood ratio test (degree of freedom) 15.5 (7) 25.8 (12) Significance level of LR test 0.03 0.01 Y = 1 correctly classified 56.1% 56.4% Y = 0 correctly classified 50.7% 53.4% Overall correct classification 53.1% 54.7%

(continued…)

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Table 3 (...continued). ** and * denote statistically significance at the 1% and 5% levels, respectively, using a one-sided z-test if the sign

on the estimated coefficient is predicted; otherwise a two-sided test is used. a The sample consists of 645 firm-year observations that reported using foreign exchange derivatives during the

sample years 1997−1999. The SEC-mandated risk disclosures are collected from Item 7A of the 10-K reports for each of the years from 1997 to 1999. The dependent variable is a binary variable that equal to one for firm-year observations with sensitivity-analysis disclosures; zero, otherwise. The explanatory variables are defined in table 2. The Likelihood ratio test indicates whether the estimated coefficients are all equal to zero (null hypothesis). The classification numbers are obtained using the Jackknifing method. Specifically, the Probit model is estimated with one observation omitted at a time and the estimated model is used to classify the omitted observation.

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Table 4 Descriptive statistics on potential losses and gains (in millions of dollars) estimated using the sensitivity analysis methodology a Panel A: Derivatives-level disclosures Potential loss Potential gain Performance Measure n Mean S.D. Min 25th Med 75th Max n Mean S.D. Min 25th Med 75th Max

Fair Values 123 73.8 202.3 0.0 5.9 20.0 48.0 1,319 64 104.3 275.1 0.1 10.5 22.0 67.4 1,535Cash Flows 0 − − − − − − − 0 − − − − − − −Earnings 8 19.8 20.1 2.5 4.3 10.5 37.6 51.0 3 61.2 13.3 45.9 45.9 67.8 70.0 70.0Combination 53 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Panel B: Entity-level disclosures Potential loss Potential gain Performance Measure n Mean S.D. Min 25th Med 75th Max n Mean S.D. Min 25th Med 75th Max

Fair Values 11 44.4 52.6 0.0 0.0 0.0 103.0 119.0 6 55.3 63.0 0.0 0.0 44.0 103.0 141.0Cash Flows 3 96.0 18.3 80.0 80.0 92.0 116.0 116.0 0 − − − − − − −Earnings 68 13.7 18.5 0.0 0.0 4.0 20.0 60.0 17 10.2 20.1 0.0 0.0 0.0 6.0 60.0Combination 76 0.0 0.0 0.0 0.0 0.0 0.0 0.0 32 0.0 0.0 0.0 0.0 0.0 0.0 0.0

a The sample consists of foreign exchange derivatives users that selected the sensitivity-analysis reporting alternative over the years from 1997 to 1999. Potential loss (gain) is the estimated loss (gain) in fair values, cash flows, earnings, or a combination of these measures under adverse (favorable) exchange rate conditions. The sensitivity analysis methodology estimates the hypothetical loss or gain from derivatives and other financial instruments as a result of a hypothetical 10% change in the end-of-period exchange rates.

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Table 5 Descriptive statistics for the Fama-MacBeth regression variables for all firm-month observations, April 1998 to March 2001 a Variablesb Mean S.D. Min 25% Median 75% Max

Panel A: Derivatives-level Fair value sensitivity (N=1938) Abs(Exchange rate exposure) 2.152 2.329 0.000 0.728 1.545 2.859 36.83 Log(Stock return volatility) 3.638 0.509 1.274 3.337 3.645 3.939 6.173 Fair value sensitivity 0.033 0.065 0.000 0.000 0.010 0.042 0.435 Log(MVE) 9.285 1.360 6.133 8.225 8.989 10.42 12.20 DTE 0.393 0.641 0.000 0.095 0.197 0.425 5.001 BTM 0.244 0.143 0.000 0.130 0.220 0.348 0.592 FC Sales / Sales 0.316 0.232 0.000 0.013 0.375 0.488 0.853 Gross NA 0.085 0.135 0.000 0.010 0.042 0.086 0.818 |∆ Net FV| 0.004 0.008 0.000 0.000 0.000 0.004 0.047 Lambda 0.738 0.148 0.406 0.611 0.754 0.845 1.058 Panel B: Entity-level Earnings sensitivity (N=1590) Abs(Exchange rate exposure) 2.141 2.225 0.000 0.740 1.546 2.830 36.83 Log(Stock return volatility) 3.636 0.482 1.386 3.347 3.646 3.942 6.173 Earnings sensitivity 0.006 0.014 0.000 0.000 0.000 0.004 0.074 Log(MVE) 9.471 1.444 6.133 8.424 9.213 10.42 12.52 DTE 0.403 0.705 0.004 0.077 0.174 0.474 5.001 BTM 0.219 0.135 0.000 0.118 0.201 0.315 0.592 FC Sales / Sales 0.335 0.228 0.000 0.137 0.352 0.515 0.853 Gross NA 0.140 0.360 0.000 0.007 0.043 0.096 2.256 |∆ Net FV| 0.002 0.005 0.000 0.000 0.000 0.002 0.047 Lambda 0.748 0.140 0.406 0.640 0.763 0.852 1.058

a The sensitivity analysis sample was identified from a sample of 342 firms that were listed in the 1997 Fortune 500. All explanatory variables are winsorized at the 1- and 99-percentiles; the dependent variables are not winsorized.

(continued…)

40

Table 5 (...continued). b Variable definitions:

Abs(Exchange rate exposure) Absolute value of the monthly exchange rate exposure, estimated using daily stock return and foreign exchange rate change in the specific month.

Log(stock return volatility) Natural logarithm of monthly stock return volatility, computed as the standard deviation of daily stock returns in the specific month.

Fair value sensitivity Estimated potential loss in fair value of derivatives from a sensitivity analysis, scaled by earnings.

Earnings sensitivity Estimated potential loss in earnings at the entity level from a sensitivity analysis, scaled by earnings or cash flow measure.

Log(MVE) Natural logarithm of the market value of equity. DTE Ratio of debt to market value of equity. BTM Ratio of book value of equity to market value of equity, winsorized from below

at 0. FC Sales / Sales Value of foreign sales and exports, scaled by net sales. Gross NA Gross notional amount of foreign exchange derivatives outstanding, scaled by

market value of equity. |∆ Net FV| Absolute change in the net fair value of foreign exchange derivatives

outstanding, scaled by market value of equity. Lambda The inverse Mills ratio estimated from the Probit selection model reported in

table 3.

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Table 6 Fama-MacBeth regression results of the exchange rate exposure and stock return volatility equations, April 1998 to March 2001 a Absolute value of future exchange rate exposure Logarithm of future stock return volatility Explanatory Predicted OLS Heckman OLS Heckman IV OLS Heckman OLS Heckman IV variables b sign (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Fair value − 0.987 1.212 18.705 -0.190 -0.146 -0.189 Sensitivity (0.86) (1.10) (1.16) (-0.89) (-0.68) (-0.05) Earnings + 13.944** 14.210** 85.893** 4.708** 4.957** 16.831* Sensitivity (4.03) (4.07) (3.10) (5.92) (6.20) (1.83) FC Sales / Sales + 0.353* 0.543* 0.461 0.070* 0.131** 0.131** (1.69) (1.84) (1.55) (1.76) (2.58) (2.50) Lambda ? -0.999 -0.145 -0.904 -1.212* -0.273* -0.063 -0.274* -0.019 (-1.28) (-1.83) (-1.09) (-1.99) (-1.98) (-0.38) (-1.99) (-0.19) Log(MVE) ? 0.001 -0.041 0.063 0.004 0.054 -0.002 0.008 -0.025* 0.025 -0.024 0.028* -0.032* (0.03) (-0.87) (1.10) (0.09) (0.87) (-0.04) (0.75) (-1.96) (1.76) (-1.87) (1.98) (-2.31) DTE ? -0.017 -0.387** 0.041 -0.350** -0.010 -0.615** -0.010 -0.175** -0.005 -0.184** -0.010 -0.181* (-0.26) (-3.43) (0.55) (-2.84) (-0.12) (-3.13) (-0.63) (-4.25) (-0.30) (-4.10) (-0.44) (-2.33) BTM ? 0.241 -0.169 0.644 0.278 0.480 0.660 0.146 -0.029 0.260* -0.010 0.267* 0.018 (0.68) (-0.40) (1.43) (0.56) (1.01) (1.20) (1.76) (-0.31) (2.45) (-0.09) (2.55) (0.11) Value of dep. + 0.181** 0.137* 0.159** 0.123* 0.152** 0.151* 0.271** 0.265** 0.271** 0.262** 0.256** 0.286** variable at t-1 (3.14) (1.97) (3.20) (1.96) (3.10) (2.39) (8.75) (7.11) (8.79) (7.17) (8.98) (7.66) Intercept ? 1.624** 2.342** 1.696** 2.727** 1.793** 2.751** 2.538** 2.961** 2.559** 2.999** 2.585** 2.967** (3.40) (4.23) (3.55) (4.68) (3.71) (4.82) (16.54) (15.41) (16.44) (15.40) (16.79) (14.89) Average R2 16.05% 14.99% 18.25% 17.73% 18.08% 18.40% 17.29% 18.32% 18.59% 21.46% 18.35% 21.14%

(continued…)

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Table 6 (...continued). ** and * denote statistically significance at the 1% and 5% levels, respectively, based on a one-sided test if the sign on the estimated coefficient is predicted;

otherwise a two-sided test is used. The statistical test is conducted using the Fama-MacBeth t-statistic. a The sample consists of a subsample of FX derivatives users that selected the sensitivity-analysis risk disclosure format. The table summarizes the means and t-

statistics (in parentheses) of the estimated coefficients from 36 cross-sectional regressions for the period form April 1998 to March 2001 (Fama-MacBeth regressions). The cross-sectional regressions are estimated by one of three methods: OLS, Heckman OLS, and Heckman IV. The Heckman OLS method includes as an additional explanatory variable the inverse Mills ratio (Lambda) from the Probit selection model reported in table 3. The Heckman IV method uses the inverse Mills ratio and all exogenous variables as instruments for the Fair Value Sensitivity and Earnings Sensitivity variables.

b Variable definitions:

Fair value sensitivity Estimated potential loss in fair value of derivatives from a sensitivity analysis, scaled by earnings;. Earnings sensitivity Estimated potential loss in earnings at the entity level from a sensitivity analysis, scaled by earnings or cash flow measure. FC Sales / Sales Value of foreign sales and exports, scaled by total sales. Lambda Inverse Mills ratio from the Probit selection model. Log(MVE) Natural logarithm of the market value of equity. DTE Ratio of debt to market value of equity. BTM Ratio of book value of equity to market value of equity, winsorized from below at 0.

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Table 7 Fama-MacBeth regression results of the exchange rate exposure and stock return volatility equations, controlling for alternative derivatives disclosures a

Absolute value of future exchange rate exposure Logarithm of future stock return volatility Explanatory Predicted OLS Heckman OLS Heckman IV OLS Heckman OLS Heckman IV variables b sign (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Fair value − 1.424 1.467 -27.918 -0.223 -0.210 -15.802* Sensitivity (1.34) (1.40) (-0.96) (-0.96) (-0.91) (-1.69) Earnings + 13.905** 14.388** 88.771** 4.472** 4.830** 14.362* Sensitivity (4.05) (4.16) (3.05) (6.13) (6.78) (1.79) Lambda ? -1.380 -1.389* -1.494 -1.592* -0.316* -0.130 -0.394** -0.156 (-1.59) (-2.14) (-1.71) (-2.38) (-2.21) (-0.73) (-2.75) (-0.70) FC Sales / Sales + 0.228 0.476 0.448 0.049 0.109* 0.128* (1.01) (1.62) (1.52) (1.24) (2.21) (2.60) Log(MVE) ? 0.013 -0.025 0.099 0.031 0.072 0.023 0.012 -0.022 0.031* -0.019 0.029* -0.025 (0.30) (-0.54) (1.59) (0.62) (1.13) (0.46) (1.07) (-1.63) (2.06) (-1.38) (2.00) (-1.77) DTE ? -0.042 -0.318** 0.006 -0.303* -0.030 -0.629** -0.018 -0.158* -0.020 -0.177** -0.025 -0.184* (-0.58) (-2.93) (0.08) (-2.55) (-0.37) (-3.05) (-1.15) (-3.76) (-1.11) (-3.61) (-1.12) (-2.35) BTM ? 0.242 -0.049 0.821 0.495 0.933 0.921 0.144 0.006 0.277* 0.050 0.385** 0.064 (0.68) (-0.11) (1.73) (0.98) (1.79) (1.56) (1.75) (0.06) (2.56) (0.43) (3.37) (0.41) Value of dep. + 0.182** 0.106* 0.158** 0.088 0.148** 0.120* 0.289** 0.284** 0.290** 0.278** 0.276** 0.297** variable at t-1 (3.15) (1.47) (3.24) (1.33) (3.15) (1.85) (8.64) (7.63) (8.75) (7.69) (9.60) (8.05) Gross NA ? 0.370 -0.023 0.628 -0.008 1.543 0.082 0.098 -0.027 0.163* -0.022 0.456 0.018 (0.76) (-0.18) (1.16) (-0.07) (1.62) (0.66) (1.29) (-1.05) (1.97) (-0.75) (1.68) (0.62) |∆ Net FV| ? -5.019 20.424 -5.831 21.765 -4.922 20.705 -0.168 8.736* -0.242 9.011* 0.490 9.155* (-0.64) (0.91) (-0.75) (0.94) (-0.61) (0.89) (-0.09) (2.26) (-0.14) (2.42) (0.28) (2.51) Intercept ? 1.525** 2.124** 1.593** 2.550** 2.000** 2.664** 2.475** 2.940** 2.464** 2.902** 2.579** 2.923** (3.29) (3.85) (3.45) (4.35) (3.86) (4.70) (15.08) (14.67) (15.11) (14.58) (15.34) (14.59) Average R2 19.59% 22.25% 22.10% 24.94% 22.26% 25.78% 20.88% 23.23% 22.16% 26.40% 23.12% 26.10%

(continued…)

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Table 7 (...continued). ** and * denote statistically significance at the 1% and 5% levels, respectively, based on a one-sided test if the sign on the estimated coefficient is predicted;

otherwise a two-sided test is used. The statistical test is conducted using the Fama-MacBeth t-statistic. a The sample consists of a subsample of FX derivatives users that selected the sensitivity-analysis risk disclosure format. The table summarizes the means and t-

statistics (in parentheses) of the estimated coefficients from 36 cross-sectional regressions for the period form April 1998 to March 2001 (Fama-MacBeth regressions). The cross-sectional regressions are estimated by one of three methods: OLS, Heckman OLS, and Heckman IV. The Heckman OLS method includes as an additional explanatory variable the inverse Mills ratio (Lambda) from the Probit selection model reported in table 3. The Heckman IV method uses the inverse Mills ratio and all exogenous variables as instruments for the Fair Value Sensitivity and Earnings Sensitivity variables.

b Variable definitions:

Fair value sensitivity Estimated potential loss in fair value of derivatives from a sensitivity analysis, scaled by earnings. Earnings sensitivity Estimated potential loss in earnings at the entity level from a sensitivity analysis, scaled by earnings or cash flow measure. FC Sales / Sales Value of foreign sales and exports, scaled by total sales. Lambda Inverse Mills ratio from the Probit selection model. Log(MVE) Natural logarithm of the market value of equity. DTE Ratio of debt to market value of equity. BTM Ratio of book value of equity to market value of equity, winsorized from below at 0. Gross NA Gross notional amount of foreign exchange derivatives outstanding, scaled by market value of equity. |∆ Net FV| Absolute change in the net fair value of foreign exchange derivatives outstanding, scaled by market value of equity.