the capital structure of swiss companies an empirical analysis

7/25/2019 The Capital Structure of Swiss Companies an Empirical Analysis

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FAME - International Center for Financial Asset Management and Engineering

HEC-University of Geneva

The capital structure of Swiss

Research Paper N 68

January 2003

Philippe GAUD

companies: An empirical analysis

using dynamic panel data

Elion JANIHEC-University of Geneva

Martin HOESLIHEC-University of Geneva, FAMEand University of Aberdeen (Business School)

Andr BENDERHEC-University of Geneva and FAME


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THE CAPITAL STRUCTURE OF SWISS COM

AN EMPIRICAL ANALYSIS USING DYNAMIC P

Philippe GAUD

Elion JANI

Martin HOESLIAndr BENDER

January 2003


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The capital structure of Swiss companies: an empi


Philippe Gaud*, Elion Jani**, Martin Hoesli*** and Andr Be

This draft: 21 January 2003

Abstract

In this paper, we analyze the determinants of the capital structure for a

companies listed in the Swiss stock exchange. Both static and dynamic tethe period 1991-2000. It is found that the size of companies, the importan

and business risk are positively related to leverage, while growth

negatively associated with leverage. The sign of these relations suggest t

order theory and trade off hypothesis are at work in explaining the capit

companies, although more evidence exists to validate the latter theor

shows that Swiss firms adjust toward a target debt ratio, but the adjustm

slower than in most other countries. It is argued that reasons for this

institutional context.

JEL classification: G32

Keywords: Capital structure, dynamic panel data, trade-off theory, peckin


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Executive summary

One of the most important decisions in the field of corporate finance

policy. Using debt financing can have both positive and negative effect

firm. On the one hand, debt financing is value-enhancing for the firm bec

shield. Furthermore, debt allows to reduce the conflicts of interest bet

shareholders. On the other hand, the use of debt may increase bankruptc

the managers of firms with growth opportunities to accept sub

opportunities. In addition, debt often does not constitute an appropriat

highly innovative start-up companies. Empirical research in this area ha

the U.S market, and less evidence exists for European countries. The ai

contribute to the empirical literature by analyzing the determinants of th

Swiss companies. We analyze a panel of 106 firms for the period 1991-20

The capital structure decisions of firms can be explained by two alternati

off theory (TOT) and the pecking order theory (POT). The TOT posits th

off between the costs and benefits of debt financing that leads to an optim

In order to maximize the value of the firm, managers should determine t

then aim at reaching that level. In contrast, according to the POT, firms a

behavior: they first use internal financing, then debt and issue equity as a

is because of informational asymmetries between managers and outside in

The debate as to which theory better explains the capital structure

unresolved. Empirical research has shown that managers have a pre

sources of financing, but this does not imply that an optimal capital stru

Indeed, from a dynamic perspective, the preference for internal financing


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that the observed debt-to-equity ratio is not the optimal level and that t

over time.

Our results are often in contradiction with pecking order theory. Fir

theory, firms with few tangible assets should be more sensitive to inform

However, we observe a positive relationship between tangible assets and

suggest that firms use tangible assets as collateral when issuing debt. Seco

POT, informational asymmetries should be more severe for small size fir

positive correlation between size and leverage. This leads us to reject the

acts as an inverse proxy for informational asymmetries, but rather that siz

for the probability of bankruptcy which is consistent with the TOT. T

growth firms are less levered than non-growth firms, which suggests th

to debt to avoid bankruptcy costs.

In our sample, we find a negative relationship between profitability and d

is usually interpreted as evidence for the pecking order theory (POT

relationship is also consistent with the TOT in the short run. For exam

TOT, despite the fact that the contemporaneous profitability is a determi

cash-flow generated during the year can be used partly to decrease the lev

Overall, our results suggest that both the pecking order theory and trade

work in explaining the capital structure of Swiss companies, although mo

validate the latter theory. Our analysis shows that Swiss firms adjust

ratio, but the adjustment process is much slower than in most other c

explanation for this is that being in disequilibrium is not costly for Swi

that reasons for this can be found in the characteristics of Swiss firm


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The capital structure of Swiss companies: an empi


1. Introduction

Since the seminal Modigliani and Miller (1958) paper showing th

conditions the impact of financing on the value of the firm is irreleva

capital structure has been expanded by many theoretical and empirical

emphasis has been placed on releasing the assumptions made by MM, in

into account corporate taxes (Modigliani and Miller, 1963), personal t

bankruptcy costs (Stiglitz, 1972; Titman, 1984), agency costs (Jensen a

Myers, 1977), and informational asymmetries (Myers, 1984). Two ma

currently the capital structure debate1: the trade off theory (TOT) and the

(POT).

The TOT posits that firms maximize their value when the benefits that ste

shield, the disciplinary role of debt, and the fact that debt suffers less from

than outside equity) equal the marginal cost of debt (bankruptcy cost

between shareholders and bondholders). The POT, developed by

consequence of informational asymmetries existing between insiders of t

(i.e. the capital market). Thereafter, addressing the issue of how com

financing mix has been primarily an empirical question, and such studie

in the last decade. However, empirical studies dealing with capital stru

(Taggart, 1977; Marsh, 1982; Jalilvand and Harris, 1984; Titman and

latter authors made a significant contribution in formulating and testing


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leveraged than those in market-oriented countries. However, they

distinction is useful in analyzing the various sources of financing. Rajan

find that the determinants of capital structure that have been reported

growth, profitability, and importance of tangible assets) are important

well. They show that a good understanding of the relevant institutional

law, fiscal treatment, ownership concentration, and accounting standar

identifying the fundamental determinants of capital structure. The anal

(2001) suggests that the same determinants of capital structure preva

countries. These studies, however, do not shed any light on the adjust

capital structure.

Other studies, which have addressed the dynamic nature of capital struct

from some limitations also. For example, the results of Taggart (1977)

Jalilvand and Harris (1984) may be biased as they use future information

proxy of the optimal debt ratio. Moreover, the tests of the target adjustme

as they are unable to reject the target adjustment hypothesis even when fi

according to POT only (Shyam-Sunders and Myers, 1999). With resp

validation of pecking order theory, Chirinko and Singha (2000) show tha

Sunders and Myers (1999) may be misleading. In addition, Frank and Go

fact that the debt level is determined fundamentally by the financing defic

of mean reversion of leverage, the adjustment process being influenced b

by Rajan and Zingales (1995).

Recent work has benefited from the advances in econometrics. Krem

Miguel and Pindado (2001), and Ozkan (2001) focus on the dynamics of

decisions offering better insight on the adjustment process toward the


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important. They argue that adjustment costs are lower in Spain than in t

because of the major role of bank financing.

The aim of this paper is to analyze the determinants of the capital struc

We add to the relatively limited literature on the dynamics of the capital

examining the dynamics of the relationship between leverage and a

variables. The analysis is conducted using panel data pertaining to 106 S

the period 1991-2000. A total of 967 observations are available for analy

that both the pecking order theory and the trade off theory are at w

evidence exists to validate the latter theory. Also, we find that the speed o

slow in Switzerland as compared to other countries. We argue that

specificity of the Swiss institutional framework help explain why Swis

too much from being away from their target ratios.

The paper is organized as follows. In section 2, we provide an overview

the determinants of the capital structure. The models and the data are pr

while the results from using both the static and dynamic models are dis

Finally, section 5 contains some concluding remarks.

2. The determinants of capital structure

In this section, we provide a review of the six main variables that have be

studies examining the determinants of capital structure.

2.1 Growth opportunities

For companies with growth opportunities, the use of debt is limite


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and Meckling, 1976; Smith and Warner, 1979). From a pecking order

growth firms with strong financing needs will issue securities less subj

asymmetries, i.e. short-term debt. If these firms have very close relat

there will be less informational asymmetry problems, and they will be a

long term debt financing as well.

A common proxy for growth opportunities is the market value to book

Firms with growth opportunities should exhibit a greater market-to-book

growth opportunities, but Harris and Raviv (1991) suggest that this is not

This will typically occur when assets whose values have increased over

depreciated, as well as when assets with high value are not accounted for

(e.g. the brand name Nestl).

Rajan and Zingales (1995) find a negative relationship between grow

leverage. They suggest that this may be due to firms issuing equity w

high. As mentioned by Hovakimian et al. (2001), large stock price i

associated with improved growth opportunities, leading to a lower debt ra

2.2 Size

Large size companies tend to be more diversified, and hence their cash fl

Size may then be inversely related to the probability of bankruptcy (T

1988; Rajan and Zingales, 1995). Ferri and Jones (1979) suggest that lar

access to the markets and can borrow at better conditions. For small

between creditors and shareholders are more severe because the manage

to be large shareholders and are better able to switch from one investme


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bankruptcy law and the Hausbanksystem which offer better protection to

case in other countries.

2.3 Profitability

One of the main theoretical controversies concerns the relationship b

profitability of the firm. According to the pecking order theory, firms

sources of financing first, then debt and finally external equity obtained

things being equal, the more profitable the firms are, the more interna

have, and therefore we should expect a negative relationship bet

profitability. This relationship is one of the most systematic findings in th

(Harris and Raviv, 1991; Rajan and Zingales, 1995; Boothet al., 2001).

In a trade-off theory framework, an opposite conclusion is expecte

profitable, they should prefer debt to benefit from the tax shield.

profitability is a good proxy for future profitability, profitable firms can

likelihood of paying back the loans is greater.

Dynamic theoretical models based on the existence of a target debt-to-e

that there are adjustment costs to raise the debt-to-equity ratio towards th

debt can easily be reimbursed with excess cash provided by internal sour

to have a pecking order behavior in the short term, despite the fact that th

their debt-to-equity ratio (Fischeret al., 1989; Leland, 1998).

2.4 Collaterals


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guarantee. Hence, firms have an incentive to do so, and one would expe

between the importance of tangible assets and the degree of leverage.

Based on the agency problems between managers and shareholders, Har

suggest that firms with more tangible assets should take more debt. This i

of managers who refuse to liquidate the firm even when the liquidation

the value of the firm as a going concern. Indeed, by increasing the levera

default will increase which is to the benefit of the shareholders. I

framework, debt can have another disciplinary role: by increasing the deb

flow will decrease (Grossman and Hart, 1982; Jensen, 1986; Stulz, 1990

former, this disciplinary role of debt should mainly occur in firms with

because in such a case it is very difficult to monitor the excessive expense

From a pecking order theory perspective, firms with few tangible assets

informational asymmetries. These firms will thus issue debt rather than eq

external financing (Harris and Raviv, 1991), leading to an expected nega

the importance of intangible assets and leverage.

Most empirical studies conclude to a positive relation between collaterals

(Rajan and Zingales, 1995; Kremp et al., 1999; Frank and Goyal, 2002)

are reported for instance by Titman and Wessels (1988).

2.5 Operating Risk

Many authors have included a measure of risk as an explanatory varia

(Titman and Wessels 1988; Kremp et al 1999; Booth et al 2001) Le


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2.6 Taxes

The impact of taxation on leverage is twofold. On the one hand, compan

to take debt because they can benefit from the tax shield. On the other h

from debt are taxed more heavily than revenues from equity, firms also

use equity rather than debt. As suggested by Miller (1977), the financia

are irrelevant given that bankruptcy costs can be neglected in equilib

Masulis (1980) show that if non-debt tax shields exist, then firms are li

debt tax shields. In other words, firms with large non-debt tax shields ha

to use debt from a tax shield point of view, and thus may use less deb

substitution effect is difficult to measure as finding an accurate proxy

that excludes the effect of economic depreciation and expenses is t

Wessels, 1988). According to Graham (2000), the tax shield accounts o

the firm value when both corporate and personal taxes are considered.

3. Models and Data

3.1 Static Model

The static model tests the Modigliani and Miller (1958) hypothesis that

variable. More specifically, the leverage is regressed on a set of explana

MM holds, then these variables should not be significant from a statistic

use explanatory variables to proxy for the determinants of capital struc

section 2. We do not take into account the tax effect for two reasons. Fir

substantially the size of our sample due to the lack of data regardi

choosing the appropriate marginal tax rate of firms is crucial in determini


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correlations that may result from a discrepancy between one of our prox

the debt-to-equity ratio computed by the managers (Titman and W

important to obtain the same sign from the various explanatory variab

market capitalization or book value. To check further the robustness of

run the regressions by only considering long term debt. Although t

reported in the tables, they are discussed whenever necessary.

Growth opportunities (GROWTH) are proxied by the market-to-book v

proxies also have been used in the literature, such as R&D and marketing

expenditures (Titman and Wessels, 1988), but such items are difficu

published financial statements, and hence are not considered in this study

logarithm of sales as proxy for size (SIZE). This measure is the most com

(Titman and Wessels, 1988; Rajan and Zingales, 1995; Ozkan, 2001).

could be the natural logarithm of total assets, but it is subject to more a

Various proxies can be used to measure profitability (PROF). We choo

assets, which is calculated as the ratio of EBIT to total assets (Rajan

Booth et al., 2001). We use the ratio of the sum of tangible assets and

asset as a proxy for collaterals (TANG). Adding inventories to the tangibl

by the fact that debts are used partly to finance inventories, and in mo

maintain some value when the firm is liquidated (Krempet al., 1999).

Proxying for the operating risk is a difficult task because such a mea

expectations concerning a firms profitability as compared to that of th

also take into account the specific nature of the firms assets. Many auth

of operating profits of each company as a proxy (Titman and Wessels

2001) Kremp et al (1999) measure the operating risk as the squared dif


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changes in the institutional context have occurred in recent years. For

prefer panel data analysis, as it is possible to include time effects as well

heterogeneity of firms by including firm-specific effects, which may

However, the fixed effects model is costly in degrees of freedom becau

the use of a dummy variable for every firm (Greene, 1993). The ra

assumes the independence between error terms and explanatory variab

performed in this study to control for the presence of firm specific effect

Hausman test is then performed to validate the exogeneity of the firm

dependent variables (Hausman, 1978). If the two null hypotheses are rej

effect model will be retained. A Wald test of the joint significance of time

also used.

In order to ease comparison, we also report simple pooled ordinary lea

pooled ordinary least squares with dummy variable for time and sector

type estimations.

Our static model to analyze firms with panel data is as follows:

ittiitit uxy +++= '

withi = 1,.,Nandt=1,.,T

and

ity : the leverage of firm i in year t

itx : a K x 1 vector of explanatory variables

: a K x 1 vector of constants

i : firm effect assumed constant for firm i over t

t : time effect assumed constant for given t over iu : error term


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( )1*

1 =

itititit yyyy

with 10


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asymptotically distributed as

2 under the null hypothesis of no relations

of the joint significance of the capital structure determinants.

Arellano and Bond (1991) show that when the number of firms is lim

standard errors associated with the two-step estimates may be biased d

the one-step estimators are less efficient than the two-step estimators

homoskedasticity of the error terms. Since the standard errors associat

estimators are more reliable to make inferences, the results using both me

this study.

3.3 Data

Our data consist of Swiss firms listed on the Swiss stock exchange SWX

2000. We use annual data extracted from Worldscope

. Banks, insuran

companies, and some other companies, whose business is highly regula

companies, are excluded from the sample. This is motivated by the fact

have to comply with very stringent legal requirements pertaining to

sample thus contains primarily industrial, commercial and service co

managers have considerable leeway concerning financial decisions.

We deflate our data using 1992 as base year. Company financial statem

stock guide are used to fill any gaps in the data, and to check th

observations (i.e. a given company in a given year) for which we have ne

balance sheet, except for retained earnings and other assets3. Ou

applying a methodology similar to that of Kremp et al. (1999). We excl

hi h fit bilit ll t l d th t iti t id th


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consecutive years for a company to be included. This leaves us with a

and a total of 967 observations.

Table I contains summary statistics for selected years of the time period

and 2000), as well as for the total period 1991-2000. The importance of

over the period, with a sharper decrease when market values of equity

when book values are used. Whereas both proxies for leverage are rough

leverage ratio amounts to 54.2% in 2000 when market values are used

book values are used. Over the period, companies on average have gro

increased the relative importance of intangible assets in their balance sh

book ratio has increased from 1.10 in 1991 to 1.87 in 2000, but remains

the fact that the Swiss market is a value market rather than a g

profitability of Swiss companies has increased over the period, and not su

return levels are accompanied by higher levels of risk.

The correlation coefficients between variables are reported in Table

generally low, except the correlation between profitability and risk. In fa

between these two variables would be expected. To check whether the

collinear, we perform a VIF test. Our VIF tests are substantially lower th

should not constitute a problem (Chaterjee and Price, 1977).

4. Results

4.1 Static Analysis

In table III we present the regression results for the static analysis. The le


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macroeconomic events and changes in the institutional context play a si

borrowing decision of Swiss firms.

SIZE plays a positive role and the coefficients are significant for all

profitability variable (PROF), we find a negative relationship with l

coefficients are again significant at the 1% level. For the other three varia

more across specifications. The RISK variable is positively related t

leverage and in all cases significant at the 1% level when the market

considered, but not when book values are considered. The GROWTH

impacts on leverage and is significant at the 1% level when market valu

is significant in all cases with panel data estimations. Tangible assets (TA

impact that is significant at the 1% level for all panel data estimatio

estimations with market values. In summary, when panel data are used, th

RISK variables have a positive impact on leverage, whereas GROWT

negative impact. All these relations are significant at the 1% level when

or book values of equity are used, except for the SIZE variable, whic

when market values are used.

When using long-term debt in lieuof total debt to compute the leverage,

firm-specific effects exist and that the random effects models give the b

obtain the same sign and the same significance for all coefficients, e

variable, which is now significant at the 5% level when using book val

SIZE, which looses its significance.

The positive impact of size on leverage is consistent with the result

studies (Rajan and Zingales 1995; Booth et al 2001; Frank and Goyal


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being not well developed in Switzerland, this allows banks to select bet

they will prefer large firms to small ones, the sign of the SIZE coefficient

As reported in several other studies, the PROF variable is negative and si

(Rajan and Zingales, 1995; Boothet al., 2001; Frank and Goyal, 2002). T

support for the pecking order theory. However, caution has to be e

dynamic nature of the relation between leverage and profitability is e

Goyal, 2002; Titman and Wessels, 1988). The positive impact of RISK

estimation when using market data implies that firms, which perform be

levered. In other words, companies with high operating risk try to c

limiting financial risk.

The coefficient of the TANG variable is positive and significant

estimations, and this result is similar to those reported in previous

Zingales, 1995; Kremp et al., 1999; Frank and Goyal, 2002). This resul

use tangible assets as collateral when negotiating borrowing, especially l

The observed sign of the relationship does not confirm the sign that wou

using the pecking order theory framework. In such a framework, firm

assets are more subject to informational asymmetries, and are more

principally short term debt - when they need external financing.

The negative sign of GROWTH confirms the hypothesis that firms with

are less levered. To analyze further this relationship, we divide our samp

using the median growth as cut-off. The negative sign and significan

remains irrespective of the leverage measure for the high growth firms.

growth firms which are typically no growth firms as the market to boo


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earnings will increase also, leading to a lower leverage ratio. Moreover,

Porta et al. (1998), these companies are often family-owned (such as L

Bucher), and have a strong tax incentive not to distribute any remaining

repurchase of shares is too costly for this type of company. For companie

(such as Axantis, and WMH), banks are reluctant to lend funds, as t

encouraging. In some cases, these companies have been forced to sell som

often with a capital gain, making it possible to reduce the debt-to-equi

market values are used, the above-mentioned financial operations will ha

impact on the market value of equity4. Therefore, the debt-to-equity ratio

4.2 Dynamic Analysis

The dynamic analysis makes it possible to study the financing behavior

time and whether there are adjustment costs. As the model is estimated in

one or more lagged variables are used as explanatory variables, our sam

967 to 755 observations. To examine the impact of profitability ov

borrowing, we add the lagged profitability in equation (3). This is motiv

we want to test the persistence of a pecking order financing, which w

model. Models such as those of Fischer et al. (1989) and Leland

existence of an optimal debt-to-equity ratio, but find pecking order beha

due to the adjustment costs. Despite the fact that the contemporaneo

determinant of the importance of leverage, the cash flow generated du

used partly to decrease the level of debt.

For the dynamic model, we test various specifications concerning the

explanatory variables. Only the results of the model that posits th

endogenous are reported in this paper Not surprisingly this is the b


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test for the one-step estimator does not confirm the validity of our ins

However, as Arellano and Bond (1991) note, the Sargan test has an te

often in the presence of heteroskedasticity.

The size of the coefficient of the lagged leverage is high and it is in all ca

1% level. For the Swiss market, the size of the coefficient is in the 0.708

one-step estimations. The coefficient is smaller when we use market

managers use market values when adjusting their leverage toward the targ

to be exercised when cross-country comparisons are made, but su

interesting however. The adjustment process is slow in Switzerland com

for other countries as reported in many studies: De Miguel and Pindado (

0.21 for Spain, Shyam-Sunder and Myers (1999) a value of 0.41 for th

(1999) a value of 0.47 for Germany, and Ozkan (2001) a value of 0.

France, the speed of adjustment is comparable to that for Switzerlan

reported by Krempet al. (1999).

The adjustment process is a trade off between the adjustment costs towa

the costs of being in disequilibrium. If the costs of being in disequilibrium

adjustment costs, then the estimated coefficient should be close to zero

example, De Miguel and Pindado (2001) explain the small coefficient t

Spanish market by the importance of bank credit. They argue that Span

low transaction costs when borrowing funds from banks, and that suc

lower agency costs between creditors and shareholders. It could seem a

same explanation for the Swiss market because companies in both coun

banks for their long term borrowing needs. According to the World Ban

debt ratio is 5 7% for Spain and 7 9% for Switzerland as compared e g t


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consider the use of debt in this case5. There is also a purely mechan

explain the lower level of debt within the framework of a dynamic TOT

prices increase due to a market boom without an increase in the size of a

of the growth options, this will lead to a decrease of the leverage, even be

Some institutional factors tend to lead to a high level of debt rather than

Hertig (1998) shows that Swiss firms have benefited from relatively ea

period under study. This is because loans were often granted base

relationships, than based upon objective criteria. The large banks were th

organization by splitting credit analysis from credit decisions, and by us

company expected earnings. In contrast, cantonal banks, in part due to p

been more inclined to continue granting loans based on the old sy

providing Swiss firms with relatively inexpensive financing given the

difficult, however, to measure the effect of the cantonal bank behavior on

policy of listed companies as they predominantly finance non-listed SM

some indirect effects however.

As far as corporate governance is concerned, Hertig (1998) shows that th

in Switzerland during the period under review is quite limited. For examp

of large banks giving them more than 10% of the voting rights in industr

than 5% of their total assets (BNS, 1992-2001). One of the consequence

of bank representatives on the boards of Swiss companies, the hi

ownership (La Porta et al., 1998), and the lack of control in bank credi

agency costs stemming from the conflicts of interest between lender

mainly borne by lenders. This will lead to an increase in the use of debt.


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of the stock market that leads companies to find themselves with a low

target. On the other hand, easy credit policy has enabled companies to bo

investments. Such firms will often have an above-target leverage. Such b

the risk premium on bank loan interest rates is too low.

An additional result from the dynamic analysis is the coefficient of the

variable (PROFit-1), which is positive and significant at the 1% leve

considered. The impact of lagged profitability on leverage, however,

current profitability. The coefficient on lagged profitability is not signifi

debt only is used. This result confirms a short-term pecking order beha

One possible reason could be that Swiss banks have made use of historica

loans; in such a context, one would expect past profitability to play an im

5. Concluding Remarks

This paper presents a study of the determinants of capital structure for Sw

analyses are performed using data pertaining to 106 firms for the peri

static and dynamic tests are conducted, and panel data specifications ar

analysis is conducted using a combination of the GMM approach and in

to check for endogeneity in variables.

Our results show that the size of companies, the importance of tangible

risk are positively related to leverage, while growth and current profita

associated with leverage. The dynamic analysis suggests that there ex

equity ratio. Lagged profitability has a positive impact on leverage,

prediction of a short term pecking order behavior towards the target


25/40

institutional framework, such as the impact of taxation and that of the re

the various sources of credit (securitized debt vs. bank debt).

From an empirical perspective, emphasis should be placed on construct

that enable to discriminate between the various factors that impact on the

impact on the speed of adjustment. Finally, focus should be placed on the

of Swiss companies to examine how firms make their financing decisions


26/40

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Table I: Descriptive statistics

This table presents descriptive statistics for the variables used in our estimatio

Worldscope

and the sample contains 106 Swiss firms listed on the Swiss stock excha

have a minimum of six consecutive years of data for the period 1991-2000. DTAB is

total assets where the total assets are measured with book values. DTAM is the ratio of

where the total assets is the sum of the book value of debt plus the market value of equSIZE is the natural logarithm of sales in real terms (base year = 1992). TANG is the ra

inventories to total assets using book values. GROWTH is the ratio of market value

assets plus market value of equity less book value of equity) to book value of assets. P

total assets. RISK is the squared difference between the firms profitability (PROF) an

of profitability for year t. To this squared measure we add the sign of the differ

profitability and the cross section mean. Summary statistics include the mean and th

years 1991, 1994, 1997, and 2000. For the total period (1991-2000), we also report the

Year 1991 1994 1997 2000

Mean Std Mean Std Mean Std Mean Std Mean

DTAB 0.573 0.135 0.575 0.153 0.563 0.151 0.542 0.157 0.566

DTAM 0.569 0.186 0.518 0 .196 0.468 0.197 0.402 0 .191 0.497SIZE 13.356 1.660 13.523 1.611 13.645 1.672 13.895 1.611 13.58

TANG 0.569 0.202 0.571 0.188 0.549 0.193 0.465 0.178 0.548

GROWTH 1.095 0.392 1.245 0.500 1.456 0.808 1.874 1.645 1.370

PROF 0.066 0.046 0.077 0 .056 0.082 0.059 0.092 0 .066 0.077

RISK 0.016 0 .401 0.053 0.783 0.011 0 .926 0.097 1.065 0.037

N 85 100 105 86


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Table II: Pearson correlation coefficients between variables and VIF

This table presents the Pearson correlation coefficients for the variables used in o

(variance inflation factor) tests between dependent variables. The data are from Worl

contains 106 Swiss firms listed on the Swiss stock exchange (SWX) for which we

consecutive years of data for the period 1991-2000. DTAB is the ratio of total debt to to

assets are measured with book values. DTAM is the ratio of total debt to total assets wh

sum of the book value of debt plus the market value of equity at the end of the year. SIZE

of sales in real terms (base year = 1992). TANG is the ratio of tangible assets plus in

using book values. GROWTH is the ratio of market value of assets (book value of ass

equity less book value of equity) to book value of assets. PROF is the ratio EBIT to

squared difference between the firms profitability (PROF) and the cross section mean

To this squared measure we add the sign of the difference between the firms profitabilmean.

DTAM DTAB SIZE TANG MTB

DTAM

DTAB 0.6985SIZE -0.0113 0.1533

TANG 0.2920 0.0581 -0.3573

MTB -0.6725 -0.2519 0.0738 -0.3056

PROF -0.6295 -0.4145 0.0748 -0.3200 0.5413

RISK -0.4480 -0.3615 0.0305 -0.2326 0.4155


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Table III: Static results

In this table we present various static estimations of the determinants of leverage. The d

stock exchange (SWX) for which we have a minimum of six consecutive years of data fo

assets are measured with book values. DTAM is the ratio of total debt to total assets wh

at the end of the year. SIZE is the natural logarithm of sales in real terms (base year =

values. GROWTH is the ratio of market value of assets (book value of assets plus mark

EBIT to total assets. RISK is the squared difference between the firms profitability (PR

add the sign of the difference between the firms profitability and the cross section mean

For the Fama-McBeth approach, we report the average of the time series of coefficien

and sector dummy variables use a dummy for each year 1992-2000 and a dummy for ea

in brackets. When appropriate, standard errors are White (1980) corrected for heteros

5% level. * indicates significance at the 10% level. Wald 1 is a test of the joint significavariables. Wald 1 and 2 are asymptotically distributed as

2under the null hypothesis o

The Hausman test is a test with H0: random effects are consistent and efficient, versus H


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OLS Fama-McBeth Estimati

Year

DTAM DTAB DTAM DTAB DTAM

Intercept 0.652 0.445 0.718 0.410

(0.051)*** (0.053)*** (0.054)*** (0.045)***

SIZE 0.010 0.017 0.010 0.018 0.010

(0.003)*** (0.003)*** (0.002)*** (0.002)*** (0.003)*** (

TANG 0.051 -0.020 0.021 -0.019 0.047 (0.027)*** (0.027) (0.025) (0.021) (0.027)*

MTB -0.105 -0.009 -0.165 0.012 -0.099

(0.018)*** (0.006) (0.023)*** (0.014) (0.018)***

PROF -2.263 -1.083 -2.054 -1.253 -2.208

(0.253)*** (0.158)*** (0.144)*** (0.249)*** (0.246)*** (

RISK 0.074 0.004 0.083 0.019 0.068

(0.015)*** (0.010) (0.016)*** (0.020) (0.015)***

R2

ajusted 0.580 0.205 0.598 0.210 0.588

R2 within

R2

between

R2

overall

Wald 1 21.1(10)

Wald 2

Chow

Hausman

N 967 967 10 10 967


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Table IV: Dynamic results

In this table we present Arellano and Bond one-step and two-step GMM estimatWorldscope

and the sample contains 106 Swiss firms listed on the Swiss stock excha

have a minimum of six consecutive years of data for the period 1991-2000. DTAB is

total assets where the total assets are measured with book values. DTAM is the ratio of

where the total assets is the sum of the book value of debt plus the market value of equ

SIZE is the natural logarithm of sales in real terms (base year = 1992). TANG is the rainventories to total assets using book values. GROWTH is the ratio of market value

assets plus market value of equity less book value of equity) to book value of assets. P

total assets. RISK is the squared difference between the firms profitability (PROF) an

of profitability for year t. To this squared measure we add the sign of the differprofitability and the cross section mean. Robust standard deviations are reported in

significance at the 1% level. ** indicates significance at the 5% level. * indicates signi

Wald 1 is a test of the joint significance of time dummy variables. Wald 3 is a test of the

estimated coefficients. Wald 1 and 3 are asymptotically distributed as 2

under th

relationship. The Sargan test of over-identifying restrictions is asymptotically distribut

instrument validity. The m2 test is a test for second order autocorrelation of residu

N(0,1).

Arellano-Bond Estimator (two-step) Arellano-Bond E

DTAM DTAB DTAM DTAB DTAM DTAB

DTit-1 0.609 0.759 0.726 0.889 0.708 0.734

(0.059)*** (0.045)*** (0.063)*** (0.050)*** (0.108)*** (0.101)***

SIZE 0.066 0.075 0.060 0.078 0.069 0.052

(0.011)*** (0.013)*** (0.011)*** (0.013)*** (0.017)*** (0.021)**

TANG 0.119 0.020 0.126 0.017 0.176 0.114

(0.042)*** (0.044) (0.045)*** (0.046) (0.092)* (0.137)

MTB -0.092 0.000 -0.095 -0.002 -0.070 -0.002

(0.011)*** (0.004) (0.012)*** (0.005) (0.019)*** (0.009)

PROF -0.989 -0.838 -0.979 -0.854 -0.954 -0.745

(0.126)*** (0.098)*** (0.118)*** (0.105)*** (0.196)*** (0.154)***

PROFit-1 0.331 0.406

(0.074)*** (0.063)***

RISK 0.025 0.012 0.028 0.016 0.025 0.006

(0 005)*** (0 005)** (0 005)*** (0 006)*** (0 008)*** (0 009)

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N 67: Linear-Quadratic Jump-Diffusion Modelling with Application to Stochastic Volati

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Y f HOU Y l U i i Xi JIN FAME d U i i f L M


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Jan ERICSSON, McGill University and Olivier RENAULT, London School of Econom

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Olivier de La GRANDVILLE, University of Geneva;February 2002

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Jean-Luc PRIGENT, Thema, Universit de Cergy-Pontoise; Olivier SCAILLET, HEC GUniversity of Geneva;January 2002

N 38: Indirect Estimation of the Parameters of Agent Based Models of Financial MarketManfred GILLI, University of Geneva; Peter WINKER, International University in Ger

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