The Optimal Interaction between a Hedge Fund Manager and Investor

ABSTRACT

This study explores hedge funds from the perspective of investors and the motivation behind their investments. We model a typical hedge fund contract between an investor and a manager, which includes the manager’s special reward scheme, i.e., partial ownership, incentives and early closure conditions. We present a continuous stochastic control problem for the manager’s wealth on a hedge fund comprising one risky asset and one riskless bond as a basis to calculate the investors’ wealth. Then we derive partial differential equations (PDEs) for each agent and numerically obtain the unique viscosity solution for these problems. Our model shows that the manager’s incentives are very high and therefore investors are not receiving profit compared to a riskless investment. We investigate a new type of hedge fund contract where the investor has the option to deposit additional money to the fund at half maturity time. Results show that investors’ inflow increases proportionally with the expected rate of return of the risky asset, but even in low rates of return, investors inflow money to keep the fund open. Finally, comparing the contracts with and without the option, we spot that investors are sometimes better off without the option to inflow money, thus creating a negative value of the option.     

Fixed income linked life insurance policies with minimum guarantees: Pricing models and numerical results

We propose a pricing model for life insurance policies in which the benefits are linked to the performance of a portfolio of interest rate sensitive assets (reference fund), and a minimum guarantee provision is present. The model is cast in the celebrated term structure framework developed by Cox, Ingersoll and Ross (1985). As for the behaviour of the investment component, we analyse two polar cases. In the first one the payments due on the reference fund when the contract is still “alive” are not reinvested, while in the second case we propose a reinvestment policy. We show how to obtain a closed form solution for the single premium in the no-reinvestment case, and how to implement a simulation approach to calculate numerically the single premium in the reinvestment case. We illustrate our analysis with numerical results that help in understanding the comparative static properties of the models proposed.     

“高水位”法下私募基金经理的激励与风险选择

私募基金契约本质上是一种复杂的委托—代理关系。本文基于现行私募基金行业标准的“2-20”合同,在委托代理框架下,结合前景理论,讨论了“高水位”法对基金经理的激励效应及其导致的冒险行为。研究发现,在高水位激励下产生“承诺升级”现象,当基金经理在评估期对基金业绩超过“高水位”持乐观信念时,经理倾向采用保守型投资策略;反之其倾向激进的投资策略。由此提出“2-20”合同改进：业绩激励乘数随着基金经理的差异信念而时变,同时纳入对基金风险范围的约束,以防范代理投资过程中的过度冒险问题。     

Hedge fund performance appraisal using data envelopment analysis

In this paper we apply data envelopment analysis (DEA) to evaluate the performance of hedge fund classifications. The purpose of alternative investment strategies such as hedge funds is to offer absolute returns, so using passive benchmarks to measure their performance could be ineffective. With the increasing number of hedge funds available, institutional investors, pension funds, and high net worth individuals urgently need a trustworthy efficiency appraisal method. DEA can achieve this. An important benefit of the DEA measure is that benchmarks are not required, thereby alleviating the problem of using traditional benchmarks to examine non-normal distribution of hedge fund returns. We suggest that DEA be used as a complimentary technique (or method) for the selection of efficient hedge funds and funds of hedge funds for investors. Using DEA can shed light and further validate hedge fund manager selection with other methodologies.     

A Higher-Order Hidden Markov Chain-Modulated Model for Asset Allocation

This paper presents an analysis of asset allocation strategies when the asset returns are governed by a discrete-time higher-order hidden Markov model (HOHMM), also called the weak hidden Markov model. We assume the drifts and volatilities of the asset returns switch over time according to the state of the HOHMM, in which the probability of the current state depends on the information from previous time-steps. The “switching” and “mixed” strategies are studied. We use a multivariate filtering technique in conjunction with the EM algorithm to obtain estimates of model parameter at a given time. This, in turn, aids investors in determining the optimal investment strategy for the next time step. Numerical implementation is applied to data on Russell 3000 value and growth indices. We benchmark the respective performances of portfolio using three classical investment measures.     

Investment–consumption with regime-switching discount rates

This paper considers the problem of consumption and investment in a financial market within a continuous time stochastic economy. The investor exhibits a change in the discount rate. The investment opportunities are a stock and a riskless account. The market coefficients and discount factor switch according to a finite state Markov chain. The change in the discount rate leads to time inconsistencies of the investor’s decisions. The randomness in our model is driven by a Brownian motion and a Markov chain. Following Ekeland and Pirvu (2008) we introduce and characterize the subgame perfect strategies. Numerical experiments show the effect of time preference on subgame perfect strategies and the pre-commitment strategies.     

跳扩散条件下波动率风险溢价及影响因素研究——基于上证50 ETF期权市场的实证

本文采用上证50 ETF及其期权交易数据,运用SVCJ模型、MCMC及傅里叶变换等方法,从P测度及Q测度中提取波动率风险溢价,并分析了其时变特征及影响因素。实证研究表明:SVCJ模型相较于SV模型及SVJ模型具有更好的市场拟合优度;傅里叶变换法能提高波动率风险溢价的估计效率;波动率风险溢价具有时变特征,在市场急剧动荡时期,波动率风险溢价基本为负,投资者厌恶波动风险,购买期权对冲波动风险的意愿较高;在市场非急剧动荡时期,波动率风险溢价基本为正,投资者偏好波动风险,购买期权对冲波动风险的意愿较低;市场收益率、波动率、换手率及投资者情绪对波动率风险溢价具有显著的影响。     

Arbres de Markov couplePairwise Markov trees

The Hidden Markov Chain (HMC) models are widely applied in various problems. This succes is mainly due to the fact that the hidden model distribution conditional on observations remains a Markov chain distribution, and thus different processings, like Bayesian restorations, are handleable. These models have been recetly generalized to “Pairwise” Markov chains, which admit the same processing power and a better modeling one. The aim of this Note is to show that the Hidden Markov trees, which can be seen as extensions of the HMC models, can also be generalized to “Pairwise” Markov trees, which present the same processing advantages and better modelling power. To cite this article: W. Pieczynski, C. R. Acad. Sci. Paris, Ser. I 335 (2002) 79–82.     

Optimal consumption and investment problem with random horizon in a BMAP model

In this paper, we consider the consumption and investment problem with random horizon in a Batch Markov Arrival Process (BMAP) model. The investor invests her wealth in a financial market consisting of a risk-free asset and a risky asset. The price processes of the riskless asset and the risky asset are modulated by a continuous-time Markov chain, which is the phase process of a BMAP. The possible consumption or investment are restricted to a sequence of random discrete time points which are determined by the same BMAP. The investor has only consumption opportunities at some of these random time points, has both consumption and investment opportunities at some other random time points, and can do nothing at the remaining random time points. The object of the investor is to select the consumption–investment strategy that maximizes the expected total discounted utility. The purpose of this paper is to analyze the impact of the consumption–investment opportunity and the economic state on the value functions and consumption–investment strategies. The general solution and the exact solution under the assumption that the consumption and the terminal wealth are evaluated by the power utility are obtained. Finally, a numerical example is presented.     

投资者注意的风险补偿对股市回报的预测

利用历史累积交易金额数据,本文构造了中国股票市场增量注意风险补偿和存量注意风险补偿,并检验其对中国股票市场收益率的预测能力。样本外检验结果显示,以上两种注意风险补偿均能显著预测下个月中国股市的超额收益率,其R²分别达到了2.68%和2.50%;与中国股票市场中其他预测变量相对比,增量注意和存量注意风险补偿表现出更强的预测能力。此外,基于不同的样本外检验期、不同的风险厌恶参数以及五种不同的变量构造方式,投资者注意风险补偿均产生显著的预测能力。围绕着经济周期波动,本文对注意风险补偿的预测能力进行了解释,同时还发现,相较于经济衰退期间,经济繁荣期间的投资者注意风险补偿样本外预测能力更强。     

贝叶斯方法在养老基金资产负债管理中的应用

把一个静态资产负债管理模型———均值方差模型应用到定额给付养老金计划的资产负债管理中,在允许无风险借贷的条件下研究养老金在无风险资产和风险资产间的分配问题,用定量分析的方法求出了最优投资组合的一般形式;又针对投资收益率特征参数未知的情况,提出了矩估计和贝叶斯估计两种方法求解最优资本配置比例,将两种方法的结果与一般形式对比,分析了影响最优投资组合的因素,得知养老基金在风险资产中的投资比例与基金经理对风险的厌恶程度、风险资产的风险益酬、风险资产收益率的波动性成负相关关系;并且随决策者掌握的历史信息增加,在风险资产上的投资比例也随之增加,投资行为逐渐趋于理性化;对上述结果进行仿真,验证了结论的有效性。     

An examination of HMM‐based investment strategies for asset allocation

We develop and analyse investment strategies relying on hidden Markov model approaches. In particular, we use filtering techniques to aid an investor in his decision to allocate all of his investment fund to either growth or value stocks at a given time. As this allows the investor to switch between growth and value stocks, we call this first strategy a switching investment strategy. This switching strategy is compared with the strategies of purely investing in growth or value stocks by tracking the quarterly terminal wealth of a hypothetical portfolio for each strategy. Using the data sets on Russell 3000 growth index and Russell 3000 value index compiled by Russell Investment Services for the period 1995–2008, we find that the overall risk‐adjusted performance of the switching strategy is better than that of solely investing in either one of the indices. We also consider a second strategy referred to as a mixed investment strategy which enables the investor to allocate an optimal proportion of his investment between growth and value stocks given a level of risk aversion. Numerical demonstrations are provided using the same data sets on Russell 3000 growth and value indices. The switching investment strategy yields the best or second best Sharpe ratio as compared with those obtained from the pure index strategies and mixed strategy in 14 intervals. The performance of the mixed investment strategy under the HMM setting is also compared with that of the classical mean–variance approach. To make the comparison valid, we choose the same level of risk aversion for each set‐up. Our findings show that the mixed investment strategy within the HMM framework gives higher Sharpe ratios in 5 intervals of the time series than that given by the standard mean–variance approach. The calculated weights through time from the strategy incorporating the HMM set‐up are more stable. A simulation analysis further shows a higher performance stability of the HMM strategies compared with the pure strategies and the mean–variance strategy. Copyright © 2009 John Wiley & Sons, Ltd.     

Capital Asset Pricing Model (CAPM) with drawdown measure

The notion of drawdown is central to active portfolio management. Conditional Drawdown-at-Risk (CDaR) is defined as the average of a specified percentage of the largest drawdowns over an investment horizon and includes maximum and average drawdowns as particular cases. The necessary optimality conditions for a portfolio optimization problem with CDaR yield the capital asset pricing model (CAPM) stated in both single and multiple sample-path settings. The drawdown beta in the CAPM has a simple interpretation and is evaluated for hedge fund indices from the HFRX database in the single sample-path setting. Drawdown alpha is introduced similarly to the alpha in the classical CAPM and is evaluated for the same hedge fund indices. Both drawdown beta and drawdown alpha are used to prioritize hedge fund strategies and to identify instruments for hedging against market drawdowns.     

Valuation of risk-based premium of DB pension plan with terminations

This paper concentrates on the premium valuation of pension insurance provided by the Pension Benefit Guaranty Corporation (PBGC). The PBGC provides a defined benefit pension sponsor with coverage in case that the pension fund fails to make pension payments as promised or that the plan sponsor does not stay in business any more. In practice, both the pension fund and the sponsor assets play a critical role in fulfilling the commitment of pension payments, and thereby it is not reasonable to isolate the risk of distress termination of the sponsor assets from that of the premature termination of the pension fund. Different from previous works in which the premature termination of the pension fund and the distress termination of the sponsor assets are analyzed separately, our model examines the situation in which retirees suffer the risk of two types of terminations at the same time. We evaluate the risk-based fair premium under the framework that the pension fund and the sponsor assets are correlated and subject to the risk of the involuntary termination (i.e., premature termination) and the distress termination, respectively. In this framework, we manage to obtain closed-form pricing formulas. Our model is more practical because of the realistic design of termination schemes. Numerical simulations are also carried out to demonstrate our findings. Our numerical experiments validate that a variable rate premium is more appropriate for the PBGC to implement.     

Production-inventory systems in stochastic environment and stochastic lead times

We consider a production-inventory system where the production and demand rates are modulated by a finite state Continuous Time Markov Chain (CTMC). When the inventory position (inventory on hand – backorders+inventory on order) falls to a reorder point r, we place an order of size q from an external supplier. We consider the case of stochastic leadtimes, where the leadtimes are i.i.d. exponential(μ) random variables, and orders may or may not be allowed to cross. We derive the distribution of the inventory level, and analyze the long run holding, backlogging, and ordering cost rate per unit time. We use simulation to study the sensitivity of the system to the distribution of the lead times.     

Optimizing Markovian modeling of chaotic systems with recurrent neural networks

In this paper, we propose a methodology for optimizing the modeling of an one-dimensional chaotic time series with a Markov Chain. The model is extracted from a recurrent neural network trained for the attractor reconstructed from the data set. Each state of the obtained Markov Chain is a region of the reconstructed state space where the dynamics is approximated by a specific piecewise linear map, obtained from the network. The Markov Chain represents the dynamics of the time series in its statistical essence. An application to a time series resulted from Lorenz system is included.     

Semi-Markov processes and α-invariant distributions

A semi-Markov process is easily made Markov by adding some auxiliary random variables. This paper discusses the I-type quasi-stationary distributions of such “extended” processes, and the α-invariant distributions for the corresponding Markov transition probabilities; and we show that there is an intimate relation between the two. The results have relevance in the study of the time to “absorption” or “death” of semi-Markov processes. The particular case of a terminating renewal process is studied as an example.     

A risk-based model for the valuation of pension insurance

In the US, defined benefit plans are insured by the Pension Benefit Guaranty Corporation (PBGC). Taking account of the fact that the PBGC covers only the residual deficits of the pension fund the sponsoring company is unable to cover and that the plans can be prematurely terminated, we consider a model that accounts for the joint dynamics of the pension fund’s and sponsoring firm’s assets in order to effectively determine the risk-based pension premium for the insurance provided by the PBGC. We obtain a closed-form pricing formula for this risk-based premium. Its magnitude depends highly on the investment portfolio of the pension fund and of the sponsoring company as well as the correlation between these two portfolios.     

A note on statistical models for individual hedge fund returns

In recent years, a large number of research papers and monographs on the analysis of hedge fund returns have been published. Typically, the authors of these studies implicitly or explicitly treat monthly returns of hedge funds as independent and identically distributed observations. The Hedge Fund Index might be able to serve that role. But the returns of an individual hedge fund are not like that. They behave autoregressively depending on the time periods. This stochastic behavior should be modeled as a combined/regime switching stochastic process of two processes: i.i.d. process and autoregressive process. This paper first depicts the autoregressiveness of hedge fund returns. Then we introduce our statistical model for returns of an individual hedge fund and then, with our retrospective view, we perform several data analyses for individual hedge funds’ return data.     

On a Local-Search Heuristic for a Class of Tracking Error Minimization Problems in Portfolio Management

In this paper we describe a 2-phase simulated annealing heuristic approach for a special class of portfolio management problems: the problem of optimizing a stock fund with respect to tracking error and transaction costs over time subject to a set of complex constraints with a linear factor return model “feeding” the objective function with data. Our results on managing two real-world funds of a major German capital investment company have shown that this meta-heuristic provides proposals for the fund manager which are feasible with respect to the investment guidelines and excellent in quality in acceptable time. Thus the approach is ideally suited to be used routinely and interactively within a decision support system to assist the fund manager in his complex task of portfolio control and optimization.