Relevant bibliographies by topics / Optimal portfolio model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Optimal portfolio model'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Optimal portfolio model"

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Nur Safitri, Indah Nur, Sudradjat Sudradjat, and Eman Lesmana. "STOCK PORTFOLIO ANALYSIS USING MARKOWITZ MODEL." International Journal of Quantitative Research and Modeling 1, no. 1 (2020): 47–58. http://dx.doi.org/10.46336/ijqrm.v1i1.6.

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A common problem that often occurs in investment is the selection of the optimal portfolio according to the wishes of investors. This thesis ueds the Markowitz Model as a basis to formed a model to choose the optimal portfolio that provided the lowest risk. Efforts to minimize risk were carried out by conducting a diversification strategy. After the selection of several companies with the criteria of capitalization value and DER (Debt Equity Ratio), a combination of stocks is formed to form a portfolio. The formed portfolio was then analyzed to determine the optimal proportion of each stock. Using the Markowitz model, which is then solved by Non Linear Programming, an optimal portfolio is obtained with the proportion of each stock minimizing risk. In general, the results of this analysis indicate that portfolios with more stocks will produce lower risks compared to portfolios with fewer stocks, thus providing optimal diversification solutions, namely portfolios with members of five stocks with optimal risk of 0.886%.
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Erwin, Dyah Astawinetu, Istiono, Hari Prastiwi Estik, and Santoso Rudy. "Optimal Portfolio Analysis on Stocks Listed in Lq45." Journal of Economics, Finance And Management Studies 07, no. 06 (2024): 3366–72. https://doi.org/10.5281/zenodo.11634966.

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The purpose of this study is to determine the optimal portfolio of stocks that are listed in the LQ-45 period (January 2023 – January 2024) and compare the return and risk in stocks that are included in the LQ-45 but not included in the optimal portfolio. The method used is the Single Index Method, which uses the ERB (Excess Return to Beta) assessment reference. The results showed that out of 45 stocks there were 10 stocks that had large ERB, which were included in the optimal portfolio were GGRM, BBTN, KLBF, EXCL, ICBP, MAPI, UNVR, CPIN, INDF, and TBIG, which provided a return of 6.61% per year and a risk of 0.08% per year. Meanwhile, the remaining thirty-five stocks were made up as well as 10 other portfolios. Each of these portfolios consists of 10 randomly selected stocks. These ten portfolios yield higher returns than optimal portfolios. However, they also have a higher risk. The results of the comparison of the coefficient of variation between the optimal portfolio and the other 10 portfolios show that the optimal portfolio is the best portfolio
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Nurhakim, Eko Sanjaya, Abdul Mukti Soma, and Irni Yunita. "Constructing Optimal Portfolios Using the Single Index Model and Markowitz Model: A Study on Cryptocurrencies." Journal of Accounting and Strategic Finance 7, no. 2 (2024): 200–218. https://doi.org/10.33005/jasf.v7i2.485.

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This study analyzes the formation of optimal portfolios on cryptocurrency assets using the single index model and the Harry Markowitz model. This study covers 79 cryptocurrencies with the largest market capitalization during the period June 2023–June 2024. We calculate the optimal portfolio using the single index model and Markowitz, and evaluate its performance using the Sharpe Ratio. The results show that the Harry Markowitz model produces better portfolio performance compared to the single index model. The Markowitz portfolio produces a positive Sharpe ratio (1.8496), a portfolio return rate of 7.678%, and lower risk (0.0415). Conversely, the single index model portfolio shows a negative Sharpe ratio (-2.0971), indicating lower returns than risk-free assets. In addition, the Markowitz model offers more efficient diversification than the single index model. However, in general, both the Single Index Model and the Markowitz Model have a significant effect on the formation of optimal portfolios, with the Sharpe Index proving to be a significant mediator in the relationship between the two models and the optimal portfolio. The R-squared value shows that the SIM variables, Markowitz Model, and Sharpe Index explain 48.4% of the variation in the optimal portfolio. This study recommends the use of the Harry Markowitz model for cryptocurrency investment because it can provide higher returns with more controlled risks. This study provides important insights for investors on the strategy of diversifying cryptocurrency asset portfolios.
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Levchenko, Valentyna, and Myroslav Ostapenko. "Formation of the optimal portfolio of insurer’s services of the voluntary types of insurance." Insurance Markets and Companies 7, no. 1 (2016): 45–51. http://dx.doi.org/10.21511/imc.7(1).2016.05.

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The article studies the possibility of using optimization modelling to form the optimal structure of insurance services’ portfolio of insurance companies. Based on the data of net insurance payments and profitability of the voluntary types of insurance in 2005-2015, the authors conducted their analysis according to the possibility to be included in the general insurance portfolio of the insurance company. The optimization model is based on the approach developed by G. Markowitz. The formation of insurance services portfolio is conducted by solving the optimization problem to maximize the portfolios’ profitability or to minimize the portfolio’s risks. The obtained results can be used in making strategic decisions by the management regarding the development of insurance companies. Keywords: insurance company, insurance service, insurance portfolio, portfolio optimization
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Yang, Hyunjun, Hyeonjun Park, and Kyungjae Lee. "A Selective Portfolio Management Algorithm with Off-Policy Reinforcement Learning Using Dirichlet Distribution." Axioms 11, no. 12 (2022): 664. http://dx.doi.org/10.3390/axioms11120664.

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Existing methods in portfolio management deterministically produce an optimal portfolio. However, according to modern portfolio theory, there exists a trade-off between a portfolio’s expected returns and risks. Therefore, the optimal portfolio does not exist definitively, but several exist, and using only one deterministic portfolio is disadvantageous for risk management. We proposed Dirichlet Distribution Trader (DDT), an algorithm that calculates multiple optimal portfolios by taking Dirichlet Distribution as a policy. The DDT algorithm makes several optimal portfolios according to risk levels. In addition, by obtaining the pi value from the distribution and applying importance sampling to off-policy learning, the sample is used efficiently. Furthermore, the architecture of our model is scalable because the feed-forward of information between portfolio stocks occurs independently. This means that even if untrained stocks are added to the portfolio, the optimal weight can be adjusted. We also conducted three experiments. In the scalability experiment, it was shown that the DDT extended model, which is trained with only three stocks, had little difference in performance from the DDT model that learned all the stocks in the portfolio. In an experiment comparing the off-policy algorithm and the on-policy algorithm, it was shown that the off-policy algorithm had good performance regardless of the stock price trend. In an experiment comparing investment results according to risk level, it was shown that a higher return or a better Sharpe ratio could be obtained through risk control.
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Gubu, La, and Muhamad Rashif Hilmi. "Pembentukan Portofolio Optimal Saham Dengan Menggunakan Model Portofolio Mean-Variance-Skewness-Kurtosis." Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika 11, no. 2 (2024): 123–33. http://dx.doi.org/10.31316/jderivat.v10i2.6218.

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This paper presents the development of Markowitz's classic Mean-Variance (MV) portfolio model, namely the Mean-Variance-Skewness-Kurtosis (MVSK) portfolio model. The MVSK portfolio model aims to overcome the fact that most stock returns in the capital market do not follow a normal distribution, and there are skewness and excessive kurtosis. The solution of the MVSK portfolio model is determined using the Newton-Raphson method. To see the advantages of the MVSK model, an empirical study was carried out on a portfolio construction using the four best stocks on the Indonesian Stock Exchange, which are included in the LQ45 index group for February-July 2023. The empirical study shows that for risk aversion the performance of portfolios formed using the MVSK model outperforms portfolios formed using the classical MV model, while for risk aversion the performance of portfolios formed using the classic MV model outperforms portfolios formed using the MVSK model. In addition, it was also found that for risk aversion , the weight and performance of the portfolio formed using the MVSK model were close to the weight and performance of the portfolio formed using the classic MV model. Keywods: portofolio, return, risk, portfolio performance, MVSK.
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Sriyono, Sriyono, Detak Prapanca, and Adelia Oktaviani. "Pengambilan Keputusan Investasi Portofolio : Pendekatan Model Indeks Tunggal Saham." Benefit: Jurnal Manajemen dan Bisnis 6, no. 2 (2021): 72–96. http://dx.doi.org/10.23917/benefit.v6i2.14489.

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Abstract. This study aims to determine the composition of the optimal portfolio formation using the Single Index method on LQ-45 shares in the Indonesia Stock Exchange period 2016 - 2018. This research was conducted on the basis of the increasing number of investors who chose to invest their funds in shares, where this is indicated from the increasing positive sentiment on stock investment compared to other investments. Portfolio formation using the Single Index model is one model that can be used to form optimal portfolios, because with this model portfolios are easily formed to fit the desired investment characteristics and objectives to be achieved. The Single Index method is a method that formulates the existence of elements of return and risk in an investment, where the risk element can be minimized through diversification and combining various investment instruments into a portfolio. By using the Single Index method, investors can take advantage of all available information as the basis for maximizing portfolio formation. The sample selection technique of this study used a purposive sampling method and 19 LQ-45 Index stocks were obtained which were used as the research sample. Based on the results of research to determine the optimal portfolio of shares using the Single Index method shows that the LQ-45 Index Shares that form the optimal portfolio are INCO, BBTN, ICBP, INTP, BMRI, BBNI, BBCA, HMSP, INDF shares , GGRM and TLKM. And this study produced 55 portfolio combinations in which there is one efficient portfolio, 26 portfolios with the same funding weight (50%: 50%). Investors choose an efficient portfolio in accordance with the preferences of the level of profit and risk they bearKeywords - Single Index Model, Optimal Portfolio, Expected Return, LQ-45
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Vanti, Eka Nur, and Epha Diana Supandi. "Pembentukan Portofolio Optimal dengan Menggunakan Mean Absolute Deviation dan Conditional Mean Variance." Jurnal Fourier 9, no. 1 (2020): 25–34. http://dx.doi.org/10.14421/fourier.2020.91.25-34.

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Penelitian ini membahas tentang pembentukan portofolio optimal menggunakan model Mean Absolute Deviation (MAD) dan model Conditional Mean Variance (CMV). Pada model MAD risiko portofolio diukur menggunakan rata–rata deviasi standar sehingga portofolio optimal dapat diperoleh dengan menggunakan pemrograman linear. Sedangkan portofolio model CMV, rata–rata return diestimasi menggunakan model Autoregressive (AR) dan risiko (variansi) diestimasi menggunakan model GARCH. Selanjutnya kedua model portofolio diterapkan dalam membentuk portofolio optimal pada saham–saham yang terdaftar dalam Indeks Saham Syariah Indonesia (ISSI) periode 4 Juli 2016 sampai 4 Juli 2018. Kinerja kedua portofolio dianalisis menggunakan indeks Sortino. Hasilnya menunjukan bahwa kinerja portofolio model CMV lebih baik dibandingkan model portofolio MAD.
 [This study discusses the formation of optimal portfolios using the Mean Absolute Deviation (MAD) model and the Conditional Mean Variance (CMV) model. The MAD portfolio model measures portfolio risk by using average standard deviations so that optimal portfolios solved by using linear programming. Meanwhile the CMV portfolio model, the average return estimated by using the Autoregressive (AR) model and the risk (variance) estimated by using the GARCH model. Furthermore, both portfolio models applied in forming optimal portfolios for stocks listed in the Indonesian Syariah Stock Index (ISSI) for the period 4 July 2016 to 4 July 2018. The performance of both portfolios analyzed by using the Sortino index. The results show that the portfolio performance of the CMV model is better than MAD portfolio model.]
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Nisani, Doron. "Portfolio selection using the Riskiness Index." Studies in Economics and Finance 35, no. 2 (2018): 330–39. http://dx.doi.org/10.1108/sef-03-2017-0058.

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PurposeThe purpose of this paper is to increase the accuracy of the efficient portfolios frontier and the capital market line using the Riskiness Index.Design/methodology/approachThis paper will develop the mean-riskiness model for portfolio selection using the Riskiness Index.FindingsThis paper’s main result is establishing a mean-riskiness efficient set of portfolios. In addition, the paper presents two applications for the mean-riskiness portfolio management method: one that is based on the multi-normal distribution (which is identical to the MV model optimal portfolio) and one that is based on the multi-normal inverse Gaussian distribution (which increases the portfolio’s accuracy, as it includes the a-symmetry and tail-heaviness features in addition to the scale and diversification features of the MV model).Research limitations/implicationsThe Riskiness Index is not a coherent measurement of financial risk, and the mean-riskiness model application is based on a high-order approximation to the portfolio’s rate of return distribution.Originality/valueThe mean-riskiness model increases portfolio management accuracy using the Riskiness Index. As the approximation order increases, the portfolio’s accuracy increases as well. This result can lead to a more efficient asset allocation in the capital markets.
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Ji, Xinyue. "Comparison of Portfolio Optimizations under Markowitz Model in Technology Sector and Financial Services Sector." Highlights in Business, Economics and Management 24 (January 22, 2024): 1194–202. http://dx.doi.org/10.54097/32f00f69.

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In the period of Covid-19, different sectors received different levels of shocks, which gave investors a degree of caution when investing in various sectors. Therefore, portfolio optimization - using specific model to assign weights of stocks to achieve a higher return while reducing risk – becomes a popular strategy. This paper chooses the Markowitz model to find optimal sector-based portfolios, specifically in technology sector and financial services sector, as well as portfolios that contains stocks in both sectors. The study uses Python to do Monte Carlo simulation, finding two optimal portfolios with maximum Sharpe ratio and minimum volatility for each sector(s), and finally comparing performances to test if the sector-based portfolio works better than the inter-sector portfolio. According to results, the minimum volatility portfolio in combined sectors reaches the same return of 0.11 as the minimum volatility portfolio in technology sector, but with lower volatility. It means the inter-sectors portfolio is better off when seeking minimum volatility. On the other hand, the maximum Sharpe ratio portfolios in technology sector, financial services sector, and combined sectors have values of returns and volatility ordering from highest to lowest. As a result, with current information, without investors’ investment preference, the optimal maximum Sharpe ratio portfolio cannot be determined and needed further exploration.
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Dissertations / Theses on the topic "Optimal portfolio model"

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Zhuang, Ziyi. "The Portfolio Optimization Project." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/285.

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This project has three parts. The first part is to use the efficient frontier and find the tangency portfolio to form our optimal portfolio. We built our portfolio using the Interactive Brokers software and rebalanced every week for 4 holding periods to see the relationship between our projected returns and actual market returns. In the second part we considered the Capital Asset Pricing Model (CAPM) and ran linear regressions on the stocks we chose in the first part of the project. This process is based on our idea of finding the systematic risk in each stock to improve our stock choosing ability. In the last part we introduce the concept of factor models and add more factors into our original CAPM model. Via a back-testing method, we test the reasonability of our factors and give advice to further improve our portfolio optimization project.
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Sharma, Amita. "Optimal portfolio selection contemplating risk propensity of investors in stock markets." Thesis, IIT Delhi, 2016. http://localhost:8080/xmlui/handle/12345678/7098.

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Demarco, Raffaella Michaela. "Optimal model points in term life insurance." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18236/.

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This thesis is focused on the problem of seeking an optimal set of the model points selection when dealing with a portfolio of term insurance policies and a LIBOR Market Model that determines the dynamics of the forward rates. Specifically, the study is associated to the problem of minimizing a specific risk functional which measures the average discrepancy between two portfolios: the given portfolio of policies and the model points, a small group of representative contracts which substitute the first one, without misrepresenting its inherent risk structure. This optimization process is aimed to reducing the computation difficulties of the valuation of the performance of any portfolios of policies, projections to be made daily by life insurance companies. In particular, in the present thesis, after a brief reference to some basic concepts in the interest rate field, there are described the LIBOR Market Model and the risk functional in a Banach space. The portfolio representation problem is also examined, because it allows to define the dynamics of those portfolios within a certain class that best represents the inherent risk structure of a given financial exposure. Finally, it is analyzed the particular case of the term life insurance.
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Gabih, Abdelali, and Ralf Wunderlich. "Optimal portfolios with bounded shortfall risks." Universitätsbibliothek Chemnitz, 2004. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200401202.

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This paper considers dynamic optimal portfolio strategies of utility maximizing investors in the presence of risk constraints. In particular, we investigate the optimization problem with an additional constraint modeling bounded shortfall risk measured by Value at Risk or Expected Loss. Using the Black-Scholes model of a complete financial market and applying martingale methods we give analytic expressions for the optimal terminal wealth and the optimal portfolio strategies and present some numerical results.
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Starck, Markus O. "Delegated investing and optimal risk budgets /." Hamburg : Kovač, 2008. http://www.verlagdrkovac.de/978-3-8300-3612-8.htm.

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Angoshtari, Bahman. "Stochastic modeling and methods for portfolio management in cointegrated markets." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:1ae9236c-4bf0-4d9b-a694-f08e1b8713c0.

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In this thesis we study the utility maximization problem for assets whose prices are cointegrated, which arises from the investment practice of convergence trading and its special forms, pairs trading and spread trading. The major theme in the first two chapters of the thesis, is to investigate the assumption of market-neutrality of the optimal convergence trading strategies, which is a ubiquitous assumption taken by practitioners and academics alike. This assumption lacks a theoretical justification and, to the best of our knowledge, the only relevant study is Liu and Timmermann (2013) which implies that the optimal convergence strategies are, in general, not market-neutral. We start by considering a minimalistic pairs-trading scenario with two cointegrated stocks and solve the Merton investment problem with power and logarithmic utilities. We pay special attention to when/if the stochastic control problem is well-posed, which is overlooked in the study done by Liu and Timmermann (2013). In particular, we show that the problem is ill-posed if and only if the agent’s risk-aversion is less than a constant which is an explicit function of the market parameters. This condition, in turn, yields the necessary and sufficient condition for well-posedness of the Merton problem for all possible values of agent’s risk-aversion. The resulting well-posedness condition is surprisingly strict and, in particular, is equivalent to assuming the optimal investment strategy in the stocks to be market-neutral. Furthermore, it is shown that the well-posedness condition is equivalent to applying Novikov’s condition to the market-price of risk, which is a ubiquitous sufficient condition for imposing absence of arbitrage. To the best of our knowledge, these are the only theoretical results for supporting the assumption of market-neutrality of convergence trading strategies. We then generalise the results to the more realistic setting of multiple cointegrated assets, assuming risk factors that effects the asset returns, and general utility functions for investor’s preference. In the process of generalising the bivariate results, we also obtained some well-posedness conditions for matrix Riccati differential equations which are, to the best of our knowledge, new. In the last chapter, we set up and justify a Merton problem that is related to spread-trading with two futures assets and assuming proportional transaction costs. The model possesses three characteristics whose combination makes it different from the existing literature on proportional transaction costs: 1) finite time horizon, 2) Multiple risky assets 3) stochastic opportunity set. We introduce the HJB equation and provide rigorous arguments showing that the corresponding value function is the viscosity solution of the HJB equation. We end the chapter by devising a numerical scheme, based on the penalty method of Forsyth and Vetzal (2002), to approximate the viscosity solution of the HJB equation.
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David, Delphine. "Contrôle optimal stochastique avec retard, asymétrie d'information, et applications en finance et en économie." La Rochelle, 2008. http://www.theses.fr/2008LAROS249.

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Nous considérons dans la première partie de cette thèse des problèmes de contrôle optimal lorsque l'état est dirigée par une équation différentielle stochastique à retard et à sauts. Nous montrons que les théorèmes classiques de contrôle tels que l'équation d'Hamilton-Jacobi-Bellman ou le principe du maximum s'étendent à notre modèle et qu'ils permettent l'obtention de résultats explicites. Nous étudions également l'existence et l'unicité d'une solution de viscosité pour l'équation d'Hamilton-Jacobi-Bellman. La deuxième partie est composée de deux travaux indépendants ayant pour thème l'étude du comportement optimal des agents pour des modèles économique et financier bien spécifiques. Le premier travail résout le problème de consommation et investissement d'un initié par le biais des intégrales forward, le deuxième analyse un modèle à générations imbriquées en temps continu. La troisième partie de cette thèse est dédiée au calcul de sensibilité d'options et plus particulièrement au calcul de l'indicateur nommé Theta. Nous donnons une expression explicite de cet indicateur pour les options européennes et digitales et présentons des simulations numériques<br>We consider, in the first part, optimal control problems when the state is driven by a stochastic differential equation with delay and jumps. We show that standard optimal control theorems as the Hamilton-Jacobi-Bellman equation or the maximum principle can be extended in our setting and that they give explicit solutions. We also study existence and uniqueness of viscosity solution for the Hamilton-Jacobi-Bellman equation. The second part of this thesis is composed of two independent works related by the study of the optimal behavior of agents in specific economic and financial frameworks. The first work solves a consumption and investment problem for an insider by means of forward integrals, the second one analyses an overlapping generations model in continuous time. The third part of this thesis is devoted to sensitivity analysis. More precisely we compute the indicator named Theta. We obtain an explicit form of this indicator for european and digital options and give some numerical simulations
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Bjurgert, Johan, and Marcus Edstrand. "Forecasting the Equity Premium and Optimal Portfolios." Thesis, Linköping University, Department of Mathematics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11795.

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<p>The expected equity premium is an important parameter in many financial models, especially within portfolio optimization. A good forecast of the future equity premium is therefore of great interest. In this thesis we seek to forecast the equity premium, use it in portfolio optimization and then give evidence on how sensitive the results are to estimation errors and how the impact of these can be minimized.</p><p>Linear prediction models are commonly used by practitioners to forecast the expected equity premium, this with mixed results. To only choose the model that performs the best in-sample for forecasting, does not take model uncertainty into account. Our approach is to still use linear prediction models, but also taking model uncertainty into consideration by applying Bayesian model averaging. The predictions are used in the optimization of a portfolio with risky assets to investigate how sensitive portfolio optimization is to estimation errors in the mean vector and covariance matrix. This is performed by using a Monte Carlo based heuristic called portfolio resampling.</p><p>The results show that the predictive ability of linear models is not substantially improved by taking model uncertainty into consideration. This could mean that the main problem with linear models is not model uncertainty, but rather too low predictive ability. However, we find that our approach gives better forecasts than just using the historical average as an estimate. Furthermore, we find some predictive ability in the the GDP, the short term spread and the volatility for the five years to come. Portfolio resampling proves to be useful when the input parameters in a portfolio optimization problem is suffering from vast uncertainty. </p>
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Li, Zejing [Verfasser], and N. [Akademischer Betreuer] Bäuerle. "Optimal Portfolios in Wishart Models and Effects of Discrete Rebalancing on Portfolio Distribution and Strategy Selection / Zejing Li. Betreuer: N. Bäuerle." Karlsruhe : KIT-Bibliothek, 2012. http://d-nb.info/1033351482/34.

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廖智生 and Chi-sang Liu. "A study of optimal investment strategy for insurance portfolio." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B31227636.

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Books on the topic "Optimal portfolio model"

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Cao, Bing-Yuan. Optimal Models and Methods with Fuzzy Quantities. Springer-Verlag Berlin Heidelberg, 2010.

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Boyle, Phelim P. Optimal portfolio selection with transaction costs. University of Toronto, Dept. of Statistics, 1994.

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Jurek, Jakub W. Optimal value and growth tilts in long-horizon portfolios. National Bureau of Economic Research, 2006.

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Elsinger, Helmut. Arbitrage and optimal portfolio choice with financial constraints. Oesterreichische Nationalbank, 2001.

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Guidolin, Massimo. Optimal portfolio choice under regime switching, skew and kurtosis preferences. Federal Reserve Bank of St. Louis, 2005.

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Aiyer, Ajay Subramanian. Optimal portfolio selection with fixed transaction costs in the presence of jumps and random drift. Cornell Theory Center, Cornell University, 1996.

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Sercu, Piet. The optimal number of contracts in cross- or delta-hedges. City University of Hong Kong, Department of Economics and Finance, 1997.

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Viciera, Luis M. Optimal portfolio choice for long-horizon investors with nontradable labor income. National Bureau of Economic Research, 1999.

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McDonnell, Philip J. Optimal portfolio modeling: Models to maximize return and control risk in Excel and R + CD-ROM. Wiley, 2008.

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Rüschendorf, Ludger. Mathematical Risk Analysis: Dependence, Risk Bounds, Optimal Allocations and Portfolios. Springer Berlin Heidelberg, 2013.

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Book chapters on the topic "Optimal portfolio model"

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Bernhard, Pierre, Jacob C. Engwerda, Berend Roorda, et al. "Merton’s Optimal Dynamic Portfolio Revisited." In The Interval Market Model in Mathematical Finance. Springer New York, 2012. http://dx.doi.org/10.1007/978-0-8176-8388-7_1.

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Quenez, Marie-Claire. "Optimal Portfolio in a Multiple-Priors Model." In Seminar on Stochastic Analysis, Random Fields and Applications IV. Birkhäuser Basel, 2004. http://dx.doi.org/10.1007/978-3-0348-7943-9_18.

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Valdez, Adrian Roy L., and Tiziano Vargiolu. "Optimal Portfolio in a Regime-switching Model." In Seminar on Stochastic Analysis, Random Fields and Applications VII. Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0545-2_22.

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Quimbayo, Carlos Andres Zapata, Diego Felipe Carmona Espejo, and Jhonatan Gamboa Hidalgo. "Optimal Portfolio Selection Using a Robust-Bayesian Model." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46739-4_7.

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Xiong, Junyu, Zhaoyi Li, Yuyao Zhang, Guoyan Chen, and Xuesong Liu. "Develop an Online Portfolio Model for Optimal Trading Strategies." In Proceedings of the 2022 3rd International Conference on Modern Education and Information Management (ICMEIM 2022). Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-044-2_119.

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Kusuma, Evelyn, Putu Anom Mahadwartha, and Endang Ernawati. "Comparison of Optimal Portfolio Before and During the Covid-19 Pandemic: Testing on LQ45." In Proceedings of the 19th International Symposium on Management (INSYMA 2022). Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-008-4_11.

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AbstractThis study forms an optimal portfolio using a single index model on LQ45 index stocks and compares its performance before and during the Covid-19 pandemic. Return, risk, Sharpe ratio, and Treynor ratio are compared between the period before and during the pandemic. The calculation of excess return to beta results obtains three stocks that make up the optimal portfolio (2016 to 2021), namely ANTM, BBCA, and INCO, with sequential proportions of 89.87%, 1.96%, and 8.17%. The different paired sample t-test results show differences in risk and Sharpe ratio performance in the portfolio before and during the Covid-19 pandemic. The risk is higher during than before the pandemic, with a higher Sharpe ratio value before the pandemic, even though both are negative. Meanwhile, the paired sample t-test comparison results for returns and Treynor ratio show no difference in portfolio performance before and during the Covid-19 pandemic.
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Ta, Bao Q., Vu T. Huynh, Khai Q. H. Nguyen, Phung N. Nguyen, and Binh H. Ho. "Maximal Predictability Portfolio Optimization Model and Applications to Vietnam Stock Market." In Credible Asset Allocation, Optimal Transport Methods, and Related Topics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97273-8_37.

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Ozenbas, Deniz, Michael S. Pagano, Robert A. Schwartz, and Bruce W. Weber. "Economics and the Equity Market: A Microeconomics Course Application." In Classroom Companion: Business. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74817-3_1.

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AbstractEconomics encompasses two broad subjects: macroeconomics and microeconomics. Macroeconomics deals with an economy in aggregate and addresses issues such as inflation, unemployment, interest rates, and economic growth. We present a macroeconomic perspective in Chap. 10.1007/978-3-030-74817-3_3. Microeconomics, the focus of this chapter, operates, as its name indicates, on the micro level, addressing household consumption decisions and the production decisions of firms. In this chapter, we focus on the parallels (and a few differences) between a standard microeconomics formulation (a household’s selection of an optimal consumption bundle) and a standard finance model (an investor’s selection of a portfolio that optimally combines a riskless asset – cash – and a risky equity portfolio). The finance formulation is the Capital Asset Pricing Model (CAPM). CAPM is a keystone of what is known as modern portfolio theory, the originator of which is Harry Markowitz who was awarded a Nobel Memorial Prize in Economic Sciences in 1990 for having developed the theory of portfolio choice. We then introduce friction (trading costs) and show how CAPM’s frictionless market equilibrium is perturbed. The analysis provides a good lead-in to the next chapter on finance.
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Sethi, Suresh P., and Michael Taksar. "A Note on Merton’s “Optimum Consumption and Portfolio Rules in a Continuous-Time Model”." In Optimal Consumption and Investment with Bankruptcy. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6257-3_3.

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Yousaf, Imran, and Shoaib Ali. "Discovering Interlinkages Between Major Cryptocurrencies Using High-Frequency Data: New Evidence from COVID-19 Pandemic." In Blockchain, Crypto Assets, and Financial Innovation. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-6839-7_13.

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Abstract Through the application of the VAR-AGARCH model to intra-day data for three cryptocurrencies (Bitcoin, Ethereum, and Litecoin), this study examines the return and volatility spillover between these cryptocurrencies during the pre-COVID-19 period and the COVID-19 period. We also estimate the optimal weights, hedge ratios, and hedging effectiveness during both sample periods. We find that the return spillovers vary across the two periods for the Bitcoin-Ethereum, Bitcoin-Litecoin, and Ethereum-Litecoin pairs. However, the volatility transmissions are found to be different during the two sample periods for the Bitcoin-Ethereum and Bitcoin-Litecoin pairs. The constant conditional correlations between all pairs of cryptocurrencies are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period. Based on optimal weights, investors are advised to decrease their investments (a) in Bitcoin for the portfolios of Bitcoin/Ethereum and Bitcoin/Litecoin and (b) in Ethereum for the portfolios of Ethereum/Litecoin during the COVID-19 period. All hedge ratios are found to be higher during the COVID-19 period, implying a higher hedging cost compared to the pre-COVID-19 period. Last, the hedging effectiveness is higher during the COVID-19 period compared to the pre-COVID-19 period. Overall, these findings provide useful information to portfolio managers and policymakers regarding portfolio diversification, hedging, forecasting, and risk management.
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Conference papers on the topic "Optimal portfolio model"

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Kazi, Monzure-Khoda, Akhilesh Gandhi, and M. M. Faruque Hasan. "Process and Network Design for Sustainable Hydrogen Economy." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.125411.

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This study presents a comprehensive approach to optimizing hydrogen supply chain network (HSCN), focusing initially on Texas, with potential scalability to national and global regions. Utilizing mixed-integer nonlinear programming (MINLP), the research decomposes into two distinct modeling stages: broad supply chain modeling and detailed hub-specific analysis. The first stage identifies optimal hydrogen hub locations, considering county-level hydrogen demand, renewable energy availability, and grid capacity. It determines the number and placement of hubs, county participation within these hubs, and the optimal sites for hydrogen production plants. The second stage delves into each selected hub, analyzing energy mixes under variable solar, wind, and grid profiles, sizing specific production and storage facilities, and scheduling to match energy availability. Iterative refinement incorporates detailed insights back into the broader model, updating costs and configurations to converge upon an optimal supply chain design. This design encapsulates macro-level network configurations, including centralization versus decentralization strategies, transportation cost analysis, and carbon footprint assessment, as well as micro-level operational specifics like renewable energy contributions, facility scale, and energy portfolio management. The methodology's robustness allows for strategic insights into hydrogen production facility siting, aligning with local energy resources and supply chain economics. This adaptable, multi-scale approach contributes to informed decision-making in the evolution of sustainable hydrogen-based energy systems, offering a roadmap for policy reforms and strategic supply chain development in diverse energy landscapes.
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Guo, Xiang, and Zixin Xu. "Optimal Portfolio Assessment Based on the Modern Portfolio Model." In Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022, Nanjing, China. EAI, 2023. http://dx.doi.org/10.4108/eai.18-11-2022.2326868.

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Wan, Shuping. "Risk Sensitive Optimal Portfolio Model under Jump Processes." In 2006 Chinese Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/chicc.2006.280664.

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Yu, Xing. "The Optimal Robust Portfolio Model Based on CDaR." In 2nd International Conference on Computer and Information Applications (ICCIA 2012). Atlantis Press, 2012. http://dx.doi.org/10.2991/iccia.2012.220.

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Chen, Zhiying, Xuanhua Peng, and Yongkui Li. "Optimal Portfolio Choice under Hidden Regime Switching Model." In Fifth Symposium of Risk Analysis and Risk Management in Western China (WRARM 2017). Atlantis Press, 2017. http://dx.doi.org/10.2991/wrarm-17.2017.43.

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Li, Bahao. "Research on Optimal Portfolio of Financial Investment Based on Genetic Algorithm." In 2019 International Conference on Economic Management and Model Engineering (ICEMME). IEEE, 2019. http://dx.doi.org/10.1109/icemme49371.2019.00104.

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Faber, Michael Havbro, Marc A. Maes, and Kazuyoshi Nishijima. "Optimal Design and Portfolio Risk Management for Groups of Structures." In ASME 2004 23rd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2004. http://dx.doi.org/10.1115/omae2004-51430.

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The present paper addresses the problem of optimal design of portfolios of fixed offshore structures. A new framework for design is developed where the effect of dependency in the performance of structures subject to common extreme load events is taken into account in the design by inclusion of the follow-up consequences resulting from the simultaneous failure of several structures in the portfolio. First the special aspects of optimal design subject to follow-up consequences are addressed from the perspective of structures portfolio risk management. Thereafter the problem of optimal design of groups of structures is defined with special considerations to the assessment of the relation between the design, the probability density function of the life cycle benefits and the number of structures considered (in a group). Using this model basis the optimum design of fixed steel offshore platforms where the capacity of the structures against extreme wave loads can be expressed as function of the Reserve Strength Ratio (RSR) is considered. Thereafter parametric studies are conducted to illustrate the significance of the number of structures considered in a group, the correlation between the extreme loads acting on the different structures and the significance of including the follow-up consequences into the design optimization problem.
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Loukeris, N., Y. Boutalis, A. Arampatzis, S. Livanis, and L. Maltoudoglou. "Computational intelligence in optimal portfolio selection — The PI model." In 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2015. http://dx.doi.org/10.1109/iisa.2015.7388004.

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Shrivastava, Akash, and Anugrah Singh. "An Optimal Stock Portfolio Construction Model Using Genetic Algorithm." In 2013 International Conference on Machine Intelligence and Research Advancement (ICMIRA). IEEE, 2013. http://dx.doi.org/10.1109/icmira.2013.32.

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He, Zhefei. "Optimal Portfolio and Consumption in Modified Black-Scholes Model." In 2011 Fourth International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2011. http://dx.doi.org/10.1109/bife.2011.90.

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Reports on the topic "Optimal portfolio model"

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Gálvez, Julio, and Gonzalo Paz-Pardo. Richer earnings dynamics, consumption and portfolio choice over the life cycle. Banco de España, 2022. http://dx.doi.org/10.53479/23686.

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Households face earnings risk which is non-normal and varies by age and over the income distribution. We show that allowing for these rich features of earnings dynamics, in the context of a structurally estimated life-cycle portfolio choice model, helps to better understand the limited participation of households in the stock market and their low holdings of risky assets. Because households are subject to more background risk than previously considered, the estimated model implies a substantially lower coeffcient of risk aversion and a lower optimal risky share for older workers with low wealth and high earnings.
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Gálvez, Julio, and Gonzalo Paz-Pardo. Richer earnings dynamics, consumption and portfolio choice over the life cycle. Banco de España, 2022. http://dx.doi.org/10.53479/23706.

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Households face earnings risk which is non-normal and varies by age and over the income distribution. We show that, in the context of a structurally estimated life-cycle portfolio choice model, allowing for these rich features of earnings dynamics helps to better understand the limited participation of households in the stock market and their low holdings of risky assets. Because households are subject to more background risk than previously considered, the estimated model implies a substantially lower coefficient of risk aversion and a lower optimal risky asset share for older workers with low wealth and high earnings.
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Sethi, Suresh P., and Michael Taksar. A Note on Merton's Optimum Consumption and Portfolio Rules in a Continuous-Time Model. Revised. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada175008.

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Chai, Jingjing, Wolfram Horneff, Raimond Maurer, and Olivia Mitchell. Extending Life Cycle Models of Optimal Portfolio Choice: Integrating Flexible Work, Endogenous Retirement, and Investment Decisions with Lifetime Payouts. National Bureau of Economic Research, 2009. http://dx.doi.org/10.3386/w15079.

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García, Salomón. The amplification effects of adverse selection in mortgage credit suply. Banco de España, 2023. http://dx.doi.org/10.53479/30138.

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This paper studies how information frictions in the securitization market amplify the response of mortgage credit supply to house price shocks. We model securitization as an optimal contracting problem between investors and banks. Banks are better informed than investors about the quality of the mortgages they originate, leading to an adverse selection problem. Investors use the quantity sold as a screening device to induce banks to reveal truthful information. We find that adverse selection amplifies the response of a bank’s mortgage originations to house price shocks. The degree of amplification is also a function of the technological differences in managing portfolios between banks and investors. The model has implications for the design of policy interventions aimed at stabilizing liquidity in the securitization market and credit provision to households in the credit market.
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Moran, Matthew. Decarbonizing Mobility with Liquid Hydrogen. SAE International, 2024. http://dx.doi.org/10.4271/epr2024015.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Liquid hydrogen (LH2) is playing a key role in decarbonization of the global energy landscape. Its large-scale continuous use in the space industry provides a foundation for transitioning state-of-the-art capabilities to other sectors. Key advancements in materials, cryogenics, and system optimization are being applied to reduce costs and increase performance for various mobile and stationary use cases. However, some unsettled topics remain to be addressed related to production, liquefaction, storage, distribution, safety, and economics. The optimal solutions to these unsettled topics will vary depending on the region, industry sector, and application.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;&lt;b&gt;Decarbonizing Mobility with Liquid Hydrogen&lt;/b&gt; provides a brief and balanced assessment of the relevant technologies, established practices, system operations, emerging trends, strategic considerations, and economic drivers. Addressing these unsettled topics is tied to the evolving economic strategies of governmental policies, public and private investment, competitive structures, regional approaches, and innovative business models.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;&lt;a href="https://www.sae.org/publications/edge-research-reports" target="_blank"&gt;Click here to access the full SAE EDGE&lt;/a&gt;&lt;sup&gt;TM&lt;/sup&gt;&lt;a href="https://www.sae.org/publications/edge-research-reports" target="_blank"&gt; Research Report portfolio.&lt;/a&gt;&lt;/div&gt;&lt;/div&gt;
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