Optimal Portfolio Allocation Research Articles

Robustness meets co-jumps: optimal consumption and portfolio choice with derivatives

In this paper, we study a robust, dynamic, continuous-time optimal consumption and portfolio allocation problem for investors with recursive preferences who have access to both stock and derivatives markets. We assume the stock price process follows a stochastic volatility model, with instantaneous precision as the unique state variable, allowing for discontinuities in all the dynamics. We obtain a closed-form approximate solution up to a system of ODEs to the optimization problem for a non-unitary value of the elasticity of intertemporal substitution of consumption, being able to derive an exact solution as a particular case. Our theoretical findings show that the optimal policies are remarkably affected by the ambiguity-aversion parameters to diffusive and jump risks. A detailed numerical analysis confirms the effectiveness of our theoretical results on real data. Finally, we prove that investors who do not believe in ambiguity may suffer considerable wealth losses.

A compound real options framework for optimal project portfolio allocation under uncertainty: The case of marginal field development under reservoir and price uncertainty

A compound real options framework for optimal project portfolio allocation under uncertainty: The case of marginal field development under reservoir and price uncertainty

Portfolio optimization with feedback strategies based on artificial neural networks

Dynamic portfolio optimization has significantly benefited from a wider adoption of deep learning (DL). While existing research has focused on how DL can applied to solving the Hamilton–Jacobi–Bellman (HJB) equation, some very recent developments propose to forego the derivation of HJB in favor of empirical utility maximization over dynamic allocation strategies expressed through artificial neural networks. In addition to simplicity and transparency, this approach is universally applicable, as it is essentially agnostic about market dynamics. We apply it to optimal portfolio allocation between cash account and risky asset following Heston model. The results appear on par with theoretical ones.

Cost‐effective portfolio allocation across quarantine, surveillance and eradication using info‐gap theory

Abstract Biosecurity activities primarily include pre‐border and border quarantine, post‐border surveillance and post‐border eradication. Budget allocated to quarantine and surveillance activities ultimately influence the expenditure and success rate of eradication campaigns. Optimal portfolio allocation examined in previous research is susceptible to potential severe uncertainties existing in ecology and in the behaviour of invasive species itself. These uncertainties, together with a limited budget, make it difficult for decision makers to allocate the total management budget to each biosecurity activity in a robust manner. Info‐gap decision theory is applied to model the severe uncertainty in invasive species management, and robust optimize the total management cost. This research shows that using a combination of pre‐border and border quarantine (to reduce the incursion probability) and post‐border surveillance (to enable early detection and rapid response), enables decision makers to be more robust to potential uncertainty. Further, it is reported that investment in quarantine that is more cost‐effective should outweigh that in surveillance, in line with precautionary principle. Increasing the estimated population threshold for surveillance detection also gains more robustness. Synthesis and applications: Portfolio allocation options developed in this research provide decision makers with a way to manage the invasive species spatially, cost‐effectively and confidently by allocating the total management budget in a robust manner. The methods outlined in this research can not only be applied to invasive species, but also the conservation of endangered species that are constrained by severe uncertainty in ecological modelling and limited resources.

Asymmetric TVP-VAR connectedness between highly traded commodities and hedging strategies: Evidence from major contagions

The objective of this study is to examine the return interconnectedness and asymmetric spillover effects in global commodity futures markets, with a focus on the impact of contagion. A competent asymmetric time-varying parameter vector autoregressive (TVP-VAR) model was employed for highly traded commodity futures (cocoa, coffee, corn, cotton, soy, sugar, wheat, and oil) between January 1, 2000, and March 31, 2024. This study investigates the connectedness of commodities in three dimensions: asymmetric spillovers, the influence of oil and oil substitutes on the network, and the impact of major contagions. The average total connectedness index (TCI) indicates that the connectedness is significant throughout the period and increases during the invasion. The findings imply that contagion effects trigger a potential alteration in the structure of the network integration level of the commodities, amplifying system-wide dynamic connectivity due to disurptions caused by oil and oil substitutes. The net plot depicts corn and soy as the net transmitters, with their magnitude increasing during the contagions. The pairwise connectedness index (PCI) revealed that corn-soy, corn‒wheat, and soy-wheat were the primary interactors, while oil became a significant interactor, particularly during the oil crash and the COVID-19 outbreak. Additionally, compared with other contagions, GFC had a potential asymmetric effect on the network. Positive returns dominate the interaction between the primary transmitter and receivers, whereas negative returns do not significantly dominate the total network. These investigations contribute to the literature on the food-fuel nexus in terms of asymmetries and the impact of contagions on the futures market. It also identified the optimal portfolio allocation based on the hedging effectiveness of three portfolio construction strategies.

ESG integration in portfolio selection: A robust preference-based multicriteria approach

We present a framework for multi-objective optimization where the classical mean–variance portfolio model is extended to integrate the environmental, social and governance (ESG) criteria on the same playing field as risk and return and, at the same time, to reflect the investors’ preferences in the optimal portfolio allocation. To obtain the three–dimensional Pareto front, we apply an efficient multi-objective genetic algorithm, which is based on the concept of ɛ-dominance. We next address the issue of how to incorporate investors’ preferences to express the relative importance of each objective through a robust weighting scheme in a multicriteria ranking framework. The new proposal has been applied to real data to find optimal portfolios of socially responsible investment funds, and the main conclusion from the empirical tests is that it is possible to provide the investors with a robust solution in the mean–variance–ESG surface according to their preferences.

Optimizing Portfolio Construction Using Nifty India Manufacturing Index: A Risk-Return Analysis with Python

This paper presents a comprehensive analysis of portfolio construction strategies aimed at maximizing risk-adjusted returns for investors. Utilizing historical data on stock returns and risks, a meticulous selection process was employed to identify 15 stocks with superior risk-return profiles. These stocks were chosen based on their outperformance relative to the dataset's average measures, prioritizing returns higher than the dataset average and risks lower than average risk levels. The selected stocks, representing a diverse range of manufacturing-related businesses, formed the cornerstone of an optimal portfolio designed to minimize sector-specific risks while maximizing growth potential. Through rigorous portfolio optimization techniques, including Markowitz's mean-variance optimization, the study identified an optimal portfolio allocation with a return of 50.22% and volatility of 13.33%. Insights from the study offer valuable perspectives for investors seeking realistic and sustainable wealth accumulation strategies. The research also identifies avenues for future exploration, including the integration of alternative asset classes, behavioral finance insights, and advancements in risk management technologies. This study demonstrates the effectiveness of Python in financial analysis and portfolio optimization while furthering the theory of portfolios and providing valuable guidance for making investment decisions. Keywords: Returns, Risk, Volatility, Optimal Portfolio Construction, Manufacturing Sector, Markowitz Portfolio Theory,

Optimizing Portfolio Allocation in the Indian Defense Sector: A Python-Based Approach

This paper presents a detailed analysis of portfolio construction strategies aimed at maximizing risk-adjusted returns for investors, utilizing Python-based methodologies. By leveraging historical data on stock returns and risks, a meticulous selection process identified 5 stocks with superior risk-return profiles, outperforming the dataset's average measures with higher returns and lower risks. The selected stocks from the defense industry formed the foundation of an optimal portfolio designed to minimize sector-specific risks while maximizing growth potential. Through rigorous portfolio optimization techniques, including Markowitz's mean-variance optimization, an optimal portfolio allocation was identified with a return of 62.40% and volatility of 27.74%. Insights from the study provide valuable perspectives for investors seeking realistic and sustainable wealth accumulation strategies. The research also highlights future avenues for exploration, such as integrating alternative asset classes, incorporating behavioral finance insights, and leveraging advancements in risk management technologies. This study contributes to advancing portfolio theory and offers practical guidance for investment decision-making.This study demonstrates the effectiveness of Python in financial analysis and portfolio optimization while furthering the theory of portfolios and providing valuable guidance for making investment decisions. Keywords: Returns, Risk, Volatility, Optimal Portfolio Construction, Defense Sector, Markowitz Portfolio Theory,

Global uncertainty and the spillover of tail risk between green and Islamic markets: A time-frequency domain approach with portfolio implications

This study first calculates the left tail risks in the renewable energy, green, sustainable, Islamic equity and Islamic bond markets using CAViaR models. Then, applying TVP-VAR based connectedness method it estimates various measures of dynamic connectedness and spillover of the tail risks at short, medium, and long term. Furthermore, through wavelet coherence analysis it examines how various global uncertainties impact such connectedness in time and frequency domain. Lastly, it estimates hedging effectiveness, optimal portfolio weights and Sharpe Ratios to provide practical implications for the market participants. The results show that the tail risk connectedness and spillover are mainly driven by the short-term dynamics emphasizing the importance of short-lived noises in the transmission of down-side risks. Usually, the connectedness of the tail risks is higher during the period of COVID-19 and Russia-Ukraine war. Though the tail risks of Islamic bond and developing Islamic equity markets are mostly isolated in the full sample showing hedging potential of these markets, the network analysis shows their increased connectedness with other markets during the period of COVID-19 and Russia-Ukraine war. In general, mature market indices such as Dow Jones Sustainable World and MSCI global environment indices, Dow Jones Islamic World, US and UK market indices are consistently net contributors/transmitters of tail risk shocks while Islamic bond and developing Islamic equity market indices are net receivers of shocks when they are connected to the network. Two uncertainty indices, that follow volatility related to gold and US dollar, demonstrate hedging potential and predictive power on the connectedness. Dow Jones Islamic World market index usually carries significant weights in optimal portfolio allocations while Islamic bonds from GCC countries have higher Sharpe Ratios indicating their promise as instruments for risk-adjusted profit.

Hedge performance of different asset classes in varying economic conditions

In the realm of long-term investment, strategic portfolio allocation is an essential tool, especially in relation to risk management and return optimisation. There are many ways to pursue optimal portfolio composition, and their effectiveness depends on many factors, including the investor’s goals, risk appetite, and investment horizon. One of the primary means of portfolio optimisation is diversification. The core idea of diversification is to maintain a diverse portfolio with weakly correlated assets that can vastly reduce portfolio exposure to different market stress factors. Diversification is a fundamental strategy in investment and portfolio management that is essential for mitigating risk and enhancing potential returns over the long term. By spreading investments across various asset classes, sectors, geographies, and investment styles, diversification helps reduce the volatility of the overall portfolio. The main subject of this study is the theoretical basis of portfolio diversification and the analysis of historical data to derive optimal strategies for using uncorrelated assets to improve portfolio performance. This paper examines the correlation dynamics between different asset classes, such as stocks, bonds, and alternative investments, and their response to changes in inflation, interest rates, and market volatility, and tests it with historical data to deduce the optimal strategies for using uncorrelated assets to improve portfolio performance. The findings of this study prove the variable relationship between asset classes under specific economic conditions. This study uses historical data to show how different asset classes can be optimally leveraged or adjusted to mitigate risks and capitalise on opportunities presented by shifting economic indicators. This reveals that the hedging benefits of equities, bonds, and gold depend greatly on interest rates, market volatility, and inflation. It also provides guidelines for investors on optimal portfolio allocation and risk management. In conclusion, dynamic portfolio management is an essential tool for reducing the portfolio’s overall volatility while maximising returns. The diversification performance of different financial asset classes depends on major economic indicators such as inflation, interest rates, and market volatility. Investors seeking to optimise their portfolios in anticipation of or in response to economic changes, aiming to maximise returns while controlling for risk, can leverage these results.

Diversified ESG Evaluation by Rating Agencies and Net Carbon Tax to Regain Optimal Portfolio Allocation

Abstract Environmental, Society, and Governance (ESG) investments have become increasingly popular in recent years, and, at the same time, many rating agencies provide ESG scores for each company. This means that the ESG investment model may have moved from the traditional two-factor model of risk-return to a three-factor model adding an ESG component to it. This paper highlights the potential for distortion of asset allocation through the shift from traditional risk-return considerations to ESG score considerations. This is equally true for green bonds, resulting in the potential for asset allocation to be distorted by green bond criteria. Furthermore, we show that imposing a net carbon tax on greenhouse gas (GHG) emissions is a measure to correct this distortion in asset allocation and make asset allocation more risk-return based, in addressing global environmental issues.

Connexion Between Cryptocurrencies Liquid and Futures Markets: Discernment from Static, Dynamic and Wavelet Approach

The knowledge of the interconnectedness between liquid and futures markets of cryptocurrencies amidst dynamic contemporary environment can be enriched through the full characterization of the direction, persistence and intensity of information flows between these markets. So, the present study attempts to investigate the static and dynamic connexions between liquid and futures markets of Bitcoin, Ethereum, Litecoin, Ripple XRP and Bitcoin Cash from June 2018 to June 2022. The connexion between their liquid and futures markets is first investigated using unconditional correlation, Johansen’s cointegration, vector error correction and Wald’s block exogeneity. Their estimates discern connexions encompassing significant long-run relationships between their liquid and futures markets; momentous unidirectional long-run causality from their futures market to liquid market; and momentous bidirectional short-run causality from their liquid market to futures market and from their futures market to liquid market. The present treatise encompasses methodological advancement in the investigation of interconnectedness between these markets by employing a dynamic conditional correlation model and a wavelet transform framework. Their discerned estimates indicate that the markets of Bitcoin, Litecoin, Ethereum and Bitcoin Cash have only momentous long-run perseverance, lingering and spillover effects of shocks’ sway on conditional correlations. However, there is momentous short- and long-run perseverance, lingering and spillover effects in the case of Ripple XRP. The wavelet coherence analysis also confirms these results by indicating a bidirectional short-run causal relation and a long-run positive comovement between liquid returns and futures returns of these cryptocurrencies. These discernments may help investors, portfolio managers and policymakers to enhance hedging effectiveness through optimal portfolio allocation and monitor financial contagion to attain and sustain financial stability in economies.

Portfolio allocation and borrowing constraints

Using the US Survey of Consumer Finances, we explore the empirical relationship between borrowing constraints and financial portfolio allocation by American households. To help motivate our empirical analysis we initially draw insights from a mean-variance model of optimal portfolio allocation with three tradable asset classes defined by increasing risk, and establish a link between borrowing restrictions and portfolio allocation in the presence of background risk. Our main contribution, however, lies in estimating the role that borrowing constraints play in shaping households' financial portfolio allocation. This is achieved using an ordered fractional probit model. In addition to revealing the significant empirical role played by household borrowing constraints in determining portfolio allocation, our analysis helps us to resolve ambiguity surrounding the behaviour of the medium-risk asset in our motivational theoretical framework. Further, the empirical distribution of medium-risk assets is found to be remarkably similar to that for high-risk assets, suggesting the presence of a more general ‘risk puzzle’, which our borrowing constraints measures partially explain.

Dynamic interlinkages between cryptocurrencies, NFTs, and DeFis and optimal portfolio investment strategies

PurposeThis study aims to scrutinize time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens and DeFi assets between 1 July 2018 and 19 February 2023 and determine optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.Design/methodology/approachThis work examines time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens, and DeFi assets between 1 July 2018 and 19 February 2023. To this end, the time-varying parameter-vector autoregression (TVP-VAR)-based connectedness methodology of Antonakakis et al. (2020) This approach is an extended version of the Diebold–Yilmaz (DY) method (Diebold and Yılmaz, 2014) and has advantages over the original DY. First, unlike the DY, it is free of the selection of a particular window size. Second, it has robustness for the outliers. Furthermore, following Broadstock et al. (2022), the author estimates time-varying optimal portfolio weights and hedging effectiveness under different portfolio construction scenarios.FindingsThis study's results indicate the following results: (1) The overall connectedness indices prominently capture well-known financial/geopolitical distress incidents; (2) the leading cryptocurrencies (ETH, BTC and BNB) are the largest transmitter of return shocks, while LINK and BTC are the largest transmitters/recipients of volatility shocks; (3) cryptocurrencies, NFTs and DeFi form distinct cluster groups in terms of return and volatility connectedness; (4) the connectedness networks estimated around the 2022 cryptocurrency crash and the FTX's filing for the bankruptcy are characterized by the strongest return and volatility interlinkages; (5) optimal portfolio strategies computed by different portfolio construction techniques display similar motifs and have sustained growth paths except for some short-lived drop backs.Research limitations/implicationsThis study's findings imply several policy suggestions for investors, stakeholders and policymakers. First, the study's time-based dynamic interlinkages can help market participants in their optimal portfolio decisions. In particular, the persistent net receiving roles of the DeFi assets and the NFTs throughout the episode, especially around the financial/geopolitical turmoil, underpin their safe haven potentials (Umar et al., 2022a, b). Finally, since the total connectedness indices (TCIs) are prone to significantly increase around financial/geopolitical burst times, these tools can be valuable for policy makers to monitor risk.Originality/valueThe contribution of knowledge is at least threefold. First, the author focuses on the dynamic time interlinkages among major cryptocurrencies, NFTs and DeFi assets in July 2018 and February 2023 considering the prominent recent financial/geopolitical incidents. Second, the author estimates network topologies of dynamic connectedness around financial/geopolitical bursts and compared them in terms of interlinkages. Finally, the author calculates the time-varying optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.

Heterogeneous Retirement Savings Strategy Selection with Reinforcement Learning.

Saving and investment behaviour is crucial for all individuals to guarantee their welfare during work-life and retirement. We introduce a deep reinforcement learning model in which agents learn optimal portfolio allocation and saving strategies suitable for their heterogeneous profiles. The environment is calibrated with occupation- and age-dependent income dynamics. The research focuses on heterogeneous income trajectories dependent on agents' profiles and incorporates the parameterisation of agents' behaviours. The model provides a new flexible methodology to estimate lifetime consumption and investment choices for individuals with heterogeneous profiles.

On the usefulness of dynamically spilled risk: An optimal portfolio allocation based on cross-sector information contagion

On the usefulness of dynamically spilled risk: An optimal portfolio allocation based on cross-sector information contagion

A Model-Based Approach Machine Learning to Scalable Portfolio Selection

This study proposes a scalable asset selection and allocation approach using machine learning that integrates clustering methods into portfolio optimization models. The methodology applies the Uniform Manifold Approximation and Projection method and ensemble clustering techniques to preselect assets from the Ibovespa and S&P 500 indices. The research compares three allocation models and finds that the Hierarchical Risk Parity model outperformed the others, with a Sharpe ratio of 1.11. Despite the pandemic's impact on the portfolios, with drawdowns close to 30%, they recovered in 111 to 149 trading days. The portfolios outperformed the indices in cumulative returns, with similar annual volatilities of 20%. Preprocessing with UMAP allowed for finding clusters with higher discriminatory power, evaluated through internal cluster validation metrics, helping to reduce the problem's size during optimal portfolio allocation. Overall, this study highlights the potential of machine learning in portfolio optimization, providing a useful framework for investment practitioners.

Optimal energy portfolio allocation method for regulable hydropower plants considering the impact of new energy generation

With the growing penetration of new energy sources, the impact of new energy generation on the income of the energy portfolio is becoming more significant. This paper proposes a risk measurement method based on the Conditional Value-at-Risk (CVaR) approach to measure the income risk from new energy generation on the energy portfolio. The superiority is proved by comparing it with the method only considering spot price fluctuations. Then, we constructed an energy allocation model of the hydropower plant to maximize the combined income-risk utility. In solving the optimal allocation of the energy portfolio, the effects of inflow and water level on the risk aversion parameters of the hydropower plant are considered. The results and comparison show that the proposed risk measurement method increases the income of the energy portfolio by 12%, and the proposed risk aversion parameter’s adjustment strategy can increase the income of the energy portfolio by 5.8% and reduces the CVaR value by 14.5%. Moreover, the method that considers the income risk from new energy generation can provide favorable conditions for improved new energy accommodation and safe operation of the system.

Co-jumps and recursive preferences in portfolio choices

This paper investigates a multivariate, dynamic, continuous-time optimal consumption and portfolio allocation problem when the investor faces recursive utilities. The economy we are considering is described through both diffusion and discontinuities in the dynamics. We derive an approximated closed-form solution to optimal rules by exploiting standard dynamic programming techniques. Our findings are manifold. First, we obtain dynamic optimal weights, inversely proportional to volatility. Second, we show that both co-jumps frequency and intensity play a crucial role, as they considerably limit potential losses in the investors’ wealth. Third, we prove that jumps in precision reinforce the effect of jumps in price, further reducing optimal allocation. Finally, we highlight how co-jumps may influence investors’ choices regarding intertemporal consumption.

Extreme dependencies and spillovers between gold and stock markets: evidence from MENA countries

This study addresses whether gold exhibits the function of a hedge or safe haven as often referred to in academia. It contributes to the existing literature by (i) revisiting this question for the principal stock markets in the Middle East and North Africa (MENA) region and (ii) using the copula-quantile-on-quantile and conditional value at risk methods to detail the risks facing market participants provided with accurate information about various gold and stock market scenarios (i.e., bear, normal, bull). The results provide strong evidence of quantile dependence between gold and stock returns. Positive correlations are found between MENA gold and stock markets when both are bullish. Conversely, when stock returns are bearish, gold markets show negative correlations with MENA stock markets. The risk spillover from gold to stock markets intensified during the global financial and European crises. Given the risk spillover between gold and stock markets, investors in MENA markets should be careful when considering gold as a safe haven because its effectiveness as a hedge is not the same in all MENA stock markets. Investors and portfolio managers should rebalance their portfolio compositions under various gold and stock market conditions. Overall, such precise insights about the heterogeneous linkages and spillovers between gold and MENA stock returns provide potential input for developing effective hedging strategies and optimal portfolio allocations.