Optimal granularity for portfolio choice

Many optimization-based portfolio rules fail to beat the simple 1/N rule out-of-sample because of parameter uncertainty. In this paper we suggest a grouping strategy in which we first form groups of equally weighted stocks and then optimize over the resulting groups only. This strategy aims at balancing the trade-off between the benefits from optimization and the losses from estimation risk. We rely on Monte-Carlo simulations to illustrate the performance of the strategy, and we derive the optimal group size for a simplified setup. Furthermore, we show that estimation risk also has an impact via the criterion by which the assets are sorted into groups (like the expected excess returns or betas), but does not negate the grouping approach. We relate our work to linear asset pricing models, and we conduct out of sample back-tests in order to confirm the validity of our grouping strategy empirically.     

Optimal design of equity-linked products with a probabilistic constraint

There is a rich variety of tailored investment products available to the retail investor. These products combine upside participation in bull markets with downside protection in bear markets. Examples include the equity-linked products sold by insurance companies and the structured products marketed by banks. This paper examines a particular contract design for products of this nature. The paper finds the optimal design from the investor's viewpoint. It is assumed that the investor wishes to maximize expected utility of the terminal wealth subject to certain constraints. These constraints include a guaranteed rate of return as well as the opportunity to outperform a benchmark portfolio with a given probability. We derive the explicit form of the optimal design assuming both constraints apply and we illustrate the nature of the solution using some specific examples.     

My global fund portfolio is not yours: the effect of home bias on European- and US-managed convertible bond fund exposures

This paper shows that global convertible bond funds (CBFs) and their resulting equity-bond exposures are regionally biased. Global bond fund managers display home bias, resulting in CBFs that are not only tilted towards the home market but also reflect the different bond-equity exposures of European and US convertibles. More specifically we find that global funds managed by a European asset management firm are more bond-like than global funds managed by a US-based asset manager. Hence, investors have to account for the asset management company's origin to avoid that the performance of the fund and its correlation with other assets is not in line with investor's ex ante expectations about globally managed portfolios. Our results also indicate that for investors of European-based CBFs this home bias has resulted in an ex post opportunity cost up to 1.38% per year, depending on the sample period.     

保险公司资产组合与最优投资比例研究

保险公司收益主要来源于承保利润和投资收益,其中承保利润受政策变动、市场条件等外部环境的影响较大,而投资收益则更多地取决于保险公司的投资能力,因此保险公司如何构建资产组合、如何确定最优投资比例就是获取投资收益最大化的重要因素。本文通过理论推导得出了保险公司的资产组合模型并运用非线性规划求解出最优投资比例,进而根据保险公司的投资数据进行了实证研究,为我国保险公司的资产组合及最优投资比例提供了一个可借鉴的思路。     

Optimal hedge fund portfolios under liquidation risk

We use an expected utility framework to integrate the liquidation risk of hedge funds into portfolio allocation problems. The introduction of realistic investment constraints complicates the determination of the optimal solution, which is solved using a genetic algorithm that mimics the mechanism of natural evolution. We analyse the impact of the liquidation risk, of the investment constraints and of the agent's degree of risk aversion on the optimal allocation and on the optimal certainty equivalent of hedge fund portfolios. We observe, in particular, that the portfolio weights and their performance are significantly affected by liquidation risk. Finally, tight portfolio constraints can only provide limited protection against liquidation risk. This approach is of special interest to fund of hedge fund managers who wish to include the hedge fund liquidation risk in their portfolio optimization scheme.