条件信息和欧洲债券基金的表现

F. Silva, M. C. Cortez
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引用次数: 68

摘要

在本文中,我们研究了欧洲债券基金的业绩,据我们所知,还没有研究。无条件模型和条件模型都用于评估基金绩效。作为条件信息,我们使用我们发现对预测欧洲市场债券市场回报有用的变量。我们还测试了债券基金表现对单一和多个基准的敏感性。结果表明,一般而言,债券基金的表现并不优于被动型策略。意大利、西班牙、葡萄牙的债券基金以及英国“金边债券”基金的负面表现更为明显。对于大多数德国基金、英国“公司”和“其他债券”基金,以及一些法国基金,我们不能拒绝中性表现的假设。这些发现对于我们使用的任何模型(无条件与条件、单指数与多指数)都是稳健的。总的来说,与单指标模型相比,多指标模型似乎增加了一些解释力。此外,当我们合并预定的信息变量时,我们可以观察到性能更好的轻微趋势,特别是对于多索引模型。这一证据与之前对股票基金的研究一致,并支持了条件模型可能有助于更好地评估业绩的观点。然而,我们的结果表明,额外的风险因素的影响似乎比纳入预定信息变量的影响更大。
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Conditioning Information and European Bond Fund Performance
In this paper we investigate the performance of European bond funds which, as far as we are aware of, have not yet been studied. Both unconditional and conditional models are used to evaluate fund performance. As conditioning information we use variables that we find to be useful in predicting bond market returns in the European Market. We also test the sensitivity of bond fund performance to single and multiple benchmarks. The results show that, in general, bond funds are not able to outperform passive strategies. The negative performance is more evident for bond funds in Italy, Spain, Portugal and also for UK "Gilt" funds. For most German funds and UK "Corporate" and "Other Bond" funds and also for several French funds we cannot reject the hypothesis of neutral performance. These findings are robust to whatever model (unconditional versus conditional and single versus multi-index) we use. In general, the multi-index model seems to add some explanatory power in relation to the single-index model. Furthermore, when we incorporate the predetermined information variables, we can observe a slight tendency towards better performance, in particular for the multi-index model. This evidence is consistent with previous studies on stock funds and comes in support of the argument that conditional models might allow for a better assessment of performance. However, our results suggest that the impact of additional risk factors seems to be greater than the impact of incorporating predetermined information variables.
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