Forecast comparison of principal component regression and principal covariate regression
Computational Statistics & Data Analysis
Stock and bond return predictability: the discrimination power of model selection criteria
Computational Statistics & Data Analysis
Intradaily dynamic portfolio selection
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
In a typical strategic asset allocation problem, the out-of-sample certainty equivalent returns for a long-horizon investor with constant relative risk aversion computed from a range of vector autoregressions (VARs) are compared with those from nonlinear models that account for bull and bear regimes. In a horse race in which models are not considered in their individuality but instead as an overall class, it is found that a power utility investor with a relative risk aversion of 5 and a 5 year horizon is ready to pay as much as 8.1% in real terms to be allowed to select models from the Markov switching (MS) class, while analogous calculation for the whole class of expanding window VARs leads to a disappointing 0.3% per annum. Most (if not all) VARs cannot produce portfolio rules, hedging demands, or out-of-sample performances that approximate those obtained from equally simple nonlinear frameworks.