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Credit default swap spreads as proxy of banking performance

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In early 2007 the financial system that seemed to be operating so well began to show some small cracks. Many companies that dealt in subprime-related activities announced that their earnings were expected to drop and along these announcements many hedge funds were liquidated after they had experienced severe losses. These events were consequences of the trend that had developed in the banking industry. Many banks switched from the traditional banking model in which they hold loans on the balance sheet to the “originate and distribute” model. The latter model led to a large reduction in the standards related to lending according to Brunnermeier (2009).

In the “originate and distribute” banking model the banks had the opportunity to create new types of products, namely the so-called “structured” products. First, diversified portfolios are created from different mortgages, corporate bonds and other assets. Next, the portfolios are divided in different tranches and are then sold to investors according to the preferences for risk of these investors, that is, the riskier tranches are sold to risk-seekin investors. The investors that have bought a tranche can insure themselves for the risk that they bear. In order to insure themselves against the risk of these tranches investors can purchase a credit default swap. The first credit default swap was negotiated in the mid-1990’s. After that first contract the credit default swap market has grown exponential in size. Lots of firms arose that traded in credit default swaps. According to Ericsson, Jacobs and Oviedo (2009) the amount of outstanding principal was more than 20 trillion dollars in 2006 and the credit default swaps accounted for a third of the trading activity.

Credit default swaps can be seen as an insurance contract between two parties. The seller, often a hedge fund, of the credit default swap provides the buyer protection against a default on the tranche for an upfront payment and a periodic fee. This periodic fee can also be

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Brunnermeier (2009) argues that it was believed that any investor that bought both a AAA-rated tranche of an asset-backed debt obligation and a credit default swap bore low risk because the chance of a default by the CDS counterparty was low.

The credit default swap is issued with risk meaning that they are exposed to counterparty risk. It may be possible that the party that provides the insurance does not have the means to pay the insurance to the buyer even though it has received the upfront payment and the fees. Risk speculators who did not own the underlying asset saw an opportunity and wanted exposure to particular assets, bonds and loans. They began speculating on certain firms of which they thought that were not able to pay back the bond holders. They also began speculating on overrated subprime mortgage pools that banks, insurance companies and hedge funds

possessed. Speculators eventually traded in trillions of dollars of insurance and speculated on the idea that the subprime mortgage pools would not default. So these instruments are the reason of massive write-downs at insurance companies, investment banks and banks. In this paper the focus will be on the credit default swaps. Credit default swaps are not transparent, are not regulated and are not traded on any exchange so therefore credit default swaps are not standardized instruments. The initial purpose of this derivative was to serve as a hedging device in the way that market participants are allowed to trade the risk that comes along with debt-related activities. However, credit default swaps are often used for other purposes. For example, the fee of the derivative is commonly used to measure the default component in the corporate spread and many times credit default swaps are used for

speculating instead of hedging of risk. Because of this alternative use of derivatives, they are very often named as weapons of destruction. Also, credit default swaps are regularly linked to the origin of the financial crisis of 2007. Thus, the aim of this paper is to examine the role of the derivative credit default swaps in the financial crisis by measuring to what extent the credit default swaps have an effect on the performance of certain banks that trade in these products. This reasoning leads to the following research question.

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I. Literature review.

Structured products had completely taken over the traditional manner of funding in the

banking industry. This is mainly due to the benefit that the product provides for the banks that create them. A large part of the risk that comes along with the product will be passed on to the financial institution that buys the product. The designer of the structured product is thus exposed for a short amount of time to “pipeline risk” as mentioned by Brunnermeier (2009). This led to falling lending standards and due to lower lending standards there existed a

substantial amount of cheap credit in the market. In July 2007 many banks that traded in these structured products and had a large exposure to this type of financial instruments on their balance sheet were uncertain about how to value these products. In a reaction to this

uncertainty people began to lose trust in the reliability of the credit ratings. Credit ratings are supposed to give a correct reflection of the probability of default.

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Callen, Livnat & Segal (2009) state that most investors mainly use earnings to evaluate the performance and the future wealth of the firm they have an interest in because earnings are a major determinant of credit risk. Furthermore, earnings can be used to estimate the true asset dynamics of the reference entity, and so its credit risk. Callen, Livnat & Segal (2009) confirm this with the findings in their paper. They find that earnings in the structure of cash flows and accruals that are created by the reference firms are negatively and significantly correlated with the level of the credit default swap spread. This finding is consistent with the insight that earnings transmit information on default risk. For example, their results show that credit default swap rates are significantly reduced, by 5 percent, due to a 1 percent increase in the ROA. Due to these findings the variable earnings will also be an important factor in this paper.

As mentioned earlier and also argued by Duffee and Zhou (2001), credit derivatives are used as a tool by banks to get round the “lemons” because they can easily be used to transfer risks and thus to manage credit risk exposure. Many banks use swaps for a short amount of time to transfer the risk of their loans to other parties. By doing so, these banks reduce their risk exposure and thus the probability that they will end up in financial distress when defaults on loans occur. Many banks that used these risk-reducing tools, like a credit default swap, were allowed to hold less capital under the Basel II Accord. This accord gives banks the

opportunity to keep on supporting their risky assets and still hold the same level of regulatory capital ratios when they use credit default swaps for capital-intensive activities or for

increasing the level of their asset bases. Shan, Tang and Yan (2014) find in their results that banks that make use of credit default swaps do not have a different capital ratio than banks that do not make use of this instrument.

Banks that are not that well capitalized are more likely to improve their capital ratios by using credit default swaps. Banks with lower capital ratios are more likely to use credit default swaps for other activities instead of credit risk management. Shan, Tang and Yan (2014) confirm this by measuring the Tier 1 ratio as proxy of bank capital quality. Tier 1 capital for banks that are active in the credit default swap market is lower than for banks that are not active in this market. A lower capital ratio may indicate that the bank is involved with more aggressive risk taking, but that does not necessarily constitute a problem. A lower capital ratio may also be the result of more efficient banking, meaning that the bank may have an

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In this situation the bank contains the same or a greater number of loans. When a bank is involved in more risk taking by lending more aggressively it can use credit default swaps for hedging a part of the credit risk exposure. So whether a bank is more efficient or risk taking depends upon its lending activity.

Banks can use credit default swaps in order to increase lending, but a larger number of lenders also leads to higher bankruptcy risk. Subrahmanyam, Tang and Wang (2014) have also found in their results that credit risk is economically significant affected by credit default swap trading. They found that for an average firm the likelihood of bankruptcy is doubled after introducing credit default swap trading in this firm. So credit default swaps can also

contribute to an increase in the probability that a borrower will default, even though they are designed to protect against defaults. Lending activity increases after the introduction of credit default swap trading indicating that more aggressive lending activities are adopted. Thus the fact that credit default swaps are recognized in bank capital regulations leads to banks adopting a larger amount of risk. In particular, both lower capital ratios and an increase in aggressive lending leads to riskier banking. From this review the following hypothesis is derived.

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The correlation between the two markets means that a downturn in the American market will very likely, due to financial linkages and the amount of trade between the American and the European market, lead to a downturn in the European market. The level of correlation between the two markets is of importance for the study because by combining the banks positioned in these two markets in the sample will grow in strength. Further, by combining the American and the European market the sample consists of banks that face the same

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II. Data and methodology

A. Data and sample

For this paper the mid-tier and top-tier international banking groups with 5-year senior credit default swaps are used. The focus of the paper will be on the United States and the European area. The decision to include American and European international banking groups in the paper is mainly due to the fact that the American and the European market, and also the countries within the European market, are linked with each other. Between the American and the European bank there is a high degree of interbank linkages so contagion is more easily spread from one market to another. As mentioned earlier, the high level of correlation between the two markets will lead to a stronger sample. Another reason for including both American and European banks is the fact that in these two markets issuance and trade in credit default swaps is largest.

The time horizon of the paper will start at 1 January 2005, due to the fact that from this year on the European banks were required to use the International Accounting Standards when preparing the consolidated financial statements. The period ends at 30 June 2014. The reason for 30 June 2014 as ending date of the period is the fact that there is no data available for dates after this period. Furthermore, in the paper of Chiaramonte & Casu (2013) the results show that in the period leading to 30 June 2014 the credit default spread values were declining and also a sign of recovery is noticed in this period.

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10 Figure I

Development credit default swap spreads in years

This table indicates the development of the credit default swap spreads in years. On the x axis the date in years is given and on the y axis the spread in basis points of the credit default swap is depicted. The three regions, United States, Europe and Asia are outlined by three different colors.

Source: Bank for International Settlements., 2010, “80th Annual Report”, Basel, 28 June 2010, pages 1-206

31 March 2009 is chosen as end date of the crisis because from this date on the credit default spreads shrank in values, but the level of the values of the credit default spread remained higher than the credit default spread level in the pre-crisis period.

The third period is the post crisis period. This period starts at 1 April 2009 and ends at 30 June 2014. In this period the credit default spread values were declining and also a sign of recovery is noticed. This development can also be noticed from the figure included above. The graph in figure I shows that from 2009 on the spreads of all three markets decline and stay at lower levels.

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B. Explanatory variables

In table I the independent variables used in the paper are outlined along with the expectation that the independent variable will have on the dependent variable, the credit default swap spread.

Table I

Independent variables and predicted sign

This table outlines the main independent variables used in the paper. The independent variables are divided into four subgroups, namely, asset quality, capital, operations and liquidity. The column ‘Description’ gives an explanation of the variables used in the paper. The predicted sign indicates the expectations of the effect of the independent variables on the dependent variable, the credit default swap spread.

Variable Description Predicted sign

Asset quality

Qa1 Loan loss reserve/gross loans (%) +

Qa2 Unreserved impaired loans /equity (%) +

Capital

Pat1 Tier 1 Ratio (%) -

Pat2 Leverage: equity/total assets (%) -

Operations

Op1 ROA = net income/average total assets (%) -/+

Op2 ROE = net income/average equity (%) -

Liquidity

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The explanatory variables used in the paper are balance sheet ratios. Eight balance ratios will be used for the paper. For asset quality two ratios are determined. The first ratio is the loan loss/gross loans (Qa1) expressed in percentage. The ratio shows the relation between the value of total credits fitted and the depreciation fund. It can be expected that as the ratio increases, the quality if the loan portfolio decreases. The second ratio for the asset quality is the unreserved impaired loans divided by equity (Qa2). The ratio can be regarded as the capital impairment ratio and the higher the ratio, the higher the probability that a firm will default.

The second category is capital and both the ratios TIER 1 (Pat1) and leverage (Pat2) can be assigned to this category. The first ratio, TIER 1, measures the capital adequacy of a particular bank to determine whether the bank can absorb losses. An increase in the ratio means that the bank can absorb losses and the credit default spread decreases. The second ratio, leverage, is calculated by dividing equity by total assets.For this ratio, the leverage of the balance sheet is used because this one is the most transparent. When banks acquire more assets by borrowing funds this will lead to an increase in the balance sheet leverage. An increase in the balance sheet leverage will immediately signal investors the change in the probability of default of a bank. Leverage has a negative relationship with the risk of a default because when equity decreases, the amount of debt increases. And with a constant amount of total assets, the risk of a default increases. Investors will respond to this development and buy credit default swaps which will lead to an increase in the spread.

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The last category is liquidity. This category consists of the ratios net loans/deposits and short-term funding (Liq1) and liquid assets/deposits and short-short-term funding (Liq2). The net loans divided by deposits ratio measures the liquidity. The relationship between the ratio and the risk of default by a term can be regarded positive or negative because in case a bank contains a smaller amount of deposits and lower equity, then this is not positively perceived by the market. This will lead to an increase in the credit default swaps. A positive response by the market is caused when the bank has a high number of loans and the same level of deposits. The relationship is in this case negative and will lead to a decrease in credit default spreads. It is positively perceived by the market because the loans are the core business of commercial banks. The ratio liquid assets to deposits and short-term funding has a negative relationship with the risk of default. An increase in the ratio means that the bank is highly liquid and therefore not very vulnerable.

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14 Table II

Summary statistics on the explanatory variables for the sample banks This table shows the summary statistics of the sample banks that will be used throughout the paper. For this paper American and European international banking groups are used. The sample period starts at 1 January 2005 and ends at 30 June 2014 and is based on quarterly data. The dependent variable in this sample is the Credit default swap spread. The explanatory variables are divided into four categories. Loan loss reserve/gross loans (Qa1) and Unreserved impaired loans /equity (Qa2) belong to the category asset quality. Tier 1 Ratio (Pat1) and Leverage = equity/total assets (Pat2) can be classified into the category capital. The variables ROA = net income/average total assets (Op1) and ROE = net income/average equity (Op2) are part of the operations category. Last, Net loans/deposits and short-term funding (Liq1) and Liquid assets/deposits and short term funding (Liq2) can be classified in the liquidity category. All variables are divided into an overall, between and within estimation. The overall estimation explains the results over time and banks. The between estimation explains the results between banks and the within estimation explains the change within banks over time. The number of observations per explanatory variables are shown and also the accompanying standard deviation, minimum value and the maximum value. The number of banks in this sample is 55.

Variable Mean Std. Dev. Min Max Observations

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In table II the summary statistics ofthe independent variables regarding the regression are depicted.The first two variables related to asset quality show an overall low mean which indicates good portfolio quality of 68% of the companies in the sample. From Table II, it can also be noted that for the qa1 variable there is less within variation than between variation. This suggests that there is more variation between the banks in the sample. For qa2 the opposite is true. The between estimation shows that between the banks occurs less variation than within a particular bank over time. With regard to the second category, capital, the mean regarding to Pat1 is low which signals that on average many banks have a low risk buffers. The variation of the position of the banks included in the sample on the other hand is large which indicates that some banks have high risk buffers which in turn has a positive effect on the level of the credit default swap spread of those banks. The mean of Pat2 suggests that banks in the sample have average debt levels. The variation of the position of the banks included in the sample is moderate and the within estimation is larger than the between estimation. Therefore, there is more variation of individual banks’ characteristics (with regard to capital) over time than there is across banks. The summary statistics of the category

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Table III below depicts the correlation matrix regarding the explanatory variables used in the study. The level of correlation between the variables is of importance for the study due to the fact that a high level of relation between two variables can lead to changes in the pattern.

Table III

Correlation matrix regarding the explanatory variables

This table shows the level of correlation between the explanatory variables used in this study. The explanatory variables are divided into four categories. Loan loss reserve/gross loans (Qa1) and Unreserved impaired loans /equity (Qa2) belong to the category asset quality. Tier 1 Ratio (Pat1) and Leverage = equity/total assets (Pat2) can be classified into the category capital. The variables ROA = net income/average total assets (Op1) and ROE = net income/average equity (Op2) are part of the operations category. Last, Net loans/deposits and short-term funding (Liq1) and Liquid assets/deposits and short term funding (Liq2) can be classified in the liquidity category.

Qa1 Qa2 Pat1 Pat2 Op1 Op2 Liq1 Liq2

Qa1 1.0000 Qa2 -0.1625 1.0000 Pat1 0.0694 0.2870 1.0000 Pat2 0.2060 0.0440 0.2031 1.0000 Op1 -0.0910 0.1694 0.2408 0.1566 1.0000 Op2 -0.0901 0.2165 0.2822 0.1017 0.7036 1.0000 Liq1 -0.0771 0.5488 0.2657 0.0677 0.2260 0.3135 1.0000 Liq2 -0.1608 0.8983 0.2584 0.0459 0.2075 0.2320 0.6068 1.0000

All variables show low correlation values with each other except for the variables Qa2 and Liq2. These two variables show a correlation value of 0.8983 which is relatively high. The VIF estimator shows a value of 1.44 and for this reason both variables will not be eliminated from the regression. However, robust standard deviation will be used in the study because the robust standard deviation has the advantage that it minimizes the variance in the values of the variables.

C. Methodology.

For all the independent variables in each of the three different periods the descriptive statistics consisting of the mean, the standard deviation, the maximum and the minimum will be

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A panel regression will be exercised in order to determine whether the balance sheet can explain the bank its credit default spreads. Panel data is considered for this examination because it provides two dimensions. First, panel data provides a cross sectional dimension, in this case banks, and panel data provides a time series dimension, in this paper quarters. In this model it is also assumed that there is correlation in the credit default swap spreads over time for a given bank, but the credit default swap spreads are independent across banks. Another reason for the choice of panel data is because it allows to control for unobservable variables, like certain variables that do not change across banks but do change over time.

The following generic model will be followed to determine the relationship.

CDSit = α + βLoan Loss Reserve/Gross Loansit + βUnreserved Impaired Loans/Equityit +

βUnreserved Impaired Loans/Equityit + βTier1Ratioit + βEquity/Total Assetsit + βROAit + βROEit + βNet Loans/Deposits and Short Term Fundingit + βLiquid Assets/Deposits and Short Term Fundingit + Dcrisis + εit

Which can also be written as

CDSit = α + β(Bankratios)it + dcrisis + εit

With

CDSit = credit default spread of bank i in quarterly time period t

α = constant

(Bankratios)it = The bank specific explanatory variables

Dcrisis = dummy variable that stands for the outbreak of the recent financial crisis which started at 1 July 2007.

εit = error

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18 III. Results

In this chapter the results regarding the performed regressions will be analyzed. In table IV the results regarding the Hausman test for the fixed versus random effects model. This test is used in order to determine whether the random effects or the fixed effects model should be used for analyzing the results of this paper.

Table IV

Hausman test for the fixed versus random effects model

This table represents the Hausman test for the fixed versus random effects model. The variables outlined in the table are defined in section 2B. For all the explanatory variables in the paper the coefficients of the fixed and the random model and their difference are given. Of this difference the accompanying standard deviation is shown in the last column of the table. Further, explanations of the b and the B coefficients are given and the null

hypothesis which will be used for the test is also included. Below these explanations the results of the Hausman test are outlined and indicate a significant result. The number of banks included in the sample is 55.

Coefficients

Variable Fixed (b) Random (B) Difference (b-B) Sqrt S.E. Asset Quality Qa1 0.6610 0.3183 0.0427 0.0727 Qa2 0.3759 0.2810 0.0949 0.0673 Capital Pat1 -0.9243 -0.9506 0.0263 0.0582 Pat2 -0.3926 -0.3452 -0.0474 0.0646 Operations Op1 -0.0618 -0.2002 0.1384 0.0485 Op2 0.1732 0.13308 0.0401 0.0356 Liquidity Liq1 -0.1401 -0.2267 0.0866 0.1508 Liq2 -0.3926 -0.2749 -0.1177 0.0547

b= consistent under H0 and Ha; obtained from xtreg

B= inconsistent under Ha, efficient under H0; obtained from xtreg Test: H0: difference in coefficients not systematic

Chi2(10) = (b-B) ‘ [(V_b-V_B)^(-1)] (b-B) = 25.45

Prob>chi2 = 0.0046

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From the “difference” column in the table can be noticed to what level the results from the fixed effects model differ from the random effects model. In order to measure this a null hypothesis has been drawn up for the Hausman test which states that the differences in the coefficients of both models are zero. The result of the Hausman test is shown in the table in the form of a Chi score with a value of 25.45. The p-value of this Chi score is very small with a significant value of 0.0046 which indicates that the coefficients of the fixed effects model differ from those of the random effects model. The significant p-value of 0.0046 leads to strong rejection to of the null hypothesis that a Random Effects model provides consistent estimates that the random effects estimator is fully efficient. Therefore, this study employs a Fixed Effects Model for further empirical testing.

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20 Table V

Panel regressions for the whole and the different periods

In this table the results of the fixed effects (within) regression are outlined. The group “Whole period” starts at 1 January 2005 and ends at 30 June 2014. The “Pre-Crisis period” begins at 1 January 2005 until 30 June 2007. The “Crisis period” runs from 1 July 2007 until 31 March 2009. The Post-Crisis period starts at 1 April 2009 and ends at 30 June 2014. The dependent variable in this regression is the credit default swap spread which is defined in section 2A. All the independent variables which are already explained in section 2B are shown in the table along with their coefficients and their robust standard errors resulting from the regression. The robust standard errors are denoted in parentheses. In this panel regression are also the dummies Pre-Crisis period, Crisis period and Post-Crisis period included. The number of observations and the number of banks included in the sample is represented in the table. Among the results the R2 broken down in the within, the between and the overall estimator can be found in the table. Last the F-statistics and the rho for all the panel regressions are shown in the table.

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In table V the results from the several panel regressions are shown. Several panel regressions have been performed with and without the dummies on the whole time period from January 2005 until 30 June 2014 and on the three specific time periods. The first subgroup, the pre-crisis period runs from 1 January 2005 until 30 June 2007. The pre-crisis period includes the period from 1 July 2007 to 30 March 2009. Last, the post-crisis period runs from 1 April 2009 to 30 June 2014.

For all these periods the regressions are run in order to see whether the explanatory variables have a statistically significant influence on the dependent variable, the credit default swap spread. In the whole period one by one the pre-crisis, the crisis and the post-crisis dummy have been included in order to examine whether the dummies have a statistically significant influence on the credit default swap spread. In the whole period the sample consisted of 55 banks with an accompanying 1879 observations and in the crisis and the post-crisis period 48 banks of the total 55 banks were included in the sample. For the crisis period the number of observations was quite lower than the number of observations in the post-crisis period. The reason for this difference is the length of the time period in which the observations are measured. The crisis period consisted of only two years and the post-crisis period included five years.

A. Panel regression results of the whole period

For the whole period, almost all explanatory variables used in the regression have a

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In the asset quality category, both explanatory variables show a significant positive relation to the credit default swap spread. For the first variable, Qa1, the standard deviation is lower which illustrates a low variation in the values of this variable among the banks. Many banks of the total banks in the sample remain around the average value. The sign of the coefficient is positive as expected prior to the study and indicates that an increase in the ratio leads to higher levels of credit default swap spreads. This means that when in this ratio the reserves of loan losses increases faster than the value of gross loans the bank is not performing well. A higher loan loss reserve signals to the market that the probability of default on loans increases. So investors will respond to this signal and buy credit default swaps.

For the second variable included in the asset quality category, Qa2, the variation in the values of this variable of the banks in the sample is a bit larger which means a larger variation in the values in Qa2 of the values of the banks. The positive coefficient is in line with the

expectations prior to the study regarding this variable, that is when the ratio increases the spread of the credit default swaps will also increase. The increase in the ratio will be interpreted by the market as a higher probability of default. The market will adjusts its expectations in the way that they will expect a decrease in the earnings of the banks. In response to these market expectations the investors will buy more credit default swaps and this will have an impact on the level of the spreads.

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Pat2, also included in the category capital, has low variation in the values which means that the range in which the values of the banks lay is not very large. This variable is based on the balance sheet concepts of the participating banks in the sample. The choice for the balance sheet concept is established on the fact that balance sheet leverage is widely accepted and most visible because it also the indebtedness of the bank. Banks for which this ratio increases, especially when due to a decrease in the level of equity, are viewed as more risky by the market than other banks. Investors in the market then respond to this increase in default risk and will protect themselves by buying credit default swap spreads.

The performance of the banks is also significantly affected by the Op2 of the category

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Investors respond to this information by not buying insurance because the bank is viewed as a strong entity. The standard deviation for this variable is high. The values of Liq2 are spread over a larger range thereby much variation exists among the banks included in the sample. B. Panel regression results of the dummy variables in the whole period

The low number of observations in the crisis period can also be seen as the main reason for the interesting result that the crisis dummy variable has no significant influence on the credit default swap spread. The pre-crisis dummy and the post-crisis dummy however do have a statistically significant influence on the credit default swap spread. First, the pre-crisis dummy has a p-value of 0.000 and a low standard deviation which means that the range of the credit default swap spreads of the individual banks is not too large. Also, the results of the model as shown by the F-statistic show a significant outcome. The model thus has a significant effect on the level of the credit default swap spreads. The rho depicts a low value of 13,83% which means that a lot of the variation in the model can be attributed to factors other than bank specific characteristics. The adjusted R2within estimator is fairly high. 39,17% of the variation of the individual banks over time can be defined by the model. The same holds for the

adjusted R2between estimator. This estimator has a strong value of 32,24% so a lot of the variation between the banks included in the sample can be attributed to bank specific traits. So overall the adjusted R2 is fairly strong in this model. The significant value of the pre-crisis dummy indicates that the performance of the bank is affected by the change in the financial circumstances. The coefficient has a negative value which indicates that the dummy has a negative influence on the level of the credit default swap spreads of the banks. This has in turn a positive impact on the profitability of the bank. Investors perceive a decrease in the default risk of the bank and are less inclined to buy credit default swaps. Due to this

perception of investors the demand for the default swaps is low in this period and this decline in demand leads to low default swap spreads.

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The adjusted R2within estimator shows a moderate value of 30,68% and the R2between estimator shows a very weak value of 7,54%. The crisis dummy itself has not a significant effect on the level of the credit default swap spreads. The accompanying standard deviation is very large which means that the spread of the credit default swaps varies large among the banks included in the sample. This insignificant result indicates that it is not possible to make any reliable inferences or conclusions with regard to the effects of the crisis on the banks’ performances.However, it must be noted that the small sample size should be taken into consideration when assessing this result. It could be very well the case that the small sample size is the main reason for the insignificant outcome.

The model in which the post-crisis dummy is included has a significant effect on the level of the credit default swap spreads. The rho is very low in this period with a result of 18,45%. The adjusted R2within and the R2between estimator depict a moderate and a very weak value of 36,90% and 8,97%. A low part of the variation in the individual banks and an even lower part of variation between banks is due to bank specific characteristics. The main variation in the values is due to idiosyncratic risk. Overall, the R2 has a value of 32,48% and thus the explaining power of the model for the variation is moderate. Furthermore, the post-crisis dummy shows a statistically significant effect on the dependent variable. In this period the standard deviation also shows a low value which indicates that the level of the credit default swap spreads for the banks are not spread out over a large range. The post-crisis dummy is significantly positive related with the level of the credit default swap spreads. When the market perceives the banks as riskier in this period it will lead to an increase in the demand for credit default swaps and this in turn leads to higher spreads. Investors view earnings as a major determinant of risk and associate the developments in this variable with a higher or lower probability of default.

Asset quality has the expected positive sign and Qa1 has a significant impact on the

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Qa2 has only in the crisis period a significant impact on the level of the credit default swap spreads. The standard deviation remains around the same value in all three periods. The significant result means that investors only in the crisis period worry about the low quality of the bank loan portfolios held by the banks in the sample. In this period an increase in the ratio results in an increase in demand for credit default swaps and in turn the accompanying spread increases.

In the capital category Pat1 has the expected negative impact and on the dependent variable and has only in the pre-crisis and in the crisis period a significant influence on the dependent variable. The standard deviation has a higher value in the crisis period. In this period the values of the capital adequacy of banks have a larger variation in the values of Pat1. The reason for this can be that some banks of the sample are more active in aggressive risk taking which the market views as risky. This behavior of banks will as a results be corrected with a higher levels of credit default swap spreads. This result is also proven by the higher

coefficient in the crisis period. Changes in the ratio have the most impact on the dependent variable in the crisis period.

The second variable of the capital category, Pat2, is significant for all three periods. For this variable the sign is negative as expected and the coefficient is slowly increasing which means that the impact of a change in the indebtedness of the bank gains more strength in time. When the level of liabilities increases the market views the entity as risky and the investors response to this change by insuring themselves against the increased change of a default. The standard deviation of the variable has remained constant in the three sub-periods. In the crisis-period the deviation increased a little, but in the post-crisis period the deviation lowered again which means that the spread of the values also decreased.

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An interesting result is the fact that coefficient shows a large increase in value in the crisis period. Earnings are a major indicator of credit risk included in the credit default swaps. In the pre-crisis period investors do not seem to be paying a lot of attention to earnings, but in the crisis period the market puts a lot of value on the level of the earnings in order to determine the probability of default. As expected, an increase in the variable immediately leads to a reduction in the dependent variable.

The negative sign of the liquidity category is in line with the expectations prior the analysis. Of the liquidity category, Liq1 is only negatively significant in the crisis-period. In the crisis period investors pay a lot more attention to the level of liquidity of the banks in the sample. Based on the level of Liq1 investors form their expectations with regard to the probability of a default. In the post-crisis period investors don’t value the liquidity anymore as much as they did in the crisis period. Changes in this ratio don’t have a significant effect on the dependent variable and over time the standard deviation shows stable values. The standard deviation of Liq2 also depicts stable values over time. The spread of the banks thus remains the same in the period. The variable is only slightly significant in the pre-crisis period. The market views liquidity as a less important indicator of credit risk. In the crisis period the variable gains more importance with regard to the probability of default, but after the crisis period the variable loses its importance and does not have a significant effect on the dependent variable. C. Panel regression results of the three sub-periods

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The model has a significant impact on the level of credit default swaps in the crisis period as shown by the F-statistic. The rho shows a value of 0.3858 which indicates that 38,58% of the variation in the results can be explained by individual bank characteristics. The value is not very high so a lot of the variation is due to idiosyncratic risk. For this period the adjusted R2 is broken down into an adjusted within, between and overall estimator. All three estimators show low values which means that the model does not explain much of the variation of an individual bank over time and between banks. Overall, the adjusted R2 indicates that the bank credit default swap spreads reflect a low amount of risk expressed by the explanatory

variables. In the crisis period only the categories asset quality and operations show a

significant influence on the credit default swap spreads and thus the performance of the banks. For the asset quality category, Qa1 has the expected positive significant effect on the

dependent variable. In this period the market values the quality of the loan portfolios of banks. The higher the ratio, the lower the quality of the portfolio and the market will perceive this low quality as risky. Investors will react to this change by buying credit default swaps as insurance. In the operations category Op2 has a negative statistical significant effect on the level of the credit default swap spreads. This is in line with the expectations. This ratio reflects the return on the banks’ own equity. An increase in the ratio means that the market views the bank as a strong and well performing entity. In reaction to this perception the demand for credit default swaps lowers.

In the post-crisis period the model has an important impact on the dependent variable with a statistical significant F-value. The rho has decreased in value in comparison to the crisis period and thus more variation in the results of the individual banks is due to idiosyncratic risk. The adjusted R2within estimator has decreased in explanatory power for this period. The adjusted R2within estimator shows that in this period only 3,30% of the variation in the credit default swap spread for the banks over time can be explained by the model. For the adjusted R2between estimator the model explains only 0,55% of the variation between banks. Overall the R2 is a weak estimator in this period. A lot of the variation cannot be explained by the model and must be attributed to idiosyncratic risk. Only the operations category has a

significant influence on the level of the credit default swap spreads. In this category, the Op1 shows a negative statistical value. A decrease in operating income leads to a lower ROA for the same level of investments and this signals a higher probability of default. As a

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IV. Conclusion

The aim of this study is examine whether credit default swap spreads are a suitable indicator of the performance of banks. In order to determine whether credit default swaps are a good proxy a sample of international American and European banks is used. From these banks senior credit default swap spreads with a maturity of five years were used as the dependent variable. For the explanatory variables balance sheet estimators of the banks included in the sample were used. The variables Loan loss reserve/gross loans (Qa1) and Unreserved impaired loans /equity (Qa2) are used to proxy the asset quality. Tier 1 Ratio (Pat1) and Leverage = equity/total assets (Pat2) can be classified into the category capital. The variables ROA = net income/average total assets (Op1) and ROE = net income/average equity (Op2) are part of the operations category. Last, Net loans/deposits and short-term funding (Liq1) and Liquid assets/deposits and short term funding (Liq2) can be classified in the liquidity

category.

The time horizon in the study is the period from 1 January 2005 until 30 June 2014. This period is subsequently divided into three sub-periods namely the pre-crisis period which starts at 1 January 2005 and ends at 30 June 2007, the crisis period which runs from 1 July 2007 until 30 March 2009 and the post-crisis period which includes the period from 1 April 2009 until 30 June 2014.

Several regressions are run to interpret whether the credit default swap spreads are good indicators of the performance of banks. First, a panel regression over the entire period is performed. The results from this regression indicate that almost all categories have a

significant effect on the height of the credit default swap spreads. The variable Op1 does not have any explanatory power in the changes of the dependent variable. For the rest of the variables, the market immediately responds to changes in the variables by increasing or decreasing the demand for credit default swaps.

In addition to this regression over the whole period, three regressions are run and each including a dummy variable. The outcomes show an interesting result, namely that the

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The pre-crisis dummy has a significant impact on the level of the credit default swap spreads. The negative influence of the dummy leads to lower level of credit default swap spreads, but to an increase in the performance of the bank. Investors are less inclined to buy credit default swaps because they perceive a decrease in the default risk of the banks. As a consequence of this behavior the demand for insurance decreases and this reduction leads to a decrease in the credit default swap spreads. The decrease in the spreads signals a stronger performance of a particular bank. Also the post-crisis dummy has a significant influence on the level of the credit default swap spreads in the sense that demand for credit default swaps increases when the market perceives the banks as riskier. This in turn will lead to higher spreads.

In the asset quality category Qa1 is in all three sub periods an important estimator for the performance of banks. In all three periods investors view the quality of the portfolio as an important indicator of probability of default. When the quality of the portfolio of a bank lowers, the ratio of the variable increases and investors will be buying more credit default swaps which results in a higher spread and lower bank performance. The variable Qa2 has only in the crisis period a significant effect. As expected, the capital category has a negative significant influence on the dependent variable. The variable Pat1 shows only in the post-crisis period an insignificant value. Pat2 has in all three periods a negative significant effect on the performance of the bank. Investors associate an increase in the ratio with a weaker entity and will be buying credit default swaps to protect themselves against default risk. As a consequence the spread will increase. The assumption that earnings have an important influence on the level of credit risk is proven by the results of the operations category. Op2 has a significant influence in all three different periods. An increase in earnings signals strength to the market and investors will immediately change their behavior by decreasing their demand for insurance. For the last category, liquidity, Liq1 is only negatively significant in the crisis period. Investors add more weight to the level of liquidity of banks in the sample in the crisis period. In the post-crisis period investors do not pay a lot attention to the value of liquidity as they did in the crisis period. Liq2 is only slightly significant in the pre-crisis period, but more significant in the crisis period. Leading up to the crisis period, the variable gains more importance.

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In order to understand the relationship between the balance sheet explanatory variables and credit default swap spreads when the macro economic conditions change two further panel regressions were conducted. Due to few observations results of the pre-crisis period could not be examined. The results of these two regressions show that only the asset quality and the operations category have a significant effect on the level of credit default swap spreads. In particular, only in the crisis period investors worry about the quality of the portfolios held by banks. Liq1 shows only in the post-crisis period a significant result and the variable Liq2 shows only in the crisis period a significant value. Overall the market regard liquidity as an important estimator for the performance of banks.

The results of the several regressions show that the four categories used in the study have a significant influence on the dependent variable, the credit default swap spreads. The market responds to changes in the variables by increasing and decreasing the demand for insurance. These changes in the variables signal the strength of a particular bank and thus its

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V. References

Acharya, V.V., Johnson, T.C., 2007, “Insider trading in credit derivatives”, Journal of Finance Economics, vol.84, pages 110-141

Annaert, J., De Ceuster, M., Van Roy, M., & Vespro, C., 2013, “What determines euro area bank CDS spreads?”, Journal of International Money and Finance, vol. 32, pages 444-461 Bank for International Settlements., 2010, “80th Annual Report”, Basel, 28 June 2010, pages 1-206

Brunnermeier, M.K., 2009, “Deciphering the Liquidity and Credit Crunch 2007-2008”, Journal of Economic Perspectives, vol. 23(1), pages 77-100

Callen, J.L., Livnat, J., Segal, D., 2009, “The Impact of Earnings on the Pricing of Credit Default Swaps”, The Accounting Review, vol.84(5), pages 1363-1394

Chiaramonte, L., Casu, B., 2013, “The determinants of bank CDS spreads: evidence from the financial crisis”, The European Journal of Finance, Routledge, vol. 17(9), pages 861-887 Duffee, G.R., Zhou, C., 2001, “Credit derivatives in banking: Useful tools for managing riks?”, Journal of Monetary Economics, vol. 45, pages 25-54

Ericsson, J., Jacobs, K., & Oviedo, R., 2009, “The Determinants of Credit Default Swap Premia”, Journal of Financial and Quantitative Analysis, vol.44(1), pages 109-132 Flannery, M.J., Houston, J.F., Partnoy, F., 2010, “Credit Default Swap Spreads as viable substitutes for credit ratings”, Univerity of Pennsylvania Law Review, vol.158(7), pages 2085-2123

Shan, S.C., Tang, D.Y., Yan, H., 2014, “Did CDS Make Banks Riskier? The Effects of Credit Default Swaps on Bank Capital and Lending”, pages 1-80

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