Optimal Asset Allocation Research Articles

When Uncertainty and Volatility Are Disconnected: Implications for Asset Pricing and Portfolio Performance

We analyze an environment where the uncertainty in the equity market return and its volatility are both stochastic and may be potentially disconnected. We solve a representative investor's optimal asset allocation and derive the resulting conditional equity premium and risk-free rate in equilibrium. Our empirical analysis shows that the equity premium appears to be earned for facing uncertainty, especially high uncertainty that is disconnected from lower volatility, rather than for facing volatility as traditionally assumed. Incorporating the possibility of a disconnect between volatility and uncertainty significantly improves portfolio performance, over and above the performance obtained by conditioning on volatility only.

A new optimisation approach to assess optimal asset allocation in European non-life insurance companies

This study addresses the allocation of optimal assets in non-life insurance companies' environment with a new vision. Most previous studies are based on the maximisation of the utility function. However, in this paper, we focused on the maximisation of technical efficiency (TE). In order to validate our objective, we select a set of European non-life insurance companies (ENIC) over the period 2008-2014. In the first step, we estimate the production function characterised by the directional output distance function (DODF). In the second one, we use two metaheuristics (PSO and GA) to assess the optimal asset allocation (OAA). The empirical results show that the proportion allocated to the 'alternative investment with high-risk high-return' (AIhh) is on average lower than those found in previous studies. However, the percentage allocated to the 'risk-free assets' (RFA) is on average different from zero. This can be explained by the attention given to the competitiveness, survival and long-term profitability respecting the maximisation of TE. So, any insurance company must give more attention to the presence of different stakeholders and resolve the conflicts of interest between them.

Stochastic volatility modelling in portfolio selection via sequential Monte Carlo simulation

This paper uses particle filter to estimate daily volatility in the Brazilian financial stocks market and obtain an optimal allocation of assets via Monte Carlo approach. Our volatility model outperforms the Kalman filter besides overcoming non-additivity and non-Gaussian disturbance pattern. The historical statistics use an optimist Black-Litterman priori view to systematise our analysis in a rolling window. Our proposed method has better out-of-sample metrics than Markowitz, Naive (equal assets weight) and Bovespa Index benchmark.

Stochastic volatility modelling in portfolio selection via sequential Monte Carlo simulation

This paper uses particle filter to estimate daily volatility in the Brazilian financial stocks market and obtain an optimal allocation of assets via Monte Carlo approach. Our volatility model outperforms the Kalman filter besides overcoming non-additivity and non-Gaussian disturbance pattern. The historical statistics use an optimist Black-Litterman priori view to systematise our analysis in a rolling window. Our proposed method has better out-of-sample metrics than Markowitz, Naive (equal assets weight) and Bovespa Index benchmark.

Sequential Learning, Asset Allocation, and Bitcoin Returns

For optimal asset allocation, mean-variance investors must learn about the joint dynamics of new and existing asset classes, not only their profitability. Bitcoin's digital gold narrative provides a unique laboratory to test this hypothesis. We find that a decrease in investors' estimate on correlation between Bitcoin and the US stock markets strongly predicts higher Bitcoin returns the next day. The same empirical pattern universally appears in out-of-sample predictions, global equity markets, and other cryptocurrencies. Our stylized model and empirical proxy for Bitcoin demand explain the predictability pattern in light of asset allocation practices and investors' learning on time-varying correlation.

The Gain-Pain Index: Asset Allocation for Individual (And Other?) Investors

Individual investors typically determine their asset allocation using investor questionnaires, which can be viewed as black boxes that generate a result without highlighting the benefits and costs of the portfolios considered. This article introduces an asset allocation tool, the gain-pain index (GPI), that overcomes this shortcoming. The tool proposed incorporates two critical variables found in investor questionnaires, the portfolio holding period and the investor’s risk tolerance, and broadens the definition of risk beyond volatility by also considering the probability of suffering a loss and the magnitude of the loss. The model is used to determine optimal asset allocations for 21 countries and the world market, for different holding periods and levels of risk aversion.

Dynamic Spending and Risk-Based Simulation in Retirement Planning

This study employed the RISK simulation to estimate the net portfolio value of a long-term investment in a diversified asset allocation. The simulations take into consideration the risk associated with that investment as presented a number of possible investment scenarios in fixed income and equity securities. The possible set of portfolio weights for combinations of the different securities are considered in each simulation. The model constraint is that asset weights in the portfolio must add up to one. The simulations construct future scenarios by randomly choosing past scenarios and assigning higher probabilities to more recent years. Further, the estimated future value of the investment is then deflated with the deflation factor to determine the present value in today’s Thai baht. For each simulated scenario, the model presents the risk associated with the investment – the value at risk (VaR) – which captures the maximum possible expected portfolio value. Finally, the paper further develops the portfolio optimization technique to determine optimal asset allocation in achieving the desired investment goal to assist retirees when planning their retirement funds by considering any risk associated with simulated scenarios.

Optimal contribution rate of PAYGO pension

ABSTRACT In this paper, we study the optimal contribution rate of pay-as-you-go (PAYGO) pension under a Nash equilibrium between the participants and the government. Given the fixed contribution rate, the participants of different cohorts choose optimal consumption and asset allocation policies to achieve their objectives. Using the variational method, we derive the closed-form solution. The value function of the participants is monotonous with respect to the PAYGO contribution rate under constant population growth rate. As such, we modify the ‘Samuelson-Aaron’ criterion that the preference between PAYGO and fully funded pensions depends not only on the demography, investment and salary parameters but also on the age of the cohort. Moreover, we establish the critical age to separate the preference and the ex-post admissible scope for the contribution rate adjustment. As a central planner, the government is fully aware of the participants' optimal feedback functions. The objective of the government is to maximize the overall utility of the participants weighted by each cohort's population. For a shrinking population, the fully funded scheme is more preferable and only the elderly cohorts prefer PAYGO scheme. However, taking into account the larger political power of the elderly cohorts in the objective function, the optimal contribution rate raises slightly.

Optimal Asset Allocation Subject to Liquidity and Withdrawal Risks

This study investigates the optimal asset allocation of a financial institution subject to liquidity risks and whose customers are free to withdraw their capital-guaranteed financial contracts at any time. Accounting for constraints on the solvency of the institution, we present a general optimization problem and provide a dynamic programming principle for the optimal dynamic investment strategies. Furthermore, we consider an explicit context, including the interest rate and credit intensity fluctuations, and show, by numerical results, that the optimal strategy improves the solvency and the asset returns of the institution compared to the baseline asset allocation.

Asset Allocation for “End-State” Portfolios

In recent years many US corporate pension plans have closed and entered their “end-state.” As end-state plans have become more prevalent, their special portfolio management challenges, including asset allocation, have gained attention. Pure immunization with public fixed income assets (“hibernation”) is a possible investment management strategy to try to minimize funding ratio variability. But this may not be sufficient for all end-state plans. For example, mortality risk could cause the actual cash liabilities to deviate from the estimated cash liabilities. This risk may argue for the inclusion of return-seeking assets. In addition, some plans have had good performance experience with their illiquid private assets (e.g., private real estate, private equity, and private credit funds). How can a CIO evaluate the potential of these private assets remaining in their end-state portfolio? We use our asset allocation framework (OASIS™) to help end-state investors solve for optimal asset allocations. An asset allocation solution seeks to maximize the end-state portfolio’s expected horizon value while meeting future cash obligations at a desired confidence level and keeping the funded status at a target level of stability over the investment horizon. We show that private assets can play an important role in helping end-state portfolios achieve their return objectives while meeting their liquidity and funded status stability constraints. CIOs managing end-state portfolios may impose additional constraints to address their specific concerns. For example, CIOs may impose an upper limit on their total allocation to private assets, impose a floor on the plan’s funding ratio, or express views on expected private asset performance and their fund-selection skills. Making asset allocation constraints more restrictive typically implies a less risky portfolio, with a lower allocation to private assets. The OASIS framework helps CIOs measure this tradeoff between performance and their constraints, allowing them to make better business decisions.

(Sub)Optimal Asset Allocation to ETFs

We study the allocation of capital to U.S. equity exchange-traded funds (ETFs). Investors tend to invest more in ETFs with lower fees and higher liquidity, consistent with common intuition. We also find, however, that investors make substantial allocations to ETFs that are similar to existing ETFs while incurring the costs of higher fees and lower liquidity. We estimate the aggregate cost to ETF investors from investing in dominated funds to be $1.1 billion to $17.5 billion since 2000 depending on the bench-marking approach. We conclude that ETF investors may be better off, as a whole, by concentrating on a relatively small set of low-fee, high-liquidity funds.

Tail risks in large portfolio selection: penalized quantile and expectile minimum deviation models

Accurate estimation and optimal control of tail risk is important for building portfolios with desirable properties, especially when dealing with a large set of assets. In this work, we consider optimal asset allocation strategies based on the minimization of two asymmetric deviation measures, related to quantile and expectile regression, respectively. Their properties are discussed in relation with the ‘risk quadrangle’ framework introduced by Rockafellar and Uryasev [The fundamental risk quadrangle in risk management, optimization and statistical estimation. Surv. Oper. Res. Manag. Sci., 2013, 18(1–2), 33–53], and compared to traditional strategies, such as the mean–variance portfolio. In order to control estimation error and improve the out-of-sample performance of the proposed models, we include ridge and elastic-net regularization penalties. Finally, we propose quadratic programming formulations for the optimization problems. Simulations and real-world analyses on multiple datasets allow to discuss pros and cons of the different methods. The results show that the ridge and elastic-net allocations are effective in improving the out-of-sample performance, especially in large portfolios, compared to the un-penalized ones.

Insurer Optimal Asset Allocation in a Small and Closed Economy: The Case of Iran’s Social Security Organization

We seek to determine the optimal amount of the insurer’s investment in all types of assets for a small and closed economy. The goal is to detect the implications and contributions the risk seeker and risk aversion insurer commonly make and the effectiveness in the investment decision. Also, finding the optimum portfolio for each is the main goal of the present study. To this end, we adopted the optimal asset-liability management (ALM) method to control the firm's risk of financial stability and growth by balancing the assets and liabilities of the firm. In the process, stochastic interest rates and inflation risks were taken into account according to the expected utility maximization framework. All assets were established and calculated by the Kalman Filter with the stochastic interest rate following the Hull-White model; an additional stochastic process models the inflation risk. To consider the stochastic process, we employed the geometric Brownian motion in the liability process to ensure a definite liability value. We chose Iran’s Social Security Organization as our sample insurer company since it has a portfolio of five types of assets and four types of liabilities, and operates in a small and closed economy. By Applying the ALM method with the stochastic control theory approach, we acquire the optimal investment strategies for insurers to minimize their risk. Our findings demonstrate the effects of model parameters, such as the degree of risk-taking on the insurer decision.

An Empirical Analysis of the Volatility Spillover Effect between World-Leading and the Asian Stock Markets: Implications for Portfolio Management

This study employs the Vector Autoregressive-Generalized Autoregressive Conditional Heteroskedasticity (VAR-AGARCH) model to examine both return and volatility spillovers from the USA (developed) and China (Emerging) towards eight emerging Asian stock markets during the full sample period, the US financial crisis, and the Chinese Stock market crash. We also calculate the optimal weights and hedge ratios for the stock portfolios. Our results reveal that both return and volatility transmissions vary across the pairs of stock markets and the financial crises. More specifically, return spillover was observed from the US and China to the Asian stock markets during the US financial crisis and the Chinese stock market crash, and the volatility was transmitted from the USA to the majority of the Asian stock markets during the Chinese stock market crash. Additionally, volatility was transmitted from China to the majority of the Asian stock markets during the US financial crisis. The weights of American stocks in the Asia-US portfolios were found to be higher during the Chinese stock market crash than in the US financial crisis. For the majority of the Asia-China portfolios, the optimal weights of the Chinese stocks were almost equal during the Chinese stock market crash and the US financial crisis. Regarding hedge ratios, fewer US stocks were required to minimize the risk for Asian stock investors during the US financial crisis. In contrast, fewer Chinese stocks were needed to minimize the risk for Asian stock investors during the Chinese stock market crash. This study provides useful information to institutional investors, portfolio managers, and policymakers regarding optimal asset allocation and risk management.

A New Optimization Approach to Assess Optimal Asset Allocation in European Non-Life Insurance companies

This study addresses the allocation of optimal assets in non-life insurance companies' environment with a new vision. Most previous studies are based on the maximisation of the utility function. However, in this paper, we focused on the maximisation of technical efficiency (TE). In order to validate our objective, we select a set of European non-life insurance companies (ENIC) over the period 2008-2014. In the first step, we estimate the production function characterised by the directional output distance function (DODF). In the second one, we use two metaheuristics (PSO and GA) to assess the optimal asset allocation (OAA). The empirical results show that the proportion allocated to the 'alternative investment with high-risk high-return' (AIhh) is on average lower than those found in previous studies. However, the percentage allocated to the 'risk-free assets' (RFA) is on average different from zero. This can be explained by the attention given to the competitiveness, survival and long-term profitability respecting the maximisation of TE. So, any insurance company must give more attention to the presence of different stakeholders and resolve the conflicts of interest between them.

Evaluation of Risk Parity Asset Allocation Strategy in Indonesia Mutual Fund During 2010–2019 Period

Indonesia as the largest country in Southeast Asia have the highest GDP among the other countries in the region. In 2011, McKinsey & Company conducted a research to understand the annual consumer spending trend in Indonesia. According to the research, financial services will be the main consumption in Indonesia in 2030. Despite those promising situations, mutual fund industry in Indonesia was facing a big challenge in 2019. During 2019, Indonesia Financial Authority (OJK) has suspended 37 mismanaged mutual fund products from several investment managers. And actively managed mutual funds will have more exposure on this mismanagement risk.
 Aside from mismanagement and underperforming risk of actively managed mutual fund, investor also facing high risk of equity asset class. Recently, IDX Composite (IHSG) experience a tremendous -28% drop in Q1 2020. Biggest peak to bottom drawdown during that period was -37,49% from 14 January 2020 until 24 March 2020. This situation creates a challenge for mutual fund sales agent (APERD).
 But a challenging situation also offers opportunities. Mutual fund sales agent (APERD) that can offer optimum asset allocation suggestion that can give maximum risk protection can take advantage of these situation. One such unique strategy that has not been applied by other mutual fund sales agent (APERD) is risk parity that became popular after 2008 financial crisis.
 This research applied risk parity framework in passively managed mutual fund because most of actively managed mutual funds in Indonesia underperform the benchmark in the 10-year period. Because of that, passively managed mutual funds provide better investment opportunity. Four equity indexes (LQ45, Jakarta Islamic Index, Bisnis 27, and Sri Kehati) and one passively managed fixed income mutual fund (ABF Indonesia Bond Index Fund) that have existed for 10-year period was chosen to apply the risk parity method. Sharpe ratio calculation is used to evaluate the portfolio performance compared to commonly used portfolio strategy in the world according to 2018 Legg Mason Global Investment Survey.
 Result of the analysis is that risk parity portfolio offer better Sharpe ratio compared to commonly used portfolio strategy in the world. Result of 2009 - April 2020 back-testing process also show that risk parity can protect investor from equity market downturn in 77.3% of the case. With above result, risk parity can offer better risk protection for Indonesia individual investor.

Employing Decentralized Trading Protocol (P2P) to Optimize Assets Allocation

As it is the power of that gives occasion for the division of labor, so the extent of this division must always be limited by the extent of this power, (Adam Smith, 1776, Book 1, Chapter. 3). This paper deals with innovations in technology that have the potential to increase the power of exchanging by making decentralized P2P barter trading protocol operational in certain markets. The unprecedented growth of the market in terms of the variety of goods and services, the number of traders and the locations in which they trade make decentralized trading protocol (exchanging) more important than ever. Economic theory suggests that the optimal exchanging system is a decentralized P2P barter trading in state contingent claims on current and future consumption or in securities that represent such claims. Until now decentralized trading protocols were not possible and global trade is carried out through centralized fiat money as a medium of exchange. Fiat money contributes to a market of monopolistic competition due to the power and opportunistic behavior of governments that issue money and of central banks that manage the money. Blockchain and decentralized trading technologies make it possible to replace centralized fiat money as medium of exchange where the media of exchange are tokens registered on blockchains. The tokens represent claims on current and future consumption. Decentralized trading protocols implemented on blockchain the distributed ledger technology are still in nascent stage, but once applied even to a small and limited segment of the market they are likely to expand and to contribute to optimal asset allocation in the market as a whole.

Digital customization of consumer investments in multiple funds: virtual integration improves risk\u2013return decisions

Digital technology in financial services is helping consumers gain wider access to investment funds, acquire these funds at lower costs, and customize their own investments. However, direct digital access also creates new challenges because consumers may make suboptimal investment decisions. We address the challenge that consumers often face complex investment decisions involving multiple funds. Normative optimal asset allocation theory prescribes that investors should simultaneously optimize risk–returns over their entire portfolio. We propose two behavioral effects (mental separation and correlation neglect) that prevent consumers from doing so and a new choice architecture of virtually integrating investment funds that can help overcome these effects. Results from three experiments, using general population samples, provide support for the predicted behavioral effects and the beneficial impact of virtual integration. We find that consumers’ behavioral biases are not overcome by financial literacy, which further underlines the marketing relevance of this research.

U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach

AbstractThis paper explores the long‐run relationship and the associated short‐run dynamics between the U.S. stock market and three major macroeconomic fundamentals, namely the U.S. industrial production index, the U.S. 10‐year Treasury bond yield and the West Texas Intermediate oil price, for the time period covering 1985–2015. The quantile autoregressive distributed lag (QARDL) model presented by Cho et al. (2015) Journal of Econometrics, 188, 281–300, which combines the autoregressive distributed lag model of Pesaran and Shin (1998), Cambridge University Press, and Pesaran et al. (2001) Journal of Applied Econometrics, 16, 289–326, and the quantile regression methodology of Koenker and Bassett (1978), Econometrica, 46, 33–50, is used. This approach analyses the links among the variables over a range of quantiles which represent different states of the equity market. The findings show a significant quantile‐varying cointegration relationship between U.S. equity prices and the three selected macroeconomic factors. The U.S. industrial production index is found to have the largest long‐run influence on the equity market. These findings highlight the importance of market players as well as policymakers to develop a greater understanding and awareness of how macro variables impact stock prices under different stock market conditions, particularly in relation to optimal asset allocation and risk management strategies, among others.

Optimal asset allocation and consumption rules for commodity-based sovereign wealth funds

This paper studies the optimal asset allocation for a sovereign wealth fund (SWF) subject to a stochastic stream of commodity-based income, where, without loss of generality, we focus on oil-based SWFs. Using CRRA utility, we assume the fund’s objective is to maximize the discounted utility of intertemporal consumption in the presence of time-varying investment opportunities, and given non-zero correlations between shocks to oil income and asset return innovations. We use the log-linear approximation method of Campbell (1993) to solve for the model’s optimal asset allocation and consumption rules. Using historical data, we estimate the model parameters using the maximization-by-parts algorithm of Song et al. (2005). We then calibrate the model to study the optimal allocation and consumption for varying levels of risk aversion, time preference and oil income volatility. Our results are of interest to SWFs seeking optimal portfolio choice in the face of changing investment opportunities which correlate with their stochastic stream of income.