Browsing by Subject "Portfolio optimization"

Now showing 1 - 6 of 6

Open Access
Dynamic mean-variance problem: recovering time-consistency
(2021-08) Düzoylum, Seyit Emre
As the foundation of modern portfolio theory, Markowitz’s mean-variance port-folio optimization problem is one of the fundamental problems of ﬁnancial math-ematics. The dynamic version of this problem in which a positive linear com-bination of the mean and variance objectives is minimized is known to be time-inconsistent, hence the classical dynamic programming approach is not applicable. Following the dynamic utility approach in the literature, we consider a less re-strictive notion of time-consistency, where the weights of the mean and variance are allowed to change over time. Precisely speaking, rather than considering a ﬁxed weight vector throughout the investment period, we consider an adapted weight process. Initially, we start by extending the well-known equivalence be-tween the dynamic mean-variance and the dynamic mean-second moment prob-lems in a general setting. Thereby, we utilize this equivalence to give a complete characterization of a time-consistent weight process, that is, a weight process which recovers the time-consistency of the mean-variance problem according to our deﬁnition. We formulate the mean-second moment problem as a biobjective optimization problem and develop a set-valued dynamic programming principle for the biobjective setup. Finally, we retrieve back to the dynamic mean-variance problem under the equivalence results that we establish and propose a backward-forward dynamic programming scheme by the methods of vector optimization. Consequently, we compute both the associated time-consistent weight process and the optimal solutions of the dynamic mean-variance problem.
Open Access
Graph neural networks for deep portfolio optimization
(Springer, 2023-07-22) Ekmekcioğlu, Ömer; Pınar, Mustafa Çelebi
There is extensive literature dating back to the Markowitz model on portfolio optimization. Recently, with the introduction of deep models in finance, there has been a shift in the trend of portfolio optimization toward data-driven models, departing from the traditional model-based approaches. However, deep portfolio models often encounter issues due to the non-stationary nature of data, giving unstable results. To address this issue, we advocate the utilization of graph neural networks to incorporate graphical knowledge and enhance model stability, thereby improving results in comparison with state-of-the-art recurrent architectures. Moreover, we conduct an analysis of the algorithmic risk-return trade-off for deep portfolio optimization models, offering insights into risk for fully data-driven models. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Open Access
Green cryptocurrencies and portfolio diversification in the era of greener paths
(Elsevier Ltd, 2024-03) Ali, Fahad; Khurram, M. U.; Sensoy, Ahmet; Vo, X. V.
The shift towards cleaner fuels from hydrocarbons has influenced nearly all market types and asset classes, and cryptocurrencies are no exception. The complex mechanism of blockchain and mining consumes high levels of electricity and surges environmental footprints in electronic waste generation. Existing studies that examine green and sustainable investments are limited to sustainable equities or green bonds; therefore, this study opens up a new research direction by considering green (energy-efficient) cryptocurrencies. First, this study develops a four-step screening process to systematically select cryptocurrencies that are greener than others. A comprehensive set of green and non-green assets and a battery of empirical tests are then employed to examine the diversification benefits of selected green cryptocurrencies against several well-diversified equity portfolios at the global, regional, and country levels. The diversification benefits of green cryptocurrencies are compared with non-green cryptocurrencies using (i) the four-moment modified value at risk and conditional value at risk, (ii) four different portfolio optimization strategies, and (iii) dynamic correlation-based hedge and safe-haven regression analyses. The results show that green cryptocurrencies provide diversification benefits that are at least comparable to, and in some cases, superior to, non-green (energy-intensive) cryptocurrencies. Cardano and Tezos are identified as green cryptocurrencies offering the most diversification benefits to investors, followed by EOS, Steller, and IOTA. This study provides valuable insights to investors and policymakers, specifically those concerned with achieving sustainability and ESG-compliance (environmental-social-governance) goals and seeking green assets to hedge and diversify various traditional investments.
Open Access
Identifying diversifiers, hedges, and safe havens among Asia Pacific equity markets during COVID-19: New results for ongoing portfolio allocation
(Elsevier BV, 2023-02-22) Ali, F.; Şensoy, Ahmet; Goodell, J. W.
We identify diversification benefits among Asian equity markets in the COVID-19 era. We find that such benefits among Asia-Pacific markets changed considerably during the pandemic, and most changes were persistent. In most cases, any of the sample equities had at least one safe-haven protection. The exceptions are Pakistan, Thailand, and Singapore, where diversification benefits are limited and vary across subperiods. The Hong Kong equity market provides safe-haven protection to most markets during periods of extreme negative returns. Further, we find that greater (lower) weightings on the Bangladeshi, Taiwanese, and Malaysian (Thai) markets provide important diversification in terms of maximizing Sharpe ratio and minimizing variance during the pandemic.
Open Access
Portfolio optimization with two coherent risk measures
(Springer, 2020) Aktürk, T. D.; Ararat, Çağın
We provide analytical results for a static portfolio optimization problem with two coherent risk measures. The use of two risk measures is motivated by joint decision-making for portfolio selection where the risk perception of the portfolio manager is of primary concern, hence, it appears in the objective function, and the risk perception of an external authority needs to be taken into account as well, which appears in the form of a risk constraint. The problem covers the risk minimization problem with an expected return constraint and the expected return maximization problem with a risk constraint, as special cases. For the general case of an arbitrary joint distribution for the asset returns, under certain conditions, we characterize the optimal portfolio as the optimal Lagrange multiplier associated to an equality-constrained dual problem. Then, we consider the special case of Gaussian returns for which it is possible to identify all cases where an optimal solution exists and to give an explicit formula for the optimal portfolio whenever it exists.
Open Access
Portfolio optimization with two quasiconvex risk measures
(Scientific and Technical Research Council of Turkey - TUBITAK,Turkiye Bilimsel ve Teknik Arastirma Kurumu, 2021-03-26) Ararat, Çağın
We study a static portfolio optimization problem with two risk measures: a principle risk measure in the objective function and a secondary risk measure whose value is controlled in the constraints. This problem is of interest when it is necessary to consider the risk preferences of two parties, such as a portfolio manager and a regulator, at the same time. A special case of this problem where the risk measures are assumed to be coherent (positively homogeneous) is studied recently in a joint work of the author. The present paper extends the analysis to a more general setting by assuming that the two risk measures are only quasiconvex. First, we study the case where the principal risk measure is convex. We introduce a dual problem, show that there is zero duality gap between the portfolio optimization problem and the dual problem, and finally identify a condition under which the Lagrange multiplier associated to the dual problem at optimality gives an optimal portfolio. Next, we study the general case without the convexity assumption and show that an approximately optimal solution with prescribed optimality gap can be found by using the well-known bisection algorithm combined with a duality result that we prove.