Relevant bibliographies by topics / Mean-Variance Portfolio

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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 'Mean-Variance Portfolio'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Mean-Variance Portfolio"

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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 (April 30, 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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Kumar, Ronald Ravinesh, Peter Josef Stauvermann, and Aristeidis Samitas. "An Application of Portfolio Mean-Variance and Semi-Variance Optimization Techniques: A Case of Fiji." Journal of Risk and Financial Management 15, no. 5 (April 19, 2022): 190. http://dx.doi.org/10.3390/jrfm15050190.

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In this paper, we apply the Markowitz portfolio optimization technique based on mean-variance and semi-variance as measures of risk on stocks listed on the South Pacific Stock Exchange, Fiji. We document key market characteristics and consider monthly returns data from SEP-2019 to FEB-2022 (T = 30) of 17/19 listed companies on the stock exchange to construct various portfolios like 1/N (naïve), maximum return, and market and minimum-variance with and without short-selling constraints. Additionally, we compute each stock’s beta using the market capitalization-weighted stock price index data. We note that well-diversified portfolios (market portfolio and minimum-variance portfolio) with short-selling constraints have relatively higher expected returns with lower risk. Moreover, well-diversified portfolios perform better than the naïve and maximum portfolios in terms of risk. Moreover, we find that both the mean-variance and the semi-variance measures of risk yields a unique market portfolio in terms of expected returns, although the latter has a lower standard deviation and a higher Sharpe ratio. However, for the minimum-variance portfolios and market portfolios without short selling, we find relatively higher returns and risks using the mean-variance than the semi-variance approach. The low beta of individual stock indicates the low sensitivity of its price to the movement of the market index. The study is an initial attempt to provide potential investors with some practical strategies and tools in developing a diversified portfolio. Since not all the portfolios based on mean-variance and the semi-variance analyses are unique, additional methods of investment analysis and portfolio construction are recommended. Subsequently, for investment decisions, our analysis can be complemented with additional measures of risk and an in-depth financial statement/company performance analysis.
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Xu, Jonathan. "MEAN VARIANCE PORTFOLIO OPTIMIZATION." European Journal of Economics and Management Sciences, no. 2 (2021): 76–81. http://dx.doi.org/10.29013/ejems-21-2-76-81.

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Lefebvre, William, Grégoire Loeper, and Huyên Pham. "Mean-Variance Portfolio Selection with Tracking Error Penalization." Mathematics 8, no. 11 (November 1, 2020): 1915. http://dx.doi.org/10.3390/math8111915.

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This paper studies a variation of the continuous-time mean-variance portfolio selection where a tracking-error penalization is added to the mean-variance criterion. The tracking error term penalizes the distance between the allocation controls and a reference portfolio with same wealth and fixed weights. Such consideration is motivated as follows: (i) On the one hand, it is a way to robustify the mean-variance allocation in the case of misspecified parameters, by “fitting" it to a reference portfolio that can be agnostic to market parameters; (ii) On the other hand, it is a procedure to track a benchmark and improve the Sharpe ratio of the resulting portfolio by considering a mean-variance criterion in the objective function. This problem is formulated as a McKean–Vlasov control problem. We provide explicit solutions for the optimal portfolio strategy and asymptotic expansions of the portfolio strategy and efficient frontier for small values of the tracking error parameter. Finally, we compare the Sharpe ratios obtained by the standard mean-variance allocation and the penalized one for four different reference portfolios: equal-weights, minimum-variance, equal risk contributions and shrinking portfolio. This comparison is done on a simulated misspecified model, and on a backtest performed with historical data. Our results show that in most cases, the penalized portfolio outperforms in terms of Sharpe ratio both the standard mean-variance and the reference portfolio.
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Deng, Zhixiang, and Yujia Han. "The Application of ARIMA and Mean-variance Models on Financial Market." BCP Business & Management 26 (September 19, 2022): 1051–57. http://dx.doi.org/10.54691/bcpbm.v26i.2069.

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This study centers on forecasting return and constructing proper portfolios with 5 typical assets rarely focused on the Chinese A-share market. This paper applies the fittest ARIMA models for each of the selected stocks to predict their trend of returns in the next 20 days. Besides, we create the efficient frontier by Monte Carlo simulation under Markowitz’s Mean-Variance framework to focus on two portfolios, i.e., the maximum Sharpe ratio portfolio and the minimum volatility portfolio. The empirical results of the ARIMA model indicate a rational prediction of return for assets in the A-share market. The maximum Sharpe ratio portfolio and the minimum volatility portfolio show that stock of Foshan Haitian Flavouring and Food Company Ltd. and stock of China Merchants Bank Co., Ltd. account for the largest proportion in the two portfolios. Further empirical results show that returns for two portfolios are higher than the market index return, which illuminates the two portfolios outperform the market index. The results in this paper will surely benefit related investors in the financial market.
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Vasant, Jiten, Laurent Irgolic, Ryan Kruger, and Kanshukan Rajaratnam. "A Comparison Of Mean-Variance And Mean-Semivariance Optimisation On The JSE." Journal of Applied Business Research (JABR) 30, no. 6 (October 21, 2014): 1587. http://dx.doi.org/10.19030/jabr.v30i6.8876.

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<p>This study investigates the effectiveness of semivariance versus mean-variance optimisation on a risk-adjusted basis on the JSE. We compare semivariance and mean-variance optimisation prior to, during and after the recent financial crisis period. Additionally, we investigate the inclusion of a fixed-income asset in the optimal portfolio. The results suggest that semivariance optimisation on the JSE in a pure equity case produces lower absolute returns, yet superior risk-adjusted returns. Further investigation suggests that semivariance metrics are effective within a certain range of portfolio sizes and diminishes in benefit once portfolios become larger. A fixed income asset scenario tested under the hypothesis of semivariance optimisation favoured greater bond weightings in optimal portfolios.<em> </em><strong></strong></p>
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Fontana, Claudio, and Martin Schweizer. "Simplified mean-variance portfolio optimisation." Mathematics and Financial Economics 6, no. 2 (April 3, 2012): 125–52. http://dx.doi.org/10.1007/s11579-012-0067-4.

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Pedersen, Jesper Lund, and Goran Peskir. "Optimal mean-variance portfolio selection." Mathematics and Financial Economics 11, no. 2 (June 20, 2016): 137–60. http://dx.doi.org/10.1007/s11579-016-0174-8.

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Bi, Junna, Hanqing Jin, and Qingbin Meng. "Behavioral mean-variance portfolio selection." European Journal of Operational Research 271, no. 2 (December 2018): 644–63. http://dx.doi.org/10.1016/j.ejor.2018.05.065.

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Jena, R. K. "Extended Mean - Variance Portfolio Optimization Model: A Comparative Study Among Swarm Intelligence Algorithms." International Journal of Accounting and Financial Reporting 9, no. 2 (April 15, 2019): 184. http://dx.doi.org/10.5296/ijafr.v9i2.14601.

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Portfolio optimization is one of the important issues in the effective management of investment. There is plenty of research in the literature addressing these issues. Markowitz’s primary portfolio selection model is a more suitable method to solve the model for obtaining fairly optimum portfolios. But, the problem of portfolio optimization is multi-objective in nature that aims at simultaneously maximizing the expected return of the portfolio and minimizing portfolio risk. The computational complexity increases with an increase in the total number of available assets. Therefore heuristic methods are more suitable for portfolio optimization in compare to deterministic methods. This research compares three well-known swarm intelligence algorithms (e.g. Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC)) for portfolio optimization. The Sharpe ratio was used as one of the important criteria for this comparison. PSO outperformed other algorithms in portfolio optimization experiments. The results were also showed that the portfolios which were made of monthly data had performed better than the yearly data.
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Dissertations / Theses on the topic "Mean-Variance Portfolio"

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Cardoso, João Nuno Martins. "Robust mean variance." Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/10706.

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Mestrado em Finanças
Este estudo empírico tem como objectivo avaliar o impacto da estimação robusta nos portefólios de média variância. Isto foi conseguido fazendo uma simulação do comportamento de 15 acções do SP500. Esta simulação inclui dois cenários: um com amostras que seguem uma distribuição normal e outro com amostras contaminadas não normais. Cada cenário inclui 200 reamostragens. O performance dos portefólios estimados usando a máxima verosimilhança (clássicos) e dos portefólios estimados de forma robusta são comparados, resultando em algumas conclusões: Em amostras normais, portefólios robustos são marginalmente menos eficientes que os portefólios clássicos. Contudo, em amostras não normais, os portefólios robustos apresentam um performance muito superior que os portefólios clássicos. Este acréscimo de performance está positivamente correlacionado com o nível de contaminação da amostra. Em suma, assumindo que os retornos financeiros têm uma distribuição não normal, podemos afirmar que os estimadores robustos resultam em portefólios de média variância mais estáveis.
This empirical study's objective is to evaluate the impact of robust estimation on mean variance portfolios. This was accomplished by doing a simulation on the behavior of 15 SP500 stocks. This simulation includes two scenarios: One with normally distributed samples and another with contaminated non-normal samples. Each scenario includes 200 resamples. The performance of maximum likelihood (classical) estimated portfolios and robustly estimated portfolios are compared, resulting in some conclusions: On normally distributed samples, robust portfolios are marginally less efficient than classical portfolios. However, on non-normal samples, robust portfolios present a much higher performance than classical portfolios. This increase in performance is positively correlated with the level of contamination present on the sample. In summary, assuming that financial returns do not present a normal distribution, we can state that robust estimators result in more stable mean variance portfolios.
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Lim, Jeffrey Cheong Kee. "Multi-period mean-variance option portfolio strategies." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337901.

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Wong, Kwok-chuen, and 黃國全. "Mean variance portfolio management : time consistent approach." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/196026.

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In this thesis, two problems of time consistent mean-variance portfolio selection have been studied: mean-variance asset-liability management with regime switchings and mean-variance optimization with state-dependent risk aversion under short-selling prohibition. Due to the non-linear expectation term in the mean-variance utility, the usual Tower Property fails to hold, and the corresponding optimal portfolio selection problem becomes time-inconsistent in the sense that it does not admit the Bellman Optimality Principle. Because of this, in this thesis, time-consistent equilibrium solution of two mean-variance optimization problems is established via a game theoretic approach. In the first part of this thesis, the time consistent solution of the mean-variance asset-liability management is sought for. By using the extended Hamilton-Jacobi- Bellman equation for equilibrium solution, equilibrium feedback control of this MVALM and the corresponding equilibrium value function can be obtained. The equilibrium control is found to be affine in liability. Hence, the time consistent equilibrium control of this problem is state dependent in the sense that it depends on the uncontrollable liability process, which is in substantial contrast with the time consistent solution of the simple classical mean-variance problem in Björk and Murgoci (2010), in which it was independent of the state. In the second part of this thesis, the time consistent equilibrium strategies for the mean-variance portfolio selection with state dependent risk aversion under short-selling prohibition is studied in both a discrete and a continuous time set- tings. The motivation that urges us to study this problem is the recent work in Björk et al. (2012) that considered the mean-variance problem with state dependent risk aversion in the sense that the risk aversion is inversely proportional to the current wealth. There is no short-selling restriction in their problem and the corresponding time consistent control was shown to be linear in wealth. However, we discovered that the counterpart of their continuous time equilibrium control in the discrete time framework behaves unsatisfactory, in the sense that the corresponding “optimal” wealth process can take negative values. This negativity in wealth will change the investor into a risk seeker which results in an unbounded value function that is economically unsound. Therefore, the discretized version of the problem in Bjork et al. (2012) might yield solutions with bankruptcy possibility. Furthermore, such “bankruptcy” solution can converge to the solution in continuous counterpart as Björk et al. (2012). This means that the negative risk aversion drawback could appear in implementing the solution in Björk et al. (2012) discretely in practice. This drawback urges us to prohibit short-selling in order to eliminate the chance of getting non-positive wealth. Using backward induction, the equilibrium control in discrete time setting is explicit solvable and is shown to be linear in wealth. An application of the extended Hamilton-Jacobi-Bellman equation leads us to conclude that the continuous time equilibrium control is also linear in wealth. Also, the investment to wealth ratio would satisfy an integral equation which is uniquely solvable. The discrete time equilibrium controls are shown to converge to that in continuous time setting.
published_or_final_version
Mathematics
Master
Master of Philosophy
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Stein, Michael. "Mean-Variance Portfolio Selection With Complex Constraints." [S.l. : s.n.], 2007. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000007246.

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Mayambala, Fred. "Mean-Variance Portfolio Optimization : Eigendecomposition-Based Methods." Licentiate thesis, Linköpings universitet, Matematiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-118362.

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Modern portfolio theory is about determining how to distribute capital among available securities such that, for a given level of risk, the expected return is maximized, or for a given level of return, the associated risk is minimized. In the pioneering work of Markowitz in 1952, variance was used as a measure of risk, which gave rise to the wellknown mean-variance portfolio optimization model. Although other mean-risk models have been proposed in the literature, the mean-variance model continues to be the backbone of modern portfolio theory and it is still commonly applied. The scope of this thesis is a solution technique for the mean-variance model in which eigendecomposition of the covariance matrix is performed. The first part of the thesis is a review of the mean-risk models that have been suggested in the literature. For each of them, the properties of the model are discussed and the solution methods are presented, as well as some insight into possible areas of future research. The second part of the thesis is two research papers. In the first of these, a solution technique for solving the mean-variance problem is proposed. This technique involves making an eigendecomposition of the covariance matrix and solving an approximate problem that includes only relatively few eigenvalues and corresponding eigenvectors. The method gives strong bounds on the exact solution in a reasonable amount of computing time, and can thus be used to solve large-scale mean-variance problems. The second paper studies the mean-variance model with cardinality constraints, that is, with a restricted number of securities included in the portfolio, and the solution technique from the first paper is extended to solve such problems. Near-optimal solutions to large-scale cardinality constrained mean-variance portfolio optimization problems are obtained within a reasonable amount of computing time, compared to the time required by a commercial general-purpose solver.
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Sezgin, Alp Ozge. "Continuous Time Mean Variance Optimal Portfolios." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613824/index.pdf.

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The most popular and fundamental portfolio optimization problem is Markowitz'
s one period mean-variance portfolio selection problem. However, it is criticized because of its one period static nature. Further, the estimation of the stock price expected return is a particularly hard problem. For this purpose, there are a lot of studies solving the mean-variance portfolio optimization problem in continuous time. To solve the estimation problem of the stock price expected return, in 1992, Black and Litterman proposed the Bayesian asset allocation method in discrete time. Later on, Lindberg has introduced a new way of parameterizing the price dynamics in the standard Black-Scholes and solved the continuous time mean-variance portfolio optimization problem. In this thesis, firstly we take up the Lindberg'
s approach, we generalize the results to a jump-diffusion market setting and we correct the proof of the main result. Further, we demonstrate the implications of the Lindberg parameterization for the stock price drift vector in different market settings, we analyze the dependence of the optimal portfolio from jump and diffusion risk, and we indicate how to use the method. Secondly, we present the Lagrangian function approach of Korn and Trautmann and we derive some new results for this approach, in particular explicit representations for the optimal portfolio process. In addition, we present the L2-projection approach of Schweizer for the continuous time mean-variance portfolio optimization problem and derive the optimal portfolio and the optimal wealth processes for this approach. While, deriving these results as the underlying model, the market parameterization of Lindberg is chosen. Lastly, we compare these three different optimization frameworks in detail and their attractive and not so attractive features are highlighted by numerical examples.
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Ramos-Elorduy, Ernesto Paolo Conconi. "Mean-variance approach for World Bank's portfolio of projects." Thesis, University of York, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399627.

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Asumeng-Denteh, Emmanuel. "Transaction costs and resampling in mean-variance portfolio optimization." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0430104-123456/.

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Wang, Yang. "Multi-Period Mean-Variance Portfolio Selection with Regime-Switching." Thesis, Curtin University, 2019. http://hdl.handle.net/20.500.11937/78725.

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This thesis studies the multi-period mean-variance portfolio selection under regime-switching with two kinds of constraints: uncertain time horizon and special market conditions. The thesis uses dynamic programming approach to obtain the optimal investment strategies and the corresponding efficient frontiers. Some special cases and numerical analysis are used to illustrate the effect of different factors on the efficient frontiers.
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Häggbom, Marcus, and Shayan Nafar. "Mean-Variance Portfolio Selection Accounting for Financial Bubbles: A Mean-Field Type Approach." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252299.

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The phenomenon of financial bubbles is known to have impacted various markets since the seventeenth century. Such bubbles are known to form when the market drastically overvalues the price of an asset, causing its market value to increase hyperbolically, only to suddenly collapse once the untenable perceived future prospects of the asset are realized. Hence, it remains crucial for investors to be able to sell off assets residing within a bubble before they burst and their value is significantly diminished. Thus, portfolio optimization methods capable of accounting for financial bubbles in stock dynamics is a field of great value and interest for market participants. Portfolio optimization with respect to the mean-field is a relatively novel approach to accounting for the bubble-phenomenon. Hence, this paper investigates a previously unattempted method of portfolio optimization, providing a mean-field solution to the mean-variance trade-off problem, as well as providing new definitions of stock dynamics capable of diverting investors from bubbles.
Finansiella bubblor är ett fenomen som har påverkat marknader sedan 1600-talet. Bubblor tenderar att skapas när marknaden kraftigt övervärderar en tillgång vilket orsakar en hyperbolisk tillväxt i marknadspriset. Detta följs av en plötslig kollaps. Därför är det viktigt för investerare att kunna minska sin exponering mot aktier som befinner sig i en bubbla, så att risken för stora plötsliga förluster reduceras. Således är portföljoptimering där aktiedynamiken tar hänsyn till bubblor av högt intresse för marknadsdeltagare. Portföljoptimering med avseende på medelfältet är ett relativt nytt tillvägagångssätt för att behandla bubbelfenomen. Av denna anledning undersöks i detta arbete en hittills oprövad lösningsmetod som möjliggör en medelfältslösning till avvägningen mellan förväntad avkastning och risk. Där-utöver presenteras även ett antal nya modeller för aktier som kan bortleda investerare från bubblor.
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Books on the topic "Mean-Variance Portfolio"

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Agarwal, Megha. Developments in Mean-Variance Efficient Portfolio Selection. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137359926.

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Developments in mean-variance efficient portfolio selection. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan, 2015.

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Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. Oxford, OX, UK: B. Blackwell, 1987.

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Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. Oxford: Basil Blackwell, 1987.

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Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. Oxford: Blackwell, 1990.

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Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. New Hope: Frank J. Fabozzi Associates, 1987.

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O'Gorman, Aongus J. Mean-risk analysis: An examination of semivariance as an alternative to the traditional risk measure of variance. Dublin: University College Dublin, 1994.

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Back, Kerry E. Mean-Variance Analysis. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0005.

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The mean‐variance frontier is characterized with and without a risk‐free asset. The global minimum variance portfolio and tangency portfolio are defined, and two‐fund spanning is explained. The frontier is characterized in terms of the return defined from the SDF that is in the span of the assets. This is related to the Hansen‐Jagannathan bound. There is an SDF that is an affine function of a return if and only if the return is on the mean‐variance frontier. Separating distributions are defined and shown to imply two‐fund separation and mean‐variance efficiency of the market portfolio.
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Agarwal, M. Developments in Mean-Variance Efficient Portfolio Selection. Palgrave Macmillan Limited, 2015.

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Agarwal, M. Developments in Mean-Variance Efficient Portfolio Selection. Palgrave Macmillan Limited, 2014.

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Book chapters on the topic "Mean-Variance Portfolio"

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Hsiao, Cheng, and Shin-Huei Wang. "Mean variance portfolio allocation." In Encyclopedia of Finance, 457–63. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/978-0-387-26336-6_45.

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De Luca, Pasquale. "Mean-Variance Portfolio Analysis." In Springer Texts in Business and Economics, 183–208. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18300-3_9.

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Hsiao, Cheng, and Shin-Huei Wang. "Mean Variance Portfolio Allocation." In Encyclopedia of Finance, 743–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91231-4_20.

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Hsiao, Cheng, and Shin-Huei Wang. "Mean Variance Portfolio Allocation." In Encyclopedia of Finance, 341–46. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-5360-4_20.

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Board, John L. G., Charles M. S. Sutcliffe, and William T. Ziemba. "Portfolio Theory: Mean-Variance Model." In Encyclopedia of Operations Research and Management Science, 1142–48. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_775.

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Thompson, Neil. "The Mean-Variance Approach." In Portfolio Theory and the Demand for Money, 4–24. London: Palgrave Macmillan UK, 1993. http://dx.doi.org/10.1007/978-1-349-22827-0_2.

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Dhankar, Raj S. "Mean–Variance Approach and Portfolio Selection." In India Studies in Business and Economics, 249–63. New Delhi: Springer India, 2019. http://dx.doi.org/10.1007/978-81-322-3950-5_16.

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Lari-Lavassani, Ali, and Xun Li. "Dynamic Mean Semi-variance Portfolio Selection." In Lecture Notes in Computer Science, 95–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44860-8_10.

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Agarwal, Megha. "Contributions to the Portfolio Theory." In Developments in Mean-Variance Efficient Portfolio Selection, 56–70. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137359926_3.

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Agarwal, Megha. "Mean-Variance Efficient Portfolio Selection: Model Development." In Developments in Mean-Variance Efficient Portfolio Selection, 71–100. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137359926_4.

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Conference papers on the topic "Mean-Variance Portfolio"

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Siyu, Lv, Wu Zhen, and Zhuang Yi. "Recursive mean-variance portfolio choice problems with constrained portfolios." In 2015 34th Chinese Control Conference (CCC). IEEE, 2015. http://dx.doi.org/10.1109/chicc.2015.7260016.

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Hoe, Lam Weng, and Lam Weng Siew. "Portfolio optimization with mean-variance model." In INNOVATIONS THROUGH MATHEMATICAL AND STATISTICAL RESEARCH: Proceedings of the 2nd International Conference on Mathematical Sciences and Statistics (ICMSS2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4952526.

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Wan, Shuping. "Mean-variance Portfolio Model with Consumption." In 2006 9th International Conference on Control, Automation, Robotics and Vision. IEEE, 2006. http://dx.doi.org/10.1109/icarcv.2006.345085.

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Chen, Guohua, and Xiaolian Liao. "Credibility Mean-Variance-skewness Portfolio Selection Model." In 2010 2nd International Workshop on Database Technology and Applications (DBTA). IEEE, 2010. http://dx.doi.org/10.1109/dbta.2010.5659059.

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Pan, Qiming, and Xiaoxia Huang. "Mean-Variance Model for International Portfolio Selection." In 2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2008. http://dx.doi.org/10.1109/euc.2008.16.

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Lai, Kin Keung, Lean Yu, and Shouyang Wang. "Mean-Variance-Skewness-Kurtosis-based Portfolio Optimization." In 2006 International Multi-Symposiums on Computer and Computational Sciences (IMSCCS). IEEE, 2006. http://dx.doi.org/10.1109/imsccs.2006.239.

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Banihashemi, Shokoofeh. "Portfolio Management by Normal Mean-Variance Mixture Distributions." In 2019 3rd International Conference on Data Science and Business Analytics (ICDSBA). IEEE, 2019. http://dx.doi.org/10.1109/icdsba48748.2019.00052.

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Signoretto, Marco, and Johan A. K. Suykens. "DynOpt: Incorporating dynamics into mean-variance portfolio optimization." In 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, 2013. http://dx.doi.org/10.1109/cifer.2013.6611696.

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Gubu, La, Dedi Rosadi, and Abdurakhman. "Robust mean-variance portfolio selection with time series clustering." In INTERNATIONAL CONFERENCE ON MATHEMATICS, COMPUTATIONAL SCIENCES AND STATISTICS 2020. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0042172.

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Zulkifli, Mohamed, Mohamed Daud, and Samat Omar. "Maximizing portfolio diversification benefit via extended mean-variance model." In 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA 2010). IEEE, 2010. http://dx.doi.org/10.1109/isiea.2010.5679379.

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Reports on the topic "Mean-Variance Portfolio"

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Cochrane, John. A Mean-Variance Benchmark for Intertemporal Portfolio Theory. Cambridge, MA: National Bureau of Economic Research, February 2013. http://dx.doi.org/10.3386/w18768.

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Beurskens, Luuk, Jaap C. Jansen, Shimon Ph D. Awerbuch, and Thomas E. Drennen. The cost of geothermal energy in the western US region:a portfolio-based approach a mean-variance portfolio optimization of the regions' generating mix to 2013. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/876243.

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