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Maximum Loss Portfolio Selection With Lsubscriptp Norm Elliptical Returns And A Generalization Of The Mean Variance Model
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Book Synopsis Maximum Loss Portfolio Selection with L[subscript]p Norm Elliptical Returns and a Generalization of the Mean-variance Model by : Ismail Ceylan
Download or read book Maximum Loss Portfolio Selection with L[subscript]p Norm Elliptical Returns and a Generalization of the Mean-variance Model written by Ismail Ceylan and published by . This book was released on 2009 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Minimax Portfolio Selection Rule with Linear Programming Solution by : Martin Young
Download or read book A Minimax Portfolio Selection Rule with Linear Programming Solution written by Martin Young and published by . This book was released on 1996 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Fat-Tailed and Skewed Asset Return Distributions by : Svetlozar T. Rachev
Download or read book Fat-Tailed and Skewed Asset Return Distributions written by Svetlozar T. Rachev and published by John Wiley & Sons. This book was released on 2005-09-15 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: While mainstream financial theories and applications assume that asset returns are normally distributed, overwhelming empirical evidence shows otherwise. Yet many professionals don’t appreciate the highly statistical models that take this empirical evidence into consideration. Fat-Tailed and Skewed Asset Return Distributions examines this dilemma and offers readers a less technical look at how portfolio selection, risk management, and option pricing modeling should and can be undertaken when the assumption of a non-normal distribution for asset returns is violated. Topics covered in this comprehensive book include an extensive discussion of probability distributions, estimating probability distributions, portfolio selection, alternative risk measures, and much more. Fat-Tailed and Skewed Asset Return Distributions provides a bridge between the highly technical theory of statistical distributional analysis, stochastic processes, and econometrics of financial returns and real-world risk management and investments.
Book Synopsis Optimal Portfolio Selection Under the Estimation Risk in Mean Return by : Lei Zhu
Download or read book Optimal Portfolio Selection Under the Estimation Risk in Mean Return written by Lei Zhu and published by . This book was released on 2008 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis investigates robust techniques for mean-variance (MV) portfolio optimization problems under the estimation risk in mean return. We evaluate the performance of the optimal portfolios generated by the min-max robust MV portfolio optimization model. With an ellipsoidal uncertainty set based on the statistics of the sample mean estimates, minmax robust portfolios equal to the ones from the standard MV model based on the nominal mean estimates but with larger risk aversion parameters. With an interval uncertainty set for mean return, min-max robust portfolios can vary significantly with the initial data used to generate the uncertainty set. In addition, by focusing on the worst-case scenario in the mean return uncertainty set, min-max robust portfolios can be too conservative and unable to achieve a high return. Adjusting the conservatism level of min-max robust portfolios can only be achieved by excluding poor mean return scenarios from the uncertainty set, which runs counter to the principle of min-max robustness. We propose a CVaR robust MV portfolio optimization model in which the estimation risk is measured by the Conditional Value-at-Risk (CVaR). We show that, using CVaR to quantify the estimation risk in mean return, the conservatism level of CVaR robust portfolios can be more naturally adjusted by gradually including better mean return scenarios. Moreover, we compare min-max robust portfolios (with an interval uncertainty set for mean return) and CVaR robust portfolios in terms of actual frontier variation, portfolio efficiency, and portfolio diversification. Finally, a computational method based on a smoothing technique is implemented to solve the optimization problem in the CVaR robust model. We numerically show that, compared with the quadratic programming (QP) approach, the smoothing approach is more computationally efficient for computing CVaR robust portfolios.
Book Synopsis Portfolio Selection in a Two-Regime World by : Moshe Levy
Download or read book Portfolio Selection in a Two-Regime World written by Moshe Levy and published by . This book was released on 2016 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: Standard mean-variance analysis is based on the assumption of normal return distributions. However, a growing body of literature suggests that the market oscillates between two different regimes - one with low volatility and the other with high volatility. In such a case, even if the return distributions are normal in both regimes, the overall distribution is not - it is a mixture of normals. Mean-variance analysis is inappropriate in this framework, and one must either assume a specific utility function or, alternatively, employ the more general and distribution-free Second degree Stochastic Dominance (SSD) criterion. This paper develops the SSD rule for the case of Mixed Normals: the SSDMN rule. This rule is a generalization the mean-variance rule. The cost of ignoring regimes and assuming normality when the distributions are actually mixed normal can be quite substantial - it is typically equivalent to an annual rate of return of 2%-3%.
Book Synopsis Characteristic-based Mean-variance Portfolio Choice by : Erik Hjalmarsson
Download or read book Characteristic-based Mean-variance Portfolio Choice written by Erik Hjalmarsson and published by . This book was released on 2009 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study empirical mean-variance optimization when the portfolio weights are restricted to be direct functions of underlying stock characteristics such as value and momentum. The closed-form solution to the portfolio weights estimator shows that the portfolio problem in this case reduces to a mean-variance analysis of assets with returns given by single-characteristic strategies (e.g., momentum or value). In an empirical application to international stock return indexes, we show that the direct approach to estimating portfolio weights clearly beats a naive regression-based approach that models the conditional mean. However, a portfolio based on equal weights of the single-characteristic strategies performs about as well, and sometimes better, than the direct estimation approach, highlighting again the difficulties in beating the equal-weighted case in mean-variance analysis. The empirical results also highlight the potential for "stock-picking" in international indexes, using characteristics such as value and momentum, with the characteristic-based portfolios obtaining Sharpe ratios approximately three times larger than the world market.
Book Synopsis Methods and Applications of the Mean-variance Portfolio Selection Model by : John William Marsh
Download or read book Methods and Applications of the Mean-variance Portfolio Selection Model written by John William Marsh and published by . This book was released on 1974 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Portfolio Selection with Parameter and Model Uncertainty by : Lorenzo Garlappi
Download or read book Portfolio Selection with Parameter and Model Uncertainty written by Lorenzo Garlappi and published by . This book was released on 2005 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Mean-Variance Portfolio Selection With 'At-Risk' Constraints and Discrete Distributions by : Gordon J. Alexander
Download or read book Mean-Variance Portfolio Selection With 'At-Risk' Constraints and Discrete Distributions written by Gordon J. Alexander and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We examine the impact of adding either a VaR or a CVaR constraint to the mean-variance model when security returns are assumed to have a discrete distribution with finitely many jump points. Three main results are obtained. First, portfolios on the VaR-constrained boundary exhibit (K 2)-fund separation, where K is the number of states for which the portfolios suffer losses equal to the VaR bound. Second, portfolios on the CVaR-constrained boundary exhibit (K 3)-fund separation, where K is the number of states for which the portfolios suffer losses equal to their VaRs. Third, an example illustrates that while the VaR of the CVaR-constrained optimal portfolio is close to that of the VaR-constrained optimal portfolio, the CVaR of the former is notably smaller than that of the latter. This result suggests that a CVaR constraint is more effective than a VaR constraint to curtail large losses in the mean-variance model.
Book Synopsis Robust Mean-Variance Portfolio Selection by : Cédric Perret-Gentil
Download or read book Robust Mean-Variance Portfolio Selection written by Cédric Perret-Gentil and published by . This book was released on 2007 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper investigates model risk issues in the context of mean-variance portfolio selection. We analytically and numerically show that, under model misspecification, the use of statistically robust estimates instead of the widely used classical sample mean and covariance is highly beneficial for the stability properties of the mean-variance optimal portfolios. Moreover, we perform simulations leading to the conclusion that, under classical estimation, model risk bias dominates estimation risk bias. Finally, we suggest a diagnostic tool to warn the analyst of the presence of extreme returns that have an abnormally large influence on the optimization results.
Book Synopsis Mean-Variance Portfolio Selection with Tracking Error Penalization by : William Lefebvre
Download or read book Mean-Variance Portfolio Selection with Tracking Error Penalization written by William Lefebvre and published by . This book was released on 2020 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 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.
Book Synopsis Tail Mean-Variance Portfolio Selection with Estimation Risk by : Zhenzhen Huang
Download or read book Tail Mean-Variance Portfolio Selection with Estimation Risk written by Zhenzhen Huang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tail Mean-Variance (TMV) has emerged from the actuarial community as a criterion for risk management and portfolio selection, with a focus on extreme losses. The existing literature on portfolio optimization under the TMV criterion relies on the plug-in approach that substitutes the unknown mean and covariance of asset returns in the optimal portfolio weight with their sample counterparts. The plug-in method inevitably introduces estimation risk and usually has poor out-of-sample performance. We propose an optimal combination of the plug-in and 1/N rules to improve out-of-sample performance. Our proposed combined portfolio consistently outperforms both the plug-in and 1/N portfolios on both simulated and real-world datasets.
Book Synopsis Out-of-sample Performance-based Estimation of Expected Returns for Portfolio Selection by : Peng-Chu Chen
Download or read book Out-of-sample Performance-based Estimation of Expected Returns for Portfolio Selection written by Peng-Chu Chen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper provides a framework for obtaining the estimator of expected asset returns for portfolio selection. The framework relies on a linear model where the expected returns are the coefficients to be estimated. The model is fitted to a synthetic dataset by Bayesian regression. The estimator is computed using a Gibbs sampler; it is consistent and asymptotically efficient when the size of the synthetic dataset grows to infinity. An empirical study shows that, under appropriate conditions, mean-variance portfolios constructed using this estimator yield better out-of-sample average returns and Sharpe ratios than benchmark portfolios, with or without a norm constraint.
Book Synopsis Optimal Mean-variance Portfolio Selection Using Historic and Fama-French Three-Factor Model Mean Estimation by : Corey P. O'Keefe
Download or read book Optimal Mean-variance Portfolio Selection Using Historic and Fama-French Three-Factor Model Mean Estimation written by Corey P. O'Keefe and published by . This book was released on 2001 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Enhancing Mean-Variance Portfolio Selection by Modeling Distributional Asymmetries by : Rand Kwong Yew Low
Download or read book Enhancing Mean-Variance Portfolio Selection by Modeling Distributional Asymmetries written by Rand Kwong Yew Low and published by . This book was released on 2015 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: Why do mean-variance (MV) models perform so poorly? In searching for an answer to this question, we estimate expected returns by sampling from a multivariate probability model that explicitly incorporates distributional asymmetries. Specifically, our empirical analysis shows that an application of copulas using marginal models that incorporate dynamic features such as autoregression, volatility clustering, and skewness to reduce estimation error in comparison to historical sampling windows. Using these copula-based models, we find that several MV-based rules exhibit statistically significant and superior performance improvements even after accounting for transaction costs. However, we find that outperforming the naive equally-weighted (1/N) strategy after accounting for transactions costs still remains an elusive task.
Book Synopsis A Comparison of the Mean-Variance-Leverage Optimization Model and the Markowitz General Mean-Variance Portfolio Selection Model by : Ph.D. Jacobs (Bruce I.)
Download or read book A Comparison of the Mean-Variance-Leverage Optimization Model and the Markowitz General Mean-Variance Portfolio Selection Model written by Ph.D. Jacobs (Bruce I.) and published by . This book was released on 2013 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: The mean-variance-leverage (MVL) optimization model (Jacobs and Levy 2012, 2013a) tackles an issue not dealt with by the mean-variance optimization inherent in the general mean-variance portfolio selection model (GPSM) -- that is, the impact on investor utility of the risks that are unique to using leverage. Relying on leverage constraints with a conventional GPSM, as is commonly done today, is unlikely to lead to the portfolio offering a leverage-averse investor the highest utility. But investors can use the MVL model to find optimal portfolios that balance expected return, volatility risk, and leverage risk. The MVL model has intuitive appeal and offers straightforward implementation for portfolio selection. In contrast, practical use of a broader application of GPSM, as suggested by Markowitz (2013), is dependent on successful future development of a stochastic margin-call model (SMCM).
Book Synopsis Growth-Oriented Portfolio Selection Based on Stochastic Holding Periods by : Thomas Burkhardt
Download or read book Growth-Oriented Portfolio Selection Based on Stochastic Holding Periods written by Thomas Burkhardt and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on the concept of time optimal portfolio selection, a specific model is developed which is designed for investors who wish to attain a certain predefined level of wealth and whose preferences can be defined on the distribution of the time at which this goal level is reached for the first time. This time marks the end of a then stochastic holding period for any risky investment strategy. In contrast to the meanwhile classic approach to portfolio selection originated by Markowitz, the portfolio choice is not based on the distribution of the portfolio value at a given future point in time, but on the distribution of the holding period after which the portfolio value reaches the desired level the first time. The model is based on assumptions which are compatible to those of the classic one period mode. A portfolio is considered the more desirable, the shorter the mean and the lower the variance of the holding period is. This implements a mean-variance-type model based on stochastic holding periods. The asset price dynamics is modeled by an arithmetic Brownian process. The resulting portfolio frontier is isomorphic to the portfolio frontier of the standard model for positive mean returns. The efficient set instead shows highly different qualitative properties, which are investigated in detail and exemplified using realistic data. The set of efficient portfolios of the time optimal model is a subset of those of the standard model.
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