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Alternative performance measures for hedge funds
... Second property is that it can rank investment even with negative threshold, whereas, it cannot be possible to rank with negative ratio in case of Sharpe ratio. Further, investors like probability of more positive returns or statistically positive skewness and dislike negative skewness and high kurtosis (skewness & kurtosis are higher moments and mean & standard deviation first two moments) (Bacmann and Scholz, 2003). ...
... The assumption of normality drew criticism from analysts and recent international papers have highlighted that return distributions are not necessarily normal; especially in the context of increasing acceptance of options, futures and hedge funds in portfolios have led to probability distributions that are far from normal. Hence, Sharpe ration inadequately account for all risk to which investor are exposed (Bacmann and Scholz, 2003;León et al., 2015). Validity can only be confirmed when distribution is normal or Gaussian. ...
... Here we can see that the Omega divides the entire universe into two parts, one above threshold and other below the threshold. Best suited returns at this level of threshold are above the horizontal line (Bacmann and Scholz, 2003). In the following diagram, we assumed that investors threshold is 0.75% per month (which is equal to average T-Bills rate in Pakistan), we can use it to partition ECDF into two areas, say G and L (G=Gain and L=Loss), therefore, we can now compute (Omega) = G / L. ...
There are more than one hundred portfolio performances, which have been proposed in financial literature, (Cogneau and Hubner, 2009), but extensively used performance measure is a Sharpe ratio and in Pakistan Asset Management Companies (AMCs) also prefer to exhibit their performance in Sharpe ratio. However, financial literature has ample of evidence that recommend Sharpe ratio is valid under normal distribution of returns. The financial returns are not distributed normally as result of which standard deviation may not adequately measure risk (Bodie et al., 2009). Whereas, standard deviation of negatively skewed distribution underestimates and positively skewed overestimates volatility that would be misleading Sharpe index. In this study, we concluded that for skewed and non-normal distribution Omega ratio or Sharpe-Omega are alternative performance measures.
... In Figures 1-3, we have demonstrated the Omega performance measure as a function of different threshold levels. As pointed out by Bacmann and Scholz [4], Omega involves all the moments of the return distribution including skewness and kurtosis and so, it is an appropriate indicator of the effectiveness of insurance strategies (see also [34]). In all figures and for high threshold levels, ...
Designing dynamic portfolio insurance strategies under market conditions switching between two or more regimes is a challenging task in financial economics. Recently, a promising approach employing the value-at-risk (VaR) measure to assign weights to risky and riskless assets has been proposed in [Jiang C., Ma Y. and An Y. "The effectiveness of the VaR-based portfolio insurance strategy: An empirical analysis" , International Review of Financial Analysis 18(4) (2009): 185-197]. In their study, the risky asset follows a geometric Brownian motion with constant drift and diffusion coefficients. In this paper, we first extend their idea to a regime-switching framework in which the expected return of the risky asset and its volatility depend on an unobservable Markovian term which describes the cyclical nature of asset returns in modern financial markets. We then analyze and compare the resulting VaR-based portfolio insurance (VBPI) strategy with the well-known constant proportion portfolio insurance (CPPI) strategy. In this respect, we employ a variety of performance evaluation criteria such as Sharpe, Omega and Kappa ratios to compare the two methods. Our results indicate that the CPPI strategy has a better risk-return tradeoff in most of the scenarios analyzed and maintains a relatively stable return profile for the resulting portfolio at the maturity.
... • g 2 : the Treynor Ratio (TR; Scholz and Wilkens 2005), defined as the ratio between the expected return and the systematic risk sensitivity with respect to a benchmark (here we consider the MSCI World index (Bacmann and Scholz, 2003)). It assumes the form of a reward-to-variability measures but it derives from the Capital Asset Pricing Model (CAPM) portfolio theory; ...
... It is the ratio of the averages of the gains above a threshold to the averages of the losses below the same threshold. Omega involves all the moments of the whole return distribution, including skewness and kurtosis, so it is an appropriate indicator of the effectiveness of portfolio strategies [Bacmann and Scholz, 2003]. The portfolio with a higher Omega ratio fares better than the one with a lower Omega. ...
The prosperity of a life insurance company is due not only to earnings in its principal business but also to investment profits of the capital at its disposal. The prolonged low interest-rate environment has been challenging these two main activities of life insurance. In this dissertation, we study portfolio optimization problems related to euro-denominated funds of life insurers. To continue to offer a favourable return to the policyholders, life insurers should allocate more risky assets to their portfolios. But, doing so, they would be exposed to not being able to guarantee the capital. Besides, the maturity of the French life insurance market creates potential conditions for massive with drawals. We apply the ruin theory to model the stochastic properties of these two processes in three case studies and derive the corresponding explicit expressions of the first two moments of the income of a life insurance company. The maximization of the expected income of a life insurance company is solved under the constraints of ruin probability. The optimal investment strategy is obtained using the upper estimation of this probability. We conducted the numerical illustration, the sensitivity analysis, and the backtesting with real data from the life insurance sector and financial markets. Our results indicate economic implications for life insurance companies in their insurance business and investment activities.
... Gupta et al. (2003) have found that estimated alpha was good estimates of the true alpha due to hedge fund manager's skills. Jean-François Bacmann and Stefan Scholz (2003) have concluded that the Sharpe ratio was used to analyze performance, some investments were mistakenly showing better or worse because all the risk features were not taken into account. So, this study also ended that omega measures and Stutzer index were used for performance measures. ...
Hedge funds are alternative investments, which use pooled funds, and different investment strategies to earn attractive returns to the investors at reduced risk. Hedge funds can be aggressively managed or make use of derivatives and leverage in domestic as well as international markets with the goal of generating high returns. Hedge funds are generally accessible to accredited investors like banks, insurance companies, wealthy individuals, endowments, and pension funds and require large amounts of investment. The study aims to measures the different hedge fund strategies and the performance of hedge fund strategies in India.
... Watanabe (2006) also considers the third and fourth central moments of a return distribution, but in a simpler form: he develops the Sortino plus skewness/kurtosis ratio as well as the Sharpe plus skewness/kurtosis ratio. Other authors (Zakamouline and Koekebakker, 2008;Campbell et al., 2001;Bachmann and Scholz, 2003;Harvey and Siddique, 2000;Pilotte and Sterbenz, 2006) examine the adjusted for skewness Sharpe ratio and the adjusted for skewness and kurtosis Sharpe ratio. ...
The Sharpe ratio emerges as one of the most popular metrics used in the evaluation of investment performance despite the wide range of alternatives that have been proposed by academics and practitioners. In the proposed research, risks and returns are analysed on the European Monetary Union bonds market, with different bonds ratings and maturities, in the period from 2005 to 2017. The past and current trends and patterns in bond returns are defined using the methods of statistic correlation and econometric analysis. It was shown that the bond returns are not normally distributed, and that the return on distribution depends on bond maturity and the economic situation in the market. The relation between volatility and bond maturity and the Sharpe ratio appeared to be non-linear and inconsistent over time. However, the hypothesis about the inverse relation between the Sharpe ratio and bond maturity is not supported by the evidence. Finally, with the help of time-series models it was proven that in the period from 2005 to 2017, the returns on European Monetary Union bonds market declined over time. ARIMA models were used to analyse the residuals from the bond returns.
... More precisely, as noted by Kazemi et al. (2004), the Omega measure is (mathematically) equal to the ratio of the expectations of a call option payoff to a put option payoff written on the risky reference asset with a strike price corresponding to the threshold. As mentioned for example in Bacmann and Scholz (2003), the main advantage of the Omega measure is that it involves all the moments of the return distribution, including skewness and kurtosis. Moreover, ranking is always possible, whatever the rational threshold, in contrast to the Sharpe ratio where this level is fixed and equal to the riskless return. ...
The purpose of this article is to analyze and compare two main portfolio insurance methods, the Option Based Portfolio Insurance (OBPI) and Constant Proportion Portfolio Insurance (CPPI) strategies using downside risk measure. For this purpose, the Omega measure is introduced. This measure take account of the entire return distribution. we determine the set of threshold values for omega performance measure and show that the CPPI method performs better than the OBPI. We compare the two strategies for the daily S&P 500 returns.
... Many studies about financial returns have shown that the mean-variance approach is not adequate to evaluate risks and performances of specific assets such as hedge funds. Indeed, the distribution of these assets returns can have negative skewness and excess kurtosis, as documented by Fung and Hsieh (1997), Ackerman et al. (1999), Brown et al. (1999), Brooks and Kat (2001), Caglayan and Edwards (2001), Bacmann and Scholz (2003), Agarwal and Naik (2004). . .. This leads to conclude that, for this type of assets, the challenge lies not only in first two moments but also in higher moments. ...
The purpose of this paper is to analyze the gap risk of dynamic portfolio insurance strategies which generalize the “Constant Proportion Portfolio Insurance” (CPPI) method by allowing the multiple to vary. We illustrate our theoretical results for conditional CPPI strategies indexed on hedge funds. For this purpose, we provide accurate estimations of hedge funds returns by means of Johnson distributions. We introduce also an EGARCH type model with Johnson innovations to describe dynamics of risky logreturns. We use both VaR and Expected Shortfall as downside risk measures to control gap risk. We provide accurate upper bounds on the multiple in order to limit this gap risk. We illustrate our theoretical results on Credit Suisse Hedge Fund Index. The time period of the analysis lies between December 1994 and December 2013.
... Les familles de critères de Fitch 28 4 Catégorie d'investissement pour Standard and Poor's 29 5 Catégorie de spéculation pour Standard and Poor's 30 6 Catégorie d'investissement à court terme pour Standard and Poor's 31 7 Catégorie spéculative court terme pour Standard and Poor's (suite) 31 8 Catégorie d'investissement pour Moody's 32 9 Catégorie de spéculation pour Moody's 32 10 Catégorie d'investissement à court terme pour Moody's 33 11 Catégorie d'investissement pour Fitch Ratings 34 12 Catégorie de spéculation pour Fitch Ratings 35 13 Catégorie d'investissement à court terme pour Fitch Ratings 36 1 Exemples de distribution de durée 51 2 Exemples de distribution de durée (suite) 52 ...
These four years were devoted to argument on the one hand in search of information (financial data ...), development of tools (specific econometric methods) and the drafting of a research paper. Specifically, for 2009-2010, we performed the following steps: - Collection of documents and information from credit rating agencies and companies, particularly to collect various opinions on the recent financial crisis - paper submitted to several conferences: 'ACD Model for Credit Ratings Migration' - Preparation of another research article on the migration of credit ratings before and after the financial crisis.
... The Omega measure introduced by Keating and Shadwick (2002) was developed with the intention to take the entire return distribution into account and is defined as the ratio of the gain with respect to a threshold L and the loss with respect to the same threshold. Then the Omega measure Ω L with respect to a threshold L is given by 8 8 In fact, the evaluation of an investment with the Omega function should be considered for thresholds between 0% and the risk free rate, see Bacmann and Scholz (2003), p. 3. ...
Eine langfristige und nachhaltige Steigerung des Unternehmenswerts als zentrales Unternehmensziel fordert eine konsequente, wertorientierte Ausrichtung aller Unternehmensteile und -aktivitäten. Das Risikomanagement, welches stets im Rahmen einer integrierten Betrachtung von Ertrags- und Risikoseite durchzuführen ist, hat das Ziel, eine wertorientierte Unternehmensführung bestmöglich zu unterstützen. Unternehmen stehen innerhalb ihres Wertschöpfungsnetzwerks jedoch vielen aktuellen und zukünftigen Herausforderungen gegenüber, und sind mit den sich daraus ergebenden Risiken konfrontiert. Vor diesem Hintergrund gibt die vorliegende Arbeit konkrete Handlungsempfehlungen für die Umsetzung eines Risikomanagements in Wertschöpfungsnetzwerken und trägt auf diese Weise zur Unterstützung einer wertorientierten Unternehmensführung bei. Mit dem Ertrags- und Risikomanagement am Beschaffungsmarkt bei Rohstoffen, dem Ertrags- und Risikomanagement am Absatzmarkt bei Kundenbeziehungen und dem Ertrags- und Risikomanagement am Finanzmarkt bei Investmentfonds werden dabei drei ausgewählte Themenbereiche herausgegriffen.
... The investor or manager should always prefer the portfolio with the highest value of Omega (Bertrand and Prigent, 2011). The main advantage of the Omega measure is that it involves all the moments of the return distribution, including skewness and kurtosis (Bacmann and Scholz, 2003). ...
This study makes a comparison between the most popular strategies of Portfolio Insurance based on Monte Carlo simulation. This work aims to define the best strategy at comparing different strategies and provide a contribution to solving some divergences in literature. Most of the previous comparisons do not take into consideration all the strategies discussed in this study and this analysis intends to add some relevant findings.The OBPI, CPPI and SLPI strategies are evaluated in terms of moments of the distribution, performance ratios (Sharpe ratio, Sortino ratio, Omega ratio and Upside Potential ratio) and stochastic dominance in different market conditions represented by an underlying asset that follows a geometric Brownian motion. In order to have a perception of a real situation in financial markets, the strategies are later also applied to three major stock indices (S&P 500, DJ EuroStoxx 50 and Nikkei 225).We find that CPPI 1 and SLPI strategies should be preferred in all scenarios according to the higher performance ratios, the higher expected returns and other measures. The choice between them is based on the preferences of the investor or manager, but we also find that the CPPI 1 strategy stochastically dominates, on second and third order, the others strategies in bear market scenarios. From our results we can state that a value of 100% for the floor should be preferred in terms of performance ratios, expected returns and other measures. This comparison allows improving the efficiency of decision making of an investor or manager in a Portfolio Insurance investment.
... Sharpe ratios tend to seriously overstate their results[Bacmann, Scholz 2003;Pride 2010;Thomas 2010]. The key results of the findings, based on the widely recognized and representative Dow Jones Credit Suisse Hedge Fund Indices, have been synopsized inTable ...
... The Omega performance measure has been introduced by Keating and Shadwick (2002) to take account of asymmetric returns, and to overcome the shortcomings of performance measures defined only from the mean and the variance of the returns. The Omega measure has been applied across a broad range of models in financial analysis, in particular to examine hedge fund style or equity funds (see, e.g., Bacmann and Scholz, 2003). More precisely, Omega is defined as the probability weighted ratio of gains to losses relative to a return threshold. ...
We introduce mixtures of probability distributions to model empirical distributions of financial asset returns. In this framework, we examine the problem of maximizing performance measures. For this purpose, we consider a large class of reward/risk ratios such as the Kappa measures and in particular the Omega ratio. This latter measure is associated to a downside risk measure based on a put component. All these measures can take account of the asymmetry of the probability distribution, which is important when dealing with mixture of distributions. We examine first a fundamental example: the ranking and maximization of Gaussian mixture distributions, according to the Omega performance measure. Then we provide a general result for the maximization of mixture distributions with respect to a very large family of performance measures, including Kappa measures.
... In order to alleviate the problems associated with the Sharpe Ratio, a number of alternative performance measures have been suggested (see, e.g., Sortino and Price (1994), Keating andShadwick (2002), Bacmann andScholz (2003)). Recently, Keating and Shadwick (2002) suggested the use of a new performance measure, which they termed the Omega Ratio. ...
The Omega Ratio is a recent performance measure. It captures both, the downside and upside potential of the constructed portfolio, while remaining consistent with utility maximization. In this paper, a new approach to compute the maximum Omega Ratio as a linear program is derived. While the Omega ratio is considered to be a non-convex function, we show an exact formulation in terms of a convex optimization problem, and transform it as a linear program. The convex reformulation for the Omega Ratio maximization is a direct analogue to mean-variance framework and the Sharpe Ratio maximization.
... Moreover, at any given threshold, the best investment should have the best Omega function (see e.g. Bacmann and Scholz (2003)). As a consequence, at low negative thresholds, the higher the logarithm of Omega, the less risky the investment is in the extreme. ...
RMF, a core investment manager of Man Investments The recent bear market has prompted a number of institutional investors to look for other sources of returns than equities. The absolute returns promised by hedge funds have proved to be particularly appealing in this environment. This is illustrated by the fact that, according to TASS, hedge funds have never seen bigger money inflows than during recent quarters. Still, many investors have concerns about hedge funds. These concerns include questions over transparency, liquidity, complexity, and the riskiness of hedge funds. While the first three issues can be addressed on a somewhat qualitative basis (see Moix and Scholz (2003a, 2003b) for an extensive discussion of these topics), the notion of risk also needs to be addressed from a quantitative point of view. In this paper, we show how to use the Omega function in order to evaluate the risk-return profile of portfolios consisting of hedge funds and traditional investments such as stocks and bonds. Difference in risk profile Adding hedge funds to traditional investments has strong implications for the risk-return profile of the resulting blended portfolio. In essence, the risks related to traditional investments and hedge funds are different. The risk drivers linked to traditional investment are linear in their performance impact and directly related to the underlying financial markets. In contrast, the risks associated with hedge funds are more complex, i.e. non-linear and usually not well understood. Thus far, two extreme schools of thought have dominated the perception of risk in hedge funds. Based on the low past volatility of most hedge fund styles, some people argue that hedge funds are not more risky than bonds and thus that they should replace them in the asset allocation. In contrast, the headline based school of thought looks at high profile blow-ups such as LTCM and concludes that hedge funds are extremely risky. As so often in life, the truth lies in between those two extremes. However, this implies that we need other measures than standard deviation and Sharpe ratio in order to appropriately quantify risk and performance.
... Further, it has been demonstrated that the proposed framework is useful for evaluating risk in large infrastructure projects using VaR and b. In reality, investors and other stakeholders have higher interest in the downside risk than upside risk [35]. That is, losses are often viewed more heavily than gains. ...
Sustainable urban development requires detailed assessment of economic, environmental and social impacts borne by major stakeholders. A framework to address these complex issues underpinning sustainable urban development is proposed to aid decision making in the face of uncertainties. An analytical approach is developed as a tool to assist decision makers by using an engineering approach, risk-based cost-benefit analysis model that encompasses concepts from “Life Cycle Costing”, “Engineering Reliability Analysis” and “Risk Management”. It aims to rank design options based on model outputs, such as rate of return, probability of loss and value at risk. This study presents the logic of this approach and tests the framework using a synthetic project formulated around the economic perspective of an investor in considering the implementation of various desalination plant technologies. Two alternative desalination plants were evaluated based on a collective of project information, and the results showed that the model output provided a clear indication of the preferred option using risk-based metrics.
... Some authors have also noted the similarity in rankings by values of Omega functions at various thresholds with those produced by Sortino ratios e.g. [1], [7]. Others, like Darsinos and Satchell [5] and Farinelli and Tibiletti [6] have suggested 'generalisations' in which Sortino ratios, Omega functions and other similar constructions appear as particular instances of a one or two parameter family of functions formed from the underlying distribution. ...
... Since all the risk characteristics are not considered, the Sharpe ratio or the Sortino ratio can be misleading in evaluating performance of investment especially in case of presence of higher moments. Bacmann & Scholz (2003) therefore advocated the use of new performance measures, namely the Stutzer index and the Omega measure, which are represented in the following. ...
Various risk measures are managed in a unique integrated framework for portfolio selection problems. They include normal risk (volatility), asymmetric risk (skewness), “fat-tail” risk (kurtosis) and downside risks, i.e. semi-variance, modified Value-at-Risk, and modified expected shortfall. The framework allows investors to change their preferences regarding these risks flexibly. The efficiency of the proposed approach is highlighted in its ability to handle investors’ risk preferences well. Verification is deployed by performing portfolio selection experiments in developed markets (e.g. the U.S. stock market), emerging markets (e.g. the South Korean stock market) and global investments. A preselection process dealing with datasets that include a large number of stocks is also introduced to eliminate stocks that have low diversification potential. Portfolios are evaluated by four performance indices, i.e. the Sortino ratio, the Sharpe ratio, the Stutzer performance index, and the Omega measure, both in-sample and out-of-sample tests. High performance and also well-diversified portfolios can be obtained if modified Value-at-Risk, variance, or semi-variance is concerned whereas emphasising only skewness, kurtosis or higher moments in general produces low performance and poorly diversified portfolios. The results also demonstrate that the proposed approach is superior in terms of overall performance and diversification level compared to a conventional higher moment portfolio optimization model. In addition, while ranking portfolios based on the four performance measures we find that these measures result in almost similar ranking outcomes in the U.S. and Korean stock investments, and slightly less similar in the global investments.
... The Omega function is defined by varying the threshold. As mentioned for example in Bacmann and Scholz (2003), the main advantage of the Omega measure is that it involves all the moments of the return distribution, including skewness and kurtosis. Moreover, ranking is always possible, whatever the threshold in contrast to the Sharpe ratio. ...
We analyze the performance of the two main portfolio insurance methods, the OBPI and CPPI strategies, using downside risk measures. For this purpose, we introduce Kappa performance measures and especially the Omega measure. These measures take account of the entire return distribution. We show that the CPPI method performs better than the OBPI. As a-by-product, we determine the set of threshold values for these risk/reward performance measures.
... Nevertheless, the ranking of investments based on the Omega ratio may vary with different threshold values. Bacmann and Scholz [2003] argue that for the purpose of investment evaluation the Omega measure should be calculated for thresholds between 0% and the risk-free rate. In our empirical analysis, the threshold return is defined as the average of 0% and the risk-free rate. ...
The financial crisis and the rescue measures taken by governments and central banks increased investors' interest in liquidity and in real assets supposed to offer a hedge against inflation. Against this background, we investigate empirically four real assets (real estate, commodities, infrastructure, and shipping) for which there are investment instruments available which trade in liquid markets. Our empirical study using data from 1999 to 2009 yields several results: First, in most cases, the addition of real assets improved portfolio performance (measured with Sharpe ratio, Sortino ratio, Omega ratio, and Modified Sharpe ratio) in comparison with a base portfolio consisting only of standard stocks and bonds. Among the four real assets, infrastructure and shipping clearly outperformed commodities and real estate. Second, the time frame chosen for the analysis matters very much. This is bad news for investors because there is no such thing as the single 'true' time frame for this purpose. Due to our analytical approach, we regard our conclusions, in spite of their general time dependence, as rather solid. Third, despite great conceptual differences, our four performance measures lead to the same conclusions. This result is interesting for investors beyond our specific setting because the selection of a specific performance measure from the vast supply of such measures does not seem to matter much.
Son zamanlarda otomatik katılım sistemi ile Türkiye'de ilgi odağı olan Bireysel Emeklilik Sistemi (BES) sistemi birçok ülkede uygulanan bir alternatif emeklilik sistemi ayağıdır. Etkin uygulamanın olduğu ülkelerde BES sistemleri tasarruf oranına ciddi katkılar sağlamaktadır. Ayrıca kurumlar farklı yöntemlerle bu fonların performanslarını ölçmekte ve bu sayede katılımcıdaki risk ve getiri algısını değiştirmeyi hedeflemektedir. Bu çalışmanın amacı emeklilik fonlarının performans analizi için kullanılmamış olan Omega oranı ile emeklilik fonları için bir performans analizi sunmaktır. Omega oranı; minimum kabul edilebilir getiriye karşılık gelen bir eşiğe göre getiriyi iki alt bölüme ayırmaktadır. Bu ölçü kazanç beklentisi (getiri eşiğin üzerindedir) ile kayıp beklentisi (getiri eşiğin altındadır) arasındaki oran olarak tanımlanmaktadır. Bu çalışmada emeklilik fonlarının performans analizi için kullanılmamış bir oran olan Omega oranı ile altın, hisse senedi, borçlanma araçları ve değişken emeklilik fonları için yeni bir performans analizi sunulmaktadır. Çalışma Omega oranın da diğer oranlar gibi performans analizinde kullanılabileceğini göstermektedir. Dört adet emeklilik yatırım fonu çeşidi için yapılan Omega performans analizinde en yüksek Omega rasyoları borçlanma araçları emeklilik fonlarınındır. En düşük omega performansları hisse senedinde gözlemlenmiştir. The Private Pension System (PPS) as a focus of attention with its automatic participation system in Türkiye, is an alternative pension system implemented in many countries. This systems make serious contributions to the savings rate in countries. Also, institutions measure the performance of these funds with different methodologies, and they aim to change the risk and return perception of the participants. The aim of this study is to present a performance analysis for pension funds with the Omega ratio not applied to analyze pension funds. Omega ratio divides the return into two subdivisions according to a threshold corresponding to the minimum acceptable return. This measure is defined as the ratio between expectation of gain (return is above the threshold) and expectation of loss. A new performance analysis is presented in this study for gold, stocks, debt instruments and variable pension funds with the Omega ratio, which is a ratio that has not been used for performance analysis of pension funds. The study shows that the Omega ratio can be used in performance analysis like other ratios. In the Omega performance analysis conducted for four types of pension investment funds, the highest omega ratios belong to debt instruments pension funds..
Son zamanlarda otomatik katılım sistemi ile Türkiye’de ilgi odağı olan Bireysel Emeklilik Sistemi (BES) sistemi birçok ülkede uygulanan bir alternatif emeklilik sistemi ayağıdır. Etkin uygulamanın olduğu ülkelerde BES sistemleri tasarruf oranına ciddi katkılar sağlamaktadır. Ayrıca kurumlar farklı yöntemlerle bu fonların performanslarını ölçmekte ve bu sayede katılımcıdaki risk ve getiri algısını değiştirmeyi hedeflemektedir. Bu çalışmanın amacı emeklilik fonlarının performans analizi için kullanılmamış olan Omega oranı ile emeklilik fonları için bir performans analizi sunmaktır. Omega oranı; minimum kabul edilebilir getiriye karşılık gelen bir eşiğe göre getiriyi iki alt bölüme ayırmaktadır. Bu ölçü kazanç beklentisi (getiri eşiğin üzerindedir) ile kayıp beklentisi (getiri eşiğin altındadır) arasındaki oran olarak tanımlanmaktadır. Bu çalışmada emeklilik fonlarının performans analizi için kullanılmamış bir oran olan Omega oranı ile altın, hisse senedi, borçlanma araçları ve değişken emeklilik fonları için yeni performans analizi sunulmaktadır. Çalışma Omega oranın da diğer oranlar gibi performans analizinde kullanılabileceğini göstermektedir. Dört adet emeklilik yatırım fonu çeşidi için yapılan Omega performans analizinde en yüksek Omega rasyoları borçlanma araçları emeklilik fonlarınındır. En düşük omega performansları hisse senedinde gözlemlenmiştir.
The Omega function is a relatively newly developed performance measure, falling in the class of downside risk measures. This measure does not make any assumptions regarding the return distributions evaluated, but incorporates the actual return distribution in the calculation.
The sensitivity of this measure to simulated changes within the class of stable distributions was investigated, within a range of parameters that was representative of the South African investment environment. The Omega and Sharpe ratios calculated for these distributions were ranked and compared. Even though the rankings were largely similar, discrepancies did occur. On investigation it was found that these discrepancies were caused by the inability of the Sharpe measure to differentiate between increased volatility caused by higher probability weighted gains (or positive skewness) and losses, as the Sharpe ratio penalises funds for volatility.
The simulated tests were extended to various distributions, which have different risk profiles and distribution shapes, and ranked. A higher incidence of ranking differences occurred due to the inability of the Sharpe ratio to differentiate between gains and losses, or to correctly account for the risk of positively skewed distributions. Negative Sharpe ratios, caused by the average realized returns being exceeded by the threshold (target) rate, resulted in incorrect rankings.
Normally, practical performance evaluation is not initiated with a detailed analysis of the return distributions in order to determine which performance measure is more appropriate. The Omega measure, however, incorporates the distribution into the calculation, which is not the case with the Sharpe measure. If the distributions are normal, the Omega measure gives exactly the same result as the Sharpe measure. However, where return distributions diverge from normality, the Omega measure will incorporate the divergence, whilst the Sharpe measure fails in this regard. The result is a ranking of performance that is more relevant to investor decision-making.
In this paper, we analyze the Kappa performance measures of portfolio returns having Johnson distributions. Kappa performance measures are based on downside risk measures, which better allows evaluating risk and performance of complex returns such as those of hedge funds. These measures take account of the whole return distribution. We illustrate how they evolve according to the four basic parameters of the Johnson distributions.
This paper provides accurate estimations of portfolio returns including several hedge funds. The main problem is to identify their dependence structure. For this purpose, we introduce goodness-of-fit tests of copula, based on the Kendall's functions. To illustrate our approach, we consider in particular different optimal portfolios, corresponding to the maximization of performance measures such as the Sharpe, Return on VaR, Return on CVaR and Omega ratios. The empirical validation is made on three hedge fund indices: the Event Driven, Long/Short and Managed Futures. The time period of the analysis is December 1993 to October 2008. Our results show that copula clearly allows a better determination of risk and performance measures of such portfolios.
Purpose – In this chapter, copula theory is used to model dependence structure between hedge fund returns series.
Methodology/approach – Goodness-of-fit tests, based on the Kendall's functions, are applied as selection criteria of the “best” copula. After estimating the parametric copula that best fits the used data, we apply previous results to construct the cumulative distribution functions of the equally weighted portfolios.
Findings – The empirical validation shows that copula clearly allows better estimation of portfolio returns including hedge funds. The three studied portfolios reject the assumption of multivariate normality of returns. The chosen structure is often of Student type when only indices are considered. In the case of portfolios composed by only hedge funds, the dependence structure is of Franck type.
Originality/value of the chapter – Introducing goodness-of-fit bootstrap method to validate the choice of the best structure of dependence is relevant for hedge fund portfolios. Copulas would be introduced to provide better estimations of performance measures.
Hedge funds typically reveal some statistical properties like serial correlation, non-normality, volatility clustering, and leverage effect, which have to be considered when risk positions of hedge funds are computed. We describe an autoregressive Markov-Switching model that captures the specific features of hedge fund returns and allows especially to fit for volatility clustering and leverage effects in the data. The model is tested using publicly available hedge fund index data from different regions. We compare two different variants of the model by means of risk and performance measures. Our case study implies that if the leverage effect appears in the data, it is worth to fit for leverage in the parameter estimation process.
We showed that traditional performance measures are not adequate for the performance evaluation of hedge funds portfolios because they take into account neither the asymmetry of returns nor the risk perception of investors. In order to overcome this problem, we made recourse to performance measures in the downside risk framework. By using the Credit Suisse/Tremont Hedge Fund database, we showed that Sortino ratio; upside potential ratio and Omega measure make it possible to overcome the drawbacks of Sharpe ratio. The results obtained also showed that the index of Mamoghli and Daboussi is the adequate measure which makes it possible to surmount the drawbacks of Treynor index and Mishra and Rahman index. Likewise, the results proved that alpha of Mamoghli and Daboussi measures more correctly than Jensen alpha and Mishra and Rahman alpha the performance of hedge funds.
The issue of performance measurement in the hedge fund industry has led to literature that is both abundant and controversial. The explanation for this complexity lies in the particular features of alternative funds. Hedge funds invest in a heterogeneous range of financial assets and cover a wide range of strategies that have different risk and return profiles. Even though the current studies on hedge fund performance appear to be confusing, due to conflicting conclusions and criticism of the methods employed in previous papers, they contribute to an improvement in the knowledge of alternative funds, and leading approaches are confirmed. The aim of this paper is to highlight some specific characteristics of hedge funds and their implications in terms of performance measurement. The most recent innovative contributions are reported.
This paper, which is being written to provide an overview of the multitude of publications we have seen on hedge fund performance, is the result of a reading and analysis of about 200 studies on this subject. About 50 publications in the most famous journals and working papers written by recognized authors have been selected to provide a dynamic and comprehensive view of the improvements in hedge fund performance measurement. The issue of performance measurement in the hedge fund industry has led to literature that is both abundant and controversial. The explanation of this complexity lies in the particular features of alternative funds. Hedge funds invest in a heterogeneous range of financial assets and cover a wide range of strategies that have different risk and return profiles. Even though the current studies on hedge fund performance appear to be confusing, due to conflicting conclusions and criticism of the methods employed in previous papers, they contribute to an improvement in our understanding of alternative funds and help to confirm the validity of leading approaches. The aim of this paper is to highlight some specific characteristics of hedge funds and their implications in terms of performance measurement.
We analyse the risk of portfolios mixing hedge funds, stocks and bonds. The risk of the portfolios is quantified by the Value-at-Risk and the Expected Shortfall derived from the Extreme Value Theory. This approach enables us to take the impact of higher moments into account. We show that the risk of a traditional portfolio is reduced by the inclusion of hedge funds. An optimal weight of 50% hedge funds is found when the traditional portfolio is mostly composed of bonds. In equity dominated portfolios, investors should incorporate as much hedge funds as possible. Furthermore we examine the extremal dependence between funds of hedge funds and stocks or bonds using multivariate Extreme Value Theory. We do not find any significant extremal dependence between hedge funds and bonds. The evidence is more mixed between stocks and funds of hedge funds. Funds of hedge funds without a significant investment in Managed Futures exhibit significant dependence in the extreme with the stock market. The August 1998 event linked to the Russian crisis and the LTCM failure is the cause of this dependence.
This paper proposes an approach to constructing the insured portfolios under the VaR-based portfolio insurance strategy (VBPI) and provides a comprehensive analysis of its hedging effectiveness in comparison with the buy-and-hold (B&H) as well as the constant proportion portfolio insurance (CPPI) strategies in the context of the Chinese market. The results show that both of the insurance strategies are able to limit the downward returns while retaining certain upside returns, and their capabilities of reshaping the return distributions increase as the guarantee or the confidence level rises. In general, the VBPI strategy tends to outperform the CPPI strategy in terms of both the degree of downside protection and the return performance.
The primary aim of the paper is to place current methodological discussions in macroeconometric modeling contrasting the ‘theory first’ versus the ‘data first’ perspectives in the context of a broader methodological framework with a view to constructively appraise them. In particular, the paper focuses on Colander’s argument in his paper “Economists, Incentives, Judgement, and the European CVAR Approach to Macroeconometrics” contrasting two different perspectives in Europe and the US that are currently dominating empirical macroeconometric modeling and delves deeper into their methodological/philosophical underpinnings. It is argued that the key to establishing a constructive dialogue between them is provided by a better understanding of the role of data in modern statistical inference, and how that relates to the centuries old issue of the realisticness of economic theories.
Eine zentrale Fragestellung in der wissenschaftlichen Auseinandersetzung auf dem Gebiet Hedgefonds stellt deren Performancemessung dar. Ausgangspunkt unserer Untersuchung bildet die in der Literatur verbreitete Meinung, dass Hedgefonds aufgrund ungewöhnlicher Ausprägungen der höheren Renditeverteilungsmomente nicht anhand der klassischen Sharpe-Ratio beurteilt werden können. Stattdessen wird die Verwendung neuerer Performancemaße, die das Verlustrisiko abbilden, empfohlen. Im Rahmen einer empirischen Untersuchung auf der Grundlage von Hedgefonds-Indizes vergleichen wir das kritisierte Performancemaß mit den neueren Ansätzen der Performancemessung. Obwohl die Renditen der Hedgefonds-Indizes deutlich von einer Normalverteilung abweichen, führen die analysierten Ansätze zu weitgehend identischen Reihungen der verschiedenen Hedgefonds-Strategien. Von daher stellen wir fest, dass die Wahl des Performancemaßes keinen entscheidenden Einfluss auf die Beurteilung der Hedgefonds-Indizes hat.
We examine factors underlying the differences in commingled real estate fund (CREF) performance using a sample of 65 CREFs during 1985-2002. More than half of the individual CREFs underperformed the employed benchmark. However, portfolios of CREFs performed well in both up and down markets, smaller CREFs outperformed larger CREFs, and top performing CREFs continued to outperform. Differential CREF performance appears to be attributable to property selection, rather than allocation across real estate sectors. Liquidity-constrained CREFs exhibited lower risk. CREFs with large benchmark tracking error experienced inferior performance. These findings indicate important cross-sectional differences among CREFs and diversification opportunities for pensions employing multiple CREF investment strategies. Copyright 2006 American Real Estate and Urban Economics Association
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