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Model uncertainty and asset return predictability: an application of Bayesian model averaging

机译：模型的不确定性和资产收益的可预测性：贝叶斯模型平均的应用

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We investigate model uncertainty associated with predictive regressions employed in asset return forecasting research. We use simple combination and Bayesian model averaging (BMA) techniques to compare the performance of these forecasting approaches in short-vs. long-run horizons of S&P500 monthly excess returns. Simple averaging involves an equally-weighted averaging of the forecasts from alternative combinations of factors used in the predictive regressions, whereas BMA involves computing the predictive probability that each model is the true model and uses these predictive probabilities as weights in combing the forecasts from different models. From a given set of multiple factors, we evaluate all possible pricing models to the extent, which they describe the data as dictated by the posterior model probabilities. We find that, while simple averaging compares quite favorably to forecasts derived from a random walk model with drift (using a 10-year out-of-sample iterative period), BMA outperforms simple averaging in longer compared to shorter forecast horizons. Moreover, we find further evidence of the latter when the predictive Bayesian model includes shorter, rather than longer lags of the predictive factors. An interesting outcome of this study tends to illustrate the power of BMA in suppressing model uncertainty through model as well as parameter shrinkage, especially when applied to longer predictive horizons.View full textDownload full textRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/00036840701736214
机译：我们调查与资产收益预测研究中使用的预测回归相关的模型不确定性。我们使用简单的组合和贝叶斯模型平均（BMA）技术来比较这些短时vs预测方法的性能。标普500每月超额收益的长期目标。简单平均涉及对预测回归中使用的因子的替代组合进行的预测加权平均，而BMA涉及计算每个模型为真实模型的预测概率，并使用这些预测概率作为权重来组合不同模型的预测。从给定的多个因素集中，我们在一定程度上评估了所有可能的定价模型，它们描述了后验模型概率所指示的数据。我们发现，虽然简单平均与具有漂移（使用10年样本外迭代周期）的随机游走模型所得出的预测相比非常有利，但BMA在更长的预测范围内优于较短的预测范围。此外，当预测贝叶斯模型包括较短而不是较长的预测因素滞后时，我们发现了后者的进一步证据。这项研究的有趣结果倾向于说明BMA通过模型以及参数收缩来抑制模型不确定性的作用，尤其是在应用于较长的预测范围时。查看全文下载全文相关的var addthis_config = {ui_cobrand：“泰勒和弗朗西斯在线” ，services_compact：“ citeulike，netvibes，twitter，technorati，美味，linkedin，facebook，stumbleupon，digg，google，更多”，发布号：“ ra-4dff56cd6bb1830b”}；添加到候选列表链接永久链接http://dx.doi.org/10.1080/00036840701736214

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