Portfolio Allocation Strategies Research Articles

Regime shifts and volatility in BRIICKS stock markets: an asset allocation perspective

Purpose– The purpose of this paper is to examine the regime shifts and stock market volatility in the stock market returns of seven emerging economies popularly called as “BRIICKS” which stands for Brazil, Russia, India, Indonesia, China, South Korea and South Africa, over the period from February 1996 to January 2012 by applying Markov regime switching (MS) in mean-variance model.Design/methodology/approach– The authors apply MS model developed by Hamilton (1989) using its mean-variance switching framework on the monthly returns data of BRIICKS stock markets. Further, the estimated probabilities along with variances have been used to calculate the time-varying volatility. The authors also examine market synchronization and portfolio diversification possibilities in sample markets by calculating the Logit transformation based cross-market correlations and Sharpe ratios.Findings– The applied model finds two regimes in each of these markets. The estimated results also helped in formulating the asset allocation strategies based on market synchronization and Sharpe ratio. The results suggest that BRIICKS is not a homogeneous asset class and each market should be independently evaluated in terms of its regime-switching behavior, volatility persistence and level of synchronization with other emerging markets. The study finally concludes that Russia, India and China as the best assets to invest within this emerging market basket which can be pooled with a mature market portfolio to achieve further benefits of risk diversification.Research limitations/implications– The study does not provide macroeconomic and financial explanations of the observed differences in dynamics among sample emerging stock markets. The study does not examine these markets under multivariate framework.Practical implications– The results highlight the role of regime shifts and stock market volatility in the asset allocation and risk management. This study has important implications for international asset allocation and stock market regulation by way of identifying and recognizing the differences on regimes and on the dynamics of the swings which can be very useful in the field of portfolio and public financial management.Originality/value– The paper is novel in employing tests of MS under mean-variance framework to examine the regime shifts and volatility switching behavior in seven promising BRIICKS stock market. Further, using MS model, the authors analyze the duration (persistence) of each identified regime across sample markets. The empirical results of MS model have been used for making portfolio allocation strategies and also examine the synchronization across markets. All these aspects of stock market regime have been largely ignored by the existing studies in emerging market context particularly the BRIICKS markets.

Better than Pre-Commitment Mean-Variance Portfolio Allocation Strategies: A Semi-Self- Financing Hamilton-Jacobi-Bellman Equation Approach

We generalize the idea of semi-self-financing strategies, originally discussed in Ehrbar, Journal of Economic Theory (1990), and later formalized in em Cui et al, Mathematical Finance 22 (2012), for the pre-commitment mean-variance (MV) optimal portfolio allocation problem. The proposed semi-self-financing strategies are built upon a numerical solution framework for Hamilton-Jacobi-Bellman equations, and can be readily employed in a very general setting, namely continuous or discrete re-balancing, jump-diffusions with finite activity, and realistic portfolio constraints. We show that if the portfolio wealth exceeds a threshold, an MV optimal strategy is to withdraw cash. These semi-self-financing strategies are generally non-unique. Numerical results confirming the superiority of the efficient frontiers produced by the strategies with positive cash withdrawals are presented.Tests based on estimation of parameters from historical time series show that the semi-self-financing strategy is robust to estimation ambiguities.

Downside vs. Symmetric Risk in Conservation Portfolio Design to Manage Climate-Change Uncertainty

Recent work uses mean-variance portfolio theory to identify optimal spatial conservation planning in the face of spatial variation in future benefits from uncertainty in climate change. Use of variance to measure risk may lead to inefficient portfolio allocation decisions when returns do not follow a multivariate normal distribution or when conservation agents are loss averse. In this paper, we use downside risk measures to evaluate the risk-reward tradeoffs involved in optimal conservation planning and compare the results against the standard mean-variance approach to identify the significant differences in optimal portfolio allocation strategies based on the measure of risk used. We use a case study of the current and potential future status of 147 bird species in the eastern United States to illustrate the difference in the optimal spatial targeting of bird conservation activity in this region subject to uncertain climate scenarios. We find that when returns are multivariate normal, the use of either risk measure arrives at identical portfolio allocation strategies. However, if the returns are not jointly normal, the choice of risk measure significantly alters the portfolio allocation decisions and also changes the risk-return profile.

Informed momentum trading versus uninformed “naive” investors strategies

We construct a zero net-worth uninformed “naive investor” who uses a random portfolio allocation strategy. We then compare the returns of the momentum strategist to the return distribution of naive investors. For this purpose we reward momentum profits relative to the return percentiles of the naive investors with scores that are symmetric around the median. The score function thus constructed is invariant and robust to risk factor models. We find that the average scores of the momentum strategies are close to zero (the score of the median) and statistically insignificant over the sample period between 1926 and 2005, various sub-sample periods including the periods examined in Jegadeesh and Titman (1993, 2001). The findings are robust with respect to sampling or period-specific effects, tightened score intervals, and the imposition of maximum-weight restrictions on the naive strategies to mitigate market friction considerations.

Informed Momentum Trading versus Uninformed 'Naive' Investors Strategies

We construct a zero-net-worth uninformed investor who uses a random portfolio allocation strategy. We then compare the returns of the momentum strategist to the return distribution of naive investors. For this purpose we reward momentum profits relative to the return percentiles of the naive investors with scores that are symmetric around the median. The score function thus constructed is invariant and robust to risk factor models. We find that the average scores of the momentum strategies are close to zero (the score of the median) and statistically insignificant over the sample period between 1926 and 2005, various sub-sample periods including the periods examined in Jegadeesh and Titman (1993 and 2001). The findings are robust with respect to sampling or period-specific effects, tightened score intervals, and the imposition of maximum-weight restrictions on the naive strategies to mitigate market friction considerations.

Non-Gaussian optimization model for systematic portfolio allocation: How to take advantage of market turbulence?

In this paper, we show how to build a systematic quantitative portfolio allocation strategy using non-Gaussian risk metrics and market turbulence detection. We propose a 3-step approach to build a reference second-order portfolio that will be optimally rebalanced according to increasing market skewness. The reference second-order portfolio uses a robust estimate of a covariance based on a semi-definite matrix calibration and incorporates risk aversion via a moving performance target depending on the volatility of the VIX index. As such, it is a dynamic strategy balancing robustness and reactivity to market volatility changes. A convex relaxation scheme allows to re-formulate the mixed second-order/third-order optimization problem as a tractable semi-definite program.

Cost drivers of operating charges and variation over time: An analysis based on semiparametric SUR models

Although building operating charges have turned out to be a major determinant of profitability for real estate investments, there is a noticeable lack of reports or studies that analyze these costs with state-of-the-art statistical techniques. Specifically, past studies usually assume linear relationships between costs and building attributes, they do not control for cluster-specific or longitudinal effects and do not account for the simultaneous structure of cost categories. Therefore, in this study we provide a novel approach to real estate cost benchmarking: we analyze the effects of building attributes on electricity, heating and maintenance costs for office buildings in Germany in a multivariate structured additive regression (STAR) model simultaneously, modeling potentially nonlinear effects as P(enalized)-splines and controlling for cluster-specific and individual heterogeneity in a three-way random effects structure. This way, we gain insights into how building attributes influence costs, and how cost levels vary across cities, companies and buildings. We furthermore construct quality-adjusted time indexes for the two major German submarkets. The results obtained can be used to derive portfolio allocation strategies and for planning, constructing, operating and redeveloping real estate.

Optimal portfolio allocation strategies with dynamic factor models

We present a framework for designing optimal allocation strategies for large stock portfolios using dynamic factor models and multivariate volatility parametrisations. We attempt to elaborate on the fundamental structure of the Fama and French (FF) factor model with a special focus on the time variation in risk and correlation between stocks returns and systematic factors. For this reason, variants of the multivariate GARCH models are employed to capture the dynamics in means, variances and covariances of the FF factors structure. Based on these models, we derive optimal capital allocation strategies in the framework of Markowitz's mean-variance portfolio theory. We outline and compare the out-of-sample performance of these mean-variance allocations with those obtained using simpler techniques, such as sample historical and exponentially weighted moving average (EWMA) estimates.

Quantitative Forecast Model for the Application of the Black-Litterman Approach

The estimation of expected security returns is one of the major tasks for the practical implementation of the Markowitz optimization. Against this background, in 1992 Black and Litterman developed an approach based on (theoretical established) expected equilibrium returns which also accounts for subjective investors’ views. In contrast to historical estimated returns, which lead to extreme asset weights within the Markowitz optimization, the Black-Litterman model generally results in balanced portfolio weights. However, the existence of investors’ views is crucial for the Black-Litterman model and with absent views no active portfolio management is possible. Moreover problems with the implementation of the model arise, as analysts’ forecasts are typically not available in the way they are needed for the Black-Litterman-approach. In this context we present how (publicly available) analysts’ dividend forecasts can be used to determine an a-priori-estimation of the expected returns and how they can be integrated into the Black-Litterman model. For this purpose confidences of the investors’ views are determined from the number of analysts’ forecasts as well as from a Monte-Carlo simulation. After introducing our two methods of view generation, we examine the effects of the Black-Litterman approach on portfolio weights in an empirical study. Finally, the performance of the Black-Litterman model is compared to alternative portfolio allocation strategies in an out-of-sample study that has not been presented in literature before to the best of our knowledge.

Passive versus Optimized Investing in Retirement Plan Portfolios

This article uses portfolios of Vanguard index funds to study the optimal portfolio allocation strategy for long-term investors who are saving for retirement. The optimization, conducted using both a single-index-hybrid model (SIHM) and the Markowitz–Sharpe optimization method, suggests that in the long run, an optimized allocation strategy will yield cumulative returns equivalent to those of a passive allocation strategy with significantly less risk. In addition, the optimized allocation strategy achieves the favorable risk and reward profile using fewer funds than the passive strategy.

The Dynamics of International Asset Price Linkages and Their Effects on German Stock and Bond Markets

The financial market turbulence in 1998, as other crises previously, produced strong price movements in the securities markets worldwide. This reflected, first, a general reassessment of credit risk, and, second, a drying-up of liquidity even in some of the largest mature securities markets. As a result, cross-market return correlations temporarily underwent dramatic changes, challenging portfolio allocation and risk management strategies which rely on constant historical comovements of asset prices. Against this background, the immediate question arises of how asset price linkages can be properly measured when they are subject to periodic changes as observed in times of market stress. The main purpose of the present paper is to address this question more thoroughly. In order to measure the dynamics of international asset price linkages, we first employ bivariate GARCH models to analyse the comovements between weekly stock and bond market returns across the G3 countries. GARCH models take account of the specific time-series properties of short-term asset returns, which is needed to obtain reliable estimates of the cross-country linkages. Next, switching-regime ARCH or 'SWARCH' models are applied which can identify different volatility regimes for short-term asset prices endogenously. We use this methodology to address two issues: first, 'Are international short-term return linkages state-dependent?,' and second, 'Do volatility spillovers affect individual segments of the domestic bond or stock market differently (i.e. are they market-segment-dependent)?' The first question may also be referred to as the 'contagion hypothesis.' This states that contagion leads to a significant increase in the cross-market correlation during states of financial market turmoil. Hence, contagion differs from mere 'interdependence' in that it demands a stronger-than-normal market linkage during periods of stress.

Optimal Life-Cycle Strategies in the Presence of Interest Rate and Inflation Risk

The worldwide shift from public pay-as-you-go pension systems to privately funded pension schemes is accompanied by a huge increase in the households capital stock mounting to trillions of dollars worldwide which pour into defined contribution pension plans. In order to assess how these funds should be optimally invested, we derive the optimal time-dependent portfolio allocation strategy taking into account long-term stock market, term structure, and inflation risk. Our results confirm that the often advised so-called life-cycle strategy is optimal if non-financial wealth is taken into account. We also benchmark common longterm defined contribution asset allocation strategies relative to the optimum in order to the assess welfare implications for varying investment horizons and risk aversions. The outcome of utility heavily depends on whether the risk aversion of the investor is estimated appropriately. Misestimation of this parameter and hence wrong investment advice can lead to utility losses of around 60 percent in terms of certainty equivalent wealth.

The design of defined-contribution pension plans using a variable-contribution lifecycle programme

Previous studies of portfolio allocation strategies over a finite time horizon have generally concluded that a constant proportion allocation is optimal. But it is common within the pensions industry to recommend a lifecycle strategy. To explain the optimality of the lifecycle approach, this paper examines the role of regular pension contributions. The analysis leads to the conclusion that variable contribution rates are important for lifecycle optimality; for a pension programme that contemplates fixed contributions, a constant proportion allocation is potentially as efficient. Essentially, lifecycle investment advice is incomplete without supplementary advice on variable contributions planning.

Handling Parameter Uncertainty in Portfolio Risk Minimization

Portfolio allocation decisions are frequently made according to optimization algorithms that treat parameters such as means, variances, and covariances of returns as given. These parameters, however, are estimated through error-prone procedures like statistical modeling or subjective evaluation. Robust optimization has become a way to incorporate uncertainty directly into the formulation of optimization problems. The technique can be applied to modeling uncertainty in the expected returns in portfolio shortfall minimization. Tests using simulated and real market data suggest that portfolio allocation strategies resulting from robust optimization formulations outperform strategies obtained using classic optimization methods.

Equilibrium Asset Pricing Under Heterogeneous Information

We analyze theoretically and empirically the implications of heterogeneous information for equilibrium asset pricing and portfolio choice. Our theoretical framework, directly inspired by Admati (1985), implies that with partial information aggregation, portfolio separation fails, buy-and-hold strategies are not optimal, and investors should structure their portfolios using the information contained in prices in order to cope with winner's curse problems. We implement empirically such a price-contingent portfolio allocation strategy and show that it outperforms economically and statistically the passive/indexing buy-and-hold strategy. We thus demonstrate that prices reveal information, in contrast with the homogeneous information CAPM, but only partially, consistent with a Noisy Rational Expectations Equilibrium. The success of our pricecontingent strategy does not proxy for the success of trading strategies based purely on historical performance, such as momentum investment.

Tracking Error and Active Portfolio Management

Persistent bear market conditions have led to a shift of focus in the tracking error literature. Until recently the portfolio allocation literature focused on tracking error minimization as a consequence of passive benchmark management under portfolio weights, transaction costs and short selling constraints. Abysmal benchmark performance shifted the literature's focus towards active portfolio strategies that aim at beating the benchmark while keeping tracking error within acceptable bounds. We investigate an active (dynamic) portfolio allocation strategy that exploits the predictability in the conditional variance-covariance matrix of asset returns. To illustrate our procedure we use Jorion's (2002) tracking error frontier methodology. We apply our model to a representative portfolio of Australian stocks over the period January 1999 through November 2002.

Market mispricings and portfolio allocation to mutual fund classes

Major factors affecting Greek household budget flows to mutual fund classes with different risk-return profiles are studied, applying the flexible functional form of the Almost Ideal Demand System to analyse allocation to equity, bond, balanced, and money market funds. An increase in household expenditure can have a positive impact; an adverse price change may erode budget benefits for a class. Volatility in possible “mispricings” in the underlying market valuation, with risk aversion attitudes, can result in asset reallocation. Cross-price effects provide insight on complementarity and substitutability between classes. Conclusions have useful policy implications for asset management and portfolio allocation strategies.

Risk aversion and portfolio allocation to mutual fund classes

The major factors affecting fund flows allocated to a range of mutual fund classes bearing different risk–return profiles are studied. The flexible functional form of the Almost Ideal Demand System (AIDS) is applied to identify the major drivers of Greek investors' demand patterns for equity, bond, balanced and money market funds, given the strong growth rates of the domestic fund market and the economy's latest entry into the EMU. An increase in household expenditure is shown to have a positive impact on mutual fund flows. An adverse price impact, however, may erode budget benefits towards a fund class, as the price factor appears to be important. The cross-price effects provide insight on complementarity and substitutability among the mutual fund classes. Variations in investors' risk aversion attitudes affect demand for mutual funds and can result in asset reallocation between the asset classes. The conclusions have useful policy implications particularly to asset fund management and portfolio allocation strategies and can be compared with established mutual fund markets.

Commercial Real Estate Return Distributions: A Review of Literature and Empirical Evidence

This paper review the literature on the distribution of commercial real estate returns. There is growing evidence that the assumption of normality in returns is not safe. Distributions are found to be peaked, fat-tailed and, tentatively, skewed. There is some evidence of compound distributions and non-linearity. Public traded real estate assets (such as property company or REIT shares) behave in a fashion more similar to other common stocks. However, as in equity markets, it would be unwise to assume normality uncritically. Empirical evidence for UK real estate markets is obtained by applying distribution fitting routines to IPD Monthly Index data for the aggregate index and selected sub-sectors. It is clear that normality is rejected in most cases. It is often argued that observed differences in real estate returns are a measurement issue resulting from appraiser behaviour. However, unsmoothing the series does not assist in modelling returns. A large proportion of returns are close to zero. This would be characteristic of a thinly-traded market where new information arrives infrequently. Analysis of quarterly data suggests that, over longer trading periods, return distributions may conform more closely to those found in other asset markets. These results have implications for the formulation and implementation of a multi-asset portfolio allocation strategy.

Optimal Diversification within Mixed-Asset Portfolios using a Conditional Heteroskedasticity Approach: Evidence from the U.S. and the U.K.

Executive Summary. In this article, portfolio allocation strategies based on a threshold autoregressive conditional heteroskedasticity model (QTARCH) are constructed for the United States and the United Kingdom and compared to a conventional asset allocation. Our procedure is based on partitioning the historical data into ‘states of the world,’ which are used to produce expectations of return and risk. Several approaches are developed to partition an initial in-sample period (1978-1983), using quarterly asset returns and economic data. These partitions are then used to test out-of-sample strategies for the next quarter. Although the conditional results are sensitive to the method of partitioning, we show that the approach can improve portfolio performance in both countries and that most of the performance improvement stems from using conditional variances-covariances.