By Robert Engle
Monetary markets reply to info almost straight away. each one new piece of data affects the costs of resources and their correlations with one another, and because the process quickly adjustments, so too do correlation forecasts. This fast-evolving setting provides econometricians with the problem of forecasting dynamic correlations, that are crucial inputs to danger size, portfolio allocation, by-product pricing, and plenty of different severe monetary actions. In watching for Correlations, Nobel Prize-winning economist Robert Engle introduces an enormous new procedure for es. Read more...
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Extra resources for Anticipating correlations : a new paradigm for risk management
In some cases the univariate variance models must be formulated with multivariate information sets. The cross products of these standardized residuals may have nonzero means that can be predicted by other functions of the data. It is this predictability that can be modeled to give time-varying conditional correlations. In the CCC model there is no predictability and consequently the conditional correlations are constant. Rather than estimating the covariance matrix and then calculating the conditional correlations from it, the DCC model uses the standardized residuals and estimates the correlation matrix directly.
By making simple assumptions on expected returns, minimum-variance portfolios can be constructed that change every time the forecast is updated. The variance of such portfolios is a measure of performance. Two criteria will be used in that section: the ﬁrst is a minimum-variance portfolio, which is equivalent to assuming that the expected returns are equal; the second is a long–short hedge portfolio, which is equivalent to assuming that one asset has a positive excess expected return while a second asset is merely expected to achieve the riskless rate.
That is, ˜ ≡ diag(Σ)1/2 , D ˜P˜−1 . 34) with components that are univariate GARCH models. Hence the conditional covariance matrix is given by ˜t2 P˜ −1 D. 34). This is a two-step estimator: ﬁrst extract the principal components from S and then estimate univariate models for each of these. The econometric analysis of this two-step process has not yet been examined. These choices of P lead to diﬀerent models. In fact there are many choices. Van der Weide (2002) has recently recognized this feature and introduced the class of generalized orthogonal GARCH, or GO-GARCH.
Anticipating correlations : a new paradigm for risk management by Robert Engle