Cryptocurrencies such as Bitcoin are establishing themselves as an investment asset and are often named the New Gold. This study, however, shows that the two assets could barely be more different. Firstly, we analyze and compare conditional variance properties of Bitcoin and Gold as well as other assets and find differences in their structure. Secondly, we implement a BEKK-GARCH model to estimate time-varying conditional correlations. Gold plays an important role in financial markets with flight-to-quality in times of market distress. Our results show that Bitcoin behaves as the exact opposite and it positively correlates with downward markets. Lastly, we analyze the properties of Bitcoin as portfolio component and find no evidence for stable hedging capabilities. We conclude that Bitcoin and Gold feature fundamentally different properties as assets and linkages to equity markets. Our results hold for the broad cryptocurrency index CRIX. As of now, Bitcoin does not reflect any distinctive properties of Gold other than asymmetric response in variance.

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As observed in the financial crisis, CDS spreads tend to increase simutaneously as a reaction to common shocks. Focusing on the spillover effects triggered by extreme events, we propose a credit risk analysis tool by applying credit default swap spread returns to the concept of CoVaR suggested by Adrian and Brunnermeier (2011). The interconnection and mutual impact on credit spreads are investigated based on CDS spreads of the biggest derivative dealers in the market. By including factors identified as determinants of CDS spreads to the set of explanatory variables such as equity return and equity volatility and implementing the variable selection technique least absolute shrinkage and selection operator (LASSO), the results demonstrate an improved performance in CDS spread VaR calculation. The enhancement is more significant in pre-crisis period but both methodologies tend to overestimate risk in turbulent period. Further, non-linear effects between CDS spreads in extreme events are captured by the introduction of a partial linear model in the CoVaR calculation.JEL: G12, G13, G23