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We provide the first systematic study of liquidity in the foreign exchange market. We find significant variation in liquidity across exchange rates, substantial illiquidity costs, and strong commonality in liquidity across currencies and with equity and bond markets. Analyzing the impact of liquidity risk on carry trades, we show that funding (investment) currencies offer insurance against (exposure to) liquidity risk. A liquidity risk factor has a strong impact on carry trade returns from 2007 to 2009, suggesting that liquidity risk is priced. We present evidence that liquidity spirals may trigger these findings.

The search for a market design that ensures stable bank funding is at the top of regulators' policy agenda. This paper empirically shows that the central counterparty (CCP)-based euro interbank repo market features this stability. Using a unique and comprehensive data set, we show that the market is resilient during crisis episodes and may even act as a shock absorber, in the sense that repo lending increases with risk, while spreads, maturities, and haircuts remain stable. Our comparison across different repo markets shows that anonymous CCP-based trading, safe collateral, and the absence of an unwind mechanism are the key characteristics to ensure market resilience.

This article studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gaussian reference model. Based on these results, we obtain optimal bounded-influence versions of the classical likelihood-based tests for parametric hypotheses. We propose a feasible and efficient algorithm for the computation of our robust estimators, which uses analytical Laplace approximations to estimate the auxiliary recentering vectors, ensuring Fisher consistency in robust estimation. This strongly reduces the computation time by avoiding the simulation of multidimensional integrals, a task that typically must be addressed in the robust estimation of nonlinear models for time series. In some Monte Carlo simulations of an AR(1)-ARCH(1) process, we show that our robust procedures maintain a very high efficiency under ideal model conditions and at the same time perform very satisfactorily under several forms of departure from conditional normality. In contrast, classical pseudo-maximum likelihood inference procedures are found to be highly inefficient under such local model misspecifications. These patterns are confirmed by an application to robust testing for autoregressive conditional heteroscedasticity.

Parametric option pricing models are widely used in finance. These models capture several features of asset price dynamics; however, their pricing performance can be significantly enhanced when they are combined with nonparametric learning approaches that learn and correct empirically the pricing errors. In this article we propose a new nonparametric method for pricing derivatives assets. Our method relies on the state price distribution instead of the state price density, because the former is easier to estimate nonparametrically than the latter. A parametric model is used as an initial estimate of the state price distribution. Then the pricing errors induced by the parametric model are fitted nonparametrically. This model-guided method, called automatic correction of errors (ACE), estimates the state price distribution nonparametrically. The method is easy to implement and can be combined with any model-based pricing formula to correct the systematic biases of pricing errors. We also develop a nonparametric test based on the generalized likelihood ratio to document the efficacy of the ACE method. Empirical studies based on S& P 500 index options show that our method outperforms several competing pricing models in terms of predictive and hedging abilities.

We propose a new method for pricing options based on GARCH models with filtered historical innovations. In an incomplete market framework, we allow for different distributions of historical and pricing return dynamics, which enhances the model's flexibility to fit market option prices. An extensive empirical analysis based on S&P 500 index options shows that our model outperforms other competing GARCH pricing models and ad hoc Black-Scholes models. We show that the flexible change of measure, the asymmetric GARCH volatility, and the nonparametric innovation distribution induce the accurate pricing performance of our model. Using a nonparametric approach, we obtain decreasing state-price densities per unit probability as suggested by economic theory and corroborating our GARCH pricing model. Implied volatility smiles appear to be explained by asymmetric volatility and negative skewness of filtered historical innovations.

We provide a model of banks' short-term funding and study the conditions influencing roll over risk. Our model reproduces the major differences between U.S. and Euro short-term funding markets. Anonymous, short-term markets are resilient against larger liquidity shocks. Non- anonymous markets however improve welfare by allocating resources efficiently. An anonymous Central Counterparty (CCP) is therefore welfare improving in a liquidity crisis but detrimental to welfare in normal times. The insurance mechanism on the CCP, which transfers wealth from high to low quality borrowers, always increases the market's resilience against a liquidity shock.

Repo markets trade off the efficient allocation of liquidity in the financial sector with resilience to funding shocks. The repo trading and clearing mechanisms are crucial determinants of the allocation-resilience tradeoff. The two common mechanisms, anonymous central-counterparty (CCP) and non-anonymous over-the-counter (OTC) markets, are inefficient and their welfare rankings depend on funding tightness. CCP (OTC) markets inefficiently liquidate high (low) quality assets for large (small) funding shocks. Two innovations to repo market design contribute to maximize welfare: a liquidity-contingent trading mechanism and a two-tiered guarantee fund.

We provide a model of banks' short-term funding and study the conditions influencing roll over risk. Our model reproduces the major differences between U.S. and Euro short-term funding markets. Anonymous, short-term markets are resilient against larger liquidity shocks. Non- anonymous markets however improve welfare by allocating resources efficiently. An anonymous Central Counterparty (CCP) is therefore welfare improving in a liquidity crisis but detrimental to welfare in normal times. The insurance mechanism on the CCP, which transfers wealth from high to low quality borrowers, always increases the market's resilience against a liquidity shock.

To study jump and volatility risk premia in asset returns, we develop a novel class of time-changed Lévy models. The models are characterized by flexible Lévy measures, and allow consistent estimation under physical and risk neutral measures. To operationalize the models, we introduce a simple and rigorous filtering procedure to recover the unobservable time changes. An extensive time series and option pricing analysis of 16 time-changed Lévy models shows that infinite activity processes carry significant jump risk premia, and largely outperform many finite activity processes.

We study the term structure of variance swaps, equity and variance risk premia. A model-free analysis reveals a significant price jump component in variance swap rates. A model-based analysis shows that investors' willingness to ensure against volatility risk increases after a market drop. This effect is stronger for short horizons and more persistent for long horizons. During the financial crisis investors demanded large risk premia to hold equities but the risk premia largely depended and strongly decreased with the holding horizon. The term structure of equity and variance risk premia responds differently to various economic indicators.