Financial Econometrics, Asset Pricing, Time Series Analysis, Volatility and Risk Management
Thursday 14:00 - 16:00
1. The Effects of Interest Rate Movements on Assets' Correlations, December 2006.
Abstract: This paper investigates whether macroeconomic variables and in particular the short term interest rate can explain the movements observed in the conditional correlations among assets. The theoretical connections between these seemingly unrelated quantities are studied within the CAPM framework. Under the assumption that the product of the relative risk aversion coefficient and the marginal utility is decreasing in consumption, original results are derived that attest the existence of a relation between the risk-free rate and the conditional correlations. The empirical findings involving the 4950 correlations of the Fama-French 100 portfolios confirm the theoretical results.
2. Sequential Conditional Correlations: Inference and Evaluation, Spring 2006.
Abstract: This paper presents a new approach to the modeling of conditional correlation matrices within the multivariate GARCH framework. The procedure, which consists in breaking the matrix into the product of a sequence of matrices with desirable characteristics, in effect converts a highly dimensional and intractable optimization problem into a series of simple and feasible estimations. This in turn allows for much richer parameterizations and complex functional forms for the single components. An empirical application involving the conditional second moments of 69 selected stock returns from the NASDAQ100 shows how the new procedure results in strikingly accurate measures of the conditional correlations.
3. Consistent Realized Covariance for Asynchronous Observations Contaminated by Market Microstructure Noise, Summer 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.
Warwick Business School
University of Warwick
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