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We examine the conjoined contagion mechanism of the inter-liability network and portfolio overlapping in shaping systemic financial risk. We first develop an iterative algorithm to compute the larg...

In this paper, we evaluate an alternative approach for bankruptcy prediction that measures the financial healthiness of firms that have coupon-paying debts. The approach is based on the framework of Leland, H. and Toft, K.B. [Optimal capital structure, endogenous bankruptcy and the term structure of credit spreads. J. Financ., 1996, 51, 987–1019], which is an extension of a widely-used model; the Black–Scholes–Merton model. Using U.S. public firms between 1995 and 2014, we show that the Leland-Toft approach is more powerful than Black–Scholes–Merton in a variety of tests. Moreover, extending popular but also contemporary corporate bankruptcy models with the probability of bankruptcy derived from the Leland-Toft model, such as Altman, E. [Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Financ., 1968, 23, 589–609], Ohlson, J.A. [Financial ratios and the probabilistic prediction of bankruptcy. J. Account. Res., 1980, 18, 109–131] and Campbell, J. Y., Hilscher, J. and Szilagyi, J. [In search of distress risk. J. Financ., 2008, 63, 2899–2939], yields models with improved performance. One of our tests, for example, shows that banks using these extended models, achieve superior economic performance relative to other banks. Our results are consistent under a comprehensive out-of-sample framework.

We consider the pricing of American-type basket derivatives by numerically solving a partial differential equation (PDE). The curse of dimensionality inherent in basket derivative pricing is circumvented by using the theory of comonotonicity. We start with deriving a PDE for the European-type comonotonic basket derivative price, together with a unique self-financing hedging strategy. We show how to use the results for the comonotonic market to approximate American-type basket derivative prices for a basket with correlated stocks. Our methodology generates American basket option prices which are in line with the prices obtained via the standard Least-Square Monte-Carlo approach. Moreover, the numerical tests illustrate the performance of the proposed method in terms of computation time, and highlight some deficiencies of the standard LSM method. ispartof: Quantitative Finance vol:19 issue:10 pages:1689-1704 status: published

Systemically important banks are connected and their default probabilities have dynamic dependencies. An extraction of default factors from cross-sectional credit default swap (CDS) curves allows u...

In credit scoring, survival analysis models have been widely applied to answer the question as to whether and when an applicant would default. In this paper, we propose a novel mixture cure proport...

We propose a behavioural portfolio selection model called collective mental accounting (CMA), which integrates all mental sub-portfolios (mental accounts) in one mathematical model. Moreover, this ...

The covariation among financial asset returns is often a key ingredient used in the construction of optimal portfolios. Estimating covariances from data, however, is challenging due to the potentia...

Considering the growing need for managing financial risk, Value-at-Risk (VaR) prediction and portfolio optimisation with a focus on VaR have taken up an important role in banking and finance. Motiv...

A fully implementable portfolio model combining mental accounting and Black-Litterman to accommodate views on expected returns with multiple attitudes to risk

© 2020, Chapman & Hall/CRC Press The Handbook of Financial Risk Management is an impressively comprehensive survey of risk management models, measurement techniques, and issues. The field of risk m...

We propose measures for systemic risk generated through intra-sectorial interdependencies in the hedge fund sector. These measures are based on variations in the average cross-effects of funds showing significant interdependency between their individual returns and the moments of the sector’s return distribution. The proposed measures display a high ability to identify periods of financial distress, are robust to modifications in the underlying econometric model and are consistent with intuitive interpretation of the results.

This paper extends the Fourier-cosine (COS) method to the pricing and hedging of variable annuities embedded with guaranteed minimum withdrawal benefit (GMWB) riders. The COS method facilitates efficient computation of prices and hedge ratios of the GMWB riders when the underlying fund dynamics evolve under the influence of the general class of Levy processes. Formulae are derived to value the contract at each withdrawal date using a backward recursive dynamic programming algorithm. Numerical comparisons are performed with results presented in Bacinello et al. (2014) and Luo and Shevchenko (2014) to confirm the accuracy of the method. The efficiency of the proposed method is assessed by making comparisons with the approach presented in Bacinello et al. (2014). We find that the COS method presents highly accurate results with notably fast computational times. The valuation framework forms the basis for GMWB hedging. A local risk minimisation approach to hedging inter-withdrawal date risks is developed. A variety of risk measures are considered for minimisation in the general Levy framework. While the second moment and variance have been considered in existing literature, we show that the value-at-risk may also be of interest as a risk measure to minimise risk in variable annuities portfolios.

Despite post-crisis reforms in over-the-counter derivatives markets, regulators are left with incomplete, but still improved, data-sets. This means that methods for reconstructing networks of bilateral exposures from incomplete data are still necessary to conduct a proper assessment of systemic risk. In this paper, we propose a modification of the network reconstruction method developed by Cont and Moussa that includes additional information which is now available to regulators through post-crisis reforms. By making use of a data-set containing all transactions on UK single name CDS contracts, we assess the suitability of the proposed methodology by examining the characteristics of reconstructed and real networks. We find that the proposed methodology allows us to reconstruct networks that both comply with the newly available information, and are as heterogeneous and sparse with fat tailed in- and out- degree distributions as the real ones.

We apply utility indifference pricing to solve a contingent claim problem, valuing a connected pair of gas fields where the underlying process is not standard Geometric Brownian Motion and the assumption of complete markets is not fulfilled. First, empirical data are often characterized by time-varying volatility and fat tails; therefore, we use Gaussian generalized autoregressive score (GAS) and GARCH models, extending them to Student’s t-GARCH and t-GAS. Second, an important risk (reservoir size) is not hedgeable. As a result, markets are incomplete which makes preference free pricing impossible and thus standard option pricing methodology inapplicable. Therefore, we parametrize the investor’s risk preference and use utility indifference pricing techniques. We use Least Squares Monte Carlo simulations as a dimension reduction technique in solving the resulting stochastic dynamic programming problems. Moreover, an investor often only has an approximate idea of the true probabilistic model underlying variables, making model ambiguity a relevant problem. We show empirically how model ambiguity affects project values, and importantly, how option values change as model ambiguity gets resolved in later phases of the projects. We show that traditional valuation approaches will consistently underestimate the value of project flexibility and in general lead to overly conservative investment decisions in the presence of time-dependent stochastic structures.

We extract from the yield curve a new measure of fundamental economic uncertainty, based on McDiarmid’s diameter and related methods for optimal uncertainty quantification (OUQ). OUQ seeks analytical bounds on a system’s behaviour, even where aspects of the underlying data-generating process and system response function are not completely known. We use OUQ to stress test a simple fixed-income portfolio, certifying its safety—i.e. that potential losses will be ‘small’ in an appropriate sense. The results give explicit tradeoffs between: scenario count, maximum loss, test horizon, and confidence level. Unfortunately, uncertainty peaks in late 2008, weakening certification assurances just when they are needed most.

Option pricing and managing equity linked insurance (ELI) require the proper modeling of stock return dynamics. Due to the long duration nature of equity-linked insurance products, a stock return model must be able to deal simultaneously with the preceding stylized facts and the impact of market structure changes. In response, this article proposes stock return dynamics that combine Levy processes in a regime-switching framework. We focus on a non-Gaussian, generalized hyperbolic distribution. We use the most popular linked equity of ELIs, the S&P 500 index, as an example. The empirical study verifies that the proposed regime-switching generalized hyperbolic (RSGH) model gives the best fit to data. In investigating the effects of stock return modeling on pricing and risk management for financial contracts, we derive the characteristic function, embedded option price, and risk measure of equity-linked insurance analytically. More importantly, we demonstrate that the regime-switching generalized hyperbolic ...

In this paper, we define the conditional risk measure under regime switching and derive a class of time consistent multi-period risk measures. To do so, we describe the information process with regime switching in a product space associated with the product of two filtrations. Moreover, we show how to establish the corresponding multi-stage portfolio selection models using the time consistent multi-period risk measure for medium-term or long-term investments. Take the conditional value-at-risk measure as an example, we demonstrate the resulting multi-stage portfolio selection problem can be transformed into a second-order cone programming problem. Finally, we carry out a series of empirical tests to illustrate the superior performance of the proposed random framework and the corresponding multi-stage portfolio selection model.

Many empirical studies have shown that financial asset returns do not always exhibit Gaussian distributions, for example hedge fund returns. The introduction of the family of Johnson distributions allows a better fit to empirical financial data. Additionally, this class can be extended to a quite general family of distributions by considering all possible regular transformations of the standard Gaussian distribution. In this framework, we consider the portfolio optimal positioning problem, which has been first addressed by Brennan and Solanki [J. Financial Quant. Anal., 1981, 16, 279–300], Leland [J. Finance, 1980, 35, 581–594] and further developed by Carr and Madan [Quant. Finance, 2001, 1, 9–37] and Prigent [Generalized option based portfolio insurance. Working Paper, THEMA, University of Cergy-Pontoise, 2006]. As a by-product, we introduce the notion of Johnson stochastic processes. We determine and analyse the optimal portfolio for log return having Johnson distributions. The solution is char...

In the probabilistic risk aversion approach, risks are presumed as random variables with known probability distributions. However, in some practical cases, for example, due to the absence of historical data, the inherent uncertain characteristic of risks or different subject judgements from the decision-makers, risks may be hard or not appropriate to be estimated with probability distributions. Therefore, the traditional probabilistic risk aversion theory is ineffective. Thus, in order to deal with these cases, we suggest measuring these kinds of risks as fuzzy variables, and accordingly to present an alternative risk aversion approach by employing credibility theory. In the present paper, first, the definition of credibilistic risk premium proposed by Georgescu and Kinnunen [Fuzzy Inf. Eng., 2013, 5, 399–416] is revised by taking the initial wealth into consideration, and then a general method to compute the credibilistic risk premium is provided. Secondly, regarding the risks represented with th...

We propose herein a new portfolio selection method that switches between two distinct asset allocation strategies. An important component is a carefully designed adaptive switching rule, which is based on a machine learning algorithm. It is shown that using this adaptive switching strategy, the combined wealth of the new approach is a weighted average of that of the successive constant rebalanced portfolio and that of the 1/N portfolio. In particular, it is asymptotically superior to the 1/N portfolio under mild conditions in the long run. Applications to real data show that both the returns and the Sharpe ratios of the proposed binary switch portfolio are the best among several popular competing methods over varying time horizons and stock pools.

Catastrophe risk bonds are always within a multi-asset class portfolio of alternative risk premia in many hedge funds. In this paper, we consider an over-the-counter insurance contract on catastrophe risk between an insurance company and a hedge-fund. The contract acts as a bond within which the insurance company, which issues the bond, pays payments higher than the market risk-free interest, in order to be insured against the risk of a predefined natural catastrophe. The contract is priced by the utility indifference pricing method. We apply our framework to price agricultural catastrophe bonds in two cities in Iran where their harvests are exposed to the risk of low temperature.

In contrast to single-period mean-variance (MV) portfolio allocation, multi-period MV optimal portfolio allocation can be modified slightly to be effectively a down-side risk measure. With this in mind, we consider multi-period MV optimal portfolio allocation in the presence of periodic withdrawals. The investment portfolio can be allocated between a risk-free investment and a risky asset, the price of which is assumed to follow a jump diffusion process. We consider two wealth management applications: optimal de-accumulation rates for a defined contribution pension plan and sustainable withdrawal rates for an endowment. Several numerical illustrations are provided, with some interesting implications. In the pension de-accumulation context, Bengen (1994)’s [J. Financial Planning, 1994, 7, 171–180], historical analysis indicated that a retiree could safely withdraw 4% of her initial retirement savings annually (in real terms), provided that her portfolio maintained an even balance between diversified equiti...

This paper develops a risk-based capital pricing model for credit insurance portfolios held by a vulnerable insurer. The model accounts for business cycles using a two-state Markov switching model, and allows for dynamic leverage adjustment by the insured firms. The new proposed model, which incorporates risk-based capital practice, is better for both the insurer and the insured firms. Based on the risk-adjusted performance metric, we found that the insurer is better off insuring short- and medium-term loans in expansion and steady states, while it is better off backing both short- and long-term loans in recessions. Our results also emphasize that macroeconomic uncertainty significantly impairs the creditworthiness of the insurer and insured firms.

Markowitz optimal portfolio theory (Markowitz 1987), also known as the Mean-Variance theory, has had a tremendous impact and hundreds of papers are devoted to this topic. This theory addresses the ...

The behaviourally based portfolio selection problem with investor’s loss aversion and risk aversion biases in portfolio choice under uncertainty is studied. The main results of this work are: developed heuristic approaches for the prospect theory model proposed by Kahneman and Tversky in 1979 as well as an empirical comparative analysis of this model and the index tracking model. The crucial assumption is that behavioural features of the prospect theory model provide better downside protection than traditional approaches to the portfolio selection problem. In this research the large-scale computational results for the prospect theory model have been obtained for real financial market data with up to 225 assets. Previously, as far as we are aware, only small laboratory tests (2–3 artificial assets) have been presented in the literature. In order to investigate empirically the performance of the behaviourally based model, a differential evolution algorithm and a genetic algorithm which are capable of dealin...

We investigate the systemic risk of the European sovereign and banking system during 2008–2013. We utilize a conditional measure of systemic risk that reflects market perceptions and can be intuitively interpreted as an entity’s conditional joint probability of default, given the hypothetical default of other entities. The measure of systemic risk is applicable to high dimensions and not only incorporates individual default risk characteristics but also captures the underlying interdependent relations between sovereigns and banks in a multivariate setting. In empirical applications, our results reveal significant time variation in systemic risk spillover effects for the sovereign and banking system. We find that systemic risk is mainly driven by risk premiums coupled with a steady increase in physical default risk.

The guaranteed minimum withdrawal benefit (GMWB), which is sold as a rider to variable annuity contracts, guarantees the return of total purchase payment regardless of the performance of the underlying investment funds. The valuation of GMWB has been extensively covered in the previous literature, but a more challenging problem is the computation of the risk based capital for risk management and regulatory reasons. One needs to find the tail distribution of the profit–loss function, which differs from its expected payoff required for pricing the GMWB contract. GMWB has embedded two option-like features: Management fees are proportional to the current value of the policyholder’s account which results in an average price of the account. Thus the contract resembles an Asian option. However, the fees are charged only up to the time of the account hitting zero which resembles a barrier option payoff. Thus the GMWB is mathematically more complicated than Asian or barrier options traded on the financial markets....

We present a new approach for pricing collateralized debt obligations (CDOs) which takes into account the issue of the market incompleteness. In particular, we develop a suitable extension of the actuarial framework proposed by Bayraktar et al. [Valuation of mortality risk via the instantaneous Sharpe ratio: Applications to life annuities. J. Econ. Dyn. Control, 2009, 33, 676–691], Milevsky et al. [Financial valuation of mortality risk via the instantaneous Sharpe-ratio: Applications to pricing pure endowments. Working Paper, 2007. Available at: http://arxiv.org/abs/0705.1302], Young [Pricing life insurance under stochastic mortality via the instantaneous Sharpe ratio: Theorems and proofs. Technical Report, 2007. Available at: http://arxiv.org/abs/0705.1297] and Young [Pricing life insurance under stochastic mortality via the instantaneous Sharpe ratio. Insurance: Math. Econ., 2008, 42, 691–703], which is based on the so-called instantaneous Sharpe ratio. Such a procedure allows us to incorporate the attitude of investors towards risk in a direct and rational way and, in addition, is also suitable for dealing with the often illiquid CDO market. Numerical experiments are presented which reveal that the market incompleteness can have a strong effect on the pricing of CDOs, and allows us to explain the high bid-ask spreads that are frequently observed in the markets.

The present study proposes a new evaluation approach aimed at estimating the cost of equity through standardized models which consider an innovative set of firm-specific information on the main unsystematic risks which are typical of any business. Our objective is extending the Capital Asset Pricing Model (CAPM) by defining a standard formula for quantifying the premium for certain idiosyncratic risks as a function of a new set of firm-specific quantitative information. We define two econometric models, for listed and non-listed firms respectively, which consider five idiosyncratic risk factors: firm size, value factor, operating risks, financial structure and stock market price volatility. The models were tested on a sample of European non-financial companies. The empirical results show that while the CAPM systematically underestimates the cost of equity, the proposed models correctly estimate its expected value; furthermore, they show a slight improvement also in terms of estimates’ volatility. Due to t...

We consider the continuous-time portfolio optimization problem of an investor with constant relative risk aversion who maximizes expected utility of terminal wealth. The risky asset follows a jump-diffusion model with a diffusion state variable. We propose an approximation method that replaces the jumps by a diffusion and solve the resulting problem analytically. Furthermore, we provide explicit bounds on the true optimal strategy and the relative wealth equivalent loss that do not rely on quantities known only in the true model. We apply our method to a calibrated affine model. Our findings are threefold: Jumps matter more, i.e. our approximation is less accurate, if (i) the expected jump size or (ii) the jump intensity is large. Fixing the average impact of jumps, we find that (iii) rare, but severe jumps matter more than frequent, but small jumps.

In this study, we propose a uniformly distributed random portfolio as an alternative benchmark for portfolio performance evaluation. The uniformly distributed random portfolio is analogous to an enumeration of all feasible portfolios without any prior on the market. Therefore, the relative ranking of a portfolio can be evaluated without peer group information. We derive a closed-form expression for the probability distribution of the Sharpe ratio of a uniformly distributed random portfolio, and conduct comparative analysis with US equity mutual funds. We find that the uniformly distributed random portfolio properly captures the historical performance distribution of equity mutual funds. In addition, we evaluate performance of cap-weighted equity portfolios via uniformly distributed random portfolios.

In his second volume on stochastic volatility and option pricing, Alan Lewis extends his previous work with a particular focus on jump modelling. The Heston or Feller 3/2 models are frequently rewo...

We develop a multi-curve term structure setup in which the modelling ingredients are expressed by rational functionals of Markov processes. We calibrate to LIBOR swaptions data and show that a rational two-factor lognormal multi-curve model is sufficient to match market data with accuracy. We elucidate the relationship between the models developed and calibrated under a risk-neutral measure Q and their consistent equivalence class under the real-world probability measure P. The consistent P-pricing models are applied to compute the risk exposures which may be required to comply with regulatory obligations. In order to compute counterparty-risk valuation adjustments, such as CVA, we show how positive default intensity processes with rational form can be derived. We flesh out our study by applying the results to a basis swap contract., 34 pages, 9 figures

This paper develops a discrete time version of the continuous time model of Bouchard et al. [J. Control Optim., 2009, 48, 3123–3150], for the problem of finding the minimal initial data for a controlled process to guarantee reaching a controlled target with probability one. An efficient numerical algorithm, based on dynamic programming, is proposed for the quantile hedging of standard call and put options, exotic options and quantile hedging with portfolio constraints. The method is then extended to solve utility indifference pricing, good-deal bounds and expected shortfall problems.

Many applications rely on the accurate prediction of company similarity to be effective. Diversification avoids similarity for risk reduction. Hedging through equity-neutral investing seeks similarity in order to ensure risk in long positions is effectively offset by short positions, and vice-versa. Relative valuation requires formation of a ‘peer group’ to which financial ratios, such as the P/E ratio, can be compared. This article considers two data-sets that have not traditionally been used for this purpose: sell-side equity analyst coverage and news article co-occurrences. Each is shown to have predictive power over future correlation, a key measure of future similarity. It is further shown that the analyst and news data can be combined with historical correlation to form groups that are on par or even exceed the quality of the Global Industry Classification System, a leading industry taxonomy.

Utilizing a specific acceptance set, we propose in this paper a general method to construct coherent risk measures called the generalized shortfall risk measure. Besides some existing coherent risk measures, several new types of coherent risk measures can be generated. We investigate the generalized shortfall risk measure’s desirable properties such as consistency with second-order stochastic dominance. By combining the performance evaluation with the risk control, we study in particular the performance ratio-based coherent risk (PRCR) measures, which is a sub-class of generalized shortfall risk measures. The PRCR measures are tractable and have a suitable financial interpretation. Based on the PRCR measure, we establish a portfolio selection model with transaction costs. Empirical results show that the optimal portfolio obtained under the PRCR measure performs much better than the corresponding optimal portfolio obtained under the higher moment coherent risk measure.

We develop an approach to optimal hedging of a contingent claim under proportional transaction costs in a discrete time financial market model which extends the binomial market model with transaction costs. Our model relaxes the binomial assumption on the stock price ratios to the case where the stock price ratio distribution has bounded support. Non-self-financing hedging strategies are studied to construct an optimal hedge for an investor who takes a short position in a European contingent claim settled by delivery. We develop the theoretical basis for our optimal hedging approach, extending results obtained in our previous work. Specifically, we derive a no-arbitrage option price interval and establish properties of the non-self-financing strategies and their residuals. Based on the theoretical foundation, we develop a computational algorithm for optimizing an investor relevant criterion over the set of admissible non-self-financing hedging strategies. We demonstrate the applicability of our approach u...

This study develops a global derivatives hedging methodology which takes into account the presence of transaction costs. It extends the Hodges and Neuberger [Rev. Futures Markets, 1989, 8, 222–239] framework in two ways. First, to reduce the occurrence of extreme losses, the expected utility is replaced by the conditional Value-at-Risk (CVaR) coherent risk measure as the objective function. Second, the normality assumption for the underlying asset returns is relaxed: general distributions are considered to improve the realism of the model and to be consistent with fat tails observed empirically. Dynamic programming is used to solve the hedging problem. The CVaR minimization objective is shown to be part of a time-consistent framework. Simulations with parameters estimated from the S&P 500 financial time series show the superiority of the proposed hedging method over multiple benchmarks from the literature in terms of tail risk reduction.

In this paper, we provide analytical valuation results for barrier options with three different types of information: continuous observations of the underlying asset value, delayed continuous observations and multiple discrete observations. Market, counterparty credit risk management and economic risk capital implications of the valuations with incomplete information are also discussed. We obtain precise analytical solutions using all the information generated by the previous discrete observations of the underlying asset, generated in a consistent fashion by path-dependent simulation. Our results have far-reaching implications for economic capital charges on path-dependent derivatives. We demonstrate that using non-conditional valuation mis-estimates the credit charges on the barrier options with respect to the precise credit charges computed with valuations conditional on the actual available information. The rest of this paper is organized as follows. Section 2 describes the classical case with full, continuous information about the underlying asset. Section 3 deals with the case of delayed continuous information. Section 4 investigates the case of discrete observations at multiple discrete times. Section 5 offers explicit closed-form solutions for the main types of single-barrier options with discrete information and parity results. Section 6 provides valuation results for double-barrier options. Section 7 presents risk management applications for foreign exchange (FX) barrier options, while section 8 concludes our paper.

Portfolio credit risk models can be distinguished by the use of a top-down approach or a bottom-up one. The main difference between these two approaches is the information of default identities. In this paper, we propose a conditional top-down approach which models the default times with a predetermined default order of identities. Thus conditioned on the default order, the default times of a bottom-up model can be constructed simply using a top-down approach. We use the tool of assumptions to separate the information of default orders from the ordered default times. The predetermined assumption (a special assumption) introduced here allows that the construction of the loss process relates to a probability on permutations. We can derive the probabilities on default orders from the known bottom-up models satisfying the predetermined assumption (e.g. Jarrow-Yu’s contagion model), and obtain new choices of probability on default orders based on some simple and interesting indices of permutations such as the ...

The proliferation of books and articles describing quantitative financial models from a theoretical perspective creates a niche for authors like Mario Wuthrich and Michael Merz (WM) to explain a su...

Option overlays on a rebalanced portfolio are designed. Inputs to the design problem are the physical and risk neutral probabilities at the option maturity. They are estimated from time series and option data, respectively. The objective for the design is the bid price of a two price economy modelled as a distorted expectation. The design is monotone increasing in the underlier with a delta constraint. The option positioning is implemented on the S&P 500 index, supposedly rebalanced every 21 days with option positions taken 10 days prior to a rebalance date with a maturity near two months. Option overlays are seen to raise performance measures and reduce drawdowns.

We propose a factor contagion model with the Marshall–Olkin copula for correlated default times and develop an analytic approach for finding the th default time distribution based on our model. We combine a factor copula model with a contagion model under the assumption that the individual default intensities follow contagion processes, and that the default times have a dependence structure with the Marshall–Olkin copula. Then, we derive an analytic formula for the th default time distribution and apply it to compute the price of portfolio credit derivatives, such as th-to-default swaps and single-tranche CDOs. To test efficiency and accuracy of our formula, we compare the theoretical prediction with existing methods.

This study tests the performance of 14 hedge fund index clones created using parsimonious out-of-sample replication portfolios consisting solely of easily accessible assets. We employ a genetic algorithm to integrate two traditional hedge fund replication methods, the factor-based and pay-off distribution replication methods, and evaluate over 4500 commonly held stocks, bonds and mutual funds as replicating portfolio components. In-sample performance indicates that hedge funds have return series similar to portfolios of commonly held assets, and out-of-sample results provide evidence that the in-sample relationships can hold with infrequent rebalancing. This hedge fund replication attempt rates well relatively to prior efforts as 11 replicating portfolios have out-of-sample correlation values of at least 60%. Overall, these results show promise for using a genetic algorithm technique to replicate hedge fund returns.