Diferenciación

In this thesis, several robustness checks are conducted. Tobit and probit models are used to check the robustness of the results from OLS model. Tobit model is used to investigate the relationship between risk-taking incentives (vega) with respect to the extent of CDS use. In

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tobit model, the extent of CDS use is regressed on risk-taking incentives provided by stock options, and other compensation variables such as salary, cash bonuses, stock grant, and ownership, as well as, control variables for other derivatives, leverage, investment opportunities, diversification, and size.

The decision of the extent of CDS use can be correlated in their unobserved components (erros) and tobit is required for the statistical analysis. The tobit specification assumes that an unobserved latent variable determines the level of the dependent variable:

In the model above, , equals CDS use of bank in year . The latent variable, , models the risk-taking incentives provided from stock options; the regressions above estimate the coefficients. Tobit regression for the CDS model (Equation 1) can be written as follow:

The extent of CDS i,t = β0 + β1.vegai,t + β2.Salary,t + β3.Cash bonusi,t + β4.Stock granti,t + β5.Ownershipi,t +

β6.Hedging derivativesi,t + β7.Trading derivativesi,t + β8 Investment opportunitiesi,t + β9.Leverage i,t + β10. Sizei,t +

β11. Diversificationi,t + μt +έ i,t

Where the extent of CDSi,t use is measured using the notional value of CDS contracts at the

end of the year and vega is the risk-taking incentives provided by stock options. The decision to use CDS may be different from the decision of how much to use CDS. It is possible that common factors affecting these decisions are either unobserved or unobservable (Adkins et al., 2007).

A bank makes two decisions: (1) whether to use CDS and (2) if the extent CDS use. In this thesis the association between the risk-taking incentives and the decision to use derivatives or not is investigated using probit model. The effect of risk-taking incentives on the decision to use derivatives can be different from the effect of the extent of using derivatives (Haushalter,

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2000; Ertugrul, et al., 2008). Probit regression for the CDS model (Equation 1) can be written as follow

CDS use decisison i,t = β0 + β1.vegai,t + β2.Salary,t + β3.Cash bonusi,t + β4.Stock granti,t + β5.Ownershipi,t +

β6.Hedging derivativesi,t + β7.Trading derivativesi,t + β8 Investment opportunitiesi,t + β9.Leverage i,t + β10. Sizei,t +

β11. Diversificationi,t + μt +έ i,t

The dependent variable in the probit estimation is a dummy variable equal to one if the bank uses CDS and zero otherwise. In the following section the key variables (dependent and independent) utilised in each model are discussed in more details. The tobit and probit models have many analogies to OLS regression: they have coefficients for every independent variable, a pseudo R-squared statistic to summarise the strength of the relationship. Unlike OLS regression, however, tobit regression in general has less stringent requirements on the normality of the variables (Davidson and Mackinnon, 2004; Gujarati, 2003). Random effects regressions are also used to predict the relationship between risk-taking incentive and CDS use for trading purposes.38

As part of a robustness check for CEO risk-taking incentives of stock option, the CEO risk- taking incentive is measured using the natural logarithm of the value stock option as utilized in many prior empirical studies (e.g., Tufano, 1996; Géczy et al., 1997).

Firm risk is measured using two proxies. The first one is firm’s Beta which is obtained from the CAPM (Nijskens and Wagner, 2011; Chen et al., 2006). The second measure is Merton distance to default (Hagendorff and Vallascas, 2011; Bai and Elyasiani, 2013).

The second part of the analysis (i.e., firm’s risk model) is also conducted using OLS regressions to predict the relationship between firm’s risk and CDS use. Furthermore, random effects regressions are employed where appropriate.

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In this thesis, regressions are expanded to broaden the scope of the investigation each round by using four sets of subsamples to examine the relationship between the variables. The first regression is based on the full sample and the control variables. The second regression is the full sample, the control variables and dummy variable based on the number of banks operating in each country (the dummy variable equals 1 if the country has more than 3 banks, 0 otherwise). The third regression is based on the full sample, control variables and countries’ dummy variables (Germany, Italy, Spain, Denmark, France, Portugal, and others).39 The fifth

regression is based on the full sample, control variables and the countries’ dummy variables based on the number of banks operating in each country (dummy equals 1 if the bank operates in one of the following countries: the UK, France, Italy, Portugal, Spain and Sweden, 0 otherwise).40 The list of the four samples and classification criteria are presented

in Appendix E.

3.9

Summary

In closing, this chapter describes the research methods and sample selection criteria used in the present thesis. The measurement methods used to measure the main variables in both the CDS model and the risk model are also explained and discussed in depth. Furthermore, sample period and data sources are presented.

This chapter highlights the importance of controlling for endogeneity problems that could exist between the risk-taking incentives of stock option compensation and CDS use when CEO risks-taking incentives and CDS usage are jointly determined. Furthermore, this chapter discusses the possible endogeneity problem in the relationship between CDS use and firms risk. The control variable used in the CDS model and in the risk model, and the various robustness checks are explained at the end of the present chapter.

39 _{These countries have the highest number of hedging observations.}

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