Relevant bibliographies by topics / Dynamic portfolio

Contents

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

Academic literature on the topic 'Dynamic portfolio'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Dynamic portfolio"

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Kortelainen, Samuli, Antero Kutvonen, and Lauri Lättilä. "Technology Portfolio Dynamics." Journal of Innovation Management 1, no. 2 (December 31, 2013): 125–39. http://dx.doi.org/10.24840/2183-0606_001.002_0009.

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Innovations are significant source of competitive advantage for firms. They are also a major source of dynamics that forces firms to adapt their capabilities to sustain competitiveness. In this study we analyzed how firms manage their technological portfolio in mobile phone industry. Our first finding is that firms have focused differently their technology portfolios. Then we identified that most firms change their technology portfolio over time. And finally we conclude that firms in mobile phone industry have different levels of dynamics where some firms change their technology portfolio faster than others. This research identifies new challenges in dynamic capabilities research related to the appropriate level of dynamics in technology management. This information is crucial in practice in order to correctly manage the firm’s dynamic processes.
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Kang, Hyoung-Goo. "Dynamic Asset Allocation under Mispricing, Predictability and Portable Alpha." International Studies Review 11, no. 1 (October 19, 2010): 73–101. http://dx.doi.org/10.1163/2667078x-01101005.

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The existing literature about portfolio management has investigated how to update a portfolio allocation, conditional on the information that possibly predicts asset returns and volatilities. We add several innovations to fill the lacuna of prior research in the contexts of global asset allocation. First, we suggest a simple method of how to rebalance portfolios automatically and dynamically in order to exploit potential market inefficiencies. The existing literature has not developed such a strategy. Out-of-sample tests demonstrate that our strategy dominates both static allocation and dynamic strategies that do not account for possible mispricing. Thus, our strategy can contribute not only to academia, but also to practical portfolio managers who endeavour to beat markets. Second, we elaborate portable alpha strategies using the new dynamic strategy. Once we add an alpha strategies using the new dynamic strategy. Once we add an alpha portfolio to existing portfolios, then they perform better in terms of mean and risk. Thus, it makes our alpha portfolio portable, i.e., we can apply the alpha portfolio to any fund and can enhance its performance. Third, our dynamic strategy implies a convenient method to estimate a conditional mean and covariance matrix as functions of predictive information matrix without consuming much computational risk managers and traders who need to control the risks of large target portfolios on a real time basis.
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Mroua, Mourad, and Fathi Abid. "Portfolio revision and optimal diversification strategy choices." International Journal of Managerial Finance 10, no. 4 (August 26, 2014): 537–64. http://dx.doi.org/10.1108/ijmf-07-2012-0085.

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Purpose – Since equity markets have a dynamic nature, the purpose of this paper is to investigate the performance of a revision procedure for domestic and international portfolios, and provides an empirical selection strategy for optimal diversification from an American investor's point of view. This paper considers the impact of estimation errors on the optimization processes in financial portfolios. Design/methodology/approach – This paper introduces the concept of portfolio resampling using Monte Carlo method. Statistical inferences methodology is applied to construct the sample acceptance regions and confidence regions for the resampled portfolios needing revision. Tracking error variance minimization (TEVM) problem is used to define the tracking error efficient frontiers (TEEF) referring to Roll (1992). This paper employs a computation method of the periodical after revision return performance level of the dynamic diversification strategies considering the transaction cost. Findings – The main finding is that the global portfolio diversification benefits exist for the domestic investors, in both the mean-variance and tracking error analysis. Through TEEF, the dynamic analysis indicates that domestic dynamic diversification outperforms international major and emerging diversification strategies. Portfolio revision appears to be of no systematic benefit. Depending on the revision of the weights of the assets in the portfolio and the transaction costs, the revision policy can negatively affect the performance of an investment strategy. Considering the transaction costs of portfolios revision, the results of the return performance computation suggest the dominance of the global and the international emerging markets diversification over all other strategies. Finally, an assessment between the return and the cost of the portfolios revision strategy is necessary. Originality/value – The innovation of this paper is to introduce a new concept of the dynamic portfolio management by considering the transaction costs. This paper investigates the performance of a revision procedure for domestic and international portfolios and provides an empirical selection strategy for optimal diversification. The originality of the idea consists on the application of a new statistical inferences methodology to define portfolios needing revision and the use of the TEVM algorithm to define the tracking error dynamic efficient frontiers.
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Jin, Xisong, and Thorsten Lehnert. "Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas." Dependence Modeling 6, no. 1 (February 7, 2018): 19–46. http://dx.doi.org/10.1515/demo-2018-0002.

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Abstract Previous research has focused on the importance of modeling the multivariate distribution for optimal portfolio allocation and active risk management. However, existing dynamic models are not easily applied to high-dimensional problems due to the curse of dimensionality. In this paper, we extend the framework of the Dynamic Conditional Correlation/Equicorrelation and an extreme value approach into a series of Dynamic Conditional Elliptical Copulas. We investigate risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) for passive portfolios and dynamic optimal portfolios using Mean-Variance and ES criteria for a sample of US stocks over a period of 10 years. Our results suggest that (1) Modeling the marginal distribution is important for dynamic high-dimensional multivariate models. (2) Neglecting the dynamic dependence in the copula causes over-aggressive risk management. (3) The DCC/DECO Gaussian copula and t-copula work very well for both VaR and ES. (4) Grouped t-copulas and t-copulas with dynamic degrees of freedom further match the fat tail. (5) Correctly modeling the dependence structure makes an improvement in portfolio optimization with respect to tail risk. (6) Models driven by multivariate t innovations with exogenously given degrees of freedom provide a flexible and applicable alternative for optimal portfolio risk management.
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Fiala, Petr. "New trends in project portfolio management." Trendy v podnikání 10, no. 3 (2021): 4–11. http://dx.doi.org/10.24132/jbt.2020.10.3.4_11.

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The use of project portfolio management is increasingly becoming a tool for promoting the strategy of the organization. Using sophisticated quantitative tools becomes a significant competitive advantage for project portfolio management. Project portfolio management is a dynamic multi-criteria decision-making problem under risk. The paper presents new approaches for analyzing the problem. A dynamic version of the Analytic Network Process (ANP) captures the network, multicriteria and dynamic structure of the problem. Multicriteria decision trees analyze risk of project portfolios. Possible projects are characterized by sets of inputs and outputs, where inputs are resources for project realization and outputs measure multiple criteria of goals of the organization. The Data Envelopment Analysis (DEA) is an appropriate approach to select efficient project portfolios.
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Škrinjarić, Tihana, and Boško Šego. "Dynamic Portfolio Selection on Croatian Financial Markets: MGARCH Approach." Business Systems Research Journal 7, no. 2 (September 1, 2016): 78–90. http://dx.doi.org/10.1515/bsrj-2016-0014.

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Abstract Background: Investors on financial markets are interested in finding trading strategies which could enable them to beat the market. They always look for best possibilities to achieve above-average returns and manage risks successfully. MGARCH methodology (Multivariate Generalized Autoregressive Conditional Heteroskedasticity) makes it possible to model changing risks and return dynamics on financial markets on a daily basis. The results could be used in order to enhance portfolio formation and restructuring over time. Objectives: This study utilizes MGARCH methodology on Croatian financial markets in order to enhance portfolio selection on a daily basis. Methods/Approach: MGARCH methodology is applied to the stock market index CROBEX, the bond market index CROBIS and the kuna/euro exchange rate in order to model the co-movements of returns and risks on a daily basis. The estimation results are then used to form successful portfolios. Results: Results indicate that using MGARCH methodology (the CCC and the DCC model) as guidance when forming and rebalancing a portfolio contributes to less portfolio volatility and greater cumulated returns compared to strategies which do not take this methodology into account. Conclusions: It is advisable to use MGARCH methodology when forming and rebalancing portfolios in terms of portfolio selection.
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Grinold, Richard C. "Dynamic Portfolio Analysis." Journal of Portfolio Management 34, no. 1 (October 31, 2007): 12–26. http://dx.doi.org/10.3905/jpm.2007.698029.

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Yu, Jiayang, and Kuo-Chu Chang. "Neural Network Predictive Modeling on Dynamic Portfolio Management—A Simulation-Based Portfolio Optimization Approach." Journal of Risk and Financial Management 13, no. 11 (November 17, 2020): 285. http://dx.doi.org/10.3390/jrfm13110285.

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Portfolio optimization and quantitative risk management have been studied extensively since the 1990s and began to attract even more attention after the 2008 financial crisis. This disastrous occurrence propelled portfolio managers to reevaluate and mitigate the risk and return trade-off in building their clients’ portfolios. The advancement of machine-learning algorithms and computing resources helps portfolio managers explore rich information by incorporating macroeconomic conditions into their investment strategies and optimizing their portfolio performance in a timely manner. In this paper, we present a simulation-based approach by fusing a number of macroeconomic factors using Neural Networks (NN) to build an Economic Factor-based Predictive Model (EFPM). Then, we combine it with the Copula-GARCH simulation model and the Mean-Conditional Value at Risk (Mean-CVaR) framework to derive an optimal portfolio comprised of six index funds. Empirical tests on the resulting portfolio are conducted on an out-of-sample dataset utilizing a rolling-horizon approach. Finally, we compare its performance against three benchmark portfolios over a period of almost twelve years (01/2007–11/2019). The results indicate that the proposed EFPM-based asset allocation strategy outperforms the three alternatives on many common metrics, including annualized return, volatility, Sharpe ratio, maximum drawdown, and 99% CVaR.
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Rizzini, Mattia, Chris Fawcett, Mauro Vallati, Alfonso E. Gerevini, and Holger H. Hoos. "Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis." International Journal on Artificial Intelligence Tools 26, no. 01 (February 2017): 1760006. http://dx.doi.org/10.1142/s0218213017600065.

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Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for domainindependent optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive empirical analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation.
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Henriques, Irene, and Perry Sadorsky. "Can Bitcoin Replace Gold in an Investment Portfolio?" Journal of Risk and Financial Management 11, no. 3 (August 14, 2018): 48. http://dx.doi.org/10.3390/jrfm11030048.

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Bitcoin is an exciting new financial product that may be useful for inclusion in investment portfolios. This paper investigates the implications of replacing gold in an investment portfolio with bitcoin (“digital gold”). Our approach is to use several different multivariate GARCH models (dynamic conditional correlation (DCC), asymmetric DCC (ADCC), generalized orthogonal GARCH (GO-GARCH)) to estimate minimum variance equity portfolios. Both long and short portfolios are considered. An analysis of the economic value shows that risk-averse investors will be willing to pay a high performance fee to switch from a portfolio with gold to a portfolio with bitcoin. These results are robust to the inclusion of trading costs.
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Dissertations / Theses on the topic "Dynamic portfolio"

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Mazibas, Murat. "Dynamic portfolio construction and portfolio risk measurement." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3297.

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The research presented in this thesis addresses different aspects of dynamic portfolio construction and portfolio risk measurement. It brings the research on dynamic portfolio optimization, replicating portfolio construction, dynamic portfolio risk measurement and volatility forecast together. The overall aim of this research is threefold. First, it is aimed to examine the portfolio construction and risk measurement performance of a broad set of volatility forecast and portfolio optimization model. Second, in an effort to improve their forecast accuracy and portfolio construction performance, it is aimed to propose new models or new formulations to the available models. Third, in order to enhance the replication performance of hedge fund returns, it is aimed to introduce a replication approach that has the potential to be used in numerous applications, in investment management. In order to achieve these aims, Chapter 2 addresses risk measurement in dynamic portfolio construction. In this chapter, further evidence on the use of multivariate conditional volatility models in hedge fund risk measurement and portfolio allocation is provided by using monthly returns of hedge fund strategy indices for the period 1990 to 2009. Building on Giamouridis and Vrontos (2007), a broad set of multivariate GARCH models, as well as, the simpler exponentially weighted moving average (EWMA) estimator of RiskMetrics (1996) are considered. It is found that, while multivariate GARCH models provide some improvements in portfolio performance over static models, they are generally dominated by the EWMA model. In particular, in addition to providing a better risk-adjusted performance, the EWMA model leads to dynamic allocation strategies that have a substantially lower turnover and could therefore be expected to involve lower transaction costs. Moreover, it is shown that these results are robust across the low - volatility and high-volatility sub-periods. Chapter 3 addresses optimization in dynamic portfolio construction. In this chapter, the advantages of introducing alternative optimization frameworks over the mean-variance framework in constructing hedge fund portfolios for a fund of funds. Using monthly return data of hedge fund strategy indices for the period 1990 to 2011, the standard mean-variance approach is compared with approaches based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investors. In order to estimate portfolio CVaR, CDaR and Omega, a semi-parametric approach is proposed, in which first the marginal density of each hedge fund index is modelled using extreme value theory and the joint density of hedge fund index returns is constructed using a copula-based approach. Then hedge fund returns from this joint density are simulated in order to compute CVaR, CDaR and Omega. The semi-parametric approach is compared with the standard, non-parametric approach, in which the quantiles of the marginal density of portfolio returns are estimated empirically and used to compute CVaR, CDaR and Omega. Two main findings are reported. The first is that CVaR-, CDaR- and Omega-based optimization offers a significant improvement in terms of risk-adjusted portfolio performance over mean-variance optimization. The second is that, for all three risk measures, semi-parametric estimation of the optimal portfolio offers a very significant improvement over non-parametric estimation. The results are robust to as the choice of target return and the estimation period. Chapter 4 searches for improvements in portfolio risk measurement by addressing volatility forecast. In this chapter, two new univariate Markov regime switching models based on intraday range are introduced. A regime switching conditional volatility model is combined with a robust measure of volatility based on intraday range, in a framework for volatility forecasting. This chapter proposes a one-factor and a two-factor model that combine useful properties of range, regime switching, nonlinear filtration, and GARCH frameworks. Any incremental improvement in the performance of volatility forecasting is searched for by employing regime switching in a conditional volatility setting with enhanced information content on true volatility. Weekly S&P500 index data for 1982-2010 is used. Models are evaluated by using a number of volatility proxies, which approximate true integrated volatility. Forecast performance of the proposed models is compared to renowned return-based and range-based models, namely EWMA of Riskmetrics, hybrid EWMA of Harris and Yilmaz (2009), GARCH of Bollerslev (1988), CARR of Chou (2005), FIGARCH of Baillie et al. (1996) and MRSGARCH of Klaassen (2002). It is found that the proposed models produce more accurate out of sample forecasts, contain more information about true volatility and exhibit similar or better performance when used for value at risk comparison. Chapter 5 searches for improvements in risk measurement for a better dynamic portfolio construction. This chapter proposes multivariate versions of one and two factor MRSACR models introduced in the fourth chapter. In these models, useful properties of regime switching models, nonlinear filtration and range-based estimator are combined with a multivariate setting, based on static and dynamic correlation estimates. In comparing the out-of-sample forecast performance of these models, eminent return and range-based volatility models are employed as benchmark models. A hedge fund portfolio construction is conducted in order to investigate the out-of-sample portfolio performance of the proposed models. Also, the out-of-sample performance of each model is tested by using a number of statistical tests. In particular, a broad range of statistical tests and loss functions are utilized in evaluating the forecast performance of the variance covariance matrix of each portfolio. It is found that, in terms statistical test results, proposed models offer significant improvements in forecasting true volatility process, and, in terms of risk and return criteria employed, proposed models perform better than benchmark models. Proposed models construct hedge fund portfolios with higher risk-adjusted returns, lower tail risks, offer superior risk-return tradeoffs and better active management ratios. However, in most cases these improvements come at the expense of higher portfolio turnover and rebalancing expenses. Chapter 6 addresses the dynamic portfolio construction for a better hedge fund return replication and proposes a new approach. In this chapter, a method for hedge fund replication is proposed that uses a factor-based model supplemented with a series of risk and return constraints that implicitly target all the moments of the hedge fund return distribution. The approach is used to replicate the monthly returns of ten broad hedge fund strategy indices, using long-only positions in ten equity, bond, foreign exchange, and commodity indices, all of which can be traded using liquid, investible instruments such as futures, options and exchange traded funds. In out-of-sample tests, proposed approach provides an improvement over the pure factor-based model, offering a closer match to both the return performance and risk characteristics of the hedge fund strategy indices.
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Liao, Chien-Hui. "Essays on dynamic portfolio management." Thesis, University of Warwick, 2003. http://wrap.warwick.ac.uk/1254/.

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Over the last three decades, there has been an increasing interest in the problem of the investor's optimal consumption and portfolio rules. Despite the substantial amount of related literature, there remain many areas for further investigation. The thesis, therefore, addresses a number of important issues relating to the theory and practice of dynamic portfolio strategies. The thesis consists of five essays. The first two essays, Chapters 3 and 4, are concerned with efficient dynamic asset allocation programs under alternative market assumptions. Chapter 3 studies a situation where the simple time-invariant portfolio strategies are efficient and provides a complete characterisation of the strategies using the efficiency arguments. The popularised constant proportion portfolio insurance (CPPI) is embedded as a special case. Chapter 4 relaxes the assumption of a constant interest rate to allow the interest rate to follow a one factor stochastic process. The factor risk premium is then determined in a way that is consistent with the underlying equilibrium. These results are then applied to solve explicitly for an investor's optimal portfolio choice problem under the special case of a Vacisek short rate model and alternative utility functions. The third essay, Chapter 5, relaxes the assumption of a constant equity risk premium to allow the risk premium to vary through time. The evolution of the market risk premium in a representative agent equilibrium (consistent with the Black-Scholes option pricing) is investigated using a unified approach. The presence of dividends and intermediate consumption proves to be the key element that enables us to obtain a stationary economy with decreasing relative risk aversion, a theoretical result that has not be established in the existing literature. The last two essays. Chapters 6 and 7. are concerned with issues of portfolio efficiency and performance measurement. Chapter 6 uses the result from Chapter 5 that, without dividends and intermediate consumption, the market risk premium must satisfy the Burgers' equation, and applies Dybvig's payoff distribution pricing model to measure the inefficiency costs incurred when this condition is violated. The numerical results show that the degree of inefficiency is not very significant, at least for the cases which we postulate, but the findings also reassure negative result predicted from the model. Finally, Chapter 7 proposes a new utility based performance measure that can be applied in the ex-post evaluation of dynamic portfolio strategies. We construct a contingent claim estimation approach to estimate the nearest efficient strategy from a single realisation and then quantify the opportunity cost resulting from the departure of the observed strategy from the nearest efficient one. The numerical examples show that the technique is remarkably robust.
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Wang, Jianshen. "Portfolio optimisation and dynamic trading." Thesis, University of Bristol, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702879.

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Gutkowska, Anna Barbara. "Essays on the dynamic portfolio choice." [Rotterdam] : Rotterdam : Erasmus Research Institute of Management (ERIM), Erasmus University Rotterdam ; Erasmus University [Host], 2006. http://hdl.handle.net/1765/7994.

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Catanas, Fernando Jorge de Lyz Girou Rodrigues. "Heuristics for the dynamic portfolio problem." Thesis, Imperial College London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322226.

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Sbuelz, Alessandro. "Essays in derivatives pricing and dynamic portfolio." Thesis, London Business School (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313275.

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He, Hua. "Essays in dynamic portfolio optimization and diffusion estimations." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14136.

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Polat, Onur. "Dynamic Complex Hedging And Portfolio Optimization In Additive Markets." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12610441/index.pdf.

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In this study, the geometric Additive market models are considered. In general, these market models are incomplete, that means: the perfect replication of derivatives, in the usual sense, is not possible. In this study, it is shown that the market can be completed by new artificial assets which are called &ldquo
power-jump assets&rdquo
based on the power-jump processes of the underlying Additive process. Then, the hedging portfolio for claims whose payoff function depends on the prices of the stock and the power-jump assets at maturity is derived. In addition to the previous completion strategy, it is also shown that, using a static hedging formula, the market can also be completed by considering portfolios with a continuum of call options with different strikes and the same maturity. What is more, the portfolio optimization problem is considered in the enlarged market. The optimization problem consists of choosing an optimal portfolio in such a way that the largest expected utility of the terminal wealth is obtained. For particular choices of the equivalent martingale measure, it is shown that the optimal portfolio consists only of bonds and stocks.
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Horneff, Wolfram Johannes. "Dynamic portfolio choice with pension annuities and life insurance /." Frankfurt, 2008. http://opac.nebis.ch/cgi-bin/showAbstract.pl?sys=000253337.

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Karamanis, Dimitrios. "Stochastic dynamic programming methods for the portfolio selection problem." Thesis, London School of Economics and Political Science (University of London), 2013. http://etheses.lse.ac.uk/724/.

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In this thesis, we study the portfolio selection problem with multiple risky assets, linear transaction costs and a risk measure in a multi-period setting. In particular, we formulate the multi-period portfolio selection problem as a dynamic program and to solve it we construct approximate dynamic programming (ADP) algorithms, where we include Conditional-Value-at-Risk (CVaR) as a measure of risk, for different separable functional approximations of the value functions. We begin with the simple linear approximation which does not capture the nature of the portfolio selection problem since it ignores risk and leads to portfolios of only one asset. To improve it, we impose upper bound constraints on the holdings of the assets and we notice that we have more diversified portfolios. Then, we implement a piecewise linear approximation, for which we construct an update rule for the slopes of the approximate value functions that preserves concavity as well as the number of slopes. Unlike the simple linear approximation, in the piecewise linear approximation we notice that risk affects the composition of the selected portfolios. Further, unlike the linear approximation with upper bounds, here wealth flows naturally from one asset to another leading to diversified portfolios without us needing to impose any additional constraints on how much we can hold in each asset. For comparison, we consider existing portfolio selection methods, both myopic ones such as the equally weighted and a single-period portfolio models, and multi-period ones such as multistage stochastic programming. We perform extensive simulations using real-world equity data to evaluate the performance of all methods and compare all methods to a market Index. Computational results show that the piecewise linear ADP algorithm significantly outperforms the other methods as well as the market and runs in reasonable computational times. Comparative results of all methods are provided and some interesting conclusions are drawn especially when it comes to comparing the piecewise linear ADP algorithms with multistage stochastic programming.
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Books on the topic "Dynamic portfolio"

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Nielsen, Lars Tyge. Performance measures for dynamic portfolio management. Fontainebleau: INSEAD, 1998.

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Bansal, Ravi. Dynamic trading strategies and portfolio choice. Cambridge, MA: National Bureau of Economic Research, 2004.

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Bansal, Ravi. Dynamic trading strategies and portfolio choice. Cambridge, Mass: National Bureau of Economic Research, 2004.

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Chiarella, Carl, Willi Semmler, Chih-Ying Hsiao, and Lebogang Mateane. Sustainable Asset Accumulation and Dynamic Portfolio Decisions. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49229-1.

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Brandt, Michael W. Dynamic portfolio selection by augmenting the asset space. Cambridge, MA: National Bureau of Economic Research, 2004.

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Liu, Jun. Dynamic asset allocation with event risk. Cambridge, MA: National Bureau of Economic Research, 2002.

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Didier, Tatiana. The current account as a dynamic portfolio choice problem. [Washington, D.C: World Bank, 2009.

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Duffie, Darrell. Dynamic asset pricing theory. 2nd ed. Princeton, N.J: Princeton University Press, 1996.

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Dynamic asset pricing theory. Princeton, N.J: Princeton University Press, 1992.

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Duffie, Darrell. Dynamic asset pricing theory. 3rd ed. Princeton, N.J: Princeton University Press, 2001.

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Book chapters on the topic "Dynamic portfolio"

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Sawik, Tadeusz. "Selection of Dynamic Supply Portfolio." In Supply Chain Disruption Management Using Stochastic Mixed Integer Programming, 43–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58823-0_3.

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Jammernegg, Werner. "Dynamic Portfolio Models under Uncertainty." In Lecture Notes in Economics and Mathematical Systems, 24–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-46646-5_3.

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Semmler, Willi. "Dynamic Portfolio Choice Models: Theory." In Asset Prices, Booms and Recessions, 203–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20680-1_17.

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Semmler, Willi. "Dynamic Portfolio Choice Models: Empirics." In Asset Prices, Booms and Recessions, 223–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20680-1_18.

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Bernhard, Pierre, Jacob C. Engwerda, Berend Roorda, J. M. Schumacher, Vassili Kolokoltsov, Patrick Saint-Pierre, and Jean-Pierre Aubin. "Merton’s Optimal Dynamic Portfolio Revisited." In The Interval Market Model in Mathematical Finance, 3–16. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-0-8176-8388-7_1.

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Sawik, Tadeusz. "Selection of Dynamic Supply Portfolio." In Supply Chain Disruption Management, 47–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44814-1_3.

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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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Chiarella, Carl, Willi Semmler, Chih-Ying Hsiao, and Lebogang Mateane. "Dynamic Saving and Portfolio Decisions-Theory." In Dynamic Modeling and Econometrics in Economics and Finance, 53–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49229-1_4.

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Zhang, Wei-Bin. "Portfolio Choice in General Dynamic Equilibrium." In The General Economic Theory, 203–15. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56204-5_10.

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Liutvinavicius, Marius, and Virgilijus Sakalauskas. "Dynamic Simulation of Pension Funds’ Portfolio." In Business Information Systems Workshops, 69–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34228-8_8.

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Conference papers on the topic "Dynamic portfolio"

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He, Chao-lin. "Dynamic Portfolio Choice under Incomplete Information." In 2009 1st International Conference on Information Science and Engineering (ICISE 2009). IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.521.

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Chen, Shea D., and Andrew E. B. Lim. "Dynamic portfolio choice with Bayesian regret." In 2012 IEEE 51st Annual Conference on Decision and Control (CDC). IEEE, 2012. http://dx.doi.org/10.1109/cdc.2012.6425943.

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Xu, Qi-Fa, Cui-Xia Jiang, and Pu Kang. "Dynamic Portfolio Selection Under Higher Moments." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370565.

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He, Chao-lin. "Dynamic Portfolio Choice under Model Uncertainty." In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.411.

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Fiala, Petr. "Dynamic Project Portfolio Management Using ANP." In International Symposium on the Analytic Hierarchy Process. Creative Decisions Foundation, 2014. http://dx.doi.org/10.13033/isahp.y2014.081.

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Feng, Keyu, Yan Yan, and Quanbao Li. "REIT Performance and Dynamic Portfolio Considerations." In 2015 Information Technology and Mechatronics Engineering Conference. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/itoec-15.2015.38.

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He Chao-lin. "Dynamic portfolio choice: Time-varying and jumps." In 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icicisys.2010.5658533.

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Lim, Andrew E. B., and Poomyos Wimonkittiwat. "Dynamic portfolio choice with market impact costs." In 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011). IEEE, 2011. http://dx.doi.org/10.1109/cdc.2011.6161506.

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Fulga, Cristinca. "Dynamic Portfolio Optimization for Utility-Based Models." In 2009 International Conference on Information and Financial Engineering, ICIFE. IEEE, 2009. http://dx.doi.org/10.1109/icife.2009.24.

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Wang, Lewen, Weiqing Liu, Xiao Yang, and Jiang Bian. "Conservative or Aggressive? Confidence-Aware Dynamic Portfolio Construction." In 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2019. http://dx.doi.org/10.1109/globalsip45357.2019.8969173.

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Reports on the topic "Dynamic portfolio"

1

Bansal, Ravi, Magnus Dahlquist, and Campbell Harvey. Dynamic Trading Strategies and Portfolio Choice. Cambridge, MA: National Bureau of Economic Research, October 2004. http://dx.doi.org/10.3386/w10820.

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Brandt, Michael, and Pedro Santa-Clara. Dynamic Portfolio Selection by Augmenting the Asset Space. Cambridge, MA: National Bureau of Economic Research, March 2004. http://dx.doi.org/10.3386/w10372.

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Horneff, Wolfram, Raimond Maurer, Olivia Mitchell, and Michael Stamos. Money in Motion: Dynamic Portfolio Choice in Retirement. Cambridge, MA: National Bureau of Economic Research, February 2007. http://dx.doi.org/10.3386/w12942.

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Li, Degui, Jia Chen, Oliver Linton, and Zudi Lu. Semiparametric dynamic portfolio choice with multiple conditioning variables. IFS, February 2015. http://dx.doi.org/10.1920/wp.cem.2015.0715.

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Cai, Yongyang, Kenneth Judd, and Rong Xu. Numerical Solution of Dynamic Portfolio Optimization with Transaction Costs. Cambridge, MA: National Bureau of Economic Research, January 2013. http://dx.doi.org/10.3386/w18709.

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Chacko, George, and Luis Viceira. Dynamic Consumption and Portfolio Choice with Stochastic Volatility in Incomplete Markets. Cambridge, MA: National Bureau of Economic Research, October 1999. http://dx.doi.org/10.3386/w7377.

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Brandt, Michael, Amit Goyal, Pedro Santa-Clara, and Jonathan Storud. A Simulation Approach to Dynamic Portfolio Choice with an Application to Learning About Return Predictability. Cambridge, MA: National Bureau of Economic Research, November 2004. http://dx.doi.org/10.3386/w10934.

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Bacchetta, Philippe, and Eric van Wincoop. Puzzling Exchange Rate Dynamics and Delayed Portfolio Adjustment. Cambridge, MA: National Bureau of Economic Research, September 2019. http://dx.doi.org/10.3386/w26259.

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Lynch, Anthony, and Sinan Tan. Labor Income Dynamics at Business-Cycle Frequencies: Implications for Portfolio Choice. Cambridge, MA: National Bureau of Economic Research, December 2004. http://dx.doi.org/10.3386/w11010.

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Hau, Harald, and Helene Rey. Can Portfolio Rebalancing Explain the Dynamics of Equity Returns, Equity Flows, and Exchange Rates? Cambridge, MA: National Bureau of Economic Research, May 2004. http://dx.doi.org/10.3386/w10476.

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