Asset Allocation Framework Research Articles

Abnormal Portfolio Asset Allocation Model: Review

It has been widely studied how investors will allocate their assets to an investment when the return of assets is normally distributed. In this context usually, the problem of portfolio optimization is analyzed using mean-variance. When asset returns are not normally distributed, the mean-variance analysis may not be appropriate for selecting the optimum portfolio. This paper will examine the consequences of abnormalities in the process of allocating investment portfolio assets. Here will be shown how to adjust the mean-variance standard as a basic framework for asset allocation in cases where asset returns are not normally distributed. We will also discuss the application of the optimum strategies for this problem. Based on the results of literature studies, it can be concluded that the expected utility approximation involves averages, variances, skewness, and kurtosis, and can be extended to even higher moments.

A Framework to Perform Asset Allocation Based on Partitional Clustering

Over the past years, many approaches to perform asset allocation have been proposed in the literature. Most of them tackle this problem as an optimization task, where the goal is to maximize return, whilst minimizing the risk. However, such approaches require the inversion of a positive-definite covariance matrix, usually resulting in the concentration of allocation, instability and low performance. Some methods have been recently introduced to solve this problem by facing it as a clustering problem. This paper introduces a framework for asset allocation based on partitional clustering algorithms. The idea is to segment the assets into clusters of correlated assets, allocate resources for each cluster and then within each cluster. The framework allows the use of different partitional clustering algorithms, intragroup and intergroup allocation methods. Also, various assessment criteria are considered, and a specialized initialization method is proposed for the clustering algorithm. The framework is evaluated with the Brazilian Stock Exchange (B3) data from the period 12/2005 to 04/2020. Different initialization methods are used for the clustering algorithm together with two intergroup and two intragroup techniques, resulting in five experimental scenarios. The results are compared with the Ibovespa index, the mean-variance model of Markowitz, and the risk-parity model recently proposed by Lopez de Prado.

Diversifying Macroeconomic Factors — For Better or for Worse

It is widely acknowledged that asset returns are driven by common sources of risk, especially in challenging times when the benefits from traditional portfolio diversification fail to realize. From a top-down perspective, investors are mostly concerned about shocks in growth or inflation that ultimately govern the pricing of broad asset classes. To this extent, we propose a natural asset allocation framework to achieve a diversified exposure to orthogonal macro risk factors and to harvest the associated long-term premia. We examine the role and usefulness of different types of macroeconomic variables, as systematic sources of risk or state variables that drive time variation in the asset returns, and compare their diversification potential across different states of the world.

Risk and Potential: A Perspective from Mean-Variance Induced Utility Functions

We propose a novel dynamic asset allocation framework based on a family of mean-variance induced utility functions that overcome the non-monotonicity and time-inconsistency problems of mean-variance optimization. The utility functions are motivated by the equivalence between the mean-variance objective and a quadratic utility function. Crucially, our framework differs from mean-variance analysis in that we allow different treatment of upside and downside deviations from a target wealth level, which naturally leads to a different characterization of investment outcomes. Risk can be viewed as the possible outcomes below the target wealth, whereas potential can be used to describe the possible outcomes exceeding the target wealth. Our proposed asset allocation framework retains two attractive features of mean-variance optimization: an intuitive explanation of the investment objective and an easily-computed optimal strategy. We establish a semi-analytical solution for the optimal trading strategy in our framework and provide numerical examples to illustrate its behavior. Finally, we discuss applications of this framework to robo-advisors.

Using a Z-score Approach to Combine Value and Momentum in Tactical Asset Allocation

We present several active strategies for combining value and momentum strategies in a tactical asset allocation (TAA) framework. We refine the basic yield approach to valuation by standardizing the value signal using the Z-score. Such standardization not only enables us to directly compare valuation measures across asset classes, but also offers insight about each asset class’s absolute valuation by its own standard. Under the nonlinear approach, it helps to identify market peaks and bottoms. We improve the momentum strategy by considering both relative and absolute performances. In the combined tactical asset allocation model, this modification to momentum acts as a simple mechanism to adjust the importance of value and momentum strategies under different market conditions. Our combined model takes advantage of both short-term momentum effects and long-term mean-reversion in valuation to achieve superior overall portfolio performance. Finally, we also provide alternative models for smaller tracking errors.

Private Equity Program Breadth and Strategic Asset Allocation

It is quite often the case that investors form views on private equity program return and cash flow profiles by analyzing broad-based swaths of the private equity industry, while side-stepping idiosyncratic risks associated with narrower, realistic baskets of private equity pools. This article remedies this by performing an empirical analysis of private equity historical performance while accounting for program diversification. Comparing a private equity program to peer group averages is somewhat unfair, as such methodology favors larger programs, arguably without merit. The authors explain a way to correct for this bias. Finally, the authors present a probabilistic strategic asset allocation framework for private equity programs that help make informed trade-offs between breadth, strategy mix, and associated costs.

The demand for eurozone stocks and bonds in a time-varying asset allocation framework

ABSTRACTThis paper analyzes the short and long-run demand for traditional financial asset classes in eleven founding eurozone members. Our sample period starts from the introduction of euro till 2017. We calculate the welfare losses stemming from ignoring the demand for domestic and eurozone equities and bonds, for various levels of risk aversion. Our results show that the bonds of eurozone countries are, in general, desirable for short-run only. However, in Ireland, Portugal and Spain the bonds are desirable for both short-run and long-run investment horizons. Stocks exhibit both short-run and long-run desirability for all countries except Greece. The Greek stocks are desirable for short- run only.

The Tradeoff between Liquidity and Performance: Private Assets in Institutional Portfolios

Institutional investors have increased their allocation to private assets to capture a potential return premium over public assets and diversification benefits. However, an allocation to private assets, which typically are less liquid than public assets, raises questions: “How does an allocation to private assets affect my portfolio’s ability to satisfy my cash liabilities?”; “What is the marginal cost, in terms of portfolio returns, to increase my confidence of always having enough liquidity to satisfy these liabilities?”; and “What is my optimal allocation to private assets and the composition of the private and public portfolios?”. “Liquidity” for investors is their degree of confidence that their portfolios will satisfy cash flow obligations. Investors who believe their portfolios are liquid are highly confident that they can meet all cash flow obligations across different economic scenarios – even if the portfolio may contain some less-liquid private assets. In contrast, investors who state that their portfolios are not very liquid have less confidence they will always be able to meet all their obligations. We propose a public-private asset allocation framework that incorporates the characteristics of common illiquid private assets (limited partnerships, or LPs). We assume the investor tries to maximize expected horizon portfolio value provided that the investor’s future cash obligations are all met with a pre-specified minimum degree of confidence. Risk to the investor is not short-term volatility but failing to meet cash obligations. The framework highlights that the optimal public-private asset allocation, and the optimal asset allocation within both the public and private portfolios, are interrelated. In addition, the model shows that increasing a portfolio’s liquidity – i.e., increasing the confidence of meeting all cash obligations – implies a less risky portfolio, with a lower allocation to less liquid, private assets. However, this may be expensive in terms of the expected future horizon value of the portfolio. The investor faces a fundamental tradeoff: liquidity vs. performance. If an investor chooses to be more confident of meeting all future cash obligations, how much will this additional liquidity cost in terms of expected future portfolio value? Some investors may conclude that their portfolios may be too liquid.

Momentum and Covered Calls Almost Everywhere

This article examines times series momentum and covered call strategies through conventional representations across ten asset classes. The performance of the two strategies generally outperform static buy and hold investments and are classified as positive and negative autocorrelation factors. The tactical overlay of time series momentum and covered call strategies onto asset classes are considered asset transformations. The two transformed replacements of the underlying asset are incorporated into well-established risk-based allocation heuristics, such as maximum diversification and equal risk contribution. The resulting portfolios show enhanced risk-adjusted performance compared with corresponding buy and hold investments or individual strategy portfolios. The authors designate this global tactical asset allocation framework as autocorrelation factor allocation (ACFA). <b>TOPICS:</b>Options, statistical methods, performance measurement, portfolio construction

Intelligent Asset Allocation via Market Sentiment Views

The sentiment index of market participants has been extensively used for stock market prediction in recent years. Many financial information vendors also provide it as a service. However, utilizing market sentiment under the asset allocation framework has been rarely discussed. In this article, we investigate the role of market sentiment in an asset allocation problem. We propose to compute sentiment time series from social media with the help of sentiment analysis and text mining techniques. A novel neural network design, built upon an ensemble of evolving clustering and long short-term memory, is used to formalize sentiment information into market views. These views are later integrated into modern portfolio theory through a Bayesian approach. We analyze the performance of this asset allocation model from many aspects, such as stability of portfolios, computing of sentiment time series, and profitability in our simulations. Experimental results show that our model outperforms some of the most successful forecasting techniques. Thanks to the introduction of the evolving clustering method, the estimation accuracy of market views is significantly improved.

Evaluation of multivariate GARCH models in an optimal asset allocation framework

This paper analyses plethora of advanced multivariate econometric models, which forecast the mean and variance-covariance of the asset returns to create optimal asset allocation models. Most existing studies use a limited number of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. In this study, we provide an in-depth knowledge of large asset modeling and optimization strategies for solving a portfolio selection problem involving the dynamic conditional correlation models (DCC). Specifically, we use symmetric GARCH models and an asymmetric version of it (GJR-GARCH). Several studies have also tried to examine the effectiveness of using parametric copula in estimating portfolio risk measures but their results have been inconclusive. We are interested in evaluating if Copula-GARCH could be an optimal model for assessing the performance of a portfolio. This study, therefore, implemented various Copula-GARCH based models using the static and dynamic estimation of the correlation. By employing different model specifications, we are able to explore the empirical applicability of the multivariate GARCH models when estimating large conditional covariance matrices. In constructing the optimal portfolios, we evaluate the minimum variance, mean-variance, maximising Sharpe ratio, mean-CVaR, and maximization of Sortino ratio. We compare the out-of-sample performance for each of the models based on the risk-adjusted performance for a portfolio with and without short sales, consisting eight stocks and four bond indices of 10 years maturity, in the United States (US), United Kingdom (UK), Germany, Japan, Netherlands, Canada and Hong Kong. Our results suggest that the dynamic models are more capable of delivering better performance gains than the static models. These models reduce portfolio risk and improve the realized return in the out-of-sample period. This paper concludes that by adding copula functions to the model, it does not give a better performance model when compared to the dynamic correlation model.

Neglecting structural breaks when estimating and valuing dynamic correlations for asset allocation

ABSTRACTThis paper assesses the econometric and economic value consequences of neglecting structural breaks in dynamic correlation models and in the context of asset allocation framework. It is shown that changes in the parameters of the conditional correlation process can lead to biased estimates of persistence. Monte Carlo simulations reveal that short-run persistence is downward biased while long-run persistence is severely upward biased, leading to spurious high persistence of shocks to conditional correlation. An application to stock returns supports these results and concludes that neglecting such structural shifts could lead to misleading decisions on portfolio diversification, hedging, and risk management.

Estimating option-implied distributions in illiquid markets and implementing the Ross recovery theorem

In this research we describe how forward-looking information on the statistical properties of an asset can be extracted directly from options market data and demonstrate how this can be practically applied to portfolio management. Although the extraction of a forward-looking risk-neutral distribution is well-established in the literature, the issue of estimating distributions in an illiquid market is not. We use the deterministic SVI volatility model to estimate weekly risk-neutral distribution surfaces. The issue of calibration with sparse and noisy data is considered at length and a simple but robust fitting algorithm is proposed. We further attempt to extract real-world implied information by implementing the recovery theorem introduced by Ross (2015). Recovery is an ill-posed problem that requires careful consideration. We describe a regularisation methodology for extracting real-world implied distributions and implement this method on a history of SVI volatility surfaces. We analyse the first four moments from the implied risk-neutral and real-world implied distributions and use them as signals within a simple tactical asset allocation framework, finding promising results.Keywords: Option-implied distributions; SVI volatility model; Ross recovery theorem; Tikhonov regularisation; illiquid derivative markets

Volatility as an Alternative Asset Class: Does It Improve Portfolio Performance?

We investigate the potential role of Exchange Traded Products (Notes) as vehicles to trade volatility (here proxied by the VIX index) as an asset class in a fully optimizing asset allocation framework, subject to long-only constraints. In back-testing, recursive exercises based on an expanding window of data from February 2010 to February 2016, we find evidence that VIX should enter with non-negligible weight most portfolio strategies and that under many circumstances, long VIX positions may generate positive risk-adjusted performance benefits. However, the volatility positions that can be managed and traded through (one of) the most popular US exchange-traded notes (VXX) fails to deliver such realized, out-of-sample benefits under all utility functions and for a range of assumptions on investors’ risk aversion. Even though the turnover implied by VXX does not appear excessive, taking into account transaction costs worsens considerably its performance and even casts doubts as to whether volatility ought to be considered as an alternative asset class altogether. Direct strategies that trade appropriate futures on the VIX improve somewhat realized performance, but not enough to tilt over the balance of our conclusions.

Predicting international stock returns with conditional price-to-fundamental ratios

Abstract Taking the perspective of international asset allocation, this paper tests if predictive regressions conditional on time-series and cross-sectional information can improve forecasts of stock index returns. We use different current price-to-fundamental ratios as predictors and condition the sample on the indicator if time-series and cross-section deliver consistent versus opposing signals. Using panel regressions, we find that only consistent ratios (i) display significant mean-reverting behavior, (ii) provide strong in-sample as well as out-of-sample evidence for return predictability, and (iii) yield economic gains in a Bayesian asset allocation framework.

Momentum and Covered Calls Almost Everywhere

We examine times series momentum and covered call strategies through conventional representations across 10 asset classes. The performance of the two strategies generally outperform static buy and hold investments and they are classified as positive and negative autocorrelation factors. The tactical overlay of time series momentum and covered call strategies onto asset classes are considered beta transformations (of previous alpha). The two “beta” replacements of the underlying asset are incorporated into well established risk based allocation heuristics such as maximum diversification and equal risk contribution. The resulting portfolios show enhanced risk adjusted performance compared to the corresponding buy and hold investments. We designate this global tactical asset allocation framework as autocorrelation factor allocation, ACFA.

Strategic Asset Allocation and Risk Budgeting for Insurers Under Solvency II

Solvency II is a new risk-based framework for setting the capital requirements of European insurance companies, in force since January 2016. The solvency capital requirement (SCR) is set such that the insurer can meet its obligations over the next 12 months with a probability of at least 99.5%. In this paper we derive expressions for an investment's marginal contribution to the solvency capital requirement, to provide insight in the risk allocation and the trade-off between return and marginal risk. In addition we derive the optimal strategic asset allocation for an insurer that maximizes the expected return on its own funds, subject to a limit on the SCR for market risk determined with the Solvency II standard formula. We then provide numerical examples to illustrate how the new framework for asset allocation and risk budgeting under Solvency II can be applied at a representative European life insurance company.

Volatility as an Alternative Asset Class: Does It Improve Portfolio Performance?

We investigate the potential role of Exchange Traded Products (Notes) as vehicles to trade volatility (here proxied by the VIX index) as an asset class in a fully optimizing asset allocation framework, subject to long-only constraints. In back-testing, recursive exercises based on an expanding window of data from February 2010 to February 2016, we find evidence that VIX should enter with non-negligible weight most portfolio strategies and that under many circumstances, long VIX positions may generate positive risk-adjusted performance benefits. However, the volatility positions that can be managed and traded through (one of) the most popular US exchange-traded notes (VXX) fails to deliver such realized, out-of-sample benefits under all utility functions and for a range of assumptions on investors’ risk aversion. Even though the turnover implied by VXX does not appear excessive, taking into account transaction costs worsens considerably its performance and even casts doubts as to whether volatility ought to be considered as an alternative asset class altogether. Direct strategies that trade appropriate futures on the VIX improve somewhat realized performance, but not enough to tilt over the balance of our conclusions.

Estimating Option-Implied Distributions in Illiquid Markets and Implementing the Ross Recovery Theorem

We describe how forward-looking information on the statistical properties of an asset can be extracted directly from options market data and how this can be used practically in portfolio management. Although the extraction of a forward-looking risk-neutral distribution is well-established in the literature, the issue of estimation in an illiquid market is not. We use the deterministic SVI volatility model to estimate weekly risk-neutral distribution surfaces. The issue of calibration with sparse and noisy data is considered at length and a simple but robust fitting algorithm is proposed. Furthermore, we attempt to extract real-world implied information by implementing the recovery theorem introduced by Ross (2015). Recovery is an ill-posed problem that requires careful consideration. We describe a regularization methodology for extracting real-world implied distributions and implement this method on a history of SVI volatility surfaces. We analyse the first four moments from the implied risk-neutral and real-world implied distributions and use them as signals within a simple tactical asset allocation framework, finding promising results.

Optimal asset allocation: Risk and information uncertainty

In asset allocation problem, the distribution of the assets is usually assumed to be known in order to identify the optimal portfolio. In practice, we need to estimate their distribution. The estimations are not necessarily accurate and it is known as the uncertainty problem. Many researches show that most people are uncertainty aversion and this affects their investment strategy. In this article, we consider risk and information uncertainty under a common asset allocation framework. The effects of risk premium and covariance uncertainty are demonstrated by the worst scenario in a set of measures generated by a relative entropy constraint. The nature of the uncertainty and its impacts on the asset allocation are discussed.