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This paper compares the performance of centralized and decentralized markets experimentally. We constrain trading exchanges to happen on an exogenously predetermined network, representing the trading relationships in markets with differing levels of connectivity. Our experimental results show that, despite having lower trading volumes, decentralized markets are generally not less efficient. Although information can propagate quicker through highly connected markets, we show that higher connectivity also induces informed traders to trade faster and exploit further their information advantages before the information becomes fully incorporated into prices. This not only reduces market efficiency, but it increases wealth inequality. We show that, in more connected markets, informed traders trade not only relatively quicker, but also more, in the right direction, despite not doing it at better prices.

This paper empirically investigates the role of banks' network centrality in the interbank market on their funding rates. Specifically we analyze transaction data from the e-MID market, the only electronic interbank market in the Euro Area and US, over the period 2006-2009 that encompasses the global financial crisis. We show that interbank spreads are significantly affected by both local and global measures of connectedness. The effects of network centrality increased as the financial crisis evolved. Local measures show that having more links increases borrowing costs for borrowers and reduces premia for lenders. For global network centrality, borrowers receive a significant discount if they increase their intermediation activity and become more central, while lenders pay in general a premium (i.e. receive lower rates) for centrality. This provides evidence of the `too-interconnected-to-fail' hypothesis.

This paper analyzes the distribution of lending and borrowing credit spreads in the European interbank market conditional on main features of banks such as their size, operating currency and nationality. This is done by means of nonparametric kernel estimation methods for the cross-sectional density of interbank funding rates over a large sample of European banks trading in the e-MID market. The analysis is repeated over consecutive non-overlapping periods in order to assess and compare the effect of the factors during crisis and non-crisis periods. We find evidence of important differences between the borrowing and lending segment of the interbank market that are augmented during crises periods. Our results strongly support the existence of a size effect in the borrowing market. Largest banks enjoy the highest lending rates and the lowest borrowing rates. The collapse of Lehman Brothers accentuates the differences in funding conditions. In both borrowing and lending segments, crises are corresponded by high volatilities in daily funding costs. Banks using the Euro currency and in countries not affected by sovereign debt crises are benefited by lower funding costs. Our nonparametric analysis of densities conditional on banks' nationality suggests that distress in the interbank market can serve as an early warning indicator of sovereign risk.

A simple approach to determining the Gaussian kernel that constitutes the backbone of the multi-factor Heston model is proposed based on a suitable expansion in powers of volatilities of volatilities. This analysis provides Black-Scholes-like formulas for pricing European vanilla options, allowing for accurate approximations of the option prices under the multi-factor Heston model up to volatilities of volatilities on the order of 50%. The analysis also leads to a simple formula for the implied volatility showing that changes in the convexity of the volatility smile are due only to price skewness, and an easy formula to reproduce volatility indices via the realized volatility. Interestingly, the variance of the Gaussian kernel is equal to the variance of the continuously compounded return in the case of the Heston model. The empirical analyses presented assess the potential of our approach to capture market distortions while adequately forecasting the dynamics of the VIX index.