We model the dynamics of volatilities and correlations of exchange rate returns using a set of specifications based on the DCC model (Engle, 2002). The DCC model offers an attractive multivariate framework for the study of correlation timing because it has the following advantages: (i) it is tractable and parsimonious with a low dimension of parameters; (ii) it is flexible and can be generalized to account for asymmetric correlations while ensuring that correlations are in the range; (iii) it provides for a positive-definite covariance matrix; and (iv) it is straightforward to implement even when the number of assets is large.

In order to assess the economic value of volatility and correlation timing, we estimate a set of multivariate models for dynamic correlations (such as the DCC model), each under a set of univariate specifications for dynamic volatility (such as the GARCH model). In the following discussion, we describe the complete set of models we estimate.

Let *y*_{t} = (*y*_{1, t}, … , *y*_{N, t})^{′} denote the *N* × 1 vector of nominal log-exchange rate returns at time *t*:

15.1

where μ = (μ_{1}, … , μ_{N})^{′} is the *N* × 1 vector of unconditional means, Σ_{t} is the *N* × *N* conditional covariance matrix, and ε _{t} = ( ...

Start Free Trial

No credit card required