Joint forecasts of Dow Jones stocks under general multivariate loss function
نویسندگان
چکیده
When forecasts are assessed by a general loss (cost-of-error) function, the optimal point forecast is not, in general, the conditional mean, and depends on the conditional volatility – which, for stock returns, is time-varying. Our aim is to provide forecasts of daily returns of 30 DJIA stocks under a general multivariate loss function. The paper’s contributions are as follows. We discuss what conditions define a multivariate loss function, and suggest a simple class of multivariate loss functions. Based on suitable combinations of univariate loss functions, these are convenient for practical applications with many variables. To keep the computational aspect tractable, we employ a flexible multivariate GARCH model to estimate forecast distributions. It easily copes with large number of series while allowing for skewness, fat tails, non-ellipticity, and tail-dependence. Based on Engle’s DCC GARCH, the model uses multivariate affine generalized hyperbolic distributions as conditional probability law, and the number of parameters to be estimated simultaneously does not depend on the number of series. We fit our model with daily data from 2002 to 2007 (keeping data from 2008 for out-of-sample forecasts), and use a bootstrap procedure to derive point forecasts under several multivariate loss functions.
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ورودعنوان ژورنال:
- Computational Statistics & Data Analysis
دوره 54 شماره
صفحات -
تاریخ انتشار 2010