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Determinants of Credit Default Swap Spread:

Evidence from the Japanese Credit Derivative Market. Determinants of Credit Default Swap Spread:. Agenda. Introduction Data Regression Analysis Robustness Check Conclusion. Introduction CDS Market Overview.

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Determinants of Credit Default Swap Spread:

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  1. Evidence from the Japanese Credit Derivative Market Determinants of Credit Default Swap Spread:

  2. Agenda Introduction Data Regression Analysis Robustness Check Conclusion

  3. Introduction CDS Market Overview In the left-hand panel of this Graph ,Remolonaand Shim (2008) shows that Asia-Pacific names comprise almost a quarter of all those traded around the world In right-hand panel, for a breakdown by economy within the region, this list shows a total of 921 names. Japan has the greatest number of names in Asia-Pacific. we consider Japan play an important role in the Asia-Pacific area. Therefore, it is of interest to study credit spread determinants on Japan dataset, The current study fills the gap in the literature by examining CDS spread in Japan market.

  4. Introduction Level Credit Spread of Economic Model Data Difference Regression Analysis Robustness Check Conclusion

  5. Credit Spread Dynamics • Structured Model • Black and Scholes(1973)‏ • Merton(1974)‏ • Collin-Dufresne, Goldstein, and Martin(2001)‏ • Ericsson, Jacobs, and Oviedo(2005)‏ Structured Model Reduce-form Model • Default Intensity Default Intensity process Firm value process • Default Prob. • Recovery Rate • Leverage level • Volatility • Risk-free Rate On the other hand, a reduced-form model assumes that a default process is unobservable and a latent factor known as default intensity determines the probability of default. Credit Spread CDS Spread In this paper, we apply the structure model to the empirical examination of the Japanese CDS market, because it obviously describes the default mechanism and enables us to analyze the relationship between credit spread and financial and macroeconomic variables.

  6. Brief summary of Credit Spread Drivers Leverage level + Volatility + Credit Spread - Risk-free Rate ? • First, The higher leverage of a firm, the higher probability of default. • Second, increasing volatility will increase the default probability. • Third, the risk-free rate determines the risk-adjusted drift of firm value and thus an increase in the risk-free rate tends to decrease risk-adjusted default probabilities. • There is a negative relation between risk-free rate and credit spread. Macroeconomic Factors

  7. Data Requirement of Dataset: cds spread from Markit Group 180-day historical volatility ,leverage ratio from PACAP database. Risk-Free Rate: Weekly data on 2-year Japan government bond yields are collected from Datastream database. Observed firm: At least 252 observations of CDS Spread 106 Japanese firms from MarkitGroup database Data Period : January 2001 to December 2004

  8. Regression Analysis We run univariate and multivariate regressions for the CDS spread and CDS Spread difference on explanatory variables based on the theory of the main determinants of credit spread. We further separate the whole sample into sub-samples by various criteria. For example by credit rating, different sample period, and financial and non-financial industries. In general, our findings remain robust after controlling these various criteria. • Dependent Variable • CDS Spread Level • CDS Spread difference in a period • Explanatory Variables • Leverage: • Historical Volatility: Sampling from 180 daily return of stock price in a shifting window • Risk-free Rate: weekly data on 2-year Japan Government Bond Yield • Square of Risk-free Rate: To Capture potential nonlinear effect for Risk-free Rate • Slope of yield Curve :Difference between 2-year and 10-year Japan Government Bond Yield which convey information on economic condition • Market Return: Proxy for the overall business climate

  9. Result • Table 1 Cross-sectional summary statistics • Table 2 Empirical results for the whole sample • Table 3 Results partitioned by credit rating • Table 4 Results partitioned by different sub-sample periods (whole 2001-2004; separate into 2001-2002 &2003-2004 ) • Table 5 Results partitioned by financial and non-financial industry From Table 3 to 5 , we reports Results partitioned by various criteria.

  10. Table 1 Panel A indicates that there are firms with very high levels of CDS spreads. Panel B shows that financial leverage, firm specific volatility, and the risk-free rate, are more related tothe CDS spread.

  11. Table 2 From table 2 to table 6, we have the same table format, In Panel A is for level data and Panel B is for difference data. From table 2 to table 6, Regressions for level data has higher explanatory power over regressions based on difference data. First, we find that the coefficients on leverage are significant and positive. Second, the coefficients on historical volatility are also positively significant. Third, the results on the risk-free rate has a significant negative relation between CDS spread level and risk-free rate.

  12. Table 3 • For lower credit rating firms, leverage ,historical volatility and risk-free rate are more sensitive to CDS spread than higher credit rating firms..

  13. Table 4 We find that, in general, the results for the sub-sample periods are very similar to each other and to the whole sample period results. In general, our results in Table 4 suggest that the theoretical explanatory variables remain robust to explain the CDS spread for different time periods in Japan.

  14. Table 5 The theoretical variables, such as leverage; historical volatility, and risk-free rate show similar results as the whole sample. • However, the results for financial industry show different pattern, compared to non-financial industry, which may be due to unique characteristics of financial firms. In general, our findings remain robust after controlling financial and non-financial industry firms.

  15. Robustness Check A robustness check using an alternative approach. Following Collin-Dufresne, Goldstein, and Martin (2001) and Ericsson, Jacobs, and Oviedo (2005), we estimate the coefficients by first running the time-series regressions for each firm Calculate the cross-sectional mean of the estimated coefficients.

  16. Table 6 The results are similar to those in the previous section.

  17. Conclusion This study investigates the determinants of CDS spread for the Japanese market and contributes the literature. Effects of level in leverage and implied volatility on CDS spread are positively significant. A negative relation between risk-free rate and CDS spread. Theoretical determinants perform well in explaining cross-sectional variation in the level of CDS spread. Limited explanatory power on the difference 0f CDS spread. Findings are consistent with the theory with statistical significance.

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