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Mon, 21 Sep 2009 00:33:12 PDT

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Mon, 21 Sep 2009 00:33:12 PDT

Special Issue on "Multivariate Volatility Models"

Garcia, R., Ghysels, E., Renault, E., Rodrigues, P. — Mon, 21 Sep 2009 00:33:13 PDT

Range-Based Covariance Estimation Using High-Frequency Data: The Realized Co-Range

Bannouh, K., van Dijk, D., Martens, M. — Mon, 21 Sep 2009 00:33:13 PDT

We introduce the realized co-range, a novel estimator of the daily covariance between asset returns based on intraday high–low price ranges. In an ideal world, the co-range is five times more efficient than the realized covariance, which uses cross-products of intraday returns, when sampling at the same frequency. In Monte Carlo simulations, we find that for plausible levels of bid–ask bounce, infrequent trading and nonsynchronous trading, the realized co-range still improves upon the realized covariance. In a volatility timing strategy for S&P500, bond and gold futures, we find that the co-range estimates are less noisy, which results in lower transaction costs and higher Sharpe ratios.

Modeling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model

Silvennoinen, A., Terasvirta, T. — Mon, 21 Sep 2009 00:33:13 PDT

In this paper, we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new double smooth transition conditional correlation (DSTCC) GARCH model extends the smooth transition conditional correlation (STCC) GARCH model of Silvennoinen and Teräsvirta (2005) by including another variable according to which the correlations change smoothly between states of constant correlations. A Lagrange multiplier test is derived to test the constancy of correlations against the DSTCC-GARCH model, and another one to test for another transition in the STCC-GARCH framework. In addition, other specification tests, with the aim of aiding the model building procedure, are considered. Analytical expressions for the test statistics and the required derivatives are provided. Applying the model to the stock and bond futures data, we discover that the correlation pattern between them has dramatically changed around the turn of the century. The model is also applied to a selection of world stock indices, and we find evidence for an increasing degree of integration in the capital markets.

CHICAGO: A Fast and Accurate Method for Portfolio Risk Calculation

Broda, S. A., Paolella, M. S. — Mon, 21 Sep 2009 00:33:13 PDT

This paper shows how independent component analysis can be used to estimate the generalized orthogonal GARCH model in a fraction of the time otherwise required. The proposed method is a two-step procedure, separating the estimation of the correlation structure from that of the univariate dynamics, thus facilitating the incorporation of non-Gaussian innovations distributions in a straightforward manner. The generalized hyperbolic distribution provides an excellent parametric description of financial returns data and is used for the univariate fits, but its convolutions, necessary for portfolio risk calculations, are intractable. This restriction is overcome by saddlepoint approximations for the Value at Risk and expected shortfall, which are computationally cheap and retain excellent accuracy far into the tails. It is further shown that the mean-expected shortfall portfolio optimization problem can be solved efficiently in the context of the model. A simulation study and an application to stock returns demonstrate the validity of the procedure.

Modeling International Financial Returns with a Multivariate Regime-switching Copula

Chollete, L., Heinen, A., Valdesogo, A. — Mon, 21 Sep 2009 00:33:13 PDT

In order to capture observed asymmetric dependence in international financial returns, we construct a multivariate regime-switching model of copulas. We model dependence with one Gaussian and one canonical vine copula regime. Canonical vines are constructed from bivariate conditional copulas and provide a very flexible way of characterizing dependence in multivariate settings. We apply the model to returns from the G5 and Latin American regions, and document three main findings. First, we discover that models with canonical vines generally dominate alternative dependence structures. Second, the choice of copula is important for risk management, since it modifies the Value-at-Risk (VaR) of international portfolios and produces a better out-of-sample performance. Third, ignoring asymmetric dependence and regime-switching in portfolio selection leads to significant costs for an investor.

A Latent Factor Model of Multivariate Conditional Heteroscedasticity

Aguilar, M. — Mon, 21 Sep 2009 00:33:13 PDT

This paper examines the joint dynamics of a system of asset returns by describing and implementing a factor multivariate stochastic volatility (factor MSV) model. The foundation for the model discussed here is the work of Doz and Renault (2006). Despite its attractive design, that model has not been adopted widely in the literature, most likely due to the difficulty encountered in its implementation. The main contribution of this paper is to illustrate that this factor MSV model can be implemented easily and with only a few modifications. Specifically, I develop a sequential testing procedure that can account simultaneously for a series of nested hypotheses and structure properly the moment conditions used for estimation. A simulation study suggests that the factor MSV model and estimation strategy presented here is able to recover accurately the number of, and dynamics for, the latent factors that drive the conditional volatility of returns.