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:
Download or read book Parameter Uncertainty in Portfolio Selection written by Apostolos Kourtis and published by . This book was released on 2012 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: The estimation of the inverse covariance matrix plays a crucial role in optimal portfolio choice. We propose a new estimation framework that focuses on enhancing portfolio performance. The framework applies the statistical methodology of shrinkage directly to the inverse covariance matrix using two non-parametric methods. The first minimises the out-of-sample portfolio variance while the second aims to increase out-of-sample risk-adjusted returns. We apply the resulting estimators to compute the minimum variance portfolio weights and obtain a set of new portfolio strategies. These strategies have an intuitive form which allows us to extend our framework to account for short-sale constraints, high transaction costs and singular covariance matrices. A comparative empirical analysis against several strategies from the literature shows that the new strategies generally offer higher risk-adjusted returns and lower levels of risk.
Download or read book Sparse and Stable Portfolio Selection with Parameter Uncertainty written by Jiahan Li and published by . This book was released on 2015 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: A number of alternative mean-variance portfolio strategies have been recently proposed to improve the empirical performance of the classic Markowitz mean-variance framework. Designed as remedies for parameter uncertainty and estimation errors in portfolio selection problems, these alternative portfolio strategies deliver substantially better out-of-sample performance. In this paper, we first show how to solve a general portfolio selection problem in a linear regression framework. Then we propose to reduce the estimation risk of expected returns and the variance-covariance matrix of asset returns by imposing additional constraints on the portfolio weights. With results from linear regression models, we show that portfolio weights derived from new approaches enjoy two favorable properties: sparsity and stability. Moreover, we present insights into these new approaches as well as their connections to alternative strategies in literature. Four empirical studies show that the proposed strategies have better out-of-sample performance and lower turnover than many other strategies, especially when the estimation risk is large.
Download or read book Optimal Portfolio Choice Under Parameter Uncertainty written by Rolf Merz and published by . This book was released on 2011 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Portfolio Choice Implications of Parameter and Model Uncertainty in Factor Models written by Deniz Kebabci and published by . This book was released on 2009 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper examines the portfolio choice implications of incorporating parameter and model uncertainty in (conditionally) linear factor models using industry portfolios. I examine a CAPM, a linear factor model with different predictor variables (dividend yield, price to book ratio, price to earnings ratio, and price to sales ratio), and a time-varying CAPM. All approaches incorporate parameter uncertainty in a mean-variance framework. I consider a time-varying CAPM with changing conditional variance. It is shown that taking into account the time variation in market betas improves the portfolio performance as measured by the ex-post Sharpe ratio compared to both an unconditional CAPM and a linear factor model with predictor variables. I also show the implications of using a Black-Litterman framework versus using a standard mean-variance approach in the asset allocation step. Black-Litterman framework can be thought as a model averaging approach and thus helps deal with both the parameter and model uncertainty problems. I show that Black-Litterman approach results in portfolios with a higher Sharpe ratio than those obtained by a standard mean-variance framework using a single model or historical averages.
Download or read book Feature Selection for Portfolio Optimization written by Thomas Bjerring and published by . This book was released on 2017 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most portfolio selection rules based on the sample mean and covariance matrix perform poorly out-of-sample. Moreover, there is a growing body of evidence that such optimization rules are not able to beat simple rules of thumb, such as 1/N. Parameter uncertainty has been identified as one major reason for these findings. A strand of literature addresses this problem by improving the parameter estimation and/or by relying on more robust portfolio selection methods. Independent of the chosen portfolio selection rule, we propose using feature selection first in order to reduce the asset menu. While most of the diversification benefits are preserved, the parameter estimation problem is alleviated. We conduct out-of-sample back-tests to show that in most cases different well-established portfolio selection rules applied on the reduced asset universe are able to improve alpha relative to different prominent factor models.
Download or read book Dynamic Portfolio Choice with Parameter Uncertainty and the Economic Value of Analysts Recommendations written by Jak[scaron Cvitani[cacute] and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We derive a closed-form solution for the optimal portfolio of a nonmyopic utility maximizer who has incomplete information about the alphas or abnormal returns of risky securities. We show that the hedging component induced by learning about the expected return can be a substantial part of the demand. Using our methodology, we perform an quot;ex antequot; empirical exercise, which shows that the utility gains resulting from optimal allocation are substantial in general, especially for long horizons, and an quot;ex postquot; empirical exercise, which shows that analysts' recommendations are not very useful. (JEL C61, G11, G24).
Download or read book Risk and Uncertainty written by Svetlozar T. Rachev and published by John Wiley & Sons. This book was released on 2011-04-22 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization The finance industry is seeing increased interest in new risk measures and techniques for portfolio optimization when parameters of the model are uncertain. This groundbreaking book extends traditional approaches of risk measurement and portfolio optimization by combining distributional models with risk or performance measures into one framework. Throughout these pages, the expert authors explain the fundamentals of probability metrics, outline new approaches to portfolio optimization, and discuss a variety of essential risk measures. Using numerous examples, they illustrate a range of applications to optimal portfolio choice and risk theory, as well as applications to the area of computational finance that may be useful to financial engineers. They also clearly show how stochastic models, risk assessment, and optimization are essential to mastering risk, uncertainty, and performance measurement. Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization provides quantitative portfolio managers (including hedge fund managers), financial engineers, consultants, and academic researchers with answers to the key question of which risk measure is best for any given problem.
Download or read book A Premium for Parameter Uncertainty in Equities written by Michael Hanke and published by . This book was released on 2014 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: The literature on the effects of parameter uncertainty on optimal portfolio choice suggests the existence of a premium for parameter uncertainty in asset returns. We use a simple extension to classical mean-variance portfolio optimization and devise a robust strategy to benefit from such a premium. Using well-known, long time series of equity returns, we show that this strategy indeed outperforms competitor strategies and yields positive and significant alphas relative to the most prominent factor models. We interpret these results to provide empirical support for the existence of a parameter uncertainty premium in equity returns.
Download or read book Parameter Uncertainty Financial Turbulence and Aggregate Stock Returns written by Sebastian Stöckl and published by . This book was released on 2017 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we develop a novel, intuitive and objective measure of time-varying parameter uncertainty (PU) based on a simple statistical test. Investors who are averse to parameter uncertainty will react to elevated levels of PU by withdrawing from the market and causing prices to fall, a behavior that is well described by the model of portfolio selection with parameter uncertainty of Garlappi et al. (2007). We show that this model in combination with our measure, outperforms all other tested variables including the strongest known predictor to date. Additionally, it is the only predictor that fulfills all criteria generally expected from a stable predictor of the equity premium. All our results are statistically and economically significant and robust to a large variety of different specifications.
Download or read book Robust Portfolio Selection Based on the Shrinkage Estimation written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: When portfolio selection is implemented by using the past sample values, parameter uncertainty may lead to suboptimal portfolios. Previous studies of portfolio selection demonstrate that classical approach based on the simple mean estimator is less reliable cause of inherent estimation error. In this paper, we investigate a shrinkage estimator based on Stein's idea in measuring the expected returns. We apply the research of Jorion (1985) to Taiwan Stock market, present the effects of estimation error on the portfolio selection and demonstrate that the shrinkage estimator is robust and dominates the classical estimator on the MSE criterion. In addition, we also examine the effect of different shrinkage target on the performance of the Bayes-Stein estimator and find that this estimator still has lower risk than the classical sample mean.
Download or read book Improvement of Portfolio Selection written by Yiyun Fang and published by . This book was released on 2011 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: "As one of the most important models in finance, efficient portfolio theory pioneered by Markowitz (1952) has been developed since 1950s. Although it has been widely used in practice, Markowitz's mean-variance model has been questioned about its validity because of its bad estimation performance especially in small samples due to the parameter uncertainty problem. Many strategies have been proposed for the purpose of lower the estimation error of mean-variance model. This paper gives a review of the existing literature with the goal of improving the performance of the Markowitz mean-variance model. We evaluate across five empirical data sets of 11 estimation methods. Among these methods, the combination rules of Tu and Zhou (2010) are practicable in terms of Sharpe ratio, and optimal two-fund rule and shrinkage on the covariance rule are praticable in terms of CEQ return. However, in comparison with the in-sample performance, these models surely still have room to improve."--Author's abstract.
Download or read book Optimal Portfolio Rule written by Hyunjong Jin and published by . This book was released on 2012 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: The classical mean-variance model, proposed by Harry Markowitz in 1952, has been one of the most powerful tools in the field of portfolio optimization. In this model, parameters are estimated by their sample counterparts. However, this leads to estimation risk, which the model completely ignores. In addition, the mean-variance model fails to incorporate behavioral aspects of investment decisions. To remedy the problem, the notion of ambiguity aversion has been addressed by several papers where investors acknowledge uncertainty in the estimation of mean returns. We extend the idea to the variances and correlation coefficient of the portfolio, and study their impact. The performance of the portfolio is measured in terms of its Sharpe ratio. We consider different cases where one parameter is assumed to be perfectly estimated by the sample counterpart whereas the other parameters introduce ambiguity, and vice versa, and investigate which parameter has what impact on the performance of the portfolio.
Download or read book Parameter Uncertainty in Multiperiod Portfolio Optimization with Transaction Costs written by Victor DeMiguel and published by . This book was released on 2014 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study the impact of parameter uncertainty on the expected utility of a multiperiod investor subject to quadratic transaction costs. We characterize the utility loss associated with ignoring parameter uncertainty, and show that it is equal to the product between the single-period utility loss and another term that captures the effects of the multiperiod mean-variance utility and transaction cost losses. To mitigate the impact of parameter uncertainty, we propose two multiperiod shrinkage portfolios and demonstrate with simulated and empirical datasets that they substantially outperform portfolios that ignore parameter uncertainty, transaction costs, or both.
Download or read book Incorporating Economic Objectives into Bayesian Priors written by Jun Tu and published by . This book was released on 2013 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: Economic objectives are often ignored when estimating parameters, though the loss of doing so can be substantial. This paper proposes a way to allow Bayesian priors to reflect the objectives. Using monthly returns of the Fama-French 25 size and book-to-market portfolios and their three factors from January 1965 to December 2004, we find that investment performance under the objective-based priors can be significantly different from that under alternative priors, with differences in terms of annual certainty-equivalent returns greater than 10% in many cases. In terms of out-of-sample performance, the Bayesian rules under the objective-based priors can outperform substantially some of the best rules developed in the classical framework.
Download or read book Modeling of Linear Uncertain Portfolio Selection with Uncertain Constraint and Risk Index written by Weiwei Guo and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since securities markets are subject to a great deal of uncertainty and complexity, the returns of securities cannot be accurately estimated by historical data. In this case, it must use experts' knowledge and judgment. Therefore, we investigate portfolio selection problems in such uncertain environments. First, this paper regards the rate of return on security as an uncertain variable which obeys linear uncertain distribution, and then provides the analytical expressions of the corresponding risk, return and risk index in the uncertain portfolio selection environment. Afterwards, we construct three types uncertain portfolio selection models with uncertain constraint, namely, the minimizing risk, the maximizing return and the maximizing belief degree. Meanwhile, in order to more intuitively reflect the investor's sense of loss, three types uncertain portfolio selection models considering both uncertain constraint and risk index are also constructed. These models are transformed into corresponding deterministic models. Furthermore, a relevant particle swarm optimization (PSO) algorithm is proposed to solve these models. Finally, through an example analysis, this paper obtains the portfolio selection strategies under different objectives, compares the results under different models, verifies the rationality and effectiveness of the models and algorithm, and analyzes the sensitivity of the parameters.
Download or read book Maxmin Portfolio Choice written by Marco Taboga and published by . This book was released on 2005 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
