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Motivated by the ongoing debate on whether optimal or naive diversification should be preferred when distributing wealth across investment alternatives, this article investigates how the choice of covariance estimator affects mean‐variance portfolio selection. In an environment tailored to ideal tradability, we construct optimal commodity futures portfolios based on 12 promising covariance matrix estimators and compare their out‐of‐sample investment performance to a simple, equally weighted investment strategy by means of bootstrap testing. We find that neither the naive allocation approach nor the advanced covariance estimators can outperform the traditional sample covariance matrix. Because this empirical result is robust to modifications of the research design (including alternative investigation periods, data frequencies, estimation window sizes, holding period lengths, weight constraint specifications, and transaction cost levels), it opposes the recurrent suggestion of the equity‐oriented literature that the sample covariance matrix should not be used for the purpose of portfolio optimization. [ABSTRACT FROM AUTHOR]

This paper presents a systematic exploration of deep reinforcement learning (RL) for portfolio optimization and compares various agent architectures, such as the DQN, DDPG, PPO, and SAC. We evaluate these agents' performance across multiple market signals, including OHLC price data and technical indicators, while incorporating different rebalancing frequencies and historical window lengths. This study uses six major financial indices and a risk-free asset as the core instruments. Our results show that CNN-based feature extractors, particularly with longer lookback periods, significantly outperform MLP models, providing superior risk-adjusted returns. DQN and DDPG agents consistently surpass market benchmarks, such as the S&P 500, in annualized returns. However, continuous rebalancing leads to higher transaction costs and slippage, making periodic rebalancing a more efficient approach to managing risk. This research offers valuable insights into the adaptability of RL agents to dynamic market conditions, proposing a robust framework for future advancements in financial machine learning. [ABSTRACT FROM AUTHOR]

The metaverse, a virtual universe in which individuals and companies can interact, has become of paramount importance in China in recent years. While the metaverses are still in their infancy, there has been a growing interest and influx of capital into these universes. Shariah-compliant corporations have been gradually attracting significant funds from Islamic countries, given the growing strong engagement and trade. Similarly, metaverse corporations have been gaining significant sizes in the Chinese market and have become cornerstones of the investment landscape. For Islamic investors, questions arise whether these new metaverse corporations provide better returns given the massive hype and media attention they have attracted, and whether a portfolio investment into these corporations deliver the benefits promised. The article provides a comparative analysis between Chinese Islamic metaverse and Shariah-compliant enterprises, where all enterprises have either A or H-shares. The performance analysis over a timespan of 10 years demonstrates that the most optimal portfolios have similar expected returns while general Shariah-compliant enterprises provide significantly lower risks as compared to the metaverse ones. This implies that Shariah-compliant enterprises provide significantly more value for the risk they are attributed and are more sustainable.

We explore the potential of machine learning (ML) models applied in two financial risk management areas, i.e., credit risk management and financial risk hedging, through two practical use cases. This comparative study starts with the issue of explainability in complex ML models used in peer-to-peer lending for credit risk management. The first use case examines the limitations of using Kernel-SHAP with dependent features and evaluates different methods for estimating these dependencies using the Lending Club dataset. Our results suggest that accounting for feature dependence improves the understanding and robustness of prediction explanations. The second use case investigates a dynamic method for hedging foreign exchange risk in international equity portfolios, emphasizing the importance of accurate forecasts of currency returns. The analysis demonstrates that predictions yielded by ML models can significantly enhance the hedging of portfolios against currency risk. These findings highlight the transformative potential of advanced ML models in financial risk management, showcasing their capability to improve financial risk measurement and management. Further, our study outlines future research directions to advance this field. [ABSTRACT FROM AUTHOR]

This study analyzes the post-pandemic dynamics and investment potential of diverse clean energy equities, including solar, wind, nuclear, and other renewable assets, highlighting nuanced differences and investment opportunities within this critical sector. The analysis reveals that nuclear energy portfolios (NLR) exhibit notable resilience, sustaining growth amidst significant market volatility. Within the mean-variance portfolio optimization (MVO) framework, this study identifies strategic investments that balance risk and return, underscoring NLR's role as a stabilizing force and return enhancer, as evidenced by its predominant allocation in both Minimum Variance and Tangency Portfolios. Employing advanced stochastic modeling and simulation techniques, the research uses a uniform distribution to generate random portfolio weights, ensuring comprehensive and unbiased exploration of the feasible solution space, thereby enhancing the robustness of the portfolio optimization process. The findings also illustrate the diversification merits of integrating clean energy equities into broader portfolios comprising traditional stocks and bonds, with nuclear-focused equity significantly enhancing the efficient frontier. Results underscore the superiority of the nuclear energy exchange-traded fund (ETF) both as a standalone investment and as a crucial component of diversified portfolios, highlighting its contribution to investment performance and risk management. This approach offers insights for investors and policymakers navigating the intersection of finance, sustainability, and economic growth post-pandemic. [ABSTRACT FROM AUTHOR]

The metaverse, a virtual universe in which individuals and companies can interact, has become of paramount importance in China in recent years. While the metaverses are still in their infancy, there has been a growing interest and influx of capital into these universes. Shariah-compliant corporations have been gradually attracting significant funds from Islamic countries, given the growing strong engagement and trade. Similarly, metaverse corporations have been gaining significant sizes in the Chinese market and have become cornerstones of the investment landscape. For Islamic investors, questions arise whether these new metaverse corporations provide better returns given the massive hype and media attention they have attracted, and whether a portfolio investment into these corporations deliver the benefits promised. The article provides a comparative analysis between Chinese Islamic metaverse and Shariah-compliant enterprises, where all enterprises have either A or H-shares. The performance analysis over a timespan of 10 years demonstrates that the most optimal portfolios have similar expected returns while general Shariah-compliant enterprises provide significantly lower risks as compared to the metaverse ones. This implies that Shariah-compliant enterprises provide significantly more value for the risk they are attributed and are more sustainable. [ABSTRACT FROM AUTHOR]

In the more than 70 years since Markowitz introduced mean-variance optimization for portfolio construction, academics and practitioners have documented numerous weaknesses in the approach. In this paper, we propose two easily understandable improvements to mean-variance optimization in the context of multi-asset class portfolios, each of which provides less extreme and more stable portfolio weights. The first method sacrifices a small amount of expected optimality for reduced weight concentration, while the second method randomly resamples the available assets. Additionally, we develop a process for testing the performance of portfolio construction approaches on simulated data assuming variable degrees of forecasting skill. Finally, we show that the improved methods achieve better out-of-sample risk-adjusted returns than standard mean-variance optimization for realistic investor skill levels. [ABSTRACT FROM AUTHOR]

This study analyzes the post-pandemic dynamics and investment potential of diverse clean energy equities, including solar, wind, nuclear, and other renewable assets, highlighting nuanced differences and investment opportunities within this critical sector. The analysis reveals that nuclear energy portfolios (NLR) exhibit notable resilience, sustaining growth amidst significant market volatility. Within the mean-variance portfolio optimization (MVO) framework, this study identifies strategic investments that balance risk and return, underscoring NLR’s role as a stabilizing force and return enhancer, as evidenced by its predominant allocation in both Minimum Variance and Tangency Portfolios. Employing advanced stochastic modeling and simulation techniques, the research uses a uniform distribution to generate random portfolio weights, ensuring comprehensive and unbiased exploration of the feasible solution space, thereby enhancing the robustness of the portfolio optimization process. The findings also illustrate the diversification merits of integrating clean energy equities into broader portfolios comprising traditional stocks and bonds, with nuclear-focused equity significantly enhancing the efficient frontier. Results underscore the superiority of the nuclear energy exchange-traded fund (ETF) both as a standalone investment and as a crucial component of diversified portfolios, highlighting its contribution to investment performance and risk management. This approach offers insights for investors and policymakers navigating the intersection of finance, sustainability, and economic growth post-pandemic.

Objective - This study aims to contribute to the field of cryptocurrency portfolio management and rebalancing strategies by empirically investigating the impact of different allocation frequencies and threshold percentages on the risk-adjusted returns of cryptocurrency portfolios. Methodology/Technique - Utilizing a simulation of 10,000 cryptocurrency portfolios comprising seven assets, including Ethereum (ETH), Bitcoin (BTC), Tether (USDT), Litecoin (LTC), Solana (SOL), Dogecoin (DOGE), and Polygon (MATIC), this study examines and compares the effects of different allocation frequencies (daily, weekly, and monthly) in time-based rebalancing and various threshold percentages (5%, 10%, and 15%) in threshold-based strategies on the portfolios' risk-adjusted returns, using the Sharpe ratio. The performance of these strategies is also compared with a passive buy-and-hold strategy. Findings -The research reveals statistically significant differences in the risk-adjusted returns between the buy-andhold strategy and the daily rebalancing and threshold-based strategies with 5% and 10% threshold percentages. The daily rebalancing strategy demonstrates a higher Sharpe ratio, while lower threshold percentages lead to better riskadjusted returns. Novelty - These empirical findings, using a simulation of 10,000 cryptocurrency portfolios, provide valuable insights into optimizing cryptocurrency portfolio performance through rebalancing strategies. Additionally, they highlight the effectiveness of implementing rebalancing techniques in cryptocurrency portfolios, contributing to the understanding of rebalancing optimization in this domain. [ABSTRACT FROM AUTHOR]

We propose a novel model to achieve superior out-of-sample Sharpe ratios. While most research in asset allocation focuses on estimating the return vector and covariance matrix, the first component of our novel model instead forecasts the future tangency portfolio, and the second component then determines the optimal investment portfolio. First, to forecast the tangency portfolio, we forecast the efficient frontier by decomposing its functional form, a square root second-order polynomial, into three interpretable coefficients, which can then be used to calculate a forecasted tangency portfolio. These coefficients can be forecasted using vector autoregressions. Second, the model invests in the portfolio on the efficient frontier that is the minimum Euclidean distance from this forecasted tangency portfolio. A motivation for our approach is to address the limitation that the tangency portfolio only maximizes the Sharpe ratio when future returns and covariances are stationary, and can be directly estimated with historical data, which often does not hold in out-of-sample data. Our approach addresses this shortcoming in a novel way by forecasting the tangency portfolio, rather than estimating return and covariance. For empirical testing, we employ two sets of assets that span the market to demonstrate and validate the performance of this novel method. [ABSTRACT FROM AUTHOR]

This paper studies a mean-variance investment-reinsurance problem under a new stochastic volatility model, namely the 4/2 stochastic volatility model. Solving this problem requires a deep understanding of a class of parabolic partial differential equations (PPDEs). By the parametrix method and the integral transform method, we derive explicit solutions to the PPDEs in several special cases. Through the Lie symmetry analysis, we obtain a four-parameter family of the 4/2 stochastic volatility models such that the corresponding PPDEs have closed-form solutions. The efficient strategy and the efficient frontier of the mean-variance problem are represented by using the closed-form solutions to PPDEs. Numerical examples for the obtained efficient frontier are provided by Monto Carlo method. [ABSTRACT FROM AUTHOR]

This study is intended as a note and provides an extension to a much-used and established test for portfolio efficiency, the Gibbons, Ross, and Shanken GRS-Wald test. Tests devised to measure portfolio efficiency are crucial to the theoretical issues related to CAPM (Capital Asset Pricing Model) testing and have applications for the fund manager who seeks to rank portfolio performance. This study looks at the GRS-Wald test for portfolio efficiency and extends it to make it visually more interpretive without any loss of generality in its structure. The geometrically recast statistic draws upon the trigonometric properties of a portfolio in the mean-variance space and a mathematical proof of the equivalence of the two statistics is provided. The GRS-Wald test is a widely used statistic in studies addressing the issue of portfolio efficiency and CAPM deviations. A simulation demonstrates the use of the recast GRS-Wald test in testing for the mean-variance efficiency of a test portfolio. The study also provides a table of the GRS-Wald test, based on a range of mean-variance locations (cosine of portfolio angles) at which the test portfolio and the efficient market portfolio can be placed. [ABSTRACT FROM AUTHOR]

Research Question: This paper investigates the performances of six portfolios constructed using robust optimization methods in the Serbian stock market. Motivation: Motivated by the lack of research that analyses the allocation strategies based on robust optimization in the other non-US markets, this paper analyses the ability of these strategies to produce positive performance in the Serbian financial market. Idea: This paper aims to check whether robust strategies can provide positive risk-adjusted performance compared to simple strategies. Data: The analysis was performed on daily data from 2017 to 2020. Tools: We used monthly portfolio rebalancing with an estimation period of 24 months, applying budget and no-short selling constraints in portfolio construction. As a benchmark, we used two simple strategies, the strategy of market index replication and the equal weighting strategy (1/N). Consequently, the performance of the portfolios is evaluated once a month and calculated for the entire investment period. Findings: Empirical results suggest that robust optimization methods improve portfolio performance on a risk-adjusted basis. The increase in performance is affected by an increase in turnover, so the stability of weights in the portfolio depends on the compliance of the model characteristics with the conditions prevailing in the market. Contribution: To the best of our knowledge, this is the first article that analyses the performance of robust optimization portfolios for the Serbian stock markets. Analysing the performance of robust optimization strategies and comparing them to two simple strategies, this paper contributes to the existing literature by checking their possibility of obtaining a positive performance in less developed markets. Additionally, all information presented in this paper could help investors optimize their risk allocation and profitability.

In quantitative finance, prediction models are traditionally optimized independently from their use in the asset allocation decision-making process. We address this limitation and present a stochastic optimization framework for integrating regression prediction models in a mean-variance optimization (MVO) setting. Closed-form analytical solutions are provided for the unconstrained and equality constrained MVO case. For the general inequality constrained case, we make use of recent advances in neural-network architecture for efficient optimization of batch quadratic programs. To our knowledge, this is the first rigorous study of integrating prediction in a mean-variance portfolio optimization setting. We present several simulations, using both synthetic and global futures data, and demonstrate the benefits of the integrated approach in comparison to the decoupled alternative. [ABSTRACT FROM AUTHOR]

We study the economic value of market timing in foreign exchange (FX) markets, that is, using information about the conditional Sharpe ratio to adjust the notional value of a conditionally mean–variance efficient currency portfolio. Our strategy trades more (less) aggressively when the conditional risk-return trade-off is more (less) favorable. This leads to a significant improvement in the out-of-sample unconditional Sharpe ratio, skewness, and maximum drawdown per 1% expected excess return. The strategy's market timing predicts returns, volatility, and skewness in FX markets. Popular currency pricing factors do not explain the strategy's high average excess returns. Our findings suggest that it is costly to impose leverage or risk (i.e., conditional volatility) limits or other inferior market timing policies when constructing currency trading strategies. [ABSTRACT FROM AUTHOR]

Research Question: This paper investigates the performances of six portfolios constructed using robust optimization methods in the Serbian stock market. Motivation: Motivated by the lack of research that analyses the allocation strategies based on robust optimization in the other non-US markets, this paper analyses the ability of these strategies to produce positive performance in the Serbian financial market. Idea: This paper aims to check whether robust strategies can provide positive risk-adjusted performance compared to simple strategies. Data: The analysis was performed on daily data from 2017 to 2020. Tools: We used monthly portfolio rebalancing with an estimation period of 24 months, applying budget and no-short selling constraints in portfolio construction. As a benchmark, we used two simple strategies, the strategy of market index replication and the equal weighting strategy (1/N). Consequently, the performance of the portfolios is evaluated once a month and calculated for the entire investment period. Findings: Empirical results suggest that robust optimization methods improve portfolio performance on a risk-adjusted basis. The increase in performance is affected by an increase in turnover, so the stability of weights in the portfolio depends on the compliance of the model characteristics with the conditions prevailing in the market. Contribution: To the best of our knowledge, this is the first article that analyses the performance of robust optimization portfolios for the Serbian stock markets. Analysing the performance of robust optimization strategies and comparing them to two simple strategies, this paper contributes to the existing literature by checking their possibility of obtaining a positive performance in less developed markets. Additionally, all information presented in this paper could help investors optimize their risk allocation and profitability. [ABSTRACT FROM AUTHOR]

We propose a novel model to achieve superior out-of-sample Sharpe ratios. While most research in asset allocation focuses on estimating the return vector and covariance matrix, the first component of our novel model instead forecasts the future tangency portfolio, and the second component then determines the optimal investment portfolio. First, to forecast the tangency portfolio, we forecast the efficient frontier by decomposing its functional form, a square root second-order polynomial, into three interpretable coefficients, which can then be used to calculate a forecasted tangency portfolio. These coefficients can be forecasted using vector autoregressions. Second, the model invests in the portfolio on the efficient frontier that is the minimum Euclidean distance from this forecasted tangency portfolio. A motivation for our approach is to address the limitation that the tangency portfolio only maximizes the Sharpe ratio when future returns and covariances are stationary, and can be directly estimated with historical data, which often does not hold in out-of-sample data. Our approach addresses this shortcoming in a novel way by forecasting the tangency portfolio, rather than estimating return and covariance. For empirical testing, we employ two sets of assets that span the market to demonstrate and validate the performance of this novel method.

We propose a dynamic method to hedge foreign exchange risk of international equity portfolios. The method is based on the currency return forecasts derived from a set of alternative machine learning models, built on the main factor components of the currency return time series. To illustrate our method, we take the perspective of a US portfolio manager investing in a global equity portfolio of developing and developed economies. The analysis of several model performance indicators, back-tested on data from 27 October 2008 to 30 December 2022, allows to conclude that accurate predictions of global currency factor returns, such as those obtained with non linear machine learning models, can improve currency risk hedging of global equity portfolios. • We propose a dynamic method to hedge foreign exchange risk of international equity portfolios. • The method is based on machine learning models built on the main factor components of currency returns. • Hedging strategy outperforms leading alternative approaches on a large set of performance metrics. • Accurate ML predictions of global factor returns improve linear currency risk hedging. [ABSTRACT FROM AUTHOR]

Provisioning multiple statistical QoS for real-time scalable video streaming over wireless fading channels is addressed in this article. A traffic rate adaptation (TRA) strategy is presented to minimize the video quality variation while keeping satisfactory average video quality by selectively delivering encoding layers of video streaming depending on dynamic channel conditions. The QoS-guaranteed TRA policy optimization is formulated based on Markov decision processes. A vector-composed policy gradient algorithm that combines gradient estimate and stochastic approximation is developed to solve the constrained mean-variance optimization problem online. It is attractive for online control and optimization with high computational efficiency and adaptability to unknown environments. Simulation results show the effectiveness of the presented strategy. [ABSTRACT FROM AUTHOR]

Planning the long-term expansion of a power sector requires anticipating future technologies, fuel costs, and new carbon policies. Many state-of-the-art models rely on exogenous data for cost and performance projections where the inherent uncertainty is either ignored or addressed only with sensitivity analysis and scenarios. For the few models accounting for uncertainty, the transition from the research field to policy making has not occurred because of important practical barriers in the latter field: higher reliance on time-tested models, impossibility to constantly adopt new models, run-time issues. To streamline this process, we present a new modular two-step methodology, based on mean-variance optimization, to help policy makers adjust for risks on costs their findings from current cost-minimizing tools, while sparing them the hurdles of adopting a new model. To illustrate this, we refine the SWITCH-China least-cost power expansion pathway by minimizing its cost uncertainty.

Planning the long-term expansion of a power sector requires anticipating future technologies, fuel costs, and new carbon policies. Many state-of-the-art models rely on exogenous data for cost and performance projections where the inherent uncertainty is either ignored or addressed only with sensitivity analysis and scenarios. For the few models accounting for uncertainty, the transition from the research field to policy making has not occurred because of important practical barriers in the latter field: higher reliance on time-tested models, impossibility to constantly adopt new models, run-time issues. To streamline this process, we present a new modular two-step methodology, based on mean-variance optimization, to help policy makers adjust for risks on costs their findings from current cost-minimizing tools, while sparing them the hurdles of adopting a new model. To illustrate this, we refine the SWITCH-China least-cost power expansion pathway by minimizing its cost uncertainty.

We propose an optimal combining strategy to mitigate estimation risk for the popular mean-variance portfolio choice problem in the case without a risk-free asset. We find that our strategy performs well in general, and it can be applied to known estimated rules and the resulting new rules outperform the original ones. We further obtain the exact distribution of the out-of-sample returns and explicit expressions of the expected out-of-sample utilities of the combining strategy, providing not only a fast and accurate way of evaluating the performance, but also analytical insights into the portfolio construction. This paper was accepted by Tyler Shumway, finance. [ABSTRACT FROM AUTHOR]

Motivated by significant changes in the dynamics of crude oil markets, we revisit the question of whether crude oil futures are a valuable addition to typical stock and bond portfolios. To answer this question, we use a mean‐variance framework with established heuristic and classic weighting schemes and, in addition, derive optimal asset weights based on forward‐looking estimates of expected returns (via univariate forecast combinations and multivariate LASSO regressions) and covariances (via DCC‐GARCH and GO‐GARCH models). We find that crude oil futures tend to be of limited benefit for investors. In recent data, their optimal weights are very low and they typically do not enhance out‐of‐sample portfolio performance. These findings hold in a variety of robustness checks with respect to different datasets, estimation windows, regression specifications, GARCH models, and transaction costs. [ABSTRACT FROM AUTHOR]

We study production planning in a multi‐product setting, in which demand for each product depends on multiple financial assets (such as commodities, market indices, etc). In addition to the production quantity decision at the beginning of the planning horizon, there is also a real‐time hedging decision throughout the horizon; and we optimize both decisions jointly. With a mean–variance problem formulation, we first derive the optimal hedging strategy, given the production quantities. This leads to an explicit objective function with which bounds on optimal production quantities are identified. Thus, optimization of the production policies can be readily solved numerically as a static minimization problem. This way, we are able to give a complete characterization of the mean–variance efficient frontier, and quantify the contribution of the hedging strategy by the variance reduction it achieves. Furthermore, the model and results are extended to allow dynamic production control that tracks the demand rates. [ABSTRACT FROM AUTHOR]

The aim of this study is to estimate the optimal portfolio weights for the mean-variance efficient optimal portfolio using linear programming technique for single and multiperiod using rebalance technique, then compare the performances and risk metrics with the equal-weighted portfolio, we used in performance metrics Sharpe and Treynor ratios, CAPM Alpha and Beta, and for the risk metrics VaR and CVaR. We found that the MV optimal portfolio performed better and less risky than the equal-weighted portfolio for both single and multi-periods. [ABSTRACT FROM AUTHOR]

This paper empirically investigates the out‐of‐sample performance of the 1/N naive rule and the Markowitz mean–variance strategies in the largest emerging market (i.e., China's A‐shares market) and provides three new findings. First, we show that some mean–variance optimization strategies can outperform the 1/N rule in China's A‐shares market, while minimum‐variance strategies cannot. Using certainty equivalent return (CER) instead of Sharpe ratios does not change our results qualitatively. Second, we find an obvious advantage of mean–variance optimization when N is large. Third, when transaction costs are taken into account, the profitability of the unconstrained mean–variance optimizations almost vanishes, while the profitability of the mean–variance optimizations with the short‐sale constraint remains. Our results are robust to using a shorter estimation window of about 60 months. These results provide support for the use of optimal diversification strategies in emerging markets. [ABSTRACT FROM AUTHOR]

Robust optimization takes into account the uncertainty in expected returns to address the shortcomings of portfolio mean-variance optimization, namely the sensitivity of the optimal portfolio to inputs. We investigate the mechanisms by which robust optimization achieves its goal and give practical guidance when it comes to the choice of uncertainty in form and level. We explain why the quadratic uncertainty set should be preferred to box uncertainty based on the literature review, we show that a diagonal uncertainty matrix with only variances should be used, and that the level of uncertainty can be chosen as a function of the asset Sharpe ratios. Finally, we use practical examples to show that, with the proposed parametrization, robust optimization does overcome the weaknesses of mean-variance optimization and can be applied in real investment problems such as the management of multi-asset portfolios or in robo-advising. [ABSTRACT FROM AUTHOR]

The authors study the performance of mean-variance optimized (MVO) equity portfolios for retail investors in various markets in the U.S. and around the world. Actively managed equity mutual funds have relatively high fees and tend to underperform their benchmark. Index funds such as exchange traded funds still charge appreciable fees, and only deliver the performance of the benchmark. The authors find that MVO portfolios are relatively easy to manage by a retail investor, and that they tend to outperform their benchmark or, at worst, equal its performance, even after adjusting for risk. Moreover, they show that the performance of these funds is not particularly sensitive to the frequency at which they are rebalanced so that, in the limit, an investor might have to rebalance his/her portfolio only once a year. This last finding translates into very low trading costs, even for retail investors. Thus, the authors conclude that MVOs offer an easy, cheap alternative to invest in the world’s equity markets.

The best constant re-balanced portfolio represents the standard estimator for the log-optimal portfolio. It is shown that a quadratic approximation of log-returns works very well on a daily basis and a mean-variance estimator is proposed as an alternative to the best constant re-balanced portfolio. It can easily be computed and the numerical algorithm is very fast even if the number of dimensions is high. Some small-sample and the basic large-sample properties of the estimators are derived. The asymptotic results can be used for constructing hypothesis tests and for computing confidence regions. For this purpose, one should apply a finite-sample correction, which substantially improves the large-sample approximation. However, it is shown that the impact of estimation errors concerning the expected asset returns is serious. The given results confirm a general rule, which has become folklore during the last decades, namely that portfolio optimization typically fails on estimating expected asset returns. [ABSTRACT FROM AUTHOR]

In this paper, we derive a time consistent investment strategy for an investor who can invest not only in a bond and stock but in a derivative as well. In order to capture typical features shown by stocks, the stock and by extension the derivative depends on stochastic volatility. We assume that the investor is interested in maximizing a mean–variance utility function. Since the problem is time-inconsistent, we formulate the problem in game theoretic way and seek a subgame Nash equilibrium as the strategy. By solving an extended HJB equation system, we derive explicit time-consistent strategy and the corresponding efficient frontier. In order to show efficiency of the derivative strategy, we compare it with a strategy for the case of a market without a derivative. Our results show that efficient frontier for an investor with a derivative is higher than without derivative. [ABSTRACT FROM AUTHOR]

We study a pricing barrier control problem in a regime-switching regulated market. In doing so, we analyze a class of one-dimensional reflected regime-switching diffusion processes. Such diffusion models arise as the key approximating processes in a regulated financial market system with the presence of regime changes. Our main goal is to determine optimal pricing barriers as solutions of long-run average mean-variance optimization problems. More precisely, the optimal barrier, if exists, will be to maximize the long-run average expected return (i.e. steady-state mean) subject to a selected level of long-run average risk (i.e. steady-state variance). [ABSTRACT FROM AUTHOR]

Covered calls are traditionally formed as an overlay on an existing portfolio. Our analysis suggests that covered calls formed in two steps by first optimizing underlying equity positions and then selecting call overwriting weights are not risk-return optimal in general. We introduce an optimization framework which simultaneously selects underlying asset positions and call options to sell to form risk-return optimal covered call portfolios. Call option market prices form a critical part of the expected return and risk expressions. Variance of the return, semivariance of the return, and conditional value-at-risk are used as risk measures. The model was first tested by forming covered call portfolios composed of three indices and then by forming portfolios using 92 U.S. equities. We find that selling call options not only reduces risk, but when selected optimally can also benefit the expected return. [ABSTRACT FROM AUTHOR]

The allocation of scarce economic resources so as to maximize societal good is at the very core of human economic development. The key contribution of Markowitz [1] was to view this age-old activity in a scientifically rigorous manner, and bring three key elements to the forefront, namely, risk, return and correlations. Since the publication of Markowitz' seminal paper, investment professionals have expensed significant resources in identifying, understanding, and monetizing new sources of uncorrelated returns. This paper moves forward that narrative by focusing on Emerging Markets (EM) and demonstrating the effectiveness of earnings acceleration factors in building significantly better mean-variance optimized portfolios.

Mean-variance portfolio analysis leads to determine tourist demands mix but it does not provide any information regarding the risk attitude of the decision maker. In this article, we propose to fill this research gap by proposing a methodological approach which combines the portfolio theory with the utility function technique. By doing so, our approach incorporates the risk attitude of the decision maker through different values of risk tolerance. We apply this methodology on destination management framework. An illustration of its strengths is suggested by using the case study of European tourists visiting France between 2008 and 2013. Our article shows that the optimal origins mix depends on the level of risk aversion of the destination manager. Accordingly, the implications of our methodological approach are highlighted. [ABSTRACT FROM AUTHOR]

In this paper, we study a mean–variance optimization problem in an infinite horizon discrete time discounted Markov decision process (MDP). The objective is to minimize the variance of system rewards with the constraint of mean performance. Different from most of works in the literature which require the mean performance already achieve optimum, we can let the discounted performance equal any constant. The difficulty of this problem is caused by the quadratic form of the variance function which makes the variance minimization problem not a standard MDP. By proving the decomposable structure of the feasible policy space, we transform this constrained variance minimization problem to an equivalent unconstrained MDP under a new discounted criterion and a new reward function. The difference of the variances of Markov chains under any two feasible policies is quantified by a difference formula. Based on the variance difference formula, a policy iteration algorithm is developed to find the optimal policy. We also prove the optimality of deterministic policy over the randomized policy generated in the mean-constrained policy space. Numerical experiments demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

• The Gerber statistic is a recently proposed co-movement measure. • It is subject to conditioning bias and differs from full-sample measures. • Statistical behaviour of Gerber statistic is explored across three proposed forms. • The Gerber statistic is best viewed as an imperfect conditional dependence metric. The Gerber Statistic is a recently proposed co-movement measure. The measure is a conditional statistic and due to conditioning bias, will naturally differ from full-sample measures. This contribution makes use of an intuitive two-asset simulation framework to better elucidate the statistical behaviour of the Gerber Statistic, across its three proposed forms. Using graphical correlation profiles, we explore the measure's behaviour across return conditioning threshold, sample size and market distribution, detailing its mechanisms of performance but also demonstrating several caveats around its understanding and use. We conclude that while interesting, the Gerber Statistic is best viewed as an imperfect conditional dependence metric. [ABSTRACT FROM AUTHOR]

Regime-based asset allocation has been shown to add value over rebalancing to static weights and, in particular, reduce potential drawdowns by reacting to changes in market conditions. The predominant approach in previous studies has been to specify in advance a static decision rule for changing the allocation based on the state of financial markets or the economy. In this article, model predictive control (MPC) is used to dynamically optimize a portfolio based on forecasts of the mean and variance of financial returns from a hidden Markov model with time-varying parameters. There are computational advantages to using MPC when estimates of future returns are updated every time a new observation becomes available, since the optimal control actions are reconsidered anyway. MPC outperforms a static decision rule for changing the allocation and realizes both a higher return and a significantly lower risk than a buy-and-hold investment in various major stock market indices. This is after accounting for transaction costs, with a one-day delay in the implementation of allocation changes, and with zero-interest cash as the only alternative to the stock indices. Imposing a trading penalty that reduces the number of trades is found to increase the robustness of the approach. [ABSTRACT FROM AUTHOR]