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Book Portfolio Selection With a Drawdown Constraint

Download or read book Portfolio Selection With a Drawdown Constraint written by Gordon J. Alexander and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: When identifying optimal portfolios, practitioners often impose a drawdown constraint. This constraint is even explicit in some money management contracts such as the one recently involving Merrill Lynch' management of Unilever's pension fund. In this setting, we provide a characterization of optimal portfolios using mean-variance analysis. In the absence of a benchmark, we find that while the constraint typically decreases the optimal portfolio's standard deviation, the constrained optimal portfolio can be notably mean-variance inefficient. In the presence of a benchmark such as in the Merrill Lynch-Unilever contract, we find that the constraint increases the optimal portfolio's standard deviation and tracking error volatility. Thus, the constraint negatively affects a portfolio manager's ability to track a benchmark.

Book Portfolio Selection with Drawdown Constraint on Consumption

Download or read book Portfolio Selection with Drawdown Constraint on Consumption written by Junkee Jeon and published by . This book was released on 2020 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study, we generalize the results of Arun (2013) on the optimal consumption and investment problem of an infinitely lived agent who does not accept her consumption falling below a fixed proportion of her historically highest level, the so-called drawdown constraint on consumption. We extend the results to a general class of utility functions. We use the martingale method to study the dual problem, which involves the choice of a maximum consumption process. The dual problem can be formulated as a two-dimensional singular control problem, with the free boundary depending on a state variable of the maximum process. We establish the duality theorem and provide semi-closed form solutions regarding the optimal strategies. To highlight our methodology, we present some special cases of utility functions that do not allow for the dimension reduction considered in Arun (2013).

Book Supply Chain and Finance

Download or read book Supply Chain and Finance written by Panos M. Pardalos and published by World Scientific. This book was released on 2004 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes recently developed mathematical models, methodologies, and case studies in diverse areas, including stock market analysis, portfolio optimization, classification techniques in economics, supply chain optimization, development of e-commerce applications, etc. It will be of interest to both theoreticians and practitioners working in economics and finance.

Book Drawdown Controlled Optimal Portfolio Selection with Linear Constraints on Portfolio Weights

Download or read book Drawdown Controlled Optimal Portfolio Selection with Linear Constraints on Portfolio Weights written by Guangliang He and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We solve the problem of constructing an optimal portfolio consisting of many risky assets to maximize the long-term growth rate of a representative agent's expected utility, subject to a set of general linear constraints on the portfolio weight vector as well as a constraint to prevent wealth drawdowns below a dynamic floor. The dynamic floor is defined as the time-decayed historical all-time high. Our results generalize those achieved by earlier authors, including Grossman and Zhou (1993) and Cvitannic and Karatzas (1994). Grossman and Zhou solved a special case of our problem by focusing on a single risky asset without portfolio weight constraints. Cvitanic and Karatzas solved a problem involving many risky assets but that ignored portfolio weight constraints and the time decay on the dynamic floor. To illustrate the usefulness of our method, we present several numerical examples based on both actual and simulated (Monte Carlo) returns. Finally, we suggest applications of our results to various practical investment management problems, including the management of hedge fund portfolios and 'principal-protected' investment strategies.

Book On Portfolio Optimization Under  drawdown  Constraints

Download or read book On Portfolio Optimization Under drawdown Constraints written by University of Minnesota. Institute for Mathematics and Its Applications and published by . This book was released on 1994 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Portfolio Optimization with Drawdown Constraints

Download or read book Portfolio Optimization with Drawdown Constraints written by Alexei Valerievich Chekhlov and published by . This book was released on 2000 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Portfolio Optimization with Drawdown Constraints

Download or read book Portfolio Optimization with Drawdown Constraints written by Alexei Valerievich Chekhlov and published by . This book was released on 2000 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Portfolio Management with Drawdown Constraint

Download or read book Portfolio Management with Drawdown Constraint written by Maxime Bonelli and published by . This book was released on 2017 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: We analyze optimal investment strategies under the drawdown constraint that the wealth process never falls below a fixed fraction of its running maximum. We derive optimal allocation programs by solving numerically the Hamilton-Jacobi-Bellman equation that characterizes the finite horizon expected utility maximization problem, for investors with power utility as well as S-shaped utility. Using stochastic simulations, we find that, according to utility maximization, implementing the drawdown constraint can be gainful in optimal portfolios for the power utility, for some market configurations and investment horizons. However, our study reveals that the optimal strategy with drawdown constraint is not the preferred investment for the S-shaped utility investor, who rather prefers the equivalent optimal strategy without constraint. Indeed, the latter investment being similar to a partial portfolio insurance, the additional drawdown constraint does not appear valuable for this investor in optimal portfolios.

Book Mean Variance Optimal Portfolio Selection with a Value At Risk Constraint

Download or read book Mean Variance Optimal Portfolio Selection with a Value At Risk Constraint written by Hui Deng and published by Open Dissertation Press. This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Mean-variance Optimal Portfolio Selection With a Value-at-risk Constraint" by Hui, Deng, 鄧惠, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b4189721 Subjects: Risk Portfolio management - Mathematical models

Book Drawdown Constraints and Portfolio Optimization

Download or read book Drawdown Constraints and Portfolio Optimization written by Marcus Davidsson and published by . This book was released on 2013 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seminal work by Markowitz in 1959 introduced portfolio theory to the world. The prevailing notion since then has been that portfolio risk is non linear i.e. you cannot use Linear Programming (LP) to optimize your portfolio. We will in this paper show that simple portfolio drawdown constraints are indeed linear and can be used to find for example maximum risk adjusted return portfolios. VaR for these portfolios can then be estimated directly instead of using computer intensive Monte Carlo methods.

Book Some Contributions of Bayesian and Computational Learning Methods to Portfolio Selection Problems

Download or read book Some Contributions of Bayesian and Computational Learning Methods to Portfolio Selection Problems written by Johann Nicolle and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present thesis is a study of different optimal portfolio allocation problems in the case where the appreciation rate, named the drift, of the Brownian motion driving the dynamics of the assets is uncertain. We consider an investor having a belief on the drift in the form of a probability distribution, called a prior. The uncertainty about the drift is managed through a Bayesian learning approach which allows for the update of the drift's prior probability distribution. The thesis is divided into two self-contained parts; the first part being split into two chapters: the first develops the theory and the second contains a detailed application to actual market data. A third part constitutes an Appendix and details the data used in the applications. The first part of the thesis is dedicated to the multidimensional Markowitz portfolio selection problem in the case of drift uncertainty. This uncertainty is modeled via an arbitrary prior law which is updated using Bayesian filtering. We first embed the Bayesian-Markowitz problem into an auxiliary standard control problem for which dynamic programming is applied. Then, we show existence and uniqueness of a smooth solution to the related semi-linear partial differential equation (PDE). In the case of a Gaussian prior probability distribution, the multidimensional solution is explicitly computed. Additionally, we study the quantitative impact of learning from the progressively observed data, by comparing the strategy which updates the initial estimate of the drift, i.e. the learning strategy, to the one that keeps it constant, named the non-learning strategy. Ultimately, we analyze the sensitivity of the gain from learning, called value of information or informative value, with respect to different parameters. Next, we illustrate the theory with a detailed application of the previous results on actual market data. We emphasize the robustness of the value added of learning by comparing learning to non-learning optimal strategies in different investment universes: indices of various asset classes, currencies and smart beta strategies. The second part tackles a discrete-time portfolio optimization problem. Here, the goal of the investor is to maximize the expected utility of the terminal wealth of a portfolio of risky assets, assuming an uncertain drift and a maximum drawdown constraint. In this part, we formulate the problem in the general case, and we solve numerically the Gaussian case with the Constant Relative Risk Aversion (CRRA) type utility function via a deep learning resolution. Ultimately, we study the sensitivity of the strategy to the degree of uncertainty of the drift and, as a byproduct, give empirical evidence of the convergence of the non-learning strategy towards a no short-sale constrained Merton problem.

Book Portfolio Benchmarking Under Drawdown Constraint and Stochastic Sharpe Ratio

Download or read book Portfolio Benchmarking Under Drawdown Constraint and Stochastic Sharpe Ratio written by Ankush Agarwal and published by . This book was released on 2016 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider an investor who seeks to maximize her expected utility derived from her terminal wealth relative to the maximum performance achieved over a fixed time horizon, and under a portfolio drawdown constraint, in a market with local stochastic volatility (LSV). In the absence of closed-form formulas for the value function and optimal portfolio strategy, we obtain approximations for these quantities through the use of a coefficient expansion technique and nonlinear transformations. We utilize regularity properties of the risk tolerance function to numerically compute the estimates for our approximations. In order to achieve similar value functions, we illustrate that, compared to a constant volatility model, the investor must deploy a quite different portfolio strategy which depends on the current level of volatility in the stochastic volatility model.

Book Linear and Mixed Integer Programming for Portfolio Optimization

Download or read book Linear and Mixed Integer Programming for Portfolio Optimization written by Renata Mansini and published by Springer. This book was released on 2015-06-10 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features. Other linear models, such as models for portfolio rebalancing and index tracking, are also covered. The book discusses computational issues and provides a theoretical framework, including the concepts of risk-averse preferences, stochastic dominance and coherent risk measures. The material is presented in a style that requires no background in finance or in portfolio optimization; some experience in linear and mixed integer models, however, is required. The book is thoroughly didactic, supplementing the concepts with comments and illustrative examples.

Book Online Portfolio Selection

Download or read book Online Portfolio Selection written by Bin Li and published by CRC Press. This book was released on 2018-10-30 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: Introduce OLPS and formulate OLPS as a sequential decision task Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art Investigate possible future directions Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. Readers are encouraged to visit the authors’ website for updates: http://olps.stevenhoi.org.

Book Financial Risk Modelling and Portfolio Optimization with R

Download or read book Financial Risk Modelling and Portfolio Optimization with R written by Bernhard Pfaff and published by John Wiley & Sons. This book was released on 2016-08-16 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Risk Modelling and Portfolio Optimization with R, 2nd Edition Bernhard Pfaff, Invesco Global Asset Allocation, Germany A must have text for risk modelling and portfolio optimization using R. This book introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. This edition has been extensively revised to include new topics on risk surfaces and probabilistic utility optimization as well as an extended introduction to R language. Financial Risk Modelling and Portfolio Optimization with R: Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk concepts and optimization with risk constraints. Is accompanied by a supporting website featuring examples and case studies in R. Includes updated list of R packages for enabling the reader to replicate the results in the book. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.

Book Worldwide Asset and Liability Modeling

Download or read book Worldwide Asset and Liability Modeling written by William T. Ziemba and published by Cambridge University Press. This book was released on 1998-11-12 with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt: The underlying theme of this volume is how to invest assets over time to achieve satisfactory returns subject to uncertainties, various constraints and liability commitments. Most investors, be they individuals or institutions, do not diversify properly across markets nor across time. The papers utilize several approaches and integrate a number of techniques as well as discussing a variety of models that have either been implemented, are close to being implemented, or represent new innovative approaches that may lead to future novel applications. Other issues address the future of asset-liability management modeling. This includes models for individuals, and various financial institutions such as banks and insurance companies. This will lead to custom products, that is, financial engineering. All in all, this will be essential reading for all involved in analysing the financial markets.