interest rate derivatives and risk exposure: evidence …
TRANSCRIPT
INTEREST RATE DERIVATIVES AND RISK EXPOSURE: EVIDENCE FROM THE LIFE INSURANCE INDUSTRY
Hui-Hsuan Liu Department of Business Administration,
National Cheng Kung University, Taiwan NO. 1, University Road, Tainan City, Taiwan 701
Tel: +886-6-2757575 ext. 53330 Cell: +886-9-28994582
Email: [email protected]
Yung-Ming Shiu Department of Business Administration,
National Cheng Kung University, Taiwan Email: [email protected]
INTEREST RATE DERIVATIVES AND RISK EXPOSURE: EVIDENCE FROM THE LIFE INSURANCE INDUSTRY
ABSTRACT
Literature has documented that risk exposure is a determinant of corporate use of derivatives. However, the reverse causality from derivative use to risk exposure has not been well examined and empirical evidence varies. In this paper, we argue that a firm’s derivative use and risk exposure decisions are simultaneously determined. Using the data on 41 life insurers from 2001 through 2006, we find that insurers with higher interest rate exposure tend to have more need for interest rate derivatives for hedging purposes and insurers that use more interest rate derivatives can operate at a higher interest rate exposure.
Keywords: Interest rate derivatives, Interest rate risk exposure, Life insurance
1
INTEREST RATE DERIVATIVES AND RISK EXPOSURE: EVIDENCE FROM THE LIFE INSURANCE INDUSTRY
INTRODUCTION
Recently there has been considerable discussion as to whether the use of derivatives increases or reduces the
risks faced by a firm. The derivatives usage on the non-financial firms, [5] indicates the 76% use derivatives to
hedge interest rate risk in the U.S. non-financial firms by a survey of financial risk management. Besides, US
firms tend to hedge in order to reduce cash flow volatility [5] [14]. Therefore, the survey indicates that
corporate interest rate risk hedging in the United States is relatively important, especially for interest
rate-sensitive firms, such as life insurers.
The most important types of risk that insurance industry faces as financial intermediary is interest-rate risk. The
raise of interest rate risk for life insurance industry depends on two conditions: first, the ratio of insurance
products scheduled has departed from the ratio of the investment return. Life insurance industry belongs to the
long-term insurance industry; an actuary must to consider the external environment factors, for instance:
inflation rate, and to forecast the conduction which related to the past investment benefit compared to the future
before defined the ratio of insurance products. If the market rate showed a very slight variation, the interest rate
risk is increase. Second, interest rate risk arises from mismatches in the rate sensitivity of the insurer’s inflows
and outflows. Inflows from assets often have maturity and liquidity characteristics that differ from those for the
outflows from liabilities. For example, when asset durations exceed those for liabilities, an increase in interest
rates causes the market value of the insurer’s assets to fall by a greater amount than for its liabilities. Changes
in interest rates can have a significant impact on a company’ risk exposure and value, especially for the life
insurers must reserved a lot for the damages. Consequently, financial institutions often face the need to manage
interest-rate risk.
Firms can use different ways to manage their interest rate risk, one technique is to match the rate sensitivities of
their assets and liabilities as closely as possible (on-balance-sheet technique); the other is to use derivatives
2
(off-balance-sheet technique). Most life insurers use financial derivatives either as part of a risk management
strategy or means of income generation. An important question that has arisen in discussions on life insurance
firms’ exposure to interest rate risk concerns the role played by derivatives.
Because the objective of this study is to evaluate the derivative use for hedge and risk exposure are
simultaneously determined in the life insurance industry, it is important to determine whether or not the
investments have any interest-rate contracts (futures/forwards, swaps, and options) by the sample firms are for
the purpose of hedging. We collect data about the firms’ derivative activities are obtained from notes to the
firms’ financial statements. If an insurer indicates that no hedge exists within the interest-rate contract, and then
the contract is not included toward the measurement of hedging by the firm, the insurer is classified as a
"non-hedger."
Derivatives usage is directly related to financial distress problem, underinvestment problem, and economics of
scales, and significantly related to the interest rate risk exposure. Modern corporate finance management theory
suggests that managers should actively use derivatives to alleviate market imperfections and reduce
firm-specific exposure to financial risks. In theory, the active use of derivatives should help firms to move
toward their desired levels of interest rate risk exposure. The result of this paper, we find that if life insurers
face the interest rate risk exposure, they will be more likely to actively engage in the use of interest rate
derivatives as a hedging strategy. Derivatives usage is thus directly related to interest rate risk exposure.
However, the key finding of this study is of a significant and positive relation between interest rate derivatives
usage and interest rate risk exposure, no matter what the extent of derivatives usage for hedging. To this end,
there remains some debate as to whether or not firms that use derivatives in this way actually reduce the risks
arising from their hedging activities or instead achieve higher levels of interest rate risk exposure from such
speculation. It should be noted that in this work the sample is restricted to firms that use derivatives purely for
hedging purposes, as this can prevent the results from being affected by other kinds of usage.
Prior corporate hedging studies are widely classified into two categories. The first contains studies that examine
3
whether or not it is in a firm’s best interests to participate in derivative markets, and the extent to which
derivatives are used (e.g. [41] [37] [11] [36]). These studies find that several firm-specific factors, such as size,
growth option, liquidity and organizational form, are potentially related to participation in such markets. The
other strand of research examines the effect of using derivatives on risks (e.g. [17] [39] [3] [13] [28] [12] [44]
[22] [29] [47]). However, prior research into whether or not this is actually the case is limited to the
management of capital market risk for financial and non-financial firms, and little is known about this topic
with regard to the insurance industry. In order to extend earlier works on the use of derivatives and risk
exposure, this work focuses on the US life insurance industry. Moreover, we emphasize the decision of interest
rate derivatives usage for hedge that incorporates the impact of derivatives usage and the underwriting risk
simultaneous. It is helpful to consider another body of work that has examined the general nature of insurance
firms' interest rate risk exposure.
Three prior studies that are closely connected to ours include [22] [16, p.310] [3]. However, several major
differences exist. First, both studies use data from non-financial firms that use derivatives, while we use data
from life insurers that use or do not use derivatives. Besides, [3] indicate that there is little relationship between
a firm’s risk exposures and the level of derivatives use because the level of derivatives usage is not large
enough to be economically significant to a firm [23]. We further examine the extent of derivatives usage to test.
Second, [16, p.310] just explore the motivation of financial derivatives usage by both the participation and the
extent model, we seeks to extend the interest rate risk exposure to related to the both participation and the
extent model. Third, [22] examines whether firms use derivatives to reduce risk, while we argue that derivative
use and risk exposure are simultaneously determined.
With regard to the derivatives usage in the financial industry, especially in the insurance industry, the research
literature is very limited. Only in the US (e.g. [11] [12]) the Australian (e.g. [9]) and the UK (e.g. [25]) is
evidence available that documents derivative hedging practices in the insurance industry. Given the importance
of risk management for the insurance industry as a global concern it is important that this shortfall is noticed.
Furthermore, prior studies just to find the determinants of firms' hedging and have concentrated on one specific
4
year (e.g. [11] [25]). We further motivate our work by extending these researches from the statutory reports of
US life insurers during the period from 2001 to 2008. It is worth mentioning that we use the Heckman
two-stage sample selection model to test for the self-selection bias, and use the fixed effect vector
decomposition (FEVD) technique to eliminate the potential endogeneity bias of the time-invariant and rarely
changing variables. This study is the first to offer insights into the derivative use and risk exposure are
simultaneously determined in the life insurance industry.
The remainder of the study is organized as follows. The following section introduces the institutional
background. Next, we review prior literature, develop our hypotheses, and describe the methodology and
empirical framework used. We then discuss our data and provide the empirical results thereafter. The
robustness checks are performed in the penultimate section. The final section concludes.
INSTITUTIONAL BACKGROUND
The National Association of Insurance Commissioners (NAIC) has found itself facing federal lawmakers a
number of times in an effort to both educate officials with regard to the best form of insurance regulation. Since
the 1990s, derivatives have become a significant financial instrument for insurers, and such firms are required
to report details of the number, type and value of their derivative contracts in their annual statements to the
NAIC. The NAIC divides the life insurers’ risk into four categories: account asset market and credit risk (C-1
risk), underwriting and pricing risk (C-2 risk), the risk of that the returns from assets will not be aligned with
the requirements of a firm’s liabilities (C-3 risk), and general business risk (C-4 risk). Specifically, C-3-2 refers
to the interest rate risk. Currently, the laws and regulations with regard to investments made by firms that are
members of the NAIC have specific constraints with regard to derivatives.
Rules governing the disclosure of derivatives information and the related reporting requirements became
stricter under SFAS 133 (Financial Accounting Standards Board [FASB], 1998), with most firms adopting the
new requirements on January 1, 2001. Under these rules, firms are required to classify their derivatives
5
activities as either assets or liabilities and measure them at fair value. Before 2001, the information contained in
firms’ annual reports was not as uniform or detailed as desired, and this information is reported in notes to
financial statements but not usually directly on the statements themselves.
As shown in Figure 1, the survey shows the participation rate and the notional value of the extent of derivatives
usage, which is related to the interest rate contracts undertaken by US life insurers between 2001 and 2006.
During this period under review, the participation rate ranged from 34% (minimum) in 2001 to 54% (maximum)
in 2005~2006, while the extent ranged from US$ 943 billion (minimum) in 2001 to US$ 14075 billion
(maximum) in 2006. The notional value of these derivatives is listed based on interest rate swap agreements,
cross-currency interest rate swap agreements, forward starting interest rate swap agreements and cross currency
swaps. In general, both the participation rate and the extent of interest rate derivatives usage increase in
proportion to the amount of interest rate derivatives a firm holds. The possible reason for this is that the growth
in derivatives trading has greatly expanded managerial opportunities to manage risk and thus enhance the value
of insurance companies. Moreover, the survey indicates that US life insurers manage their risk exposure by
undertaking hedging with large derivatives positions, and thus this sample is suitable for this study.
(Insert Figure 1 about here)
HYPOTHESES DEVELOPMENT
Previous research has reported a number of possible linkages between a firm’s propensity to use derivatives and
its incentives. However, relatively few studies can be found on the impact of derivatives being used for hedging
on such aggregate measures as firm risk. In addition, this study emphasizes the derivative usage for hedge and
risk exposure are simultaneously determined in the life insurance industry.
Effects of Risk on Derivatives Usage
Firms attempt to reduce risks if they have poorly diversified and risk-averse owners, face progressive taxes,
6
suffer large costs from potential bankruptcy, face potential for wealth transfers from bondholders to
shareholders due to agency costs, and suffer from information asymmetry between the insiders and outsiders of
the firm. [45] show that hedging the interest rate risk can increase firm value by lowering the bankruptcy
transaction cost. [19] argue that firms should hedge in order to avoid the cost of external financing when they
experience low internal cash-flow. In many instances, such risk reduction can be achieved with derivatives.
Derivative users have an advantage in the risk-shifting process, as the inclusion of derivatives allows for a low
cost method of hedging. For life insurance firms, the growth in derivatives usage has greatly expanded
managerial opportunities to manage risks and enhance the value of companies. Moreover, [30] points out that as
the value of long-term insurance contracts, particularly those with guaranteed returns, is sensitive to interest
rate fluctuations and inflation, it is likely that derivatives usage would be more appropriate for life insurance
firms than general insurers. Extending the investigation to other industries, [47] supports the hypothesis that the
gold mining industry can use derivatives to reduce risks. In addition, [1] analyzes the relation between a bank’s
characteristics and its interest rate risk management behavior, and the result indicates that derivatives usage can
minimize the risk of external shocks on a firm's operating policies. [2] provides evidence about the interest rate
risk management activities of lodging firms. His findings show a significant negative relation between the use
of interest rate derivatives and interest rate risk exposure. In sum, a number of studies examining a variety of
different firms have found that the use of derivatives for hedging purposes is an efficient way to reduce risk.
More specifically, hedging with derivatives can limit the degree of interest rate risk exposure that a firm has
(e.g. [29]). [8] note that interest rate risk exposure is an important factor that influences the value of a life
insurer, as the equity of such firms is sensitive to long-term interest rates. [13] find when firms have larger
interest rate risk exposure, they tend to actively use interest rate derivatives to offset this. Moreover, [29]
finds that an increase in the use of interest rate derivatives corresponds to greater interest rate risk exposure for
a sample of US banks. Accordingly, we affirm that life insurers will use derivatives for hedging purposes if
they face significant interest rate risk exposure. The interest rate risk exposure can affect the both the propensity
to undertake such a strategy and the extent of interest rate derivatives usage. In order to examine the issues
7
raised above in more detail, we construct the following hypothesis:
H1: Life insurers with a higher interest rate risk exposure still have a greater propensity to hedging using
interest rate derivatives.
Effects of Derivatives Usage on Risk
Derivatives provide a way for firms to more easily reduce their interest rate risk exposure, and many studies
find that companies actively pursue this strategy (e. g. [1] [2] [13] [44] [22] [47]). However, a number of prior
studies take the opposite view on the relation between derivatives usage and risk exposure. [46] points out that
carrying out risk management activities (such as hedging by using derivatives) does not increase firm value. [40]
also find that the options usage increases the interest rate risk exposure of banks. [6] examine the effects of
using derivatives on the volatility of firms' returns, and find that the use of derivatives increases risk exposure.
In addition, [44] [24] both find that increased use of derivatives by banks tends to result in higher levels of
interest rate risk exposure. Furthermore, [28] find that the use of derivatives by firms does not measurably
increase or decrease the volatility of their returns. In addition, studies have found that firms’ risk exposure to
variations in interest rates is not directly related to their derivatives positions. Specifically, [42] find no
significant relationship between derivatives usage and the interest rate risk exposure, while [34] shows that
there is no statistical difference in the risk measured and return performance between derivatives users and
nonusers in the mutual fund industry. Moreover, [29] finds that there is no significant relationship between the
extent of derivatives activities and interest rate risk exposure. In sum, a review of the literature shows that
whether or not interest rate derivatives can be used to hedge against interest rate risk is still inconclusive.
However, the related arguments are quite different from established theories of corporate risk management, and
this paper thus aims to consolidate these different strands in the literature. We thus speculate that even if life
insurers use more derivatives to hedge, they can not completely remove the risks they face, and thus we present
the following hypothesis:
H2: Life insurers have a greater propensity to hedging using interest rate derivatives, they still with a higher
interest rate risk exposure.
8
THE METHODOLOGY AND EMPIRICAL FRAMEOWRK
As discussed earlier, an insurer’s interest rate derivative use and risk exposure can be jointly determined. We,
therefore, construct a two-equation simultaneous equations model to account for the simultaneity between these
two. This model is constructed as follows:
( ) titititi eCVIREXfIRDU ,,11,,1,1, , += − (1)
( ) titititi eCVIRDUfIREX ,,21,,2,2, , += − (2)
where tiIRDU , represents a dummy variable (interest rate derivative user= 1; nonuser= 0) to represent the
participation decision on interest rate derivative use or a continuous variable (proxied by the balance of
year-end notional value of interest rate derivatives scaled by total assets) to represent the extent decision by life
insurer i in year t. 1 tiIREX , denotes the interest rate risk exposure of life insurer i in year t. CV1 and CV2 are
two different sets of control variables that are identified based on the theoretical arguments proposed and
empirical evidence presented in the derivatives and finance literature, such as [22] [28]. Following [33] [22]
[21], we lag control variables to correct for the endogeneity problem. 2 tie ,1 and tie ,2 are classical
disturbances.
Since IRDU and IREX are jointly dependent variables, they are correlated with the disturbances. The least
squares estimation will be subject to simultaneous equations bias, leading to inconsistent estimators of
parameters. As suggested in [21, p. 373], we use the 2SLS method to estimate the simultaneous equations
model. Following [18] [3], we measure the interest rate exposure as the coefficient 1β from the following
1 The FASB reiterated its belief that this notional value provides a useful indication of the extent of derivatives activity. In addition, the findings of previous studies (Guay, 1999 and Colquitt and Hoyt, 1997) are consistent with the idea that disclosures of notional amounts of derivatives are useful in explaining the interest rate risk that firms face. 2 The variable addition test proposed by Wu (1973) is performed to examine the endogeneity of the control variables. As expected, the unreported results show that several explanatory variables (e.g., leverage and quick ratio) are at least partially endogenous. Following Kennedy (1998), Guay (1999), and Greene (2008), we lag independent variables to control for possible endogeneity.
9
equation:
titmtittitti eRIRSR ,,,,2,,1,0, +++= βββ (3)
where tiSR , is the common stock return of life insurer i in year t; t,0β a constant, tIR is the percentage
change in the 6-month Libor rate in month t; tmR , is the monthly returns on the CRSP equal-weighted index
for month t, and tie , is the error term. ti,,1β represents the interest rate risk exposure measured as a
percentage change in the rate of return on the life insurer’s common stock due to a 1% change in interest rates.
The proxy used to examine the relationship between interest rate risk exposure and the participation decision
and the extent decision. The six-month Libor rate is used in the model because it is the benchmark used for
most floating rate debt. ti,,2β represents the rate of return on the CRSP equal-weighted market index for NYSE,
AMEX, and NASDAQ firms.
DATA AND VARIABLES
Data
Our initial sample consists of 45 life insurers based on a Compustat search of firms with the four-digit standard
industrial classification (SIC) code of 6311, life insurance. Of 45these insurers, forty one disclose detailed
derivatives information in their 10-K filings and annual reports for the period 2001 to 2006 that allows us to
examine the relation between interest rate derivative use and risk exposure.
Prior to SFAS 133 that became effective in 2001, the information on derivative disclosed in firms’ annual
reports was not as uniform or detailed as desired. More importantly, this rule requires firms to classify their
derivative contracts as assets or liabilities at fair value.
Our analysis period starts from 2001, thereby avoiding the potentially confounding effects that major legislative
changes could have on our results. A sample of derivative disclosure for the AEGON in 2006 is provided in the
10
appendix. Besides, this study is unlikely to be subject to survivorship bias, as our data set includes all of the life
insurers which existed during the period 2001 to 2006, even if they failed to survive until the end of the period.
(Insert Appendix about here)
Measure of Variables
Prior research suggests a number of factors which may affect the participation decision and extent decision for
use of derivatives for hedging and its risk exposure. These factors on the dependent variables in both equations
are examined as follows and a list of variables and their definitions are described in Table 1.
(Insert Table 1 about here)
Measure of Derivatives Usage
Prior researches use dummy variable and notional values as a continuous variable to measure the derivative
usage (e.g. [22] [28]). Notional value of derivatives represents the principal amounts on which the interest
payments are based and thus important information on the magnitude of a firm’s hedging program. Prior
research indicates derivatives that are speculative or fail to qualify for hedge accounting treatment are excluded
from this measure because speculative derivatives generally increase risk exposures leading to earnings higher
volatility. Only the interest rate derivative is qualified for hedge accounting treatment under the current
accounting guidelines. We use the notional values and a dummy variable as an alternative variable to designate
a derivative user firm as equal to one otherwise zero. Furthermore, use the notional value of interest rate
derivatives scaled by total assets to measure the extent decision.
Measure of Control variables in the Participation Decision
With regard to the control variables, we refer to previous research to construct these in order to investigate the
reasons why companies may choose to use derivatives for hedging, and thus our three variables are leverage,
convertible bonds, and affiliation. In addition, past research has suggested that a number of insurer
11
characteristic variables must be included in the regression to avoid an omitted variables bias, and these include
cash flow and firm size.
Leverage. Leverage is introduced to account for the fact that an insurer’s capital structure may be related to its
underinvestment problem. This problem is more pronounced when there is more debt in a firm's capital
structure, as companies with higher leverage are likely to hedge more (e.g. [36]), and thus we expect to see a
positive relationship between leverage and the use of derivatives.
Convertible bonds. [19] propose that convertible bonds and preferred stock are substitutes for the use of
derivatives. That is, if a firm uses more convertible bonds, it will use fewer derivatives, and we thus set a
dummy variable to test this, anticipating that an inverse relationship will be found.
Affiliation. [12] [44] suggest that the level of participation in derivatives transactions is mainly contingent upon
a firm’s ability to manage firm risk, such as via a holding company or group affiliation. This is because a firm
affiliated to another group or companies has more resources and information to engage in complicated
derivatives strategies. The relation between affiliation and derivatives usage is thus expected to be positive.
Cash flow. [19] claim that the level of cash available for investment is inversely related to the need for external
financing, and thus also for derivatives. We thus set a ratio of the cash flow per share scaled by total assets to
proxy for cash flow, and expect the inverse relation between them.
Firm size. To measure the size of the life insurers, we use the natural logarithm of the life insurance companies’
total assets to proxy the firm size. Prior studies have found that firm size is related to using derivatives. Some
researchers think that larger firms have more resource to engage in risk management, and derivatives as a tool
of hedging exist informational economies (e.g. [11]) and scale economies. [11] find that while larger insurers
are more likely to use derivatives than smaller insurers. In addition, another strand of research (e.g. [48]) think
that the cost of bankruptcy is not proportionally to firm size, so the smaller firm has the incentive to engage
12
hedging. Although past studies find that firm size is related to the use of derivatives (e.g. [13] [12] [48] [11]
[45]), we cannot speculate on the results for this given the ambiguous sign of firm size in these earlier works.
Measure of Control variables in the Extent Decision
We follow [2] [32] [4] [15] [47] in using the following control variables to test the interest rate risk exposure:
firm size, floating rate debt, interest coverage ratio, quick ratio, underinvestment costs and assets-liabilities
management. All these variables are considered in theory to be related to the insurers’ interest rate risk exposure.
Overall, only firm size and quick ratio are expected to positively affect the interest rate risk exposure.
Firm size. To measure the size of the life insurers, it proxied by the natural log of total assets, is commonly used
in financial research to control for the inherent skewness of this variable (e.g. [2]).
Floating rate debt. [2] and Antonios et al. (2009) argue that firms face exposure from the interest rate
sensitivity of their debt. In addition, a firm can adjust the exposure of its debt by refinancing, using derivatives
and issuing new debt (e.g., issuing bonds). The interest rate sensitivity of debt is an important factor that affects
a firm’s interest rate risk exposure, and we use the floating rate debt to proxy for this, expecting that the
relationship between the two will be negative.
Interest coverage ratio. we use an interest coverage ratio to proxy the financial distress cost, based on the fact
that firms with higher coverage ratios face lower exposure (negative relation), due to their ability to service debt
payments and absorb unexpected shocks.
Quick ratio. The quick ratio is included as a proxy variable for hedging substitutes. Firms with higher amounts
of internal funds (higher liquidity) can withstand unexpected shocks and reduce potential financial distress costs
(e.g. [2] [4]).
Underinvestment costs. [2] argue that the underinvestment problem is an increasing function of the proportion
13
of growth opportunities in the investment opportunity set. Following [22], we use the book-to-market ratio to
proxy underinvestment cost. The relation between underinvestment costs and exposure is thus expected to be
negative.
Assets-liabilities management. Insurers enter into derivative transactions to hedge or mitigate the risk to their
assets, liabilities and surplus from fluctuations in interest rates, credit quality, foreign currency exchange rates
and equity market valuation. Furthermore, when following this strategy they seek to replicate an asset by
pairing a cash market instrument with derivatives to offset the risk. Life insurers are thus be able to adjust their
interest rate risk exposures mainly by altering the composition of their assets and liabilities. We anticipate that
if life insurers use the balance-sheet to manage interest rate risk, then their interest rate risk exposure will
probably meet their expectations. We expected an inverse relation between these items.
EMPIRICAL RESULTS
Univariate Analysis
The sample is comprised of 41 life insurers from 2001 to 2006, with a total of 244 firm-years observations, 133
of which reported the relevant information about using interest rate derivatives, and 111 did not. We separately
present the summary statistics with regard to the use and non-use of interest rate derivatives for all variables in
Table 2. In addition, the Table 2 reports the differences in the means of user and non-user groups, as well as a
nonparametric Wilcoxon signed-rank test of the differences between the distributions. Moreover, we show a
Pearson correlation coefficient matrix for all the variables, which indicates the strength and direction of the
linear relationship between them. The results show a significant relation between use of the interest rate
derivative strategy and the relevant control variables. The extent of interest rate derivatives use is significantly
related to the control variables, except with regard to convertible bonds. In addition, the interest rate risk
exposure is significantly related to the relevant control variables. Moreover, all the coefficient values for all the
control variables in this study are less than 0.5, and this indicates that there is no collinear relationship between
them. In addition, the descriptive test shows that the interest rate risk exposure, the interest rate derivative
participation and the extent of interest rate derivative usage appear to have a significant relation to each other.
14
(Insert Table 2 about here)
Multivariate Analysis
Overall these regressions, we first ensure that there are no collinear relationships within our analysis as all the
calculated VIFs are smaller than 10. The results of the heteroskedasticity test of the participation decision
(extent decision) using the Breusch-Pagan chi-squared test show that the calculated values of 12.2605, 8.7634,
11.2794, and 9.3421 (10.5954, 10.3991 and 11.0031). These are all smaller than the χ2
value (12.5919),
meaning that we cannot reject the hypothesis of homoskedasticity on this evidence.
In addition, with regard to the autocorrelation problem, the results of the participation decision (extent decision)
by DW test are inconclusive (conclusive), critical values: dL= 1.73752< 1.7852< dU= 1.83992; dL= 1.72883<
1.7343< dU= 1.84876 (critical values: 1.8421> dU= 1.83992; 2.0229, 2.1397> dU= 1.84876, meaning we
cannot reject the null hypothesis). We further examine the problem within the participation decision by the LM
test, then the results show non-autocorrelation (0.6839, 0.1121 > 0.05). These regressions all do not have the
problem of autocorrelation. Furthermore, tests for endogeneity indicate a potential problem among the control
variables. In an effort to solve this problem, lagged values for all control variables are utilized, as suggested by
[20] [33].
The procedure of the interest rate-related participation decision was estimated by the Heckman two-stage model.
We use the Heckman two-stage model (HTSR) to test the self-selection bias, the estimates of the bias
parameters (IMR= the inverse Mills ratio) is not statistically significant, and thus its mission would not lead to
biased standard errors. We just use the OLS model to estimate the participation model after the robustness test
by using the Heckman two-stage sample selection model. The Pseudo- R2
value (= 0.3617) is calculated using
the PROBIT method developed by Zavoina and McElvey (1975), and the results indicate a reasonably good fit.
In addition, as expected, most control variables are significant within the models, apart from the cash flow. The
results for the cash flow only fell short of expectations; in that it is positive and significant, meaning that life
15
insurers with higher cash flow have a greater opportunity to engage in the use of interest rate derivatives
although they face a higher interest rate risk exposure. Overall, the results of the participation decision show
that the interest rate-related participation in such derivatives is significantly and positively related to the interest
rate risk exposure, and vice versa. This implies that life insurers with a higher-then-average propensity to
participate the interest rate derivatives are associated with a higher interest rate risk exposure.
(Insert Table 3 about here)
The procedure of the interest rate-related extent decision was estimated by the Lagrange Multiplier (LM) and
Hausman tests to determine the most appropriate model.3 The results of the LM test suggesting that the panel
regression is the most appropriate model (with a calculated p-value of 0.0334 < 0.05). Furthermore, we use the
fixed effect vector decomposition (FEVD) technique to eliminate the potential endogeneity bias of the
time-invariant and rarely changing variables. Then, we check the results of the Hausman test to test the model’s
robustness trend with regard to the Fixed Effect (FE) model (value = 8.86 < χ2
value (12.59159), meaning that
we cannot reject the null hypothesis). As for robustness to using alternatives estimation techniques, it can be
seen that the estimated coefficients are similar for both the FE and FEVD models. Overall, the findings support
the notion that interest rate risk exposure positively affects the extent of interest rate derivatives usage, and vice
versa. We find that life insurers face a high degree of interest risk exposure even if they are likely to manage
their interest rate risk exposure by taking significant interest rate derivatives position. Furthermore, we
emphasized that derivatives use for hedge and risk exposure is simultaneously determined in the life insurance
industry.
(Insert Table 4 about here)
3 The LM test is employed to examine the relative efficiency of the heterogeneous panel data models (FE/RE models) against the homogeneous pooled OLS estimation. If the LM test statistic is greater than the critical chi-squared value, this suggests that the panel data models are more appropriate than the OLS specification. If the computed LM test statistic argues in favor of panel data models, the Hausman specification test is then used to check for efficiency and bias in the estimation of coefficients obtained using the FE specification by demeaning or the RE specification based on a generalized least squared estimation procedure. If the Hausman test statistic is greater than the critical chi-squared value, this suggests that the FE model is more suitable than the RE model (Hausman, 1978).
16
CONCLUSIONS
In this paper, we provide an empirical analysis to assess whether interest rate risk exposure is related to the use
of interest rate derivatives as a hedging strategy, as well as to the extent of such usage.
Derivatives usage is directly related to financial distress problem, underinvestment problem, and economics of
scales, further significantly related to the interest rate risk exposure. We find that life insurers with a
higher-then-average propensity to participate the interest rate derivatives are associated with a higher interest
rate risk exposure. Additionally, life insurers still face a high degree of interest risk exposure even if they are
likely to manage their interest rate risk exposure by taking significant interest rate derivatives position.
Derivatives usage is thus directly related to interest rate risk exposure; it is not consistent with the modern
corporate finance management theory. The paper emphasized the derivative use for hedge and risk exposure is
simultaneously determined in the life insurance industry.
Moreover, we eliminate the potential endogeneity bias of the time-invariant and rarely changing variables by
utilizing the FEVD technique to check the robustness of the hypotheses. In addition, a comprehensive analysis
of derivatives usage has not previously been carried out in the US insurance sector. Therefore, the results of this
study could be used to compare with and evaluate the results reported in studies carried out elsewhere, notably
in the UK. (e.g. [12] [11]).
Results emphasize the importance of both the participation decision and the extent decision with regard to
hedging for interest rate risk. Life insurance firms are relatively conservative industry, while they face risk
exposure; they decided to use derivatives for hedge. The derivative use and risk exposure are simultaneously
determined.
17
REFERENCES
[1] Amiyatosh P. Interest Rate Derivatives at Commercial Banks: An Empirical investigation, Journal of Monetary Economics, 2007, 54, 1769–1808. [2] Amrik, S. The interest rate exposure of lodging firms, International Journal of Hospitality Management, 2009, 28 (1), 135-143 [3] Bali, T. G., Hume, S. R. & Martell, T. F. A New Look at Hedging with Derivatives: Will Firms Reduce Market Risk Exposure? Journal of Futures Markets, 2007, 27, 1053-1083. [4] Bartram, S. The Interest Rate Exposure of Non-Financial Companies, European Finance Review, 2002, 6, 101-125. [5] Bodnar, G. M. & Gebhardt, G. Derivatives usage in risk management by us and German non-financial firms: A comparative survey, Working Paper 6705, 1998. [6] Bondnar, G. M., Hayt, G. S. & Marston, R. C. 1995 Wharton Survey of Derivatives Usage by US Non-Financial Firms, Financial Management, 1996, 25, 113-133. [7] Breusch, T. & Pagan, A. A Simple Test for Heteroscedasticity and Random Coefficient Variation, Econometric, 1979, 47, 1287-1294. [8] Brewer, E., Carson, J. M., Elyasiani, E., Mansur, I. & Scott, W. L. Interest Rate Risk and Equity Values of Life Insurance Companies: A GARCH-M Model, The Journal of Risk and Insurance, 2007, 74, 401-423. [9] Ceuster, Marc De, Flanagan, L., Hodgson, A. & Tahir, M. I. Determinants of Derivative Usage in the Life and General Insurance Industry: The Australian Evidence, Review of Pacific Basin Financial Markets and Policies, 2003, 6, 405-431. [10] Clatworthy, M. A., Makepeace, G. H. & Peel, M. J. Selection Bias and the Big Four Premium: New Evidence Using Heckman and Matching Models, Accounting and Business Research, 2009, 39, 139-166. [11] Colquitt, L. L. & Hoyt, R. E. Determinants of Corporate Hedging Behavior: Evidence from the Life Insurance Industry, Journal of Risk and Insurance, 1997, 64, 649-671. [12] Cummins, J. D., Phillips, R. D. & Smith, S. D. Derivatives and Corporate Risk Management: Participation and Volume Decisions in the Insurance Industry, Journal of Risk and Insurance, 2001, 68, 51-92. [13] Daniel, M. C. & Steven, A. S. Do Nonfinancial Firms Use Interest Rate Derivatives to Hedge? Working Paper, 2005. [14] De Ceuster, Marc J. K., Durinck, E., Laveren, E. & Lodewyckx, J. A Survey into the Use of Derivatives by Large Non-financial Firms Operating in Belgium, European Financial Management, 2000, 6, 301-318. [15] Faff, R. W. & Howard P. F. Interest Rate Risk of Australia Financial Sector Companies in Period of Regulatory Change, Pacific –Basin Finance Journal, 1999, 7, 83–101. [16] Faff, R. & Nguyen, H. Further Evidence on the Corporate Use of Derivatives in Australia: The Case of Foreign Currency and Interest Rate Instruments, Australian Journal of Management, 2003, 28, 307-317. [17] Ferreira Carneiro, L. A. & Sherris, M. Corporate Interest Rate Risk Management with Derivatives in Australia: Empirical Results, Revista Contabilidade and Financas-USP, 2008, 19, 86-107. [18] Flannery, M. J. & James, C. M. The Effect of Interest Rate Changes on the Common Stock Returns of Financial Institutions, Journal of Finance, 1984, 39, 1141-1153. [19] Froot, K. A., Scharfstein, D. S. & Stein, J. C. A Framework for Risk Management, Harvard Business Review, 1994, 72, 91-102. [20] Greene, W. H. Sample Selection Bias as a Specification Error: Comment, Econometric, 1993, 49, 3. [21] Greene, W. H. Econometric Analysis, 5th ed. Prentice Hall, Upper Saddle River, New Jersey, 2008,. [22] Guay, W. R. The Impact of Derivatives on Firm Risk: An Empirical Examination of New Derivatives Users, Journal of Accounting and Economics, 1999, 26, 319-351. [23] Guay, W. & Kothari, S. P. How much do firms hedge with derivatives? Journal of Financial Economics, 2003, 70, 423–461. [24] Gunther, J. & Siems, T. F. Who’s Capitalizing on Derivatives? Financial Industries Studies, Federal Reserve Bank of Dallas, 1995, 1-8. [25] Hardwick, P. & Adams, M. The Determinants of Financial Derivatives Use in the United Kingdom Life Insurance Industry, ABACUS, 1999, 35(2), 163-184. [26] Hausman, J. A. Specification Tests in Econometrics, Econometric, 1978, 46, 1251-1271. [27] Heckman, J. J. Sample Selection Bias as a Specification Error, Econometric, 1979, 47, 153-161. [28] Hentschel, L. & Kothari, S. P. Are Corporations Reducing or Taking Risks with Derivatives? Journal of
18
Financial and Quantitative Analysis, 2001, 36, 93-118. [29] Hirtle, B. J. Derivatives, Portfolio Composition, and Bank Holding Company Interest Rate Risk Exposure, Journal of Financial Services Research, 1997, 12, 243-266. [30] Hoyt, R. E. Use of Financial Futures by Life Insurers. Journal of Risk and Insurance, 1989, 56, 740-749. [31] Hsiao, C. Analysis of Panel Data, 2nd Edition, New York: Cambridge: Cambridge University Press, 2003. [32] Hue Hwa, A. Y., Faff, R. & Chalmers, K. Derivative Activities and Asia-Pacific Banks’ Interest Rate and Exchange Rate Exposures, Journal of Financial Markets, 2009, 19, 16-32. [33] Kennedy, P. A Guide to Econometrics, 4th ed. Cambridge, MA: MIT Press, 1998. [34] Koski, J. L. & Pontiff, J. How are Derivatives Used? Evidence from the Mutual Fund Industry, Journal of Finance, 1999, 54, 791-816. [35] Maddala, G. S. Introduction to Econometrics, 3rd ed., New York: John Wiley and Sons, 2001. [36] Nance, D. R., Smith, Jr., Clifford, W. & Smithson, C. W. On the Determinants of Corporate Hedging, Journal of Finance, 1993, 48, 267-284. [37] Philip, H. & Mike, A. The Determinants of Financial Derivatives Use in the United Kingdom Life Insurance Industry, ABACUS, 1999, 35, 163-184. [38] Plümper, T. & Troeger, V. E. Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analysis with Unit Fixed Effects, Political Analysis, 2007, 15, 124-139. [39] Purnanandam, A. Interest Rate Derivatives at Commercial Banks: An Empirical Investigation, Journal of Monetary Economics, 2007, 54, 1769–1808. [40] Reichert, A. & Shyu, Y. M. Derivative activities and the risk of international banks: A market index and VaR approach, International Review of Financial Analysis, 2003, 12, 489-511. [41] Shiu, Y. An Empirical Investigation on Derivatives Usage: Evidence from the United Kingdom General Insurance Industry, Applied Economics Letters, 2007, 14, 353–360. [42] Simons, K. Interest Rate Derivatives and Asset-Liability Management by Commercial Banks, New England Economic Review, Federal Reserve Bank of Boston, 1995, 17-28. [43] Singh, A. 2009, The Interest Rate Exposure of Lodging Firms, International Journal of Hospitality Management, 28: 135–143. [44] Sinkey Jr., J. F. & Carter, D. A. Evidence on the Financial Characteristics of Banks that Do and Do Not Use Derivatives, The Quarterly Review of Economics and Finance, 2000, 40, 431-449. [45] Smith, C. W. & Stulz, R. M. The Determinants of Firms’ Hedging Policies, Journal of Financial and Quantitative Analysis, 1985, 20, 391-405. [46] Stulz, R. M. Risk Management and Derivatives, Thomson-Sough-Western College Publishing, 2003. [47] Tufano, P. Who Manages Risk? An Empirical Examination of Risk Management Practices in the Gold Mining Industry, Journal of Finance, 1996, 51, 1097-1137. [48] Warner, J. B. Bankruptcy Costs: Some Evidence, Journal of Finance, 1977, 32, 337-347. [49] Wu, D. Alternative tests of independence between stochastic regressors and disturbances, Econometric, 1973, 41, 733-750.
19
APPENDIX
In the appendix, we use AEGON’s 2006 annual report with regard to interest rate hedging contracts from the
AEGON’s website as an example. We find that AEGON classifies its derivatives position into five categories,
but only the first part of interest rate contracts relates to the current research. The derivatives for hedge are
separated into forwards, swaps, options, and futures. We add all the interest rate contracts to be the total volume
of interest rate derivatives. Moreover, we present details on how the firm claims it uses interest rate derivatives
for hedging are extracted AEGON’s 2006 annual report in the table Appendix B.
Derivatives instruments designated as fair value hedges include the interest rate swap agreements and
cross-currency interest rate swap agreements. For the years ended December 31, 2006, 2005 and 2004, AEGON
recognized gains and losses related to the ineffective portion of designated fair value hedges of EUR 5 million,
EUR 32 million and EUR 37 million respectively. No portion of derivatives was excluded when assessing
hedge effectiveness.
Derivatives instruments designated as cash flow hedges include the interest rate swap agreements, forward
starting interest rate swap agreements and cross currency swaps. AEGON is hedging its exposure to the
variability of future cash flows from the interest rate movements for terms up to five and a half years for hedges
converting existing floating-rate assets and liabilities to fixed-rate assets. According to the forward starting
interest rate swap agreements, fair value adjustments for these interest rate swaps are deferred and recorded in
equity until the occurrence of the forecasted transaction at which time the interest rate swaps will be terminated.
The accumulated gain or loss in equity will be amortized into investment income as the acquired asset affects
income. AEGON is hedging its exposure to the variability of future cash flows from interest rate movements for
terms up to sixteen and a half years. These transactions will affect the profit and loss for approximately 40 years.
For the year ended December 31, 2006, none of AEGON’s cash flow hedges has been discontinued, as it was
probable that the original forecasted transactions would occur by the end of the originally specified time period
documented at inception of the hedging relationship.
20
AEGON funds its investments in insurance subsidiaries with a mixture of debt and equity. AEGON aims to
denominate debt funding in the same currency as the functional currency of the investment. Investments outside
the Eurzone, United States, United Kingdom and Canada are funded in euro. When the debt funding of
investments is not in the functional currency of the investment, AEGON uses derivatives to swap the currency
exposure of the debt instrument to the appropriate functional currency. AEGON utilizes various financial
instruments as designated hedging instruments of its foreign investments.
The following two tables represents aggregate notional amounts and fair values of derivatives, held for own
account as well as for account of policyholders. The notional amounts listed for interest rate contracts will not
be exchanged by parties and, thus, do not reflect an exposure of the company. The amounts listed for cross
currency swaps, included under ‘Foreign exchange contracts’ will be exchanged at amounts calculated on the
basis of the notional amounts and the terms of the derivatives, which are related to interest rates, exchange rates
and/or certain indices.
21
APPENDIX TABLE 1 The Details from AEGON’s 2006 Annual Report Related to Derivatives Usage
Interest rate contracts Foreign exchange contracts
Credit contracts
Equity contracts Other derivatives
Exchange traded
contracts
Exchange traded contracts
Forwards Swaps Options
Future
Forwards Swaps Swaps Swaps Options
Future Options
Embedded derivatives
Notional value
5223 48296 5577 3047 1402 4153 832 629 1127 681 9 1766
Assets fair value
97 1142 201 17 17 225 7 38 102 6 1 30
Notional value
22538 24 558 1393 814 862 1244 1260 1588 9721
Liabilities fair value
512 8 13 377 5 7 10 22 834
Unit: US$ million
22
APPENDIX TABLE 2 The Purpose, Motivation and Agreement Content of The Interest Rate Derivatives Usage as Detailed in AEGON’s 2006 Annual Report
Purpose Instrument Type Motivation Agreement content Interest rate swap agreements
Swaps To effectively convert certain fixed-rate assets and liabilities to a floating-rate basis (generally to six months or less LIBOR), in order to more closely match the performance of the assets and liabilities within AEGON’s portfolio.
These agreements involve the payment or receipt of fixed-rate interest amounts in exchange for floating-rate interest amounts over the life of the agreement without the exchange of the underlying principal amounts.
As fair value hedges
Cross-currency interest rate swap agreements
Option To effectively convert certain foreign currency fixed-and floating-rate assets and liabilities to US dollar floating-rate assets and liabilities.
These agreements involve the exchange of the underlying principal amounts.
Interest rate swap agreements
Swaps To effectively convert certain variable-rate assets and liabilities to a fixed-rate basis in order to match the performance of the assets and liabilities within AEGON’s portfolio more closely.
These agreements involve the payment or receipt of variable rate interest amounts in exchange for fixed-rate interest amounts over the life of the agreement without the exchange of the underlying principal amounts.
Forward starting interest rate swap agreements
Forwards To hedge the variability in future cash flows associated with the forecasted purchase of fixed-income assets.
These agreements reduce the impact of future interest rate changes on the forecasted transaction.
As cash flow hedges
Cross currency swaps Future To convert variable foreign currency cash flows into fixed cash flows in local currencies. The cash flows from these hedging instruments are expected to occur over the next 30-35 years.
These agreements involve the exchange of the underlying principal amounts. Immaterial amounts of hedge ineffectiveness were recorded in the income statement during 2006, 2005 and 2004. The amount of deferred gains or losses to be reclassified from equity into net income during the next twelve months is expected to be immaterial.
As net foreign investment hedges
Subordinated borrowings, long-term, short-term borrowings, short-term debts to credit institutions, cross currency swap contracts and forward foreign exchange contracts
To swap the currency exposure of the debt instrument to the appropriate functional currency.
To ensure that total capital will reflect currency movements without distorting debt to shareholders’ equity ratios.
23
943 1131 1307
10944 1119614075
34%
41%
46%
51% 54% 54%
0
2000
4000
6000
8000
10000
12000
14000
16000
2001 2002 2003 2004 2005 20060%
10%
20%
30%
40%
50%
60%
National value(Unit: US$ billion) %
Extent Participation rate
FIGURE 1: The Participation Rate and Extent of Interest Rate Derivatives Usage by Life Insurance Companies
24
TABLE 1 Variables and Definitions
Variable Definition Panel A: Endogenous variables Interest rate derivative participation Participation decision: 1 for interest rate derivative users, 0 otherwise Interest rate derivative usage Ratio of the year-end notional volume of interest rate derivatives by total assets Interest rate risk exposure Ratio of the return on the life insurer’s common stock due to a 1% change in interest rates Panel B: Control variables Leverage Ratio of the book value of total liabilities to the market value of equity Convertible bonds Dummy variable = 1 if the life insurer use convertible bonds, 0 otherwise Affiliation Dummy variable = 1 if the life insurer affiliates to a financial group, 0 otherwise Cash flow Ratio of the cash flow per share scaled by total assets Firm size Natural logarithm of total assets Floating rate debt Ratio of the floating rate debt to total long-term debt Interest coverage ratio Ratio of the operation income before depreciation to the interest expense Quick ratio Ratio of quick assets to current liabilities Underinvestment costs Ratio of the book value of equity capital to the market value of equity capital Asset-Liability management Dummy variable = 1 if the life insurer uses the balance-sheet to the interest rate risk management, 0 otherwise
25
TABLE 2 Descriptive Statistics of The Users and Non-users The Interest Rate Derivatives by Life Insurers and Correlation Matrix
Users (N = 133) Non-users (N = 111) Variable Mean Std. Dev. Min. Max. Mean Std. Dev. Min. Max. Means of the Wilcoxon signed-rank test
Panel A: Descriptive statistics Interest rate risk exposure 0.2491 0.2957 0.0491 1.9366 0.0000 0.2491 0.2491 0.2491 12.4502 (0.0000) Leverage 17.8024 15.6271 0.9286 82.1745 9.0571 8.2287 1.2164 48.3368 12.2905 (0.0000) Convertible bonds 0.7803 0.4156 0.0000 1.0000 0.6857 0.4665 0.0000 1.0000 2.8483 (0.0044) Affiliation 0.4773 0.5014 0.0000 1.0000 0.0571 0.4972 0.0000 1.0000 5.7151 (0.0000) Cash flow 0.0486 0.0569 0.0006 0.5407 0.0569 0.5002 0.0015 0.2305 1.4366 (0.0514) Firm size 10.8156 2.3959 0.0000 13.7947 8.1989 2.2481 0.0000 11.8874 12.0068 (0.0000) Floating rate debt 2.9934 2.6448 0.0000 9.8504 3.7289 3.6115 0.0000 15.4137 10.6255 (0.0000) Interest coverage ratio 4.0632 4.6193 0.0000 28.9301 2.9025 2.8901 0.0000 10.8900 9.9374 (0.0000) Quick ratio 11.9673 12.3630 0.3830 56.7513 14.3755 15.5462 0.0100 78.8000 12.1309 (0.0000) Underinvestment costs 12.1923 7.9557 0.0101 46.6071 9.7184 12.9953 0.0084 71.3901 11.6521 (0.0000) Asset-Liability management 0.5935 0.4932 0.0000 1.0000 0.6191 0.4880 0.0000 1.0000 0.2250 (0.0823) Panel B: Correlation matrix (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) Interest rate derivative participation (1) — Interest rate derivative usage (2) - 0.4751* — Interest rate risk exposure (3) - 0.0919* - 0.0721* — Leverage (4) - 0.3133* - 0.1263* - 0.0248 — Convertible bonds (5) - 0.0312* - 0.0044 - 0.0808 - 0.1746 — Affiliation (6) - 0.1449* - 0.1348** - 0.0896 - 0.1281* - 0.0775 — Cash flow (7) - 0.0221* - 0.2686** - 0.0348 - 0.2019** - 0.1151 - 0.0381* — Firm size (8) - 0.4775* - 0.3014** - 0.0281** - 0.1275 - 0.4278** - 0.3012* - 0.0353* — Floating rate debt (9) - 0.1285 - 0.0198 - 0.0056** - 0.0352 - 0.1295 - 0.1102 - 0.1045 - 0.2006** — Interest coverage ratio (10) - 0.1275 - 0.1156 - 0.0102* - 0.1946 - 0.0852 - 0.0266 - 0.0042 - 0.0026* - 0.0556* — Quick ratio (11) - 0.0756 - 0.0471 - 0.0054* - 0.0969 - 0.1556 - 0.0959 - 0.0512 - 0.0499 - 0.0289 - 0.1081* — Underinvestment costs (12) - 0.1428 - 0.0403 - 0.0116* - 0.0452 - 0.0551 - 0.1719 - 0.0381 - 0.0956 - 0.0297 - 0.0091* - 0.0933 — Asset-Liability management (13) - 0.0787 - 0.1849** - 0.0875** - 0.0677 - 0.0169 - 0.0802 - 0.1063 - 0.0501** - 0.0902 - 0.0949 - 0.0445* 0.2668** — Note: Panel A separately reports the descriptive statistics for all independent variables between users and non-users, and reports the differences in the means of user and non-user groups, as well as a nonparametric Wilcoxon signed-rank test of the differences between the distributions. Panel B reports the pair wise of the Pearson correlation matrix for all variables. ***, **, and * represent statistical significance at the 0.01, 0.05, and 0.1 levels, respectively.
26
TABLE 3 Relation between Interest Rate Derivative Participation and Risk Exposure
Dependent variable = Interest rate derivative participation Dependent variable = Interest rate risk exposure Independent variable Excepted sign PROBIT TSLS VIF HTSR OLS TSLS VIF Constant - 3.822626***
( 0.0000) - 0.579326*** ( 0.0000 ) - 328.182531***
( 0.0000 ) - 375.122395*
( 0.0914 ) - 353.106737**
( 0.0281 )
Interest rate derivative participation + 563.520787** -( 0.0271 )
- 569.122530** ( 0.0265 )
590.331279** ( 0.0272 ) 1.350
Interest rate risk exposure + - 0.000927** ( 0.0425 )
- 0.000721** ( 0.0323 ) 1.018
Leverage + - 0.059862*** ( 0.0000 )
- 0.012356*** ( 0.0000 ) 1.094
Convertible bonds - - 0.156641* ( 0.0539 )
- 0.071866* ( 0.0712 ) 1.251
Affiliation + 0.032988* ( 0.0873 )
- 0.005444* ( 0.0919 ) 1.095
Cash flow - 3.154307* ( 0.0619 )
- 0.668418** ( 0.0457 ) 1.076
Firm size +/- 0.330799*** ( 0.0000 )
- 0.102412*** ( 0.0000 ) 1.318 --8.339682*
- ( 0.0744 ) - 10.450821* ( 0.0912 )
- -8.189543* ( 0.0594 ) 1.337
Floating rate debt - - - 2.733631* --( 0.0575 )
- 2.384317** ( 0.0484 )
- 2.314374** ( 0.0475 ) 1.051
Interest coverage ratio - - --1.342496* --( 0.0681 )
- 1.427042* ( 0.0985 )
- 1.241044* ( 0.0798 ) 1.040
Quick ratio + --2.904657* ( 0.0714 )
- 2.858954* ( 0.0623 )
- -2.952926* ( 0.0773 ) 1.028
Underinvestment costs - --5.709654 --( 0.1454 )
- 6.463847 ( 0.1672)
- 5.463575 ( 0.1423 ) 1.232
Asset-Liability management - --2.976375 --( 0.1355 )
- 5.241119 ( 0.1021)
- 3.265793 ( 0.1003 ) 1.211
IMR - --0.048627 - - - ( 1.6436 )
Number of observations - 244 244 0 - 244 244 0 - -244
Adjusted R 2 - 0.3493 0.2955 -- -0.1402 0.1247 -- 0.1486
F test ( p- value) 22.20*** ( 0.0000 ) 17.81*** ( 0.0000) -0.86* ( 0.0577 ) 0.57** ( 0.0358) - 0.93* ( 0.0831 )
Pseudo- R 2 - 0.3617
DW Test - Autocorrelation -1.7852 -1.9459 1.7343 2.0244 LM Test – Autocorrelation -0.1656 ( 0.6839 ) 0.1795 ( 0.1121 ) Breusch - Pagan chi-squared – Heteroskedasticity -12.2605 -8.7634 11.2794 9.3421 Notes: Within our analysis, no collinear relationship within our analysis (all calculated VIFs are smaller than 10). Results of the heteroskedasticity test by use the Breusch - Pagan chi-squared test (calculated values: 12.2605, 8.7634, 11.2794 and 9.3421) are smaller than 2χ value (12.59159), mean that we cannot reject the hypothesis of homoskedasticity on this evidence. In addition, with regard to the autocorrelation problem, we first use the DW test. However, the results are inconclusive (critical values: dL= 1.73752< 1.7852< dU= 1.83992; dL= 1.72883< 1.7343< dU= 1.84876), we further use the LM test to examine. The results include non-autocorrelation (0.6839, 0.1121 > 0.05). In addition, we use the Heckman two-stage model (HTSR) to test the self-selection bias, the estimates of the bias parameters (IMR= the inverse Mills ratio) is not statistically significant, and thus its mission would not lead to biased standard errors. We just use the OLS model to estimate the participation model after the robustness test by using the Heckman two-stage sample selection model. Then, after we test the endogeneity with the method proposed in Hausman (1978), we conclude that explanatory variables are endogenous. We use the TSLS model to test the robustness of the endogeneity problem. In addition, we use the LOGIT model to conduct a robustness test. Although the result is not recorded here, the tenor of the results is qualitatively unchanged. Furthermore, tests for endogeneity indicate a potential problem among the control variables. In an effort to solve this problem, lagged values for all control variables are utilized, as suggested by Greene (1997) and Kennedy (1998). The PROBIT, OLS and TSLS method are lags to test within this model. ***, **, and * represent statistical significance at the 0.01, 0.05, and 0.1 levels, respectively.
27
TABLE 4 Relation between Interest Rate Derivative Usage and Risk Exposure
Dependent variable = Interest rate derivative usage Dependent variable = Interest rate risk exposure Independent variable Excepted sign TOBIT FEVD TSLS VIF OLS FEVD TSLS VIF Constant - 0.885557***
( 0.0000 ) - 0.677114*** ( 0.0000 )
- 0.262266***(0.0001)
- 204.865084* ( 0.0819 )
- 54.407050* ( 0.0604 )
- 287.757584* ( 0.0772 )
Interest rate derivative usage + 166.322884** ( 0.037 )
27.341537** ( 0.0263 )
138.397502** ( 0.0473 ) 1.022
Interest rate risk exposure + - 0.000328* ( 0.0612 )
- 0.000018** ( 0.0144 )
0.000234* ( 0.0596 )
1.018
Leverage + - 0.007063*** ( 0.0000 )
- 0.001125*** ( 0.0000 )
0.002527** ( 0.0168 )
1.034
Convertible bonds - - 0.138041** ( 0.0221 )
- 0.830698*** ( 0.0000 )
- 0.096921***( 0.0081 )
1.021
Affiliation + - 0.056617* ( 0.0823 )
-0.746897*** ( 0.0000 )
0.133624 ( 0.2625 )
1.095
Cash flow - - 2.205607*** ( 0.0000 )
- 1.783747*** ( 0.0000 )
- 1.674727***( 0.0000 )
1.076
Firm size +/- - 0.082495*** ( 0.0000 )
- 0.128342*** ( 0.0000 )
0.038077***( 0.0000 )
1.056 3.130528* ( 0.0669 )
1.607813* ( 0.0616)
1.530521* ( 0.0587 )
1.164
Floating rate debt - - 1.248694**( 0.0463 )
- 1.577121** ( 0.0357)
- 1.278983**( 0.0214 )
1.051
Interest coverage ratio - - 1.368694* ( 0.0768 )
- 0.788203* ( 0.0614)
- 3.001967* ( 0.0669 )
1.030
Quick ratio + 1.348282* ( 0.0892 )
1.161797* ( 0.0519)
1.118188* ( 0.0461 )
1.024
Underinvestment costs - - 0.755751 ( 0.1592 )
- 1.932272 ( 0.1949)
- 1.530342 ( 0.1805 )
1.020
Asset-Liability management - - 0.335353 ( 0.2445 )
- 1.312873* ( 0.0965)
- 1.238677* ( 0.083 )
1.017
Number of observations 244 244 244 - 244 244 244
Adjusted R 2 0.7353 0.3323 0.4219 0.1687 0.1365 1.9108
F test 11.48*** 14.47*** 15.77*** 4.46* 5.80*** 5.23** ( p- value) ( 0.0000 ) ( 0.0000 ) ( 0.0000 ) ( 0.0782 ) ( 0.0000) ( 0.0427)
DW Test – Autocorrelation 1.8421 2.0229 2.1397 Breusch - Pagan chi-squared– Heteroskedasticity 10.5954 10.3991 11.0031 Notes: We ensure there is no collinear relationship within our analysis (all calculated VIFs are smaller than 10), and we get the result of the regression, including the variance consistent statistic (the Breusch - Pagan chi-squared test = 10.5954, 10.3991, 11.0031) are smaller than 2χ value (12.59159). Besides, we use the DW test to sure the model includes non-autocorrelation (critical values: 1.8421> dU= 1.83992; 2.0229, 2.1397> dU= 1.84876). As for robustness, the results of the LM test suggest that the panel regression is the most appropriate model. Then according to the result of the Hausman test, the model trends to the fixed effect (FE) model. However, we have time-invariant and rarely changing variables within the model. We further use the FEVD technique to eliminate the potential endogeneity bias by Plumper and Troeger (2007, p.129). Then, we find the result of the estimated coefficients is similar for both FE and FEVD models. Besides, due to the extent model uses the censored data, we use the TOBIT model to conduct a robustness test. Furthermore, after we test the endogeneity by the method in Hausman (1978), we conclude that explanatory variables are endogenous. We still use the TSLS model to test the robustness of the endogeneity problem. In addition, tests for endogeneity indicate a potential problem among the control variables. In an effort to solve this problem, lagged values for all control variables are utilized, as suggested by Greene (1997) and Kennedy (1998). The TOBIT, FEVD and OLS method are lags to test within this model. ***, **, and * represent statistical significance at the 0.01, 0.05, and 0.1 levels, respectively.