Synthetic Hedge Funds: Performance And The Replication Success
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Technische Universität München (TUM)
Robeco Asset Management - Quantitative Strategies; Technische Universität München (TUM)
Robert A. Heigermoser
Technische Universität München (TUM)
October 1, 2015
We provide evidence on the performance and the replication success of a broad sample of 72 synthetic hedge funds from January 2009 to December 2013. Thereby, we assign the term 'synthetic hedge fund' to mutual funds and exchange-traded funds with hedge fund indices as their benchmarks. Replication success is measured through different perspectives from distributional characteristics to risk-adjusted performance. We find an overall significant underperformance of synthetic hedge funds compared to an appropriate benchmark index. Furthermore, mutual funds (associated with active portfolio management) can produce return characteristics closer to hedge fund benchmarks than exchange-traded funds (associated with passive management). From a single strategy perspective, we find a picture of heterogeneity. Regarding the market environment, we show larger return differences for unusual market conditions than for regular ones.
Synthetic Hedge Funds: Performance And The Replication Success - Introduction
In recent years, financial markets have experienced a series of unprecedented crises: a liquidity crunch that seriously affected the interbank lending market, a burst of the US housing bubble, and the subsequent banking and sovereign debt crisis. This series of events has led to a global economic downturn while the consequences are still felt today. With the drop of global base rates to a historical trough, many investors have to cope with negative real interest rates. This specifically affects institutional investors such as endowments, pension funds, and insurance companies, which are typically committed to long-term agreements that have been entered at times when interest rates were on higher levels. Consequently, institutional investors are searching for alternatives to traditional investments in order to achieve the returns needed to fulfill their obligations. Hedge funds have received noticeably increased attention in recent years. This increased interest of institutional investors reveals a significant gap between characteristics of hedge funds and institutional investors’ expectations. Institutional investors typically have high transparency requirements and impose restrictions on their investment mandates, as they are bound to strict regulatory standards. It is moreover usual that they require a certain degree of liquidity to meet their contractual obligations. On the other hand, the hedge fund industry is marketed as an absolute return industry where returns depend on manager skills. Therefore, it is common that hedge fund managers do not provide position-level transparency, have limited capacity, and resist any restrictions in their investment process. This behavior is backed by the argument that any kind of restriction cuts down performance. In addition, hedge funds charge extremely high management fees compared to traditional mutual funds and commonly require lock-up periods.
Since it is apparent that expectations of both parties – institutional investors and hedge funds – are incompatible, ideas to obtain returns similar to hedge funds without directly investing in those funds have been brought up. Those concepts are combined under the terms ’hedge fund replication’, ’hedge fund clones’, ’hedge fund tracking’, or simply ’synthetic hedge funds’. Replicating the returns of hedge funds has gathered significant academic and practitioner interest since the beginning of the century. As a first step, it was necessary for academia to substantiate the claim that hedge fund returns are not entirely driven by manager skill.1 After the theoretical framework had been investigated, the development of two different hedge fund replication approaches could be observed: a factor-based approach which uses linear factor models of investable assets to model the time series of hedge fund returns and a payoff distribution approach which models the distributional properties of hedge fund returns. With both attempts delivering appealing results, it was only a question of time until commercially available synthetic hedge funds were issued by financial institutions.
Those products are usually marked by several advantages when compared to real hedge fund investments. Because synthetic hedge funds are arranged as mutual funds or exchange-traded funds, higher transparency and higher liquidity is natural. Furthermore, synthetic hedge funds do not rely on a specific manager. On the one hand, this eliminates manager-specific risks. On the other hand, this prevents possible benefits from managerial skills. Since the latter comes at a high cost with the typical 2-20 fee structure of hedge funds, institutional investors are faced with the question: Is the skill of hedge fund managers worth the relatively high fees or can synthetic hedge funds provide similar net returns for investors, as their low fees might compromise for less flexibility? If so, institutional investors might receive additional benefits from synthetic hedge funds in form of higher liquidity and more transparency. As empirical evidence on the performance of these products is weak due to their short history, the present study sheds light on several aspects of synthetic hedge funds and extends previous research.
To the best of our knowledge, we analyze the largest sample by number of considered synthetic hedge funds and by number of monthly observations to date. Besides the overall performance of this new asset class, we are the first to investigate the question of whether mutual funds or exchange-traded funds are better suited for achieving hedge fund-like returns. Furthermore, we examine the performance of synthetic hedge funds on a single strategy basis. We argue that the hedge fund universe is very heterogeneous and an overall performance comparison should be interpreted with caution. Standard hedge fund indices might not have the same style composition as the investigated sample of synthetic hedge funds. In addition to using self-constructed benchmark indices that match the style composition of our sample, we investigate both approaches of synthetic hedge funds: the factor-based approach (by using factor models and tracking errors) and the payoff distribution approach (by testing return distributions).
The remainder of this study is organized as follows. Section 2 provides a theoretical overview of synthetic hedge funds and the two most popular replication approaches. Also included is a literature review regarding the empirical performance of synthetic hedge funds. Our unique dataset of 72 commercially available synthetic hedge funds and the portfolio construction is described in Section 3. Section 4 provides our empirical results as well as robustness tests. Finally, Section 5 summarizes our main results.