Los experimentos críticos

Employing generalised information share (GIS) of Lien and Shrestha (2014), Chapter 2 addresses the issue that CDS spread and stock implied credit spread may not have a one-to-one cointegration relation required by Hasbrouck’s (1995) information share (IS) and Gonzalo and Granger’s (1995) component share (GG). Nevertheless, all the three measures provide qualitatively similar empirical results. The stock market generally leads the CDS market in capturing credit risk news, except for the period of 2008–2010. Eliminating transitory price components, such as the liquidity effect, increases the informational efficiency of the CDS market in the earlier period of the sample. Another finding is that the CDS of investment-grade firms contributes more to credit risk discovery compared with that of speculative-grade firms. Further, the overall economy condition and funding cost negatively affect the credit risk discovery contribution of the CDS market. Finally, CCP seems to hinder CDS from capturing credit risk news first, which supports that the CDS market may be driven largely by insider trading.

Chapter 2 contributes to the existing literature in the following aspects. First, while GIS technique may be theoretically stronger than IS and GG methods, our findings suggest that it does not make material difference in the relative price discovery contribution of CDS contracts. Second, this chapter provides support to several previous papers which demonstrate the general dominant role of the stock market in credit risk discovery, e.g., Forte and Peña (2009) and Narayan et al. (2014). However, during the period of 2008– 2010, the CDS market is found to dominate the stock market, which supports Xiang et al. (2013). Third, the current understanding of the impact of eliminating transitory

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components on credit risk discovery is extended. This chapter finds that for the U.S. firms, the impact is generally insubstantial but time-varying, complementing Forte and Lovreta’s (2015) study. By suggesting new factors, it adds to the extant literature of the drivers of the informational efficiency of CDS and stock markets. The negative effects of funding cost and central clearing service on the market efficiency of the CDS market may help investors to design better trading strategies and benefit regulators in terms of effectively regulating the CDS market.

Using Diebold and Yilmaz’s (2015a) VECM-based connectedness measures, Chapter 3 finds a significant rise in the total credit risk transmission among the G-SIFIs during the period of severe financial events; as the financial crises intensified, so too did the cross-border spillovers of default risk, with a significant threat carrying over from the large U.S. banks and insurers to the other G-SIFIs in the EU and Asia. While there are bilateral linkages between G-SIBs and G-SIIs, the threat to the global financial stability that a large bank would pose if it were to fail is generally greater than that of an insurer. The changes in interbank lending, unconventional banking activity, regulatory leverage ratio, and extra loss absorbency requirement can have a significant impact on a G-SIB’s role in credit risk transmission. A G-SII’s role in credit risk spillovers can be positively determined by its non-traditional non-insurance activity, size, and global business.

Chapter 3 adds to the literature in a number of aspects. Firstly, it improves the current understanding of credit risk transmission across financial firms, e.g., Yang and Zhou (2013), by focusing on the G-SIFIs identified by the FSB. Second, unlike Diebold and Yilmaz (2015a), this chapter suggests that the empirical findings of VECM model and that of VAR model are qualitatively similar. It implies that although VECM model is econometrically more robust than VAR model as it allows for possible cointegration relations shared by the G-SIFIs’ credit risk, it may not necessarily provide substantially

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different empirical results. Third, this study adds to the existing literature of systemic importance of each financial firm by proposing a ‘too-interconnected-to-fail’ ranking to identify which G-SIFI is the major credit risk provider or receiver. Since this ranking is derived directly from CDS market data, it is complementary to the FSB’s list that is based on accounting data. Regulators may combine the two lists to obtain a ‘composite’ ranking that considers diverse sources of information. Finally, it offers further evidence of the drivers of credit risk spillovers of financial institutions, which complements the extant literature, e.g., Yang and Zhou (2013). The findings of regulatory leverage ratio and extra loss absorbency requirement may help regulators improve regulation in terms of curbing the G-SIBs to be more systemically important.

Using asymmetric dynamic conditional correlation model with exogenous variables (ADCC-X), Chapter 4 finds that in contrast with equity market, sovereign CDS market is more sensitive to domestic sovereign rating events or surprises. The arrivals of rating events/surprises are accompanied with an increase of the negative correlation of the two assets. Both symmetric and asymmetric reactions of returns and volatility of two assets to positive and negative rating news are found. Two rating events symmetrically affect the negative asset correlation in Spain, Italy, and Cyprus, while they exert asymmetric influence on the correlation in Portugal, Ireland, Netherlands, Finland, and the United States. Bailout news is accompanied by wider CDS spreads and worse equity market performance. Asset volatility increases and two assets are more correlated. Compared with domestic sovereign rating events, bailout news has stronger and more significant influence on individual assets as well as asset correlation. Greek rating events generate spillover effect on sovereign CDS and equity markets in several sample countries. The two assets becomes less negatively correlated when Greek rating events occur.

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Chapter 4 contributes to the existing literature on several dimensions. First, this chapter adopts a more general measure to define sovereign credit rating events and it calculates rating surprises, which complements the existing methods used by Gande and Parsley (2005) and Drago and Gallo (2016). Second, it adds to the existing literature relating macro news to the returns and volatility of different assets as well as the correlation between assets, such as Andersen et al. (2007) and Brenner et al. (2009). It finds that the conditional correlation of sovereign CDS and equity index is not a simple indicator of their relationship, but can be driven by the releases of sovereign credit rating and bailout events. Brenner et al. (2009) suggest that the changes of asset correlation on the announcement days of macro news may be attributable to any cross-asset trading which is jointly induced by information spillovers, portfolio rebalancing, wealth effects, and increased degree of disagreement among investors. Moreover, bailout news exerts more significant impact. Finally, it extends the current understanding of the spillover effect of sovereign rating events, e.g., Ismailescu and Kazemi (2010), by showing that Greek sovereign rating news can affect not only the returns and volatility of two assets, but also their correlation in several sample countries.