Optimal Portfolio Allocation Research Articles

Dynamic spillover effect and hedging between the gold price and key financial assets. New evidence from Vietnam

ABSTRACT This study investigates the interlinkage of gold markets and Vietnamese asset classes at multiple investment horizons using a hybrid wavelet-based VAR-GARCH-BEKK approach. The findings show that the spillover effects between time series are time-varying across various wavelet scales in terms of direction and strength. The connectedness for various market pairs is weak in the short run but eventually strengthened towards the long run. We also analyse the multiscale behaviour of hedge ratio for optimal portfolio allocation decisions, which decompose volatility spillovers, allowing investors to adapt their hedging strategies.

Optimal Target-Date Funds for Observed Savings Rates

We study optimal life-cycle portfolio allocation and its application to target-date fund (TDF) design. We show that optimal TDF allocation must be explicitly linked to a savings rate; for example, a higher savings rate generating higher financial wealth accumulation should necessarily come with a more conservative TDF. Further, we quantify the extent to which accumulated wealth, the investing environment, and participant characteristics affect TDF allocations and compare the resulting optimal allocations against the observed universe of U.S. TDF products. Plan sponsors and asset managers can use these findings to improve TDF selection and design.

Multi-Period Goals-Based Portfolio Optimization

Portfolio managers regularly have views on capital markets that span multiple periods. A portfolio manager, for example, may expect a recession with high probability over the coming period, followed by a recovery in the subsequent period. Currently, there are few methods for optimal portfolio allocation across these multiple periods that match how practitioners operate in the real world. Herein, we present a multi-period optimization method using a goals-based framework that allows practitioners to develop multi-period capital market expectations and optimize their portfolios across those periods.

From Black Wednesday to Brexit: Macroeconomic shocks and correlations of equity returns in France, Germany, Italy, Spain, and the United Kingdom

AbstractThis paper investigates whether macroeconomic shocks, such as the UK's referendum decision to leave the European Union (“Brexit”), the 2008 Financial Crisis, the 1992 ERM Crisis (“Black Wednesday”), and the 1987 stock market crash (“Black Monday”), had a positive impact on portfolio risk diversification. We estimate weekly dynamic conditional correlations and then optimal sectoral portfolio allocations between 1973 and 2019. Our results show that correlations of equity returns increased as a consequence of economic integration among European countries from the mid‐1980s until the late 2000s, and decreased in the United Kingdom after Black Wednesday and the Brexit referendum. We tested the existence of a correlation change‐point on June 27, 2016 by applying Wied et al. (2012)'s [Econometric Theory, 28(3), 570–589] correlation structural break test, which we modified to account for dynamic conditional correlations. Application of this test confirms that the referendum date was a break‐point in nearly all UK manufacturing industries. The failure of Lehman Brothers and the 1987 stock market crash were also identified as structural breaks in equity correlations. Moreover, our findings suggest that the Brexit vote may constitute a long‐term trend reversal of the convergence of equity return correlations in European markets, akin to Black Wednesday, rather than a shock like the 1987 and 2008 financial crises, which merely intensified a historical upward trend in correlations of European equity returns.

Cointegration, information transmission, and the lead‐lag effect between industry portfolios and the stock market

AbstractThis paper shows that lagged information transmission between industry portfolio and market prices entails cointegration. We analyze monthly industry portfolios in the US market for the period 1963–2015. We find cointegration between six industry portfolio and market prices. We show that the equilibrium error, the long‐term common factor between industry portfolio and market cumulative returns, has strong predictive power for excess industry portfolio returns. In line with gradual information diffusion across connected industries, the equilibrium error proxies for changes in the investment opportunity set that lead to industry return predictability by informed investors. Forecasting models including the equilibrium error have superior forecasting performance relative to models without it, illustrating the importance of cointegration between the industry portfolio and market prices. Overall, our findings have important implications for investment and risk‐management decisions, since the out‐of‐sample explanatory power of the equilibrium error is economically meaningful for making optimal portfolio allocations.

Co-movements between Bitcoin and other asset classes in India

Purpose This study aims to analyze the dynamic relationship between the Bitcoin market and the conventional asset classes in India Design/methodology/approach This paper aims to cast light on the dynamic linkages between Bitcoin prices and other conventional asset classes in India by using the wavelet transform frameworks, which can allow us to analyze components of time series without losing the information. To do that, the techniques used with the data set include wavelet-based covariance, correlation, coherence spectrum, continuous power spectrum and Granger causality test. Findings The findings of the study suggest that interrelationships between Bitcoin and the key financial asset returns are statistically significant at low, medium and high frequencies. This study also finds the existence of the unidirectional connectedness between Bitcoin the other assets in India. Practical implications The outcome of the analysis calls for substantial policy implications for investors, portfolio management in India. This research on the existence of the interconnectedness between Bitcoin and other conventional asset classes in a specific country context, India can, therefore, make a significant contribution to the contemporary debate about the speculative nature of the cryptocurrencies. It casts light on whether Bitcoin provides any diversification and risk management benefits for Indian, as well as global investors. Originality/value To the best of the author’s knowledge, this is the first paper investigating the interrelatedness between Bitcoin and key conventional asset classes in India. This research makes methodological advancements by using the wavelet coherence transform. The findings provide empirical bases from which to deal with issues regarding hedging purposes and optimal portfolio allocation for an increasing number of investors in the Indian context. Therefore, the main contribution of this study to related literature in this field is significant.

Dynamic asymmetric optimal portfolio allocation between energy stocks and energy commodities: Evidence from clean energy and oil and gas companies

This paper investigates returns and volatility transmission between SPGCE (S&P Global Clean Energy), SPGO (S&P Global Oil), two non-renewable energy commodities (natural gas and crude oil), and three products of crude oil distillation (heating oil, gasoline, and propane). We estimate a VAR(1) asymmetric BEKK-MGARCH(1,1) using daily U.S. data from March 1, 2010, to February 25, 2020. The empirical findings reveal a vast heterogeneity in spillover patterns of returns, volatilities, and shocks. We employ the empirical results to derive optimal portfolio weights, hedge ratios, and effectiveness measures for SPGCE and SPGO diversified portfolios. We find dynamic diversification advantages of energy commodities, especially heating oil, for energy-related stock markets. We also find that SPGCE and SPGO stocks possess the highest average optimal weight and hedging effectiveness for each other, implying that the positive performance of SPGCE stocks considerably compensates for the negative performance of SPGO stocks. For investors and regulators, the advancement and implementation of clean energy programs and policies, while reducing environmental debt and enhancing “green” growth and sustainable development, provide instruments and strategies to hedge the equity risks inherent in the oil and gas industry.

A study of robust portfolio optimization with European options using polyhedral uncertainty sets

We consider the problem of maximizing the worst-case return of a portfolio when the manager can invest in stocks as well as European options on those stocks, and the stock returns are modeled using an uncertainty set approach. Specifically, the manager knows a range forecast for each factor driving the returns and a budget of uncertainty limiting the scaled deviations of these factors from their nominal values. Our goal is to understand the impact of options on the optimal portfolio allocation. We present theoretical results regarding the structure of that optimal allocation, in particular with respect to portfolio diversification. Specifically, we show that the presence of options only leads to limited diversification across the financial instruments available. We compare our robust portfolio to several benchmarks in numerical experiments and analyze how the optimal allocation varies with the budget of uncertainty. Our results indicate that our approach performs very well in practice.

Experiments on Portfolio Selection: A Comparison between Quantile Preferences and Expected Utility Decision Models

This paper conducts a laboratory experiment to assess the optimal portfolio allocation under quantile preferences (QP) and compare the model's predictions with those of the expected utility theory using a mean-variance (MV) utility function. We estimate the risk aversion coefficients associated to the individuals' empirical portfolio choices under the QP and MV theories, and evaluate the relative predictive performance of each theory. The experiment assesses individuals' preferences through a portfolio choice experiment constructed from two assets that may include a risk-free asset. The results of the experiment confirm the suitability of both theories to predict individuals' optimal choices. Furthermore, the aggregation of results by individual choices offers support to the MV theory. However, the aggregation of results by task, which is far more informative, provides more support to the QP theory. The overall message that emerges from this experiment is that individuals' behavior is better predicted by the MV model when it is difficult to assess the differences in the lotteries' payoff distributions but better described as QP maximizers, otherwise.

Multi-Period Portfolio Optimization with Investor Views under Regime Switching

We propose a novel multi-period trading model that allows portfolio managers to perform optimal portfolio allocation while incorporating their interpretable investment views. This model’s significant advantage is its intuitive and reactive design that incorporates the latest asset return regimes to quantitatively solve managers’ question: how certain should one be that a given investment view is occurring? First, we describe a framework for multi-period portfolio allocation formulated as a convex optimization problem that trades off expected return, risk and transaction costs. Using a framework borrowed from model predictive control introduced by Boyd et al., we employ optimization to plan a sequence of trades using forecasts of future quantities, only the first set being executed. Multi-period trading lends itself to dynamic readjustment of the portfolio when gaining new information. Second, we use the Black-Litterman model to combine investment views specified in a simple linear combination based format with the market portfolio. A data-driven method to adjust the confidence in the manager’s views by comparing them to dynamically updated regime-switching forecasts is proposed. Our contribution is to incorporate both multi-period trading and interpretable investment views into one framework and offer a novel method of using regime-switching to determine each view’s confidence. This method replaces portfolio managers’ need to provide estimated confidence levels for their views, substituting them with a dynamic quantitative approach. The framework is reactive, tractable and tested on 15 years of daily historical data. In a numerical example, this method’s benefits are found to deliver higher excess returns for the same degree of risk in both the case when an investment view proves to be correct, but, more notably, also the case when a view proves to be incorrect. To facilitate ease of use and future research, we also developed an open-source software library that replicates our results.

Risk Appetite and Jumps in Realized Correlation

This paper examines the role of non-cash flow factors over correlation jumps in financial markets. Utilizing time-varying risk aversion measure as a proxy for investor sentiment and the cross-quantilogram method applied to intraday data, we show that risk aversion captures significant predictive power over realized stock-bond correlation jumps at different quantiles and lags. The predictive relation between correlation jumps and time-varying risk aversion is found to be asymmetric, as we detect a heterogeneous dependence pattern across different quantiles and lag orders. Our findings underline the importance of non-cash flow factors over correlation jumps, highlighting the role of behavioral factors in optimal portfolio allocations and the effectiveness of diversification strategies.

Optimal portfolio allocation using option‐implied information

AbstractThis paper explores option‐implied information measures for optimal portfolio allocation. We introduce two state variables constructed from option prices. The first state variable is the risk‐premium on the risky asset and the second variable is the market price of risk. We also explore a lognormal distribution, a mixture of lognormal distributions, and a binomial tree for constructing the implied risk‐neutral density function. Using a combination of statistical and economic measures applied to a portfolio given by the 1‐month US Treasury bill and the S&P 500 Index we show the good performance of option‐implied information measures for optimal portfolio allocation.

Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis

ABSTRACT In this article, we develop a nonparametric functional data analysis (NP-FDA) model to forecast the term-structure of Brazil, Russia, India, China and South Africa (BRICS). We use daily data over the period of January 1, 2010 to December 31, 2016. We find that, while it is in general difficult to beat the random-walk model in the shorter-horizons, at longer-runs our proposed NP-FDA approach outperforms not only the random-walk model, but also other popular competitors used in term-structure forecasting literature. In addition, the NP-FDA model is also found to produce economic gains, besides statistical gains, over the random-walk model. Our results have important implications for both policymakers aiming to stabilize the economy, and for optimal portfolio allocation decisions of financial market agents.

Asymmetric volatility spillovers between world oil prices and stock markets of the G7 countries in the presence of structural breaks

AbstractThe purpose of this paper is to examine the volatility transmission between oil prices and the seven major stock markets (G7) using symmetric and asymmetric bivariate GARCH models incorporating structural breaks. Since these countries play a crucial role in the international economy, it is important for financial market participants to understand the volatility transmission process for designing optimal portfolio allocations. We examine weekly data over the period 1998–2017 and endogenously detect structural breaks in variance using an iterated algorithm in order to estimate the volatility dynamics with greater accuracy. The models were estimated using the maximum likelihood method, and the BFGS algorithm was employed to obtain the variance–covariance estimation and the corresponding standard errors. Overall, we find volatility spillovers between markets together with the fact that the oil market has a leading role. More importantly, we show that neglecting structural changes in variance and/or asymmetries may lead to biased volatility spillover effects. Furthermore, the results highlight that models ignoring structural breaks are significantly overestimating the effect of their own past volatility on the current volatility. In addition, we find that disregarding asymmetries could also lead to erroneous estimates of these effects. Finally, we compute dynamic risk‐minimizing hedge ratios and optimal portfolio weights, however, no significant discrepancies are found among the models considered.

Dynamic Asymmetric Optimal Portfolio Allocation between Energy Stocks and Energy Commodities: Evidence from Clean Energy and Oil and Gas Companies

This paper investigates returns and volatility transmission between SPGCE (index of clean energy stocks), SPGO (index of oil and gas stocks), two non-renewable energy commodities (natural gas and crude oil), and three products of crude oil distillation (heating oil, gasoline, and propane). We estimate a VAR(1) asymmetric BEKK-MGARCH(1,1) using daily U.S. data from March 1, 2010, to February 25, 2020. The empirical findings reveal a vast heterogeneity in spillover patterns of returns, volatilities, and shocks. We employ the empirical results to derive optimal portfolio weights, hedge ratios, and effectiveness measures for SPGCE and SPGO diversified portfolios. We find dynamic diversification advantages of energy commodities, especially heating oil, for energy-related stock markets. We also find that SPGCE and SPGO stocks possess the highest average optimal weight and hedging effectiveness for each other, implying that the positive performance of SPGCE stocks considerably compensates for the negative performance of SPGO stocks. For investors and regulators, the advancement and implementation of clean energy programs and policies, while reducing environmental debt and enhancing “green” growth and sustainable development, provide instruments and strategies to hedge the equity risks inherent in the oil and gas industry.

Sources of Uncertainty and Subjective Prices

Abstract We develop a general framework to study source-dependent preferences in economic contexts. We behaviorally identify two key features. First, we drop the assumption of uniform uncertainty attitudes and allow for source-dependent attitudes. Second, we introduce subjective prices to compare outcomes across different sources. Our model evaluates profiles source-wise, by computing the source-dependent certainty equivalents; the latter are converted into the unit of account of a common source and then aggregated into a unique evaluation. By viewing time and location as instances of sources, we show that subjective discount factors and subjective exchange rates are emblematic examples of subjective prices. Finally, we use the model to explore the implications on optimal portfolio allocations and home bias.

Optimal portfolio allocation with imposed price limit constraint

Daily price limits are adopted by many securities exchanges in countries such as the USA, Canada, Japan and various other countries in Europe and Asia, in order to increase the stability of the financial market. These limits confine the price of the financial asset during all trading stages of any trading day to a range, usually determined based on the previous day’s closing price. In this paper we study the portfolio optimization problem with impose the price limit constraint. The dynamic programming technique is applied to derive the Hamilton–Jacobi–Bellman equation and the method of Lagrange multiplier is used to tackle the constraint. Optimization problem solution results, using numerical method show that the equilibrium path of wealth and investment in risky assets has a different way than in the absence of price limits.

Portfolio Selection Under Systemic Risk

This paper proposes a novel methodology to construct optimal portfolios that explicitly incorporates the occurrence of systemic events. Investors maximize a modified Sharpe ratio that is conditional on a systemic event, with the latter interpreted as a low market return environment. We solve the portfolio allocation problem analytically under the absence of short-selling restrictions and numerically when short-selling restrictions are imposed. This approach for obtaining an optimal portfolio allocation is made operational by embedding it in a multivariate dynamic setting using dynamic conditional correlation and copula models. We evaluate the out-of-sample performance of our portfolio empirically on the US stock market over the period 2007 to 2020 using ex-post wealth paths and systemic risk metrics against mean-variance, equally-weighted, and global minimum variance portfolios. Our portfolio maximizing a modified Sharpe ratio outperforms all competitors under market distress and remains competitive in non-crisis periods.

Risk reduction in two-pillar mandatory pension system under regulatory constraints: simulation-based evidence from Poland

ABSTRACT The regulatory framework of the Polish pension system is far-removed from the optimal lifecycle portfolio approach, which recommends a time-varying asset mix to minimize portfolio risk. Nevertheless, a question emerges on how large diversification gains can grow under the existing rules. This study accounts for the current restrictions and estimates risk-reduction opportunities under the two-pillar pension system. We compare the outcomes with our previous results for optimal portfolio allocation and, as in the previous study, use a Monte Carlo approach with a copula function to simulate the distribution of replacement rates for the Polish pension system. We conclude that the regulations hinder, to a significant extent, the opportunity to minimize shortfall risks, an unsatisfactory outcome for Polish pensioners.

Semimartingale theory of monotone mean–variance portfolio allocation

AbstractWe study dynamic optimal portfolio allocation for monotone mean–variance preferences in a general semimartingale model. Armed with new results in this area, we revisit the work of Cui et al. and fully characterize the circumstances under which one can set aside a nonnegative cash flow while simultaneously improving the mean–variance efficiency of the left‐over wealth. The paper analyzes, for the first time, the monotone hull of the Sharpe ratio and highlights its relevance to the problem at hand.