【精品文档】41中英文双语外文文献翻译成品：私募股权母基金的风险状况

此文档是毕业设计外文翻译成品（ 含英文原文+中文翻译），无需调整复杂的格式！下载之后直接可用，方便快捷！本文价格不贵,也就几十块钱！一辈子也就一次的事！外文标题：The Risk Profile of Private Equity Fund-of-Funds外文作者：Tom Weidig, PhD, Quantexperts, Esch-Alzette, Luxembourg, Andreas Kemmerer 文献出处:Social Science Electronic Publishing , 2018 , 7 (4) :935-944(如觉得年份太老，可改为近2年，毕竟很多毕业生都这样做)英文1389单词，6988字符(字符就是印刷符)，中文2278汉字。The Risk Profile of Private Equity Fund-of-FundsTom Weidig, PhD, Quantexperts, Esch-Alzette, Luxembourg, Andreas Kemmerer ABSTRACTPrivate equity fund-of-funds (FoF) investments are now contributing more than 10% of the capital to private equity, i.e. venture capital and buyout. However, their risk profile is not well understood due to the opaque and illiquid market, and the limited access to performance figures. FoFs need to understand their risk profile, if they are to convince potential investors of their lower risk. Research on direct and funds investment exists. Directs show significant variability of returns with a significant probability of a total loss and extreme profits. Funds are less risky, because they invest in up to twenty direct investments. We show that FoFs even further significantly reduce the risk due to diversification. To this aim, we present a framework to construct the risk profile of FoFs using funds performance data. We also discuss the choosendata source, and the results of the simulations.INTRODUCTIONA fund-of-funds (FoF) collects capital from investors to invest in about 20 or more funds on their behalf. FoF investors are typically pension funds, banks, insurance companies, corporate investors, and other FoFs. The first private equity FoF was raised in 1978. FoFs have increased their share considerably over the last years, and now provide around 10% of the capital of funds. In 2002, 67 FoF managers existed in the US, and 49 FoF managers in Europe. They manage about $130 billion. FoFs allow investors to easily invest and diversify on a global basis. They typically charge a management fee of around 0.5% per year, and participate in the profits with 5% to 10%. Internal cost savings for the FoF investor and the FoF managers added value partially or more than fully compensate these fees, which are often wrongly perceived as fees on fees.A FoF, which has a portfolio of funds, should have a reduced risk in comparison to a single funds investment due to a non-perfect correlation between funds. A fund has about 20 direct investments in its portfolio, and a FoF, e.g. with 20 funds, has 400 direct investments. Thus, FoF investors should have clear diversification benefits through a second level of diversification, and can also consist of small and mediumsized investors. The aim of this paper is to clarify the diversification benefits. It is difficult to find historical FoFs return data. Therefore, historical FoFs need to be modeled. Historical FoF are constructed by creating portfolios of historical funds that are randomly selected from the VE database while respecting the timeline. Fifty thousand such portfolios are created, and the historical distribution of a FoF is obtained. For example, to create a set of historical FoF with two funds invested over two years, two funds would repeatedly be randomly selected from the historical funds for each of two consecutive vintage years. Both years are sample in the sample portfolio to give each vintage year the same weight. Our data source is the Venture Economics database, with 282 (195) European and 745 (401) US VC funds (buyout funds). The results show the advantages of an investment in a FoF comparable to a single fund. Therefore, FoFs have higher returns and lower distributions of returns than direct investments. The diversification effect by increasing the number of investments is larger than the effect by increasing the investment period. Contrary to individual funds, European FoFs seem to provide better investment opportunities to an investor than the US counterpart. American buyout funds have a lower level of risk than VC funds. This relationship does not apply for the European market.FRAMEWORKIt is needed to construct historical FoFs, because performance data on historical FoFs does not exist, and standard portfolio theory cannot be used for model FoFs using funds. Firstly, continuous transaction- based pricing does not exist for funds. Fluctuations of VC funds values do therefore not necessarily reflect any intrinsic variability of returns and hence an underlying risk. Secondly, returns of VC are not normally distributed, which violates a major assumption of standard portfolio theory. And finally there is no reliable data on correlations available, especially at the level of funds. As standard concepts of financial theory are not applicable for the construction of VC portfolios, other techniques to analyse the risk profile of this asset class have to be applied. For that reason Monte Carlo simulation is used to compute the performance of FoFs.To construct a historical fund, a hypothetical FoF is situated in a chosen year, and funds are assigned, in which it could have invested during its investment period, to its portfolio. Every fund is drawn only once. The performance of the FoF is the average performance of the underlying funds. Ideally, all possible combinations of the portfolio of a FoF should be constructed. However, this is not feasible. For example, a FoF with only two funds in its portfolio and with a dataset of 100 funds has 100! (i.e. 100*99*98) combinations, which is much higher than one million. Therefore, a simple Monte Carlo simulation is used, which results in randomly choosing the year, and the funds within a year. The more portfolios are constructed randomly, the closer the sample comes to the population of all possible combinations. The simulation with 50,000 historically possible FoFs delivers a very good approximation to the population of all possible combinations. FoFs are simulated of up to 50 funds invested within a five year investment period. The sample of the performance of the constructed FoFs contains all information needed for the risk profile, e.g. average performance and standard deviation. Additionally, the probability of losing capital and the average loss in case a loss of capital is being recorded.A historical analysis of FoFs would imply that we keep the historical proportion of FoFs for each vintage year. In the past, the number of FoFs has significantly increased, and all historical funds would be considerably biased towards the past few years. Therefore, we will treat every vintage year equal, and assume a constant number of FoFs for each year.An important issue remains: Is the risk profile of a fund influenced by factors like stage, market, size, and vintage year? If this is the case, we need to distinguish between different FoF types. For example, if the difference between the US sample and the European sample is statistically significant, we consider this in the construction of FoFs. Thus, US FoFs and European FoFs have to be treated differently. A statistical analysis needs to be done on the data source to determine performance influencing factors. The link between performance and vintage year is more difficult. Due to the insistence on a historically feasible selection of funds, the vintage year is automatically taken into account. If no correlation between vintage years and performance is found, it will be possible to relax this constraint and randomly select the funds from all vintage years.The performance of a fund is only a summarising snapshot of the many cashflows of the fund, and the timing and amount of the cash flows is not reflected in this framework. Unfortunately, the VE database does not deliver any specific information on the cash flow. REFERENCESBygrave, D., and Timmons, J. (1992). Venture Capital at the Crossroads. Boston: Harvard Business School Press.Chen, P., Baierl, G., and Kaplan, P. (2002). Venture Capital and its Role in Strategic Asset Allocation. The Journal of Portfolio Management, 28, No. 2, 83-89.Gilson R., and Black, B.(Winter 1999). Does Venture Capital Require an Active Stock Market? Journal of Applied Corporate Finance, 36-48.Jeng L., and Wells P. (2000). The Determinants of Venture Capital Funding: Evidence across Countries. Journal of Corporate Finance, 6, 241-289.Kaplan S., and Schoar, A. (2003). Private Equity Performance: Returns, Persistence and Capital. NBER working paper, No. 9807. Terhaar, K., Staub, R. and Singer, B. (Spring 2003). Appropriate Policy Allocation for Alternative Investments. The Journal of Portfolio Management, 29, No. 3, 101-110.译文：私募股权母基金的风险状况Tom Weidig, PhD, Quantexperts, Esch-Alzette, Luxembourg, Andreas Kemmerer摘要现如今，私募股权母基金（FoF）投资在私募股权资本（即风险投资和买断）中占比超过10%。然而由于市场的不透明和流动性不足，它们的风险状况并不完全为人所了解，而且获得的业绩数据很有限。对于母基金，需要了解它们的风险状况，以便说服潜在投资者降低风险。本文对现存的直接投资和基金投资进行了研究。直接投资显示其回报率的重大变化，并且很可能出现全面亏损和极高利润回报。基金投资则风险较小，因为它们投资了多达20个直接投资。我们的研究表明由于多元化的投资策略从而进一步显着降低了母基金投资风险。为此，我们提出了一个框架，用基金绩效数据构建母基金风险概况。我们还讨论了选择的数据来源和模拟结果。引言母基金（FoF）是从投资者那里收集资金并代表他们投资约20个或更多的子基金。母基金投资者通常是养老基金、银行、保险公司、企业投资者以及其他母基金。在1978年，出现了第一个私募股权母基金。在过去几年里，母基金FoFs的份额大幅增加，现在提供了约10的资金。 2002年，美国有67个FoF管理人员，欧洲有49个FoF管理人员。他们管理着约1300亿美元。 FoFs允许投资者在全球范围内进行自由投资和多元化投资。他们通常每年收取约0.5的管理费，并以5至10的比例参与利润分红。FoF投资者的内部节约的成本、FoF管理者的增值部分或这两者全部补偿的费用，往往被错误地看作是叠加的费用。由于基金之间没有建立健全的相互关系，与单一基金投资相比，具有资金投资组合属性的母基金应该具有低的投资风险。基金在其投资组合中有大约20个直接投资，而FoF例如拥有20个子基金，有400个直接投资。因此，FoF投资者应该通过第二层级的多元化投资获得明确的多元化收益，它也可以由中小型投资者组成。本文的目的是要说明多元化投资的好处。很难找到以往关于FoFs的数据。因此，需要对以往的FoF进行建模。以往的FoF是按照时间序列从VE数据库中随机选择的基金组合来构建的。通过创建了五万个这样的投资组合，获得了FoF的历史分布。例如，要创建一套两年期的投资两个基金的历史FoF，两个基金会连续两年从历史基金中随机选择两个基金。这两年都是样本组合中的样本，每年的数据都是同样重要。我们的数据来源是Venture Economics数据库，其中282（195）个来自欧洲和745（401）个来自美国风险投资基金（收购基金）。结果显示，与单一基金相比， FoF投资的优势明显。因此，与直接投资相比，FoF具有较高的回报率和较低的投资回报分派。相比于受投资时间的影响，投资的多元化受投资数量的影响更大。与个别基金相反，欧洲FoF似乎为投资者提供比美国投资者更好的投资机会。美国收购基金的风险水平低于风险投资基金。这种情况不适用于欧洲市场。架构由于以往的FoFs相关数据不存在而且标准投资组合理论不能适用于基金的FoFs模型，因此需要对以往的FoFs数据进行构建。首先，基金中不存在持续的基于交易的定价。因此，VC基金价值的波动不一定能反映出任何回报的内在变化，因此也不会反映潜在风险。其次，风险投资的回报不是正态分布的，这违背了标准投资组合理论的主要假设。最后，还没有得到相关可用的可靠数据，特别是在资金水平方面。由于金融理论的标准概念并不适用于风险投资组合的构建，因此必须应用其他技术来分析此类资产的风险状况。 出于这个原因，蒙特卡罗模拟被用来计算FoF的绩效表现。为了构建一个以往基金的框架，假设FoF在选定的某一年，并且在其投资期内可以投入资金到其投资组合中去。每个基金只抽出一次。 FoF的绩效表现是相关基金的平均表现。在理想情况下，要构建FoF投资组合中所有可能的组合。但是，这是不可行的。例如，在资料库中有100只基金，而在投资组合中只有两只基金（即100 * 99 * 98 .）的组合，远高于一百万。因此，使用简单的蒙特卡洛模拟，其得到的结果是源自随机选择的年份以及一年内的资金。随机构建的投资组合越多，总体上样本就越接近所有可能的基金组合。用50,000个以往的FoFs进行的模拟几乎可以很好地描述所有组合的总体情况。在五年投资期内，FoF可以模拟多达50个基金投资。所构建的FoF的绩效样本包含风险所需的任何信息，例如，平均绩效表现和标准差。此外，它还记录了资金流失的可能性和资本损失情况下的平均损失。对FoF的历史分析意味着我们要记录每个年份FoF所占的比例。 在过去，FoF的数量已经显着增加，所有的历史资金数量在过去几年都会有相当大的偏差。 因此，我们均衡地看待每一年份的数量并假设每年的FoF数量不变。但仍然存在一个重要问题是