DP2008/14

Over the hedge? Exporters optimal andselective hedging choices

Richard Fabling and Arthur Grimes
October 2008
JEL classification: D21, F31, G15
www.rbnz.govt.nz/research/discusspapers
Discussion Paper Series

ISSN 1177-7567

DP2008/14
Over the hedge? Exporters optimal andselective hedging choices*
Richard Fabling and Arthur Grimes
Abstract
How do exporting firms manage currency exposures? We examine this issue
at the firm level using comprehensive data from the prototype LongitudinalBusiness Database recently developed by Statistics New Zealand. We use
these data to test both optimal and selective hedging theories. Optimal
hedging theory hypothesises that firms hedging choices depend on theprobability and cost of financial distress, underinvestment risks, scale,managerial risk aversion, information asymmetry, governance, ownership
structures and tax rules. Recent literature suggests that some firms varyhedging positions relative to their optimal position in a selective attempt tobeat the market . We examine whether hedging behaviour is consistent
with hypotheses derived from optimal hedging theories, and test whether
hedging positions change (possibly sub-optimally) when the NZD/AUD isperceived to be high or low relative to an historical average. Optimal andselective hedging theories are both supported by the data. Estimation is overJuly 2000 to March 2007 (monthly) a period during which the NZD/AUD
varied substantially, making this a particularly pertinent period to test
exporters currency risk management practices.

*

The authors wish to thank Statistics New Zealand for access to the data. We thank Motu
staff, University of Waikato seminar participants, and participants at the 2007 New Zealand
Econometric Study Group meeting and 2008 ESAM/NZAE conference for feedback on thepaper. Any remaining errors are the responsibility of the authors. The views expressed inthis paper are those of the authors and do not necessarily reflect those of the Reserve Bankof New Zealand.
Richard Fabling, Reserve Bank of New Zealand (richard.fabling@rbnz.govt.nz);
Motu Economic and Public Policy Research. Arthur Grimes, Motu Economic andPublic Policy Research (arthur.grimes@motu.org.nz); University of Waikato
ISSN 1177-7567 ©Reserve Bank of New ZealandDisclaimer
This research uses data that was accessed while Richard Fabling was on
secondment to Statistics New Zealand in accordance with security andconfidentiality provisions of the Statistics Act 1975. Only people authorisedby the Act are allowed to see data about a particular business ororganisation. The results of this work have been confidentialised to protect
individual businesses from identification. The analysis and interpretation ofthese results were undertaken while the authors were at the Reserve Bank of
New Zealand and Motu, respectively. The opinions, findings,
recommendations and conclusions expressed in this report are those of theauthors. Statistics New Zealand, the Reserve Bank of New Zealand, Motu
and the University of Waikato take no responsibility for any omissions or
errors in the information contained here.
The results are based in part on tax data supplied by Inland Revenue toStatistics New Zealand under the Tax Administration Act 1994. This tax
data must be used only for statistical purposes, and no individual
information is published or disclosed in any other form, or provided back toInland Revenue for administrative or regulatory purposes. Any person who
had access to the unit-record data has certified that they have been shown,
have read and have understood section 81 of the Tax Administration Act
1994, which relates to privacy and confidentiality. Any discussion of data
limitations or weaknesses is not related to the data s ability to support
Inland Revenue's core operational requirements.
Statistics New Zealand protocols were applied to the data sourced from the
New Zealand Customs Service. Any discussion of data limitations is not
related to the data s ability to support that agency s core operational
requirements.

1

Introduction

We analyse the currency hedging behaviour of goods exporters using a richand comprehensive longitudinal panel of exporting firms. Exporters
potentially face major risks arising from currency fluctuations. Under the
specific conditions considered by Modigliani and Miller (1958) there is no
gain in firm value through hedging these risks; thus hedging will not occur
where positive administrative and/or transactions costs to hedging arepresent. However, hedging of foreign exchange risk by some exporters does
occur in practice. A body of theoretical and empirical work on optimal
hedging practices explains why such behaviour may be observed. Morerecently, another phenomenon has been observed: some firms appear tohedge on a selective basis (ie, to alter their hedge positions relative to some
optimal level) in an attempt to beat the market .
Our analysis tests both for optimal hedging determinants and for thepresence of selective hedging behaviour. We are able to do so at theindividual firm level using data from the prototype Longitudinal Business
Database (LBD) recently developed by Statistics New Zealand (SNZ), thecountry s official statistical agency. The LBD covers virtually all New
Zealand firms. It includes SNZ firm-level survey data (used, for example, tocompile the national accounts) and administrative data that include tax
filings relating to firm s annual accounts as well as GST (value added tax)
and PAYE (employee income tax) obligations. These data sources enable
construction of a wide range of firm-level financial variables that may
influence optimal hedging decisions. Additionally, we have daily Customs
merchandise trade shipment data linked to firms. This is our source forcurrency exposures and hedging decisions. It includes data on the currency
that each trade was conducted in, a variable indicating whether the trade washedged back into New Zealand dollars (NZD) and, if so, the exchange rateof the hedging contract. We are therefore able to track exporters currency
hedging decisions on a high frequency longitudinal basis, while at the sametime controlling for optimal hedging determinants.
Our study builds on Fabling and Grimes (2008) which presented descriptive
data on New Zealand exporters hedging practices and used aggregated (asopposed to longitudinal unit record) data to test selective hedging behaviour.
Fabling and Grimes found considerable differences in hedging behaviour
across different sectors, both in a static sense (mean hedge ratios) and a
dynamic sense (correlation of hedge ratios). As predicted by some optimal2

hedging hypotheses (discussed further below), large firms hedge more thansmaller firms. However, small firms are the next most comprehensive
hedgers, with intermediate-sized firms hedging a lower proportion ofcurrency exposures than either large or small firms. Competing
determinants of optimal hedging choices (eg, scale versus potential financial
distress) may be behind such observed behaviour; what is clear from thisprior study is that hedging propensity is not monotonically related to firmsize. However, the study did find a monotonically increasing relationshipbetween hedging propensity and export intensity (exports as a ratio of total
sales).
Australia is New Zealand s largest trading partner accounting for 20.6
percent of merchandise exports (and approximately half of manufactured
exports) in 2007. Fabling and Grimes (2008) found some tentative evidenceof selective hedging behaviour with regard to exporters Australian dollar(AUD) exposures; aggregate hedge ratios were consistently negatively
related to the value of the NZD/AUD cross rate,1 consistent with exporters
locking in perceived low exchange rates. Despite this observed behaviour,
statistical tests found no evidence that selective hedging behaviour isbeneficial for firms; specifically there was no explanatory power of hedging
practices for future exchange rate changes. Fabling and Grimes also foundno evidence that changes in forward points (ie, short term interest rate
differentials) alter firms hedging decisions.
These results are consistent with other, mostly recent, explorations of thephenomenon of selective hedging. Building on the ideas of Stulz (1996) andWorking (1962), Brown et al (2006) and Meredith (2006) examined whether
selective hedging occurs for commodities in the gold and the oil/gas
industries respectively. Firms may selectively hedge profitably if they
possess a comparative advantage relative to other firms in a market withrespect to future price trends (eg, because of specialised supply-side
knowledge). Evidence of selective hedging is found in both studies when
prices deviate from historical averages. However, neither study finds
evidence indicating that selective hedging leads to superior operating or
financial performance. Thus firms in both industries may believe that they
have a comparative advantage in predicting industry trends which in fact
they do not possess.

1

We express the exchange rate in its mathematical sense; ie, NZD/AUD = x
xAUD.

3

1NZD =

The use of selective hedging in interest rate and currency markets appears to
be much more widespread than can be explained solely by firms
comparative advantage in specific markets (Dolde, 1993; Bodnar, Hayt andMarston, 1996; Glaum, 2000 and 2002; Faulkender, 2005). It is possible thatthe practice is influenced by managerial characteristics and incentive sets
within the firm (Beber and Fabbri, 2006). For instance, managers
remuneration may be more closely tied to upside performance relative tobudget (through bonuses) than to downside results. Alternatively, managers
may mistakenly believe that markets are mean-reverting when they are not;or, at least, more mean-reverting than they actually are.2 It is, however,possible that actions which appear to represent selective hedging behaviour(eg, observed changes in hedge ratios based on the use of forward exchange
rate contracts) are offset by changes in other forms of hedging, such asbalance sheet hedges, use of natural hedges and invoicing exports in local
currency. These last two alternatives are explored in our empirical work.
In order to model selective hedging behaviour, one must first model optimal
hedging behaviour. We define optimal hedging as follows: Let VjZt be themarket value of firm j, with a set of characteristics, Z, in period t. The firmchooses an optimal hedging policy, h*, from a feasible vector of hedging
choices, H =(h1, , h*, , hN) such that VjZt |h* =sup(VjZt |hi, i=1, ,N). If
firm j has the same characteristics, Z, in period t+1 as in period t then, withefficient markets, h* will again be the optimal hedging choice. By contrast,
if the firm varies its hedging choice (after controlling for its characteristics),
and especially if the variation is in response to market movements of thevariable to be hedged, we define the firm to be practicing selective hedging.
Determinants of optimal hedging choices explored in prior theoretical andempirical studies chiefly reflect responses to maximise firm value in thepresence of deviations from frictionless, full information markets. Suchdeviations may include: the existence of financial distress costs, which may
induce increased hedging by highly leveraged firms and firms with poor
liquidity (Smith and Stulz, 1985; Nance et al, 1993); underinvestment costs
which may increase hedging by firms with strong growth prospects, so
2Some implied New Zealand evidence on this view comes from Brookes et al (2000), whoreport that corporates consider forward rate contracts advantageous for short-term hedging
transactions owing to their relative flexibility: Contracts can readily be rolled forward, orclosed out, according to the firm s view of the exchange rate (p.27). They indicate that
selective hedging based on the level of the exchange rate relative to historical averages (ie,on perceived mean-reverting exchange rate behaviour) is practiced by a sizeable portion ofexporters.

4preserving internally generated funds to be used for expansion(Bessimbinder, 1991; Froot, Scharfstein and Stein, 1993); scale and export
intensity, leading to increased hedging by larger firms and/or by firms withlarge ratios of exports/sales (Graham and Rodgers, 2002; Lel, 2004); convex
tax schedules which, even with a proportional tax schedule (as in New
Zealand), may induce greater hedging by firms with existing tax losses(Smith and Stulz, 1985); and country-specific factors such as accounting
conventions, regulatory restrictions or the nature of capital markets (Bodnar
and Gebhardt, 1999; Bodnar et al, 2003). In some cases, optimal hedging
may reflect maximisation of the managerial value function (rather than thatof shareholders), being impacted by managerial risk aversion andgovernance characteristics (Breedan and Viswanath, 1998).
Our access to New Zealand s LBD enables formulation of longitudinalfinancial proxies representing a range of potential optimal hedging
determinants hypothesised in the studies cited above. One feature that sets
our study apart from prior studies of firm hedging behaviour is the breadthof our coverage. Almost invariably, prior studies have concentrated on small
subsets of firms that are often quite homogeneous in certain respects, forinstance very large US firms (Bodnar and Gentry, 1993; Geczy et al, 1997;
Allayannis and Ofek, 2001; Hentschel and Kothari, 2001), large European
firms (De Ceuster et al, 2000) or firms in specific commodity markets(Tufano, 1996; Haushalter, 2000; Brown et al, 2006; Meredith, 2006). These
selective samples mean that most such results are not generalisable acrossthe great bulk of firms in an economy, most of which are not exchangelisted and which cover a wide range of sectors. By contrast, our data source
includes almost all private sector firms across the country, with currency
hedging information available for virtually all firms that have exported amerchandise item at any time between 2000 and 2007. This provides wide
coverage of firms across sectors and across size and age cohorts. It alsoenables us to use estimation methods that minimise selection issues (forinstance, regarding which firms choose to export to a certain market in acertain currency at a certain time).
Another key factor that sets our study apart from others is the longitudinal
nature of our data. Rather than using a single cross-section as in many priorstudies (eg, Geczy et al, 1997), we use longitudinal data (aggregated to amonthly frequency) over seven years. Thus our results are less subject to thecriticism of cross-sectional studies that the results may be time-specific andso not generalisable under different economic conditions. The longitudinal
nature of the data is key to estimating whether selective hedging occurs.

5In section 2 of the paper, we outline our hypotheses and discuss modellingissues that must be dealt with. In particular, we are careful to delineate two
separate approaches to dealing with potential selection bias. Section 3
outlines our data sources and provides some descriptive statistics of relevant
variables. Section 4 presents our results, both with respect to optimal
hedging determinants and selective hedging practices. Our major results,
particularly with respect to selective hedging, are robust to a variety of
specification tests, splitting the population across various dimensions, anddifferent ways of handling selection issues. Section 5 concludes anddiscusses future directions for research. A major unresolved issue, given wefind strong evidence that many firms practice selective hedging, is why theyshould do so when prior evidence (and efficient markets) indicates that such
behaviour, on average, adds no value to the firm. Our split population
results give one clue in this direction but cannot fully explain the propensityof firms across all types to engage in selective hedging.

2

Hypotheses and Modelling Issues

We estimate the determinants of exporters hedging decisions, focusing on
currency hedging decisions of New Zealand firms that export merchandise
goods to Australia. Between 2004 and 2007, almost equal proportions ofthese exports were denominated in Australian dollars (AUD) and New
Zealand dollars (NZD), at 47.1 percent and 43.3 percent respectively(Fabling and Grimes, 2008). A small proportion was denominated in othercurrencies, chiefly USD, but these trades are not our focus for the remainderof the paper. The aggregate share of AUD-denominated exports hedged
back to NZD varied between 20 percent and 32 percent over the same
period.
The presence of exports in both AUD (hedged and unhedged) and NZD
raises a definitional and modelling issue to be addressed: Is the currency ofdenomination a choice variable of exporters, or are exporters currencytakers , at least over the relevant time horizon? If currency is not a choicevariable for the exporter, we can define hedged transactions as AUD exportsthat are explicitly hedged back to NZD (eg, by forward contracts).3 If
currency is a choice variable, we need to define hedged transactions as also
including all NZD-denominated exports. We adopt two different model
specification approaches catering for each of these possibilities.
3

Brooks et al (2000) find that forward contracts are the predominant form of currency
hedging used by New Zealand exporters.

6

In the first approach, we treat currency of denomination as exogenous andmodel the decisions of firms to hedge (or leave unhedged) their AUD
exports to Australia. In the second approach, we model the decisions offirms to hedge either by explicitly covering their AUD-denominated exportsback to NZD or by denominating exports in NZD versus the alternative ofdenominating exports in AUD and leaving those transactions unhedged.
Under either approach, because of the nature of our longitudinal panel, we
are able to model optimal hedging determinants and selective hedging
decisions together.
A number of econometric issues prevent use of simple OLS regression
under either approach. In particular we face selection and truncation issues.
Using the first approach (exogenous currency of denomination) as ourbaseline model, let Hit be the proportion of firm i s AUD-denominatedexports to Australia in month t that are hedged,4 given that firm i exports in
AUD in t; and noting that 0<Hit<1. This is a truncated regression problem
with both selection effects and a limited range for the observed dependent
variable. The selection issue arises since we are conditioning only on firmsthat export in AUD in month t. This variable may be a choice variable of thefirm not only for currency denomination reasons (as in our second
approach) but also because the export decision itself, including its timing,
may be a choice variable.
Specifically, consider two latent variables, H it* and Z it* , generated by thebivariate process in (1) where Xit and Wit are vectors of observations on
exogenous (or predetermined) variables, and are unknown parameter
vectors, is the standard deviation of it, and is the correlation between it
and it. We only observe the sign of Z it* so the variance of it is restricted to
1.
H it*

X it

it

it

Z it*

Wit

it

it

2

~ NID

,

1

(1)

The variables that we observe are Hit (the proportion of firm i s AUD
exports in t that are hedged) and Zit (a binary variable denoting whether firm
i exports in AUD in t) where:
Hit = H it* if Z it* > 0;
4

Hit unobserved otherwise

Value calculations for this variable use AUD as numeraire.

7

Zit = 1

if Z it* > 0;

Zit = 0 otherwise(2)

To deal with these selection and truncation issues we use Heckman s twostep (Heckit) method involving a selection equation plus a structural
equation that estimates the parameters of interest.5 The selection equation isa probit estimating whether firm i exports in AUD in t. This equation isused to obtain consistent estimates of which, in turn, are used to constructestimates of it.
The structural equation estimates the parameters of the hedging function
given the decision to export in AUD. Specifically, we estimate the tobit
equation:
Hit = Xit

+

it

+ eit

(3)

where the inverse Mills ratio (using Wit and the estimated from the probit
equation) is used to proxy it. This approach yields consistent estimates ofconditional on the assumption of bivariate normality. Since
0, the tstatistic on the inverse Mills ratio (IMR) in (3) can be used to test the null
hypothesis of =0. The precision of estimates is dependent on theinformation in Wit relative to Xit; accordingly, we include extra elements inthe selection equation that do not appear in the structural equation. We test
robustness of our approach by estimating an alternative structural equationthat divides the IMR observations into deciles, then using these deciles inplace of the IMR in (3), so not relying on the linearity assumption implicitin (1) and (3).
In our application, the elements of Xit comprise variables hypothesised to beimportant in the optimal hedging literature together with dynamic exchange
rate variables to test for the presence of selective hedging. Additional
explanatory variables are available for inclusion in Wit since the selection
equation includes variables that predict whether firms (a) export; and (b)export in AUD. These variables are not included in the structural equationthat predicts whether firms will choose to hedge any resulting AUD
exposures.

5

See Davidson and MacKinnon (2004). Full information maximum likelihood (FIML)
provides an alternative estimation method. In a recent application using both full sample
and truncated sample data, Johansson (2007) finds that, while similar point estimates are
obtained, the FIML estimates are less efficient than those obtained from the Heckit method.

8Under our first approach (assuming that exporters are currency-takers ),the dependent variable in the first stage regression is a binary variable.
Specifically, XAUDit = 1 if firm i exports to Australia in AUD in month t,and zero otherwise.6 The dependent variable in the second stage regression(H1it, taking the role of Hit) is the proportion of AUD-denominated exports
of firm i hedged in month t.
In the second approach (where the exporter chooses the currency of trade),the dependent variable in the first stage regression is a binary variable(XAU2) where XAU2it = 1 if firm i exports to Australia in AUD or NZD inmonth t, and zero otherwise. The dependent variable in the second stage
regression (H2it) is the proportion of firm i s exports to Australia in t that areeither denominated in NZD, or in AUD and hedged back to NZD.
Variables included in Xit that are hypothesised to influence optimal hedging
decisions are listed in the appendix (together with the expected coefficient
sign in the tobit equation). Two hedging experience variables are included:
ZHK and MHK; the former (binary) variable measures whether firm i hasever hedged an export previously (ie, since August 1997, the date whenthese data were first captured) while MHK measures the inverse of thenumber of months since the last hedging transaction. Both are expected to
be positive,7 consistent with hedging being more likely where there is somein-house expertise. An alternative specification tests whether the results arerobust to the specification of the functional form in MHK by replacing
MHK with three dummy variables depending on whether the most recent
hedging transaction was less than 1 year ago, between 1 and 3 years ago,and over three years ago; these are denoted MHK_1YR, MHK_3YR and
MHK_>3YR respectively.8
Our access to taxation and other financial data enables us to specify two
financial variables related to the probability and cost of financial distress:
DER is a measure of the debt-equity ratio (defined here as debt/(debt +equity)); and ICR is the interest coverage ratio. In each case, an increase in
the variable indicates a financially more fragile position (ceteris paribus), so
increasing the incentive to hedge currency risk (thus coefficients are

6

That is, XAUDit takes the role of Zit; i and t subscripts are henceforth suppressed in the
text where the meaning is clear.
7
Here and elsewhere, we describe the alternative hypothesis against the null of zero.
8
Corresponding variables are included for our second approach, with hedging experience
defined also to include prior exports denominated in NZD.

9hypothesised to be positive).9 Another financial variable relates to the firm s
tax position. New Zealand has a proportional (linear) company tax regime
for firms with positive profits. However, a firm with a tax loss carryforward faces a convex tax schedule, so has the incentive to lock in a
tranche of tax-free profits. The variable ZTX is a binary denoting whether afirm has a tax-loss carry-forward position, with the hypothesis implying a
positive coefficient.
Considerable evidence exists in prior literature that hedging is moreprevalent in larger firms than in smaller firms; this has generally beeninterpreted as a scale effect (eg, Marsden and Prevost, 2005). However,there is reason to doubt the scale argument. For instance, Geczy et al (1997)
find a positive relationship between firm size and hedging propensity
amongst Fortune 500 firms. All such firms must reasonably be expected tohave sufficient scale to be able to hedge currency and other financial risks,
so the positive relationship may reflect other factors. Almost uniquely, we
are able to differentiate between a pure scale (firm size) effect and otherfactors that may be positively correlated with scale but imply different
causal links; for instance, reflecting diversification. Our scale variable is(log of) real total sales, LSAL. Other variables that may be correlated withsales but that reflect different channels are measures of diversification: DPC(number of product types10 exported in the past year), DMC (number ofmarkets exported to in the past year), DCC (number of currencies used toexport in the past year) and DIC (number of countries imported from in thepast year). In each case, we hypothesise that the greater the diversification,
the less the need to hedge any particular transaction (so negative coefficientsare expected in each case). These diversification variables are likely to becorrelated, so we also estimate a specification in which the four variables
are replaced by their first principal component (PCA).
Export and import intensity are hypothesised to be potentially important
determinants of hedging behaviour, although their impacts on specifichedging choices will be affected by the currency exposures for thesetransactions. (These variables can be considered as supplements to theexplicit diversification variables.) For our first approach, a firm with a highproportion of NZD denominated exports relative to sales (FXNS) may
already be well hedged and so choose not to hedge AUD denominated9

We also have data on the quick ratio ; however this variable has a correlation coefficientwith the debt-equity ratio of 0.83 (in the tobit sample) and so is dropped from the analysis.
10
Defined at the Harmonised System ten-digit (HS10) level.

10exports (a negative coefficient). Conversely, a firm with a high proportionof AUD denominated exports relative to sales (FXAS) will be heavily
exposed to movements in the NZD/AUD and so choose to hedge a greater
proportion of its AUD denominated exports (a positive coefficient). A firmwith a high proportion of other currency (non-AUD and non-NZD) exports
relative to sales (FXOS) has some degree of currency diversification inplace and so may choose not to hedge AUD denominated exports. However,such a firm is also likely to have strong experience of currency markets andthis may make it more likely that it will hedge; thus the hypothesised
coefficient on FXOS is of indeterminate sign. In each of these three cases,
we split off re-exports (FXNRS, FXARS, FXORS) since the hypothesised
sign in each case is less clear than for standard exports (thus each coefficient
has an indeterminate sign). In the second approach, the set of export
intensity variables is replaced by simpler variables measuring exports (andre-exports) as a proportion of sales since, in that approach, currency istreated as a choice variable.
Imports may also provide a form of currency hedge. We do not havecurrency denomination data for imports and do not have data on firms
indirect purchases of imported goods. Instead, we include the proportion ofAustralian imports relative to sales (FMAS) and the proportion of othercountry imports relative to sales (FMOS). These are (possibly poor) proxies
for offsetting currency exposures stemming from imports. To the extent thatthese variables proxy for firms currency import exposures, we hypothesisethat FMAS will have a negative coefficient reflecting a natural hedge
position; FMOS is of indeterminate sign reflecting a balance of experienceand diversification influences.
A strong body of theory (but not such a strong body of empirical results)
indicates that firms faced with underinvestment risks are likely to hedge
more than other firms in order to lock-in internally generated funds tofinance expansion. Traditionally, the difficulty in testing this hypothesis isfinding adequate proxies to identify such firms. Our access to balance sheet
data enables us to form a relevant proxy: the intangible asset ratio (ITA),
defined as the ratio of intangible assets to total assets of the firm. Firms witha high ITA are expected to have greater likelihood of strong growthprospects, and so hedge risks more comprehensively (positive coefficient).
Conversely, companies with a high dividend to profit ratio (DTP) signal thatthey are not constrained by internal capital shortages and thus have less ofan incentive to lock in expected profits (negative coefficient). Firms withhigh capital requirements may face a relatively high need to lock in

11internally generated funds to finance depreciation or further expansion; weproxy this influence by the depreciation to total expenses ratio (DTE), withthe hypothesis implying a positive coefficient.
Governance and ownership structures may be important for determining
hedging decisions owing to different risk appetites. We control for four
different types of ownership structure: sole proprietor (ZBT_SP),
partnership (ZBT_PART), state-owned enterprise (ZBT_SOE) and company(the omitted category in the equation). We have no priors on the coefficientsigns for these variables. In addition, we include a control variable(ZBT_FOR) for whether the firm is defined as a foreign-controlled firm in
any of the data sources. We hypothesise that foreign owned firms will hedge
AUD exposures less than do New Zealand owned firms (thus have anegative coefficient) for two reasons. First, they may be owned by anAustralian company in which case the translation exposure offsets thetransaction exposure and no hedging is required. Second, non-Australianowned firms will tend to have greater diversification across markets than
New Zealand owned firms and so have less incentive to hedge any one
source of currency risk.
We include 97 sector controls (HS_k). These take the form of binary
variables equal to one if the firm has ever exported a good in the (two-digit)
HS Chapter (between 1997 and 2007) and zero otherwise. These variables
are not reported (owing to confidentiality restrictions) but are included toensure that the results are not driven either by sectoral differences in
hedging propensities or by sectoral differences in the means of the
explanatory variables.
Extra variables are required in the probit equation (ie, variables that appear
in Wit that are not included in Xit). Under our first approach, we include
variables in the probit that help predict whether a firm will export toAustralia in AUD (but that do not help explain the subsequent hedging
decision). These include dummy binary variables respectively for whether
the firm has ever exported prior to month t (FEX), has exported to Australia
prior to or in t (FEA and XAU respectively), and has ever exported in AUD
prior to t (FAU). We also include variables indicating the (inverse of the)length of time since these actions occurred (MEX, MEA and MAU
respectively). Month dummy variables (ZMk) are included given theseasonality in exports.

12For the probit equation, we replace ZHKit and MHKit (ie, variables includedin the tobit indicating whether the firm has hedged before, and time sincehedging) with their respective sector averages (ZHK_ind and MHK_ind).
We do so to ensure that we have an independent predictor of currency
hedging experience in the probit that is not based on the firm s own hedging
experience.
The selective hedging variable, that appears in both Xit and Wit, is thedeviation of the NZD/AUD exchange rate from its three-year lagged moving
average, AUD3:
AUD3t

NZD / AUDt
36

NZD / AUDt i / 36
i 1

We include AUD3t plus twelve lagged changes in AUD3 (AUD3d1, ,AUD3d12, where AUD3dx = AUD3t-x+1 AUD3t-x). This specification isequivalent to including the current plus twelve lags of AUD3; it enables us
to summarise the overall impact of the selective hedging decision solely
with reference to the coefficient on AUD3t. We choose the deviation of
NZD/AUD from an historical average reference point since the descriptive
and anecdotal evidence (eg, Brookes et al, 2000) indicates that at least some
exporters make hedging decisions based on backward-looking benchmarksof normal exchange rate levels; ie, they assume at least some degree ofmean reversion in the NZD/AUD rate. Fabling and Grimes (2008) used avariety of backward-looking windows in their aggregate hedging study andfound similar results when using one, three, five and ten year windows thethree-year window had slightly higher explanatory power for aggregate
hedging movements than the other windows. Use of an historical window
has the advantage over potential smoothing filters (such as a HodrickPrescott filter or a Baxter-King band-pass filter) that it does not use any
future data in its construction; thus it includes only information actuallyavailable to the firm at time t.
Our null hypothesis, consistent with joint hypotheses of rational behaviour
and efficient markets, is that firms do not hedge selectively; the alternative
hypothesis of selective hedging implies a negative coefficient on AUD3.
Previously cited cross-sectional studies for different countries suggest thatmany firms engage in some degree of selective hedging in interest rate andexchange rate markets. The puzzle that arises from these studies is why such
behaviour might occur, especially within deeply traded financial markets.

13In order to investigate this issue more closely we invert the problem. We
hypothesise that certain types of firm are more likely than others to display
consistent hedging behaviour arising from explicit adoption of firm-specificfinancial policies. Specifically, we hypothesise that firms for which
exporting is a major activity will be most likely to have explicit hedging
policies in place and so will not be as prone to engage in selective hedging
as less intensive exporters. The latter group may export only irregularly, andso may not have developed an explicit hedging policy. In that situation, thechoice of whether or not to hedge a specific export shipment must be
addressed on each occurrence. We hypothesise that it is these latter firmsthat are most likely to be influenced in their hedging decision by the current
level of the exchange rate relative to some perceived norm.
This hypothesis implies that firms with low export intensity (exports/total
sales) will be more prone to selective hedging than firms with high export
intensity. We explicitly test this hypothesis. Similar reasoning applies tofirm differences according to degree of trade intensity ((exports + imports)/
total sales), size and foreign-ownership. Financially fragile firms (eg, highleverage) or firms that wish to lock in profits because they are in a tax-loss
situation may behave differently to exchange rate fluctuations than do otherfirms; we test whether selective hedging propensity differs according tothese criteria.
Firms as a whole may be more prone to selective hedging at some parts ofthe perceived exchange rate cycle than at others. Accordingly, we test
whether selective hedging behaviour differs according to whether AUD3 isabove or below unity (ie, whether NZD/AUD is above or below its past
three year average). We also test stability of selective hedging practices
across AUD3 quartiles in case distance from the recent mean level affects
selective hedging propensity. Thus, not only do we test for the existence of
selective hedging within the population, we also try to infer from our firmand exchange rate groupings the processes that might lie behind any
observed selective hedging behaviour.

3

Data

Our comprehensive panel comprises all New Zealand-based firms that everexported to Australia between July 2000 and March 2007, subject to14

minimum threshold requirements.11 This period (81 months) constitutes ourestimation period and is fixed by the availability of lagged financial data,
noting that we use previous financial year data to minimise any endogeneity
issues.12 Subject to the exporting requirement, a firm will be included in thepanel if it is economically active over two consecutive years from 20002007. That is, we adopt an unbalanced panel (although most firms arepresent in all years). To be economically active a firm must be observedin our broad-ranging administrative data as either: selling products,
purchasing intermediate inputs, employing staff or holding physical capital.
The population includes firms in all sectors other than foreign-locatedfirms,13 households, and not-for-profits. These restrictions leave us withapproximately 12,500 firms in our population.
Our main set of estimates is conducted for firms that have no imputed data
attributed to them. We do so to ensure data reliability. With this sample, ourprobit equation (using our first approach to defining hedging) comprises
647,952 firm-month observations; the tobit equation in our first approach(which includes only firms that export to Australia in AUD in month t)comprises 38,892 firm-month observations. The potential drawback of using
only unimputed data is that we may incur some selection bias if firms
requiring imputation constitute a non-random sample. To check robustnessof our results, we also estimate our baseline probit and tobit equations withall firms that have both unimputed and imputed data;14 this increases theobservation count in the two equations to 948,120 and 53,868, respectively.
The size distribution of firms included in our study is shown in the kernel
density graph, Figure 1, for LSAL (logarithm of real sales).15 The
distribution of annual real sales is remarkably symmetric and covers a verybroad range of firm sizes extending from well below the mandatory
Customs filing threshold (of NZD1,000) to over NZD1billion per annum.
11

The key threshold requirements are at NZD$40,000 p.a. of income (to be subject tomandatory GST filing), and $1,000 consignment value (to be subject to mandatory Customs
filing). On average over the observation period, 1NZD=0.87AUD=0.57USD. Given theextremely low nature of the thresholds, we can be virtually certain that our data excludes
very few trading firms.
12
In a very small number of cases financial data has been projected back or forward at thestart or end of the observation period to avoid being further limited by rare balance dates.
13
Foreign-owned firms located in New Zealand are included.
14
The imputed data is supplied by Statistics New Zealand and is constructed using a mix ofhistorical, donor and linear interpolation methods.
15
The kernel density is calculated excluding the top 1% and bottom 1% of the distribution
due to confidentiality restrictions.

15

Figure 1
Distribution of LSAL (logarithm of real sales)
0.25

Density

0.20
0.15
0.10
0.05
0.00
8

9

10

11

12

13

14
15
LSAL

16

17

18

19

20

* Kernel density plot using Epanechnikov kernel function. Top and bottom one percent of distribution
excluded to comply with Statistics NZ confidentiality rules

Figure 2
Distribution of DER (Debt/(Debt+Equity))
1.4
1.2

Density

1.0
0.8
0.6
0.4
0.2
0.0
0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

DER
* Kernel density plot using Epanechnikov kernel function. Observations at zero (17.7%) and one(15.2%) excluded to enable the distribution at intermediate values to be seen more clearly.

The distribution of firms financial states also varies considerably. Figure 2
plots the kernel density of DER (debt to equity ratio), excluding firms with
DER=0 (17.7 percent of firms) and firms with DER=1 (15.2 percent of16

firms). Slightly fewer than half of firms have DER less than 0.5. Bycontrast, Figure 3 shows that ITA (intangible assets/total assets) is heavily
skewed towards zero; 73.6 percent of firms have ITA=0.16 For the large
bulk of firms, intangible assets comprise less than 10 percent of total assets.

Figure 3
Distribution of ITA (Intangible Assets/Total Assets)
14
12

Density

10
8
6
4
2
0
0.0

0.1

0.2

0.3

0.4

0.5
ITA

0.6

0.7

0.8

0.9

1.0

* Kernel density plot using Epanechnikov kernel function. Observations at zero (73.6%) excluded toenable the distribution at other values to be seen more clearly.

The mean monthly value of H1 (the hedging variable in our first approach)is shown in Figure 4. For the first one to two years of the observation period
it ranges mostly between 15 percent and 20 percent, before dropping to
between 5 percent and 10 percent over the final five years. The drop
coincided with a sharp rise in AUD3 (deviation of NZD/AUD from its
lagged three year average), also shown in Figure 4. Thereafter, several
periods of inverse movements between the two series occur. The correlation
coefficient between the two series over the study period is -0.35, consistent
with the aggregate indicators of selective hedging in Fabling and Grimes(2008). In our econometric work, presented in section 4, we are able to test
whether this inverse relationship holds up at the unit record firm level once
we control for other (optimal hedging) influences; we also test whether the
relationship is consistent across the different quartiles of AUD3.
16

Our econometric estimates in section 4 include firms with DER=0, DER=1 and ITA=0.
These observations have only been excluded from Figures 2 and 3 to enable the distribution
at other values to be seen more clearly.

17

Figure 4
Mean Monthly Value of H1 (AUD Hedging Proportion) and
AUD3 (NZD/AUD deviation from lagged 3 year average)
0.25

1.15
Mean(H1)

0.20

AUD3 (RHS)

1.10

0.15

1.05

0.10

1.00

0.05

0.95

0.00
July 2000

0.90
July 2001

July 2002

July 2003

4

Results

4.1

Estimated Equations

July 2004

July 2005

July 2006

In reporting results, we concentrate on those using our first definition ofhedging (where the currency of denomination is exogenous to the firm) anduse only unimputed data. Subsequently, we report results that incorporate
imputed data and describe results using our second hedging definition(including NZD exports as hedged exports). Our reporting focuses primarilyon the tobit (rather than the probit) results since our interest is in thecurrency hedging decision rather than the prior export decision. We report
both the optimal hedging findings and the selective hedging results which
are estimated together. The latter constitute the most novel aspect of ourstudy. In particular, the extensive controls that we include for optimal
hedging determinants and the comprehensive nature of firm sizes andsectors in our dataset make the results with respect to selective hedging
particularly rigorous compared with those in prior studies.17
Table A.2 in the appendix presents results using the first hedging definition.
All columns, except the final two, report results for the sample that uses
17

The 97 sector controls are jointly significant (p=0.000) in every equation, but theircoefficients are not reported for confidentiality reasons.

18

only unimputed data. Column 1 presents the probit equation used to predictwhether firm i exports to Australia in AUD in month t (p-values are shownin square parentheses for each coefficient). We are able to predict theexporting choice with a high degree of precision (pseudo-R2=0.682). We
calculate the inverse Mills ratio from the probit regression for inclusion in
the tobit equation.
Our main tobit equation (column 2) contains the optimal hedging andselective hedging variables described in section 2 plus the inverse Mills
ratio (IMR). The latter is highly significant implying that the selection
equation is required. Note, however, that when we omit this ratio (column 5)
the results do not change markedly. One potential criticism of including the
raw IMR is that the implied linearity assumption may not be appropriate. To
test sensitivity of the results to an alternative specification, we include a setof binary dummy variables indicating whether the observation is in thesecond to tenth IMR deciles (the first decile being the omitted variable).
These results are reported in column 6; again other results are not sensitive
to this alternative specification. Thus we concentrate on the main results incolumn 2.

4.2

Optimal Hedging

All but one of the optimal hedging variables that are significant at the 5
percent level have the expected (alternative hypothesis) sign. The hedging
experience variables (ZHK and MHK) are both highly significant (p=0.000)
indicating that firms have an increased potential to hedge if they have pasthedging experience.
Natural hedging opportunities affect the propensity to hedge AUD exports.
Firms that export in a large number of currencies (DCC) and that importfrom a large number of countries (DIC) are less likely to hedge their AUD
exposures. Other natural hedge proxies (DPC and DMC) are not significant,possibly because these four variables are highly correlated with one another(the six bivariate correlation coefficients range between 0.31 and 0.68). In
column 4, we replace the four variables with their first principal component(PCA). The principal component is highly significant and negative
indicating that natural hedge opportunities reduce the need to hedge
explicitly. Other results are not materially affected.
The hypothesis that firms facing under-investment risks tend to hedge more
intensively is supported. Firms with high intangible asset ratios (ITA) hedge

19

a higher proportion of their AUD exposures (this result holds even with theinclusion of extensive sector controls in the equation). This result is robust
across all specifications including when imputed data is used (column 11),although the significance and size of the coefficient falls in that case. One ofthe difficulties in testing the under-investment hypothesis in past work has
been the difficulty of finding a suitable proxy for firms with high growthprospects. The intangible asset ratio provides an appealing proxy for such
firms, but has not generally been available to past researchers, andimputation techniques are unlikely to be able to replicate such a variablewith high precision. The drop in size and significance of ITA in column 11
is consistent with these observations.
Three other financial variables are significant in the main tobit regression.
The tax-loss carry-forward position (ZTX) is significantly positive
indicating that such firms wish to lock in profits so as to ensure use is madeof their tax-loss position. The debt-equity ratio (DER) is significantly
positive, indicating that firms with a more fragile balance sheet structure(relative to sector norms) are more likely to hedge. However, the interest
coverage ratio (ICR) is significantly negative. This is the only variable,
significant at 5 percent, that has a sign different from that hypothesised. One
problem with inclusion of both DER and ICR is that the two variables aremoderately positively correlated (correlation coefficient = 0.27). In column
3, we omit ICR; DER continues with a positive coefficient (p = 0.116);other coefficients are little changed. The results for the financial variables,
therefore, provide some support for the hypothesis that firms with morefragile balance sheets (and/or with tax losses) tend to increase their hedging
of exchange rate exposures.
One important finding relates to firm size. Our firm scale variable (LSAL) is
not significant (even at the 20 percent level) in the main tobit regression;
when imputed data is added (column 11) the variable remains insignificant(and the point estimate is slightly negative). The insignificance of scale isrobust across the specifications without ICR, with PCA, and without IMR(columns 3-5), albeit with some limited significance (p=0.087) when thealternative IMR specification is used.
One reason why prior studies may have (mistakenly) reported a firm scale
hedging effect is if a positive correlation existed between firm size and otherfirm characteristics that were omitted from those analyses. To test thispossibility, column 7 presents results for an equation that omits the hedging

20

experience and diversification (and related) variables.18 These variables
have moderate to high positive correlations with firm size. The results incolumn 7 indicate a sizeable and significant firm scale effect (p=0.000 for
LSAL) when these variables are omitted. Column 8 reintroduces the two
hedging experience variables, but continues to omit the diversification
variables; firm scale returns to insignificance. Together, these results
indicate that larger firms tend to have experience in hedging exchange raterisk. However, once firms (of whatever size) gain this experience, there isno subsequent firm scale effect. We can thus provide a rationale for, and aninterpretation of, the firm scale effects found in prior studies.
To test the robustness of this result we include an alternative definition of
the hedging experience variables in column 9. Rather than including adummy variable indicating prior hedging experience plus a variablecalculated as the inverse of the number of months since last hedging
experience, column 9 includes just three binary dummy variables indicating
whether a firm has most recently hedged (a) in the past year; (b) one to three
years ago; or (c) more than three years prior (with never having hedged
being the omitted variable). The three coefficients diminish in size as themost recent experience is extended back in time, with only the two more
recent variables being significant. These results accord with the hypothesisthat prior (but not too distant prior) hedging experience is an important
determinant of current hedging choices. Firm size is now found to besignificant, albeit with a much smaller coefficient than in column 7.
Nevertheless, the explanatory power of this variant of the equation is not ashigh as for the main equation, so the main equation is preferred.

4.3

Selective Hedging

We find no evidence that the decision to export to Australia in AUD isdriven by the level of the NZD/AUD relative to its past three year average(AUD3). This result, from the probit equation, holds regardless of whether
imputed data is included or excluded with p 0.45 on AUD3 in columns 1
and 10. In turn, this finding implies that our first hedging definition isappropriate since currency of export denomination is unaffected by actual
exchange rate levels.

18

That is, omitting ZHK, MHK (hedging experience) and FXNS, FXNRS, FXAS, FXARS,FXOS, FXORS, FMAS, FMOS, DPC, DMC, DCC, DIC (diversification).

21

In the tobit equations, by contrast, we find consistent evidence that AUD3
affects the proportion of AUD exposures that is hedged. All specifications,
including when imputed data is included, have a negative coefficient on
AUD3 with a high degree of significance (p=0.000). Our estimates imply
that a 1 percent rise in the NZD/AUD cross rate relative to its lagged threeyear average (with all other variables held at their means) induces an initial
fall in H1 (the proportion of AUD export exposures that is hedged) from amean level of 9.4 percent to 8.1 percent.19 This confirms that the selective
hedging effect is of a material economic magnitude as well as being
statistically significant. The coefficient on AUD3 is extremely stable acrossspecifications except when the hedging experience variables (ZHK, MHK)
are omitted (column 7). In this case, the coefficient jumps markedly inabsolute value. This finding, which suggests that there is an interplay
between hedging experience and the propensity to hedge selectively, iscongruent with our hypothesis that firms with different exporting experience
may respond differently to exchange rate fluctuations.
We explore this hypothesis in more depth by allowing for different AUD3
coefficients for different types of firm in the tobit specification. Row 1 ofTable 1 reports the AUD3 coefficient for two classes of firm definedaccording to their export intensity; low firms ( high firms ) have export
intensity below (above) the median for the population. We interact a dummy
variable delineating the two subsets of firms with AUD3 (and with the
AUD3dx variables) and report the resulting AUD3 coefficients and p-values
for each firm class. The final column of the table reports the p-value andconclusion for the null hypothesis that the two AUD3 coefficients are thesame.
We are able to reject the null hypothesis of identical AUD3 coefficients
when firms are split according to their export intensity. High export
intensity firms hedge more consistently across NZD/AUD fluctuations thando less export intensive firms. While this result is consistent with ouralternative hypothesis, we nevertheless find that even high export-intensive
firms engage in selective hedging (p=0.000).
We perform similar tests along other dimensions, dividing firms into two
categories for each of trade intensity (row 2, Table 1), scale (row 3), foreign
19

This calculation combines the effects of the coefficients on AUD3t and AUD3d1t (since
AUD3t appears in both terms). The dynamic effect on hedging propensities beyond theinitial period will depend on the subsequent dynamic behaviour of NZD/AUD (and henceof AUD3) which we do not model here.

22versus domestic ownership (row 4), leverage (row 5) and tax-loss position(row 6). In each of these cases, we cannot reject the hypothesis that selective
hedging behaviour is identical across the two firm classes.

Table 1
AUD3 coefficients for different firm and AUD3 splits
including tests of coefficient difference
Coefficient
On Low

Coefficient
On High

Different?

Split according to:
Export Intensity(Low = below median)-8.318[0.000]-5.948[0.000]YES[0.024]

Trade Intensity(Low = below median)-7.695[0.000]-6.789[0.000]NO[0.390]Scale (LSAL)(Low = below median)-7.164[0.000]-7.208[0.000]NO[0.967]

Foreign Ownership (ZBT_FOR)(Low = domestic ownership)-7.079[0.000]-7.725[0.000]NO[0.601]

Leverage (DER)(Low = below median)-6.643[0.000]-7.697[0.000]NO[0.318]

Tax-Loss Position (ZTX)(Low = no tax-loss carry-forward)-7.138[0.000]-7.553[0.000]NO[0.752]

AUD3(Low = below one)

-10.967[0.000]-8.883[0.000]NO[0.428]

Tobit equation results corresponding to Column 2 of Table A.2, but with AUD3 (and AUD3dx)
coefficients interacted with binary dummy variables. Binary dummy variables created as indicated foreach case in the initial column. AUD3 coefficients only are reported. p-values in brackets.

Row 7 reports results from applying the same test (for all firms) where thesplit depends on whether AUD3 is greater or less than unity. We cannot
reject symmetry in selective hedging behaviour in this case. We investigate
this result further by dividing AUD3 into its four quartiles recognising thepossibility that the intensity of selective hedging behaviour may differ
according to the degree of exchange rate departure from recent norms aswell as the direction of that departure. We find that the selective hedging
coefficient is not significantly different from zero when AUD3 is in thethird quartile (ie, the exchange rate is a little below its recent past) but it
remains significantly negative for each of the other three quartiles.
Furthermore, we cannot reject the null hypothesis of equal coefficients23

across all four AUD3 quartiles, indicating that behaviour is consistent acrossdifferent directions and magnitudes of exchange rate fluctuation.
The tests reported in Table 1 indicate that selective hedging behaviour isprevalent across disparate classes of firm and across different exchange rate
outcomes relative to history. The only case where we find a significant
difference in firm selective hedging behaviour is where firms differ in theirexport intensity. Firms that are more heavily reliant on exports tend to hedge
more consistently than do other, perhaps opportunistic, exporters.
Nevertheless, even here, we find that high export intensity firms still tend toengage significantly in selective hedging.
The analysis above has been conducted using our first hedging definition,
which appears to be the appropriate definition given our probit results.
Nevertheless, we have also estimated the models using our second hedging
definition (including NZD-denominated exports to Australia as hedgedtransactions). We again find no impact of AUD3 within the probit equation(p=0.566),20 while continuing to find a statistically significant impact of
AUD3 in the tobit specification (p=0.000), albeit with a smaller absolutemagnitude for the coefficient (-1.487). Thus even if firms have the ability tochoose their currency of denomination, their broad hedging decision
remains responsive to the level of NZD/AUD relative to its historical
average.

5

Conclusions

Firms that have currency exposures must decide whether they should hedge
these exposures and, if so, how. Costs of financial distress, underinvestment risks, tax considerations, expertise, experience and the presenceof natural hedges all potentially impact on the optimal hedging decision.
Firms must also decide whether to maintain a consistent hedging policy orwhether to vary their hedging positions in response to currency movements.
Efficient markets theory, and prior empirical evidence, suggests that thelatter strategy ie, selective hedging is not commonly profitable for firms.
However, recent studies suggest that this behaviour is neverthelessobserved.
This study is the first to examine both optimal and selective currency
hedging behaviour by exporters across a comprehensive longitudinal panel
20

Full regression results are not reported in this paper, but are available from the authors onrequest.

24

of exporting firms. Our access to administrative (official statistical, taxationand trade) data for almost all private sector firms in the economy enables us
to control for selection effects as well as to track each individual firm s
currency hedging choices over 81 months (July 2000 March 2007). We
focus on the hedging decisions of New Zealand exporters exposed to
NZD/AUD risk through the denomination of their exports to Australia in
AUD. Our panel contains over 38,000 firm-month observations onexporters currency hedging choices drawn from over 600,000 firm-monthobservations on exporting and non-exporting firms activities.
Over our observation period, firms hedge an average of 9.4 percent of their
AUD exposure back to NZD. However, this ratio varies over time from amonthly average of 4.3 percent to one of 22.4 percent. Figure 4 indicatesthat the propensity to hedge falls as the NZD/AUD rises relative to its
lagged three year average (AUD3), indicative of some degree of selective
hedging. The pervasiveness of selective hedging behaviour is confirmed inour estimates. Even after controlling for a large range of factors that may
influence optimal hedging decisions, the proportion of exporters AUD
exposures that is hedged is influenced significantly by the level of AUD3.
Consistent with prior studies on selective hedging, we therefore find thatfirms with no apparent comparative advantage in the currency markets
nevertheless seek to time the market . This result is robust across all
subsets of firms that we have tested. The only case where different classesof firm have a significant difference in selective hedging behaviour is thefinding that high export intensity firms hedge more consistently than do low
export intensity firms; even here, however, significant selective hedging
behaviour is observed across both groups.
Our optimal hedging results shed light on some hitherto curious findings in
the literature. Perhaps most significant is that we do not find a firm scaleeffect once controls are included for other relevant firm characteristics that
may influence optimal hedging choices. In particular, once we control forfirms prior hedging experience, firm size has no effect on the hedging
propensity; however, when experience is omitted, firm size is statisticallysignificant. Prior studies that have found firm size effects on hedging
behaviour, even amongst listed S&P and Fortune 500 firms, may therefore
suffer from a lack of controls for hedging expertise and experience that may
be positively correlated with firm size. Our results in this respect appear to
make more sense than the competing conjecture that some Fortune 500 firm
have insufficient scale to undertake currency hedging activity.

25

Other key optimal hedging results indicate that firms with high growthprospects (proxied by a high ratio of intangible to total assets) hedge more
intensively as do firms in tax-loss situations. There is tentative evidence thatfirms with high leverage also hedge more intensively. Each of these findings
is consistent with prior theory. Firms that have well diversified trade andcurrency transactions tend to undertake less intensive explicit hedging of
AUD exposures, consistent with the existence of natural hedges.
The strong
and largely expected
optimal hedging results, obtained
within a comprehensive longitudinal dataset on virtually all private sector
firms involved in New Zealand s most economically important traderelationship, mean that the selective hedging results are unlikely to be a
product of omitted variable bias. Rather, our ability to track the same firms
over 81 months presents the opportunity to detect deviations of firms
hedging activities from levels implied by the optimal hedging determinants.
We cannot fully explain why firms deviate from their optimal strategies
when they have no comparative advantage in the currency markets. Part ofthe explanation appears to be that firms for which exporting is relatively
unimportant (low export intensity) display less consistency in their hedging
decisions. This may be because they have not developed explicit internal
policies within the firm to deal with this situation. However, this cannot be
the full explanation since even high export intensity firms still tend toengage in selective hedging behaviour. Future work may fruitfully explore
whether differing managerial characteristics help explain this observed
behaviour and/or whether firms respond to external hedging strategy advice(eg, from banks or specialist advisers). Whatever the explanations turn out
to be, our results indicate that selective hedging behaviour exists andappears to be ubiquitous across many classes of firm.

26References
Allayannis, George & Eli Ofek (2001) Exchange rate exposure, hedging,and the use of foreign currency derivatives , Journal of International
Money and Finance 20, 273-296
Beber, Alessandro & Daniela Fabbri (2006) Who times the foreignexchange market? Corporate speculation and CEO characteristics ,mimeo, University of Lausanne, Switzerland
Bessimbinder, H. (1991) Forward contracts and firm value: Investment
incentive and contracting effects , Journal of Financial andQuantitative Analysis, 519-532
Bodnar, G.M., G.S. Hayt & R.C. Marston (1996) 1995 Wharton survey ofderivatives usage by US nonfinancial firms , Financial Management,
113-133.
Bodnar, G. & W. Gentry (1993) Exchange rate exposure and industry
characteristics: Evidence from Canada, Japan and the U.S. , Journalof International Money and Finance 12, 29-45
Bodnar, Gordon & Genther Gebhardt (1999) Derivatives usage in risk
management by US and German non-financial firms: A comparative
survey , Journal of International Financial Management andAccounting 10, 153-187
Bodnar, Gordon, Abe de Jong & Victor Macrae (2003) The impact of
institutional differences on derivatives usage: A comparative study of
US and Dutch firms , European Financial Management 9, 271-297
Breedan, D. & S. Viswanath (1998) Why do firms hedge? An asymmetric
information model , Working Paper, Duke University
Brookes, Andy, David Hargreaves, Carrick Lucas & Bruce White (2000)
Can hedging insulate firms from exchange rate risk? Reserve Bank of
New Zealand Bulletin, 63(1), 21-34
Brown, Gregory W., Peter R. Crabb & David Haushalter (2006) Are firms
successful at selective hedging? , Journal of Business, 79, 2925-2949
Davidson, Russell and James MacKinnon (2004) Econometric Theory andMethods, New York: Oxford University Press

27

De Ceuster, Mark J.K., Edward Durinck, Eddy Laveren & Jozef Lodewyckx(2000) A survey into the use of derivatives by large non-financial
firms operating in Belgium , European Financial Management, 6,
301-318
Dolde, Walter (1993) The trajectory of corporate financial risk
management , Journal of Applied Corporate Finance, 6, 33-41
Fabling, Richard & Arthur Grimes (2008) Do Exporters Cut the Hedge?
Who Hedges, When and Why , Occasional Paper 08/02, Wellington:
Ministry of Economic Development
Faulkender, Michael (2005) Hedging or market timing? Selecting theinterest rate exposure of corporate debt , Journal of Finance, 60, 931962
Froot, K., D. Scharfstein & J. Stein (1993) Risk management: Coordinating
corporate investment and financing policies , Journal of Finance 48,
1624-1658
Geczy, C., B. Minton & C. Schrand (1997) Why firms use currency
derivatives? , Journal of Finance 52, 1324-1354
Glaum, Martin (2000) Finanzwirtschaftliches Risikomanagement deutscher
Industrie- und Handels-unternehmen, Frankfurt/Main: Moderne
Wirtschaft
Glaum, Martin (2002) The determinants of selective hedging: Evidence
from German non-financial corporations , Working Paper, JustusLieberg-Universitat Giessen
Graham, John & Daniel Rogers (2002) Do firms hedge in response to tax
incentives? , Journal of Finance 57, 815-839
Haushalter, G. David (2000) Financing policy, basis risk, and corporate
hedging: Evidence from oil and gas producers , Journal of Finance,
55, 107-152
Hentschel, Ludger & S.P. Kothari (2001) Are corporations reducing or
taking risks with derivatives , Journal of Financial and Quantitative
Analysis, 36, 93-118

28

Johansson, Fredrik (2007) How to Adjust for Nonignorable Nonresponse:
Calibration, Heckit or FIML? , Working Paper 2007:22, Departmentof Economics, Uppsala Universitet
Lel, Ugur (2004) Currency risk management, corporate governance, andfinancial market development , Working Paper, University of Indiana
Marsden, Alastair & Andrew Prevost (2005) Derivatives use, corporate
governance, and legislative change: An empirical analysis of New
Zealand listed companies, Journal of Business, Finance andAccounting, 32(1), 255-295
Meredith, Joseph (2006) Selective hedging: A test of two commodities ,Paper presented to 2006 Financial Management Association
conference,
Salt
Lake
City,
www.fma.org/SLC/Papers/selectivehedgttcj.pdf
Modigliani, Franco & Merton H. Miller (1958) The cost of capital,
corporation finance and the theory of investment , American
Economic Review, 48(3), 261-297
Nance, DR, CW Smith & CW Smithson (1993) On the determinants ofcorporate hedging , Journal of Finance, 267-284
Smith, C. & R. Stulz (1985) The determinants of firms hedging policies ,Journal of Financial and Quantitative Analysis 20, 391-405
Stulz, RM (1996) Rethinking risk management , Journal of Applied
Corporate Finance, 9, 8-24
Tufano P. (1996) Who manages risk? An empirical examination of risk
management practices in the gold mining industry , Journal of
Finance 51, 1097-1137
Working, Holbrook (1962) New concepts concerning futures markets andprices , American Economic Review, 52, 431-459

29

Appendix
Table A.1
Variables, Data Sources, Description, Expected Sign (tobit)
Variable

Sub

SourceDescription

E(sign,tobit)

Dependent Variables: Specification 1 (Currency is Exogenous)
1st stage: Firm exports in AUD in t(=1 if so; 0 otherwise)
2nd stage: Proportion of firm i's
AUD exports in t that are hedged,
Customs
H1
itcalc in AUD (0<H1<1), H1=(AUD
exports hedged)/(total AUD exports)
Dependent Variables: Specification 2 (Cover/Currency are Joint
Decisions)
1st stage: Firm exports to Australia
Customs
XAU2
itin AUD or NZD in t (=1 if so; 0
otherwise)
2nd stage: Proportion of firm i's
XAU2-defined exports that are
hedged, calc in NZD (0<H2<1),
Customs
H2
it
H2=(total XAU2 exports - unhedged
AUD OZ exports)/(total XAU2
exports)[py means "previous year" defined
Independent Variables
by the firm's financial reporting
dates]
Dummy=1 if firm has hedged any
Customs
ZHK
itexport before
1/(No. months since firm last hedged
CustomsMHK
it
any export); 0 if never
Alternatespecificationfor
ZHK,MHK:x 1yr,
1<x 3yrs,
Customs
MHK_x
itx>3yrs. Dummy=1 if firm mostrecently hedged during period x but
not earlier
Debt
toequityratio:
IR10/AES
DER
it
Debt/(Debt+Equity) [py]Interest coverage ratio: Interest
IR10/AES expenses/(Earnings before interest &ICRittax) [py]XAUD

itCustoms

+
+
+
+
+

LSAL

it

IR10/AES

Natural log of real total sales [py]

+

FXNS

it

Customs;
IR10/AESNZD denominated exports excluding
reexports (calc in NZD)/total sales
[py]

-

30

Table A.1 (continued from previous page)

Variable

Sub

SourceDescription

FXNRS

it

Customs;
IR10/AES

FXAS

it

Customs;
IR10/AES

FXARS

it

Customs;
IR10/AES

FXOS

itCustoms;
IR10/AES

FXORS

it

FMAS

it

FMOS

it

DTE

it

IR10/AES

ITA

it

IR10/AES

DTP

it

IR10/AES

ZBT_x

it

LBF

ZBT_FORit

LBF/IR4

ZTX

it

IR4

DPC

it

Customs

DMC

it

Customs

DCC

it

Customs

DIC

it

Customs

PCA

it

Customs

AUD3

t

RBNZ

NZD denominated reexports (calc in
NZD)/total sales [py]AUD denominated exports excluding
reexports (calc in NZD)/total sales[py]
AUD denominated reexports (calc in
NZD)/total sales [py]Other fx denominated exports
excludingreexports(calc
inNZD)/total sales [py]Other fx denominated reexports (calcin NZD)/total sales [py]Australian
imports(calc
inNZD)/total sales [py]Other country imports (calc in
NZD)/total sales [py]Depreciation to expenses ratio:
Depreciation/total expenses [py]Intangible asset ratio: Intangible
assets/total assets [py]Dividends to profit ratio: Dividends
paid/Profit [py]Business_type
dummies:Sole
proprietor, Partnership, State-owned
Enterprise; (Company is omitted)
Dummy=1 for known foreign-owned
firm (0 otherwise)
Company carrying a tax loss forward
to current year
No. of HS10 products exported overpast 12 months (running)
No. of markets exported to over past
12 months (running)
No. of currencies exported in overpast 12 months (running)
No. of countries imported from overpast 12 months (running)
The first principal component of
DPC, DMC, DCC & DIC
Deviation of AUD from 3 year
moving average

Customs;
IR10/AES
Customs;
IR10/AES
Customs;
IR10/AES

31

E(sign,tobit)

?
+
?
?
?
?
+
+
?
+
-

Table A.1 (continued from previous page)
Variable

Sub

Source

AUD3dx

t

RBNZ

IMR

it

Probit

IMR_decx

it

ProbitHS_ki

Customs

Description
Monthly change in deviation of AUD
from 3 year moving average(x=1 12)Inverse Mills ratio from probit
equation
Dummy = 1 if inverse Mills ratio in
decile x (x=2 10) (decile 1 isomitted)Dummy=1 if 2-digit HS good
(k=1, ,97) ever exported by firm
over August 1997 to March 2007

Extra independent variables for 1st stage (probit)
*_ind

it

Customs

FEX

itCustoms

FEA

itCustoms

FAU

it

Customs

MEX

it

Customs

MEA

it

Customs

MAU

it

Customs

XAU

itCustoms

ZMkt

Industry averaged variable
Dummy =1 if firm has ever exported
beforeDummy =1 if firm has ever exported
to Australia before
Dummy =1 if firm has ever exportedin AUD before
1/(No. months since firm lastexported); 0 if never1/(No. months since firm lastexported to Australia); 0 if never1/(No. months since firm lastexported in AUD); 0 if neverFirm exports to Australia in t (=1 ifso; 0 otherwise)
Month dummies k=1-11 (Jan-Nov)

32

E(sign,tobit)

?
?
?
?

Table A.2
Probit and tobit regression results

ZHK_ind

MHK_ind

1

2

3

4

5

6

7

8

9

10

11

Probit

Tobit

tobit

tobit

tobit

tobit

tobit

tobit

tobit

Probit

tobit

Main

Main

No ICRPCA

No IMR

IMR dec s

Fin only

Fin & hedge

alt. MHK

incl imp.

incl imp.

0.233

0.516***[0.160][0.000]0.352

0.701*[0.442][0.061]

ZHK

MHK

1.005***

1.008***

0.989***

0.825***

0.990***

0.977***[0.000][0.000][0.000][0.000][0.000][0.000]1.169***[0.000]5.186***

5.190***

5.218***

5.055***

5.267***

5.203***

5.632***[0.000][0.000][0.000][0.000][0.000][0.000][0.000]

MHK_1yr

4.767***

MHK_3yr

0.652***[0.000][0.000]0.176

MHK_>3yr[0.188]DER

ICR-0.0160.150**

0.113

0.151**

0.216***

0.149**[0.272][0.040][0.116][0.039][0.004][0.041]

0.009-0.162**-0.160**-0.179***-0.165**[0.469][0.012][0.013][0.006][0.010][0.000]33

0.266***

0.138*

0.211**-0.041***0.127*[0.006][0.058][0.012][0.000][0.054]-0.364***-0.195***-0.1140.024**-0.152***[0.003][0.122][0.015][0.007]

Table A.2 (continued from previous page)
1

2

3

4

5

6

7

8

9

10

11

LSAL

0.018***

0.025

0.026

0.004

-0.0160.035*

0.356***-0.0280.089***

0.013***-0.027[0.000][0.226][0.214][0.849][0.448][0.087][0.000][0.124][0.000][0.000][0.123]FXNS-0.366***-0.187-0.215-0.067-0.059-0.1070.066-0.381***-0.391***[0.000][0.250][0.184][0.675][0.722][0.517][0.723][0.000][0.000]FXNRS-0.647***-1.161-1.198-1.0220.398-0.668-0.484-0.171***-0.154[0.000][0.159][0.146][0.213][0.612][0.407][0.625][0.000][0.528]

FXAS

FXARS

FXOS

FXORS

FMAS

FMOS

DTE

2.423***-0.286-0.299-0.229-1.854***-0.321-0.2361.140***-0.125[0.000][0.150][0.132][0.248][0.000][0.340][0.283][0.000][0.285]1.983***-2.994***-2.985***-2.765***-5.741***-2.623**-7.137***

0.482***-0.328[0.000][0.005][0.005][0.009][0.000][0.014][0.000][0.000][0.261]

0.483***

0.685***

0.676***

0.810***

0.382**

0.624***

1.151***

0.129***

0.138[0.000][0.000][0.000][0.000][0.044][0.001][0.000][0.000][0.289]-0.414**-11.512***-11.689***-11.563***-11.618***-11.820***-13.565***-0.224***-0.857***[0.016][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.007]

0.063

0.218

0.231

0.165

0.382*

0.142

0.108

0.073***

0.159[0.126][0.329][0.300][0.462][0.091][0.522][0.689][0.002][0.229]-0.096***

0.521***

0.515***

0.287**

0.757***

0.392***

0.822***-0.120***0.219**[0.000][0.000][0.000][0.021][0.000][0.003][0.000][0.000][0.023]0.375***

0.407

0.310

0.396

-0.3470.588

1.490*

0.226-0.5120.107*

0.492[0.000][0.521][0.625][0.534][0.593][0.349][0.080][0.722][0.484][0.087][0.129]34

Table A.2 (continued from previous page)
ITA

DTP

1

2

3

4

5

6

7

8

9

10

11

0.052

0.689***

0.694***

0.653***

0.558**

0.710***

1.206***

0.662***

0.918***

0.024

0.489**[0.268][0.003][0.003][0.005][0.019][0.002][0.000][0.004][0.001][0.518][0.017]-0.012

0.108

0.118

0.097

0.118

0.108

0.122

0.119

-0.010-0.011-0.023[0.485][0.198][0.161][0.251][0.164][0.196][0.279][0.161][0.918][0.433][0.756]-0.097*

0.240

0.285

0.168

0.422

0.200

0.383

0.145

0.485

-0.019-0.078[0.066][0.500][0.422][0.640][0.236][0.571][0.452][0.684][0.259][0.585][0.762]-0.107***0.702***

0.724***

0.704***

0.889***

0.696***

0.916***

0.650***

1.037***-0.114***0.372**[0.006][0.002][0.002][0.002][0.000][0.002][0.005][0.005][0.000][0.000][0.035]ZBT_SOE-0.132-0.540-0.514-0.377-0.570-0.7060.071-0.6440.054-0.0220.196[0.130][0.216][0.239][0.378][0.208][0.104][0.904][0.145][0.910][0.717][0.529]ZBT_FOR-0.045***-0.066-0.050-0.100-0.011-0.065-0.898***-0.081-0.149*-0.056***-0.022[0.001][0.352][0.472][0.155][0.878][0.355][0.000][0.229][0.066][0.000][0.728]-0.0190.169***

0.115*

0.156**

0.169***

0.171***

0.714***

0.178***

0.195***-0.0030.150***[0.146][0.009][0.059][0.016][0.010][0.008][0.000][0.006][0.009][0.733][0.009]-0.003**ZBT_SPZBT_PART

ZTX

0.000

-0.001-0.001-0.002-0.001-0.003**0.000[0.568][0.478][0.465][0.164][0.547][0.035][0.960][0.019]DMC-0.001

0.003

0.003

0.007

0.003

0.028***

0.001

0.012***[0.278][0.554][0.567][0.166][0.566][0.000][0.587][0.004]DCC

0.127***-0.188***-0.188***-0.274***-0.185***-0.215***0.133***-0.233***[0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000]

DPC

35

Table A.2 (continued from previous page)
DIC

1

2

3

-0.005***-0.031***-0.031***[0.000][0.000][0.000]

4

5

6

9

10

11

-0.029***-0.031***7

8-0.050***-0.007***-0.026***[0.000][0.000][0.000][0.000][0.000]-8.322***-0.103***PCA[0.000]0.109-7.220***-7.122***-7.231***-7.745***-7.112***-15.650***-7.523***-9.271***0.089[0.453][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.457][0.000]

AUD3d1-0.704*5.995***

5.855***

5.947***

6.902***

5.794***

17.740***6.389***

7.780***-0.644**7.072***[0.051][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.031][0.000]

AUD3d2-0.659*3.016*

2.961*

2.965*

3.116*

2.873*

9.749***

3.238**

3.935**-0.4673.408**AUD3

AUD3d3AUD3d4[0.074][0.059][0.064][0.064][0.054][0.071][0.000][0.044][0.032][0.127][0.023]-0.3859.000***

8.905***

9.029***

9.367***

8.810***

18.180***9.412***

9.439***-0.3869.770***[0.293][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.202][0.000]-0.511

6.766***

6.757***

6.875***

7.042***

6.759***

15.086***7.125***

8.055***

0.208

7.857***[0.152][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.481][0.000]8.607***

0.094

7.675***

7.582***

7.679***

8.350***

7.646***

19.470***8.123***

10.188***-0.329[0.794][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.270][0.000]

AUD3d6-0.688*5.958***

5.887***

6.028***

6.710***

5.634***

15.710***6.564***

6.873***-0.547*

6.334***[0.057][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.067][0.000]

AUD3d7-0.751**4.700***

4.633***

4.617***

5.168***

4.737***

13.635***5.118***

6.369***-0.621**

6.177***[0.039][0.003][0.003][0.004][0.001][0.003][0.000][0.001][0.000][0.039][0.000]

AUD3d536

Table A.2 (continued from previous page)
AUD3d8AUD3d9AUD3d10

AUD3d11

AUD3d12

1

2

3

4

5

6

7

8

9

10

11

0.220

8.073***

8.035***

8.113***

8.482***

7.781***

16.823***8.598***

9.310***-0.1577.870***[0.547][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.604][0.000]0.071

8.113***

8.068***

8.167***

8.667***

8.087***

16.787***8.621***

9.642***

0.319

10.474***[0.838][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.269][0.000]5.994***

0.073

7.301***

7.236***

7.270***

7.445***

7.379***

16.781***7.866***

9.639***-0.208[0.835][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.000][0.475][0.000]-0.637*2.822*

2.750*

2.822*

3.133**

2.652*

10.538***3.239**

4.817***-0.3724.845***[0.064][0.073][0.080][0.073][0.049][0.091][0.000][0.041][0.007][0.192][0.001]

0.032

7.647***

7.552***

7.592***

7.930***

7.518***

17.145***8.266***

9.374***-0.1138.645***[0.000][0.000][0.685][0.925]IMR

IMR_dec2[0.000][0.000][0.000]0.930***

0.931***

0.944***[0.000][0.000][0.000]0.063[0.604]

IMR_dec3-0.045[0.756]

IMR_dec4-0.180[0.265]

IMR_dec5-0.129[0.467]37[0.000][0.000][0.000]-0.269***0.997***

0.670***

0.991***[0.000][0.000][0.000][0.000][0.000]Table A.2 (continued from previous page)
1

2

3

4

5

6

7

8

9

10

0.201

IMR_dec6[0.276]
IMR_dec70.517***

IMR_dec80.987***

IMR_dec91.505***[0.004][0.000][0.000]1.854***

IMR_dec10[0.000]-0.077*-0.030[0.099][0.437]

FEA-0.061-0.100***[0.130][0.003]FAU

0.464***

0.472***[0.000][0.000]MEX-0.102***-0.076***[0.000][0.000]-0.754***-0.761***

FEX

MEA

MAU

[0.000][0.000]2.162***

2.192***[0.000][0.000]38

11

Table A.2 (continued from previous page)
1
XAU

2

3

4

5

6

7

8

9

10

2.651***

2.598***[0.000][0.000]11

Obs.

647,952

38,89238,89238,89238,89238,89238,89238,89238,892

948,12053,868

Pseudo R2

0.682

0.374

0.374

0.373

0.361

0.378

0.074

0.369

0.296

0.672

0.375

p-values in brackets; *significant at 10%; **significant at 5%; ***significant at 1%. Constant & sector dummies included in all equations; month dummies included in probits.

39