PUBLICATIONS

"Sequential Conditional Correlations: Inference and Evaluation", Journal of Econometrics, 2009.

WORKING PAPERS

Beatlestrap, 2010.
Abstract: The bootstrap of test statistics requires the re-estimation of the model's parameters for each bootstrap sample. When parameter estimates are not available in closed form, this procedure becomes computationally demanding as each replication requires the numerical optimization of an objective function. This paper investigates the feasibility of the Beatlestrap, an optimization-free approach to bootstrap. It is shown that, ex-post, M-estimators may be expressed in terms of simple arithmetic averages therefore reducing the bootstrap of Wald statistics to the bootstrap of averages. Similarly, it is shown how the Lagrange Multiplier and the Likelihood Ratio statistics may be bootstrapped bypassing the objective function's multiple optimizations. The proposed approach is extended to simulation based Indirect Estimators. The finite sample properties of Beatlestrap are investigated via Monte Carlo simulations.

The Effects of Interest Rate Movements on Assets' Conditional Second Moments, 2009.
Abstract: This paper investigates whether the short term interest rate may explain the movements observed in the conditional second moments of asset returns. The theoretical connections between these seemingly unrelated quantities are studied within the C-CAPM framework. Original results are derived that attest the existence of a relation between the risk-free rate and the conditional second moments of asset returns. The empirical findings, involving 165 stock returns quoted at the NYSE, confirm that the interest rate is a determinant of the 165 conditional variances and 13530 conditional correlations.

Consistent Realized Covariance for Asynchronous Observations Contaminated by Market Microstructure Noise, 2006.
Abstract: This paper proposes a consistent estimator for the realized covariance of high frequency and asynchronous assets' returns that are contaminated by microstructure noise. The main contribution is the introduction of the pseudo-aggregation which transforms the observations into series with the same number of data points without incurring in any loss of information. This in turn makes it possible to construct an unbiased and consistent covariance estimator by merging techniques from the literature relating to the asynchrony and the market microstructure contamination. Monte Carlo simulations confirm the theoretical results and highlight the outstanding performance of the proposed estimator.