Trend-following hedge funds and multi-period asset allocation

Abstract

Selected hedge funds employ trend-following strategies in an attempt to achieve superior risk-adjusted returns. We employ a lookback straddle approach for evaluating the return characteristics of a trend-following strategy. The strategies can improve investor performance in the context of a multi-period dynamic portfolio model. The gains are achieved by taking advantage of the funds' high level of volatility. A set of empirical results confirms the advantages of the lookback straddle for investors at the top end of the multi-period efficient frontier.     

Asset Allocation with a High Dimensional Latent Factor Stochastic Volatility Model

We investigate the implications of time-varying expected returnand volatility on asset allocation in a high dimensional setting.We propose a dynamic factor multivariate stochastic volatility(DFMSV) model that allows the first two moments of returns tovary over time for a large number of assets. We then evaluatethe economic significance of the DFMSV model by examining theperformance of various dynamic portfolio strategies chosen bymean-variance investors in a universe of 36 stocks. We findthat the DFMSV dynamic strategies significantly outperform variousbenchmark strategies out of sample. This outperformance is robustto different performance measures, investor’s objectivefunctions, time periods, and assets.     

Bond portfolio optimization using dynamic factor models

A general class of dynamic factor models is used to obtain optimal bond portfolios, and to develop a duration-constrained mean-variance optimization, which can be used to improve bond indexing. An empirical application involving two large data sets of U.S. Treasuries shows that the proposed portfolio policy outperforms a set of yield curve strategies used in bond desks. Additionally, we propose a dynamic rule to switch among alternative bond investment strategies, and find that the benefits of such dynamic rule are even more pronounced when the set of available policies is augmented with the proposed mean-variance portfolios.     

Wolf in Sheep's Clothing: The Active Investment Strategies behind Index Performance

This paper argues that the commonly used market indices imply forms of active investment management in disguise. The selection and rebalancing rules make these indices highly exclusive and dynamic regarding their underlying components and significantly bias their performance. Any passive investment tracking these indices turns into an active strategy characterised by market timing and state‐dependent performance. Evidence is provided that exclusive indices outperform (underperform) more inclusive peer indices in upward (downward) markets. The constitution and maintenance rules of exclusive indices correspond to a set of active trading and investment rules similar to momentum strategies.     

Portfolio performance measurement using APM-free kernel models

A general asset-pricing framework is used to derive a conditional asset-pricing kernel that accounts efficiently for time variation in expected returns and risk, and is suitable to perform (un)conditional evaluations of passive and dynamic investment strategies. The positive abnormal unconditional performance of Canadian equity mutual funds over the period 1989–1999 becomes negative with conditioning, and is robust to the removal of ex post index mimickers. The reversal in the size-based performance results with limited information conditioning is alleviated somewhat with an expansion of the conditioning set. The performance statistics are weakly sensitive to changes in the level of relative risk aversion of the uninformed investor. Unconditional positive performances based on averages of individual fund performances lose their significance when cross-correlations are accounted for using the block-bootstrap method. Estimates of survivorship bias due to the elimination of funds with shorter lives, which range from 36 to 58 basis points per year, are stable across performance models but differ across groupings by fund objective.     

A bootstrap-based comparison of portfolio insurance strategies

This study presents a systematic comparison of portfolio insurance strategies. We implement a bootstrap-based hypothesis test to assess statistical significance of the differences in a variety of downside-oriented risk and performance measures for pairs of portfolio insurance strategies. Our comparison of different strategies considers the following distinguishing characteristics: static versus dynamic protection; initial wealth versus cumulated wealth protection; model-based versus model-free protection; and strong floor compliance versus probabilistic floor compliance. Our results indicate that the classical portfolio insurance strategies synthetic put and constant proportion portfolio insurance (CPPI) provide superior downside protection compared to a simple stop-loss trading rule and also exhibit a higher risk-adjusted performance in many cases (dependent on the applied performance measure). Analyzing recently developed strategies, neither the TIPP strategy (as an ‘improved’ CPPI strategy) nor the dynamic VaR-strategy provides significant improvements over the more traditional portfolio insurance strategies.     

An economic evaluation of stock–bond return comovements with copula-based GARCH models

Owing to their importance in asset allocation strategies, the comovements between the stock and bond markets have become an increasingly popular issue in financial economics. Moreover, the copula theory can be utilized to construct a flexible joint distribution that allows for skewness in the distribution of asset returns as well as asymmetry in the dependence structure between asset returns. Therefore, this paper proposes three classes of copula-based GARCH models to describe the time-varying dependence structure of stock–bond returns, and then examines the economic value of copula-based GARCH models in the asset allocation strategy. We compare their out-of-sample performance with other models, including the passive, the constant conditional correlation (CCC) GARCH and the dynamic conditional correlation (DCC) GARCH models. From the empirical results, we find that a dynamic strategy based on the GJR-GARCH model with Student-t copula yields larger economic gains than passive and other dynamic strategies. Moreover, a less risk-averse investor will pay higher performance fees to switch from a passive strategy to a dynamic strategy based on copula-based GARCH models.     

Investing in the size factor

This paper investigates the role of the size factor for constructing investment portfolios and proposes a dynamic extension that accommodates the risk-free asset and time-varying weights. These weights are determined by a set of state variables given by the term structure of sovereign interest rates, variables describing market risk aversion such as the VIX index and the CRB Industrial return, and indexes reflecting investor sentiment towards the economic outlook. The empirical section explores the suitability of these state variables and analyses the out-of-sample performance of size factors idiosyncratic to the US, the UK and European financial markets that are compared against the dynamic version that optimizes the weights in each period. The results provide support to the different size factors except for periods of economic distress in which the optimal dynamic strategies are clearly superior.     

Trend-following trading strategies in commodity futures: A re-examination

This paper examines the performance of trend-following trading strategies in commodity futures markets using a monthly dataset spanning 48 years and 28 markets. We find that all parameterizations of the dual moving average crossover and channel strategies that we implement yield positive mean excess returns net of transactions costs in at least 22 of the 28 markets. When we pool our results across markets, we show that all of the trading rules earn hugely significant positive returns that prevail over most subperiods of the data as well. These results are robust with respect to the set of commodities the trading rules are implemented with, distributional assumptions, data-mining adjustments and transactions costs, and help resolve divergent evidence in the extant literature regarding the performance of momentum and pure trend-following strategies that is otherwise difficult to explain.     

Predictability and diversification benefits of investing in commodity and currency futures

We re-examine diversification benefits of investing in commodities and currencies by considering a risk-averse investor with mean-variance preferences who exploits the possibility of predictable time variation in asset return means, variances, and covariances. We implement unconditional and conditional efficient portfolio strategies designed to exploit this predictability, together with more traditional and/or ad hoc ones yet hitherto relatively unexplored in this context (including the equally weighted, fixed weight, volatility timing, and reward-to-risk timing strategies). We find that, for all portfolio strategies, commodities and currencies do not improve the investment opportunity set of the investor with an existing portfolio of stocks, bonds and T-bills, and an investment horizon of one month. Our findings, which reverse the conclusions of previous studies that focus on static portfolio strategies, are robust across several performance metrics.     

Dynamic factor long memory volatility

In this paper, we develop a long memory orthogonal factor (LMOF) multivariate volatility model for forecasting the covariance matrix of financial asset returns. We evaluate the LMOF model using the volatility timing framework of Fleming et al. [J. Finance, 2001, 56, 329–352] and compare its performance with that of both a static investment strategy based on the unconditional covariance matrix and a range of dynamic investment strategies based on existing short memory and long memory multivariate conditional volatility models. We show that investors should be willing to pay to switch from the static strategy to a dynamic volatility timing strategy and that, among the dynamic strategies, the LMOF model consistently produces forecasts of the covariance matrix that are economically more useful than those produced by the other multivariate conditional volatility models, both short memory and long memory. Moreover, we show that combining long memory volatility with the factor structure yields better results than employing either long memory volatility or the factor structure alone. The factor structure also significantly reduces transaction costs, thus increasing the feasibility of dynamic volatility timing strategies in practice. Our results are robust to estimation error in expected returns, the choice of risk aversion coefficient, the estimation window length and sub-period analysis.     

An examination of the benefits of dynamic trading strategies in U.K. closed-end funds

We examine the after-cost out-of-sample performance of the unconditional mean–variance (UMV) strategy in the presence of conditioning information (Ferson and Siegel (2001)) using portfolios of U.K. equity closed-end funds. We find that the performance of the UMV strategy significantly improves when using lagged information variables with the highest persistence (first-order autocorrelation) levels and reduces turnover. This strategy is able to outperform alternative dynamic trading strategies and performs well across different subperiods. At low levels of trading costs, the UMV strategy is able to deliver significant value added to investors.     

Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix

The estimation of the inverse covariance matrix plays a crucial role in optimal portfolio choice. We propose a new estimation framework that focuses on enhancing portfolio performance. The framework applies the statistical methodology of shrinkage directly to the inverse covariance matrix using two non-parametric methods. The first minimises the out-of-sample portfolio variance while the second aims to increase out-of-sample risk-adjusted returns. We apply the resulting estimators to compute the minimum variance portfolio weights and obtain a set of new portfolio strategies. These strategies have an intuitive form which allows us to extend our framework to account for short-sale constraints, transaction costs and singular covariance matrices. A comparative empirical analysis against several strategies from the literature shows that the new strategies often offer higher risk-adjusted returns and lower levels of risk.     

Omega performance measure and portfolio insurance

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.     

The Dynamic Relations between the World's Leading Computer Companies and Their Corresponding OEM/ODM Firms

This paper explores the return dynamics between the world's major computer OBM firms and their corresponding OEM/ODM companies in Taiwan. We adopt a systematic multiple hypotheses testing method, the VAR test methodology, to test the dynamic relations. The result shows that there exist strong dynamic relations between the stock returns of the own-brand firms and their corresponding OEM/ODM firms. Specifically, returns of the OBM firms tend to lead those of their corresponding OEM/ODM companies. And the extent of this return lead-lag pattern increases with the closeness of the relationship between those OBM firms and their OEM/ODM partners. This implies that the OBM-OEM/ODM partnership is an important factor in the information set of the investors' trading strategies. In addition to the return dynamics, we also examine the volatility association and spillover effect between returns of these two types of firms. The result indicates a significant spillover effect of the current volatility of the OBM firms on future volatility of their corresponding OEM/ODM firms. Our results imply that the information transmission process between performance of the OBM firms and earnings power of the OEM/ODM companies is not only channeled through the first moment return lead-lag pattern, but also conducted by the second moment volatility spillover effect.     

Forecasting the performance of hedge fund styles

This article predicts the relative performance of hedge fund investment styles using time-varying conditional stochastic dominance tests. These tests allow for the construction of dynamic trading strategies based on nonparametric density forecasts of hedge fund returns. During the recent financial turmoil, our tests predict a superior performance for the Global Macro investment style compared with the other strategies of ‘Directional Traders’. The Dedicated Short Bias investment style is stochastically dominated by the other directional styles. These results are confirmed by simple nonparametric tests constructed from realized excess returns. Further, by utilizing a cross-validation method for optimal bandwidth parameter selection, we discover the factors that have predictive power regarding the density of hedge fund returns. We observe that different factors have forecasting power for different regions of the returns distribution and, more importantly, that the Fung and Hsieh factors have power not only for describing the risk premium but also, if appropriately exploited, for density forecasting.     

Basis risk in static versus dynamic longevity-risk hedging

This paper provides a tractable, parsimonious model for assessing basis risk in longevity and its effect on the hedging strategies of Pension Funds and annuity providers. Basis risk is captured by a single parameter, that measures the co-movement between the portfolio and the reference population’s longevity. The paper sets out the static, full and customized swap-hedge for an annuity, and compares it with a dynamic, partial, and index-based hedge. We calibrate our model to the UK and Scottish populations. The effectiveness of static versus dynamic strategies depends on the rebalancing frequency of the second, on the relative costs, and on basis risk, which does not affect fully-customized, static hedges. We show that appropriately calibrated dynamic hedging strategies can still be reasonably effective, even at low rebalancing frequencies.     

Efficient Option Replication in the Presence of Transactions Costs

In the presence of transaction costs, a risk-return trade-off exists between the quality and the cost of a replicating strategy. In that context, I show how to expand the set of all possible time-based strategies through the introduction of a multi-scale class of strategies, which consist in rebalancing different fractions of an option portfolio at different time frequencies. The method, based on time-scale diversification, is to dynamic replication what investment in diversified portfoliosis to static portfolio selection: in a dynamic context, one may enjoy the benefits of diversification by using different time scales in trading the same asset. This revised version was published online in November 2006 with corrections to the Cover Date.     

A moment computation algorithm for the error in discrete dynamic hedging

This paper develops a computational approach to determining the moments of the distribution of the error in a dynamic hedging or payoff replication strategy under discrete trading. In particular, an algorithm is developed for portfolio affine trading strategies, which lead to portfolio dynamics that are affine in the portfolio variable. This structure can be exploited in the computation of moments of the hedging error of such a strategy, leading to a lattice based backward recursion similar in nature to lattice based pricing techniques, but not requiring the portfolio variable. We use this algorithm to analyze the performance of portfolio affine hedging strategies under discrete trading through the moments of the hedging error.     

Strategic asset?allocation with switching dependence

This paper revisits the problem of the strategic asset allocation between stocks and bonds. The novelty of our approach is to model the influence of economic cycles on the marginal distributions of asset returns and their dependence structure by a single hidden Markov chain. After a brief review of selected statistical distributions (Student’t and Weibull) and copulas (elliptic and Archimedian), we describe how the switching regime model is calibrated using two indices: the CAC 40 for stocks and the SGI Bond 10 years, for bonds. We then propose a dynamic investment policy based on the estimated probabilities of sojourn in each state of the Markov chain. Even though the Markov chain ruling the assets dynamics is hidden, a Bayesian procedure can be used to infer the probabilities of being in a certain state of the economy. The asset allocation can then be adapted to provide the highest yield given the most likely state. Having calibrated and estimated the parameters of the model, the performance of static and dynamic strategies are compared by conducting Monte Carlo simulations. Our results show that dynamic strategies, which exploit the additional information relating the probable regime state, perform better than static policies with a limited risk and an acceptable number of reallocations.