103,724 research outputs found
Risk forecasting models and optimal portfolio selection.
Get PDFThis study analyses, from an investor's perspective, the performance of several risk forecasting models in obtaining optimal portfolios. The plausibility of the homoscedastic hypothesis implied in the classical Markowitz model is dicussed and more general models which take into account assymetry and time varying risk are analysed. Specifically, it studies whether ARCH-type based models obtain portfolios whose risk-adjusted returns exceed those of the classical Markowitz model. The same analysis is performed with models based on the Lower Partial Moment (LPM) which take into account the assymetry in the distribution of returns. The results suggest that none of the models achieve a clearly superior average performance. It is also found that models based on semivariance perform as well as those based on the variance, but not better than, even if the evaluation criterion is based on the Reward-to-Semivariance ratio. When attention turns to the analysis of worst case performance, the results are clearly different. Models which employ LPM with a high degree of risk aversion (n>2) as the risk measure are consistently superior to those which employ a symmetric measure, either homoscedastic or heteroscedastic.
Minimização do risco em carteira: aplicação da moderna teoria do portfólio
Get PDFTCC (graduação) - Universidade Federal de Santa Catarina. Centro Sócio-Econômico. Economia.O elevado nível de oscilação no preço dos ativos no mercado acionário, ocasionado por expectativas dos agentes econômicos resulta em alto risco para os investidores, uma vez que tal comportamento torna-se imprevisível na presença de choques econômicos. Nesse sentido, os investidores procuram ao máximo inibir o componente aleatório dos preços dos ativos financeiros, por meio de um processo de diversificação de ativos. Dentre as metodologias existentes para a minimização do risco estão a de Markowitz e Sharpe. A metodologia de Markowitz é caracterizada pela otimização do trade-off entre risco e retorno, e permite delinear uma fronteira eficiente de portfólios, no qual se identifica as melhores composições de ativos para cada nível de risco assumido. Enquanto isso, o método de Sharpe, mais conhecido como CAPM, complementa a base da teoria moderna do portfólio. Dessa forma, o objetivo do presente estudo é utilizar uma metodologia de otimização de portfólio capaz de minimizar o risco e identificar seu maior retorno médio para cada nível de risco assumido. A partir da inclusão de um ativo livre de risco à fronteira eficiente de Markowitz, é possível determinar seu retorno exigido para os ativos. Conclui-se que a aplicação dos métodos à um caso real, por meio da utilização do software MatLab, apresentou-se eficaz, uma vez que foi possível identificar a minimização do risco mediante o procedimento de diversificação de ativos e a composição ideal para o alcance do ponto máximo do índice Sharpe
Letter, [Author unclear] to Paulina T. Merritt
No full textHandwritten letter to Paulina Merritt from an unknown author, October 1, 1876.
Robust Portfolio Optimization with a Hybrid Heuristic Algorithm
Get PDFEstimation errors in both the expected returns and the covariance matrix hamper the constructing of reliable portfolios within the Markowitz framework. Robust techniques that incorporate the uncertainty about the unknown parameters are suggested in the literature. We propose a modification as well as an extension of such a technique and compare both with another robust approach. In order to eliminate oversimplifications of Markowitz’ portfolio theory, we generalize the optimization framework to better emulate a more realistic investment environment. Because the adjusted optimization problem is no longer solvable with standard algorithms, we employ a hybrid heuristic to tackle this problem. Our empirical analysis is conducted with a moving time window for returns of the German stock index DAX100. The results of all three robust approaches yield more stable portfolio compositions than those of the original Markowitz framework. Moreover, the out-of-sample risk of the robust approaches is lower and less volatile while their returns are not necessarily smaller.Hybrid heuristic algorithm, Markowitz, Robust optimization, Uncertainty sets.
Linear statistical inference for global and local minimum variance portfolios
Get PDFTraditional portfolio optimization has been often criticized since it does not account for estimation risk. Theoretical considerations indicate that estimation risk is mainly driven by the parameter uncertainty regarding the expected asset returns rather than their variances and covariances. This is also demonstrated by several numerical studies. The global minimum variance portfolio has been advocated by many authors as an appropriate alternative to the traditional Markowitz approach since there are no expected asset returns which have to be estimated and thus the impact of estimation errors can be substantially reduced. But in many practical situations an investor is not willing to choose the global minimum variance portfolio, especially in the context of top down portfolio optimization. In that case the investor has to minimize the variance of the portfolio return by satisfying some specific constraints for the portfolio weights. Such a portfolio will be called 'local minimum variance portfolio'. Some finite sample hypothesis tests for global and local minimum variance portfolios are presented as well as the unconditional finite sample distribution of the estimated portfolio weights and the first two moments of the estimated expected portfolio returns. --Estimation risk,linear regression theory,Markowitz portfolio,portfolio optimization,top down investment,minimum variance portfolio
Sparse and stable Markowitz portfolios
Get PDFWe consider the problem of portfolio selection within the classical Markowitz meanvariance optimizing framework, which has served as the basis for modern portfolio theory for more than 50 years. Efforts to translate this theoretical foundation into a viable portfolio construction algorithm have been plagued by technical difficulties stemming from the instability of the original optimization problem with respect to the available data. Often, instabilities of this type disappear when a regularizing constraint or penalty term is incorporated in the optimization procedure. This approach seems not to have been used in portfolio design until very recently. To provide such a stabilization, we propose to add to the Markowitz objective function a penalty which is proportional to the sum of the absolute values of the portfolio weights. This penalty stabilizes the optimization problem, automatically encourages sparse portfolios, and facilitates an effective treatment of transaction costs. We implement our methodology using as our securities two sets of portfolios constructed by Fama and French: the 48 industry portfolios and 100 portfolios formed on size and book-to-market. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve portfolio comprising equal investments in each available asset. In addition to their excellent performance, these portfolios have only a small number of active positions, a desirable feature for small investors, for whom the fixed overhead portion of the transaction cost is not negligible. JEL Classification: G11, C00Penalized Regression, Portfolio Choice, Sparse Portfolio
Bill T. Jones Still/Here
No full textA look at dancer and choreographer Bill T. Jones's highly acclaimed dance: Still/Here. At workshops around the country, people facing life-threatening illnesses are asked to remember the highs and lows of their lives, and even imagine their own deaths. They then transform these feelings into expressive movement, which Jones incorporates into the dance Still/Here. Jones demonstrates the movements of his life story: his first encounter with white people, confusion over his sexuality, his partner Arnie Zane's untimely death from AIDS, and Jones's own HIV status.Danced by The Bill T. Jones/Arnie Zane Dance Company. Cinematography, Joel Shapiro and Don Lenzer ; edited by Geof Bartz ; music & lyrics for Still / by Kenneth Frazelle ; sung by Odetta. Music for Here / composed and arranged by Vernon Reid
Risk measures and their applications in asset management
Get PDFSeveral approaches exist to model decision making under risk, where risk can be broadly defined as the effect of variability of random outcomes. One of the main approaches in the practice of decision making under risk uses mean-risk models; one such well-known is the classical Markowitz model, where variance is used as risk measure. Along this line, we consider a portfolio selection problem, where the asset returns have an elliptical distribution. We mainly focus on portfolio optimization models constructing portfolios with minimal risk, provided that a prescribed expected return level is attained. In particular, we model the risk by using Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). After reviewing the main properties of VaR and CVaR, we present short proofs to some of the well-known results. Finally, we describe a computationally efficient solution algorithm and present numerical results.conditional value-at-risk;elliptical distributions;mean-risk;portfolio optimization;value-at-risk
sj-docx-1-jls-10.1177_0261927X231220404 - Supplemental material for Generative AI Are More Truth-Biased Than Humans: A Replication and Extension of Core Truth-Default Theory Principles
No full textSupplemental material, sj-docx-1-jls-10.1177_0261927X231220404 for Generative AI Are More Truth-Biased Than Humans: A Replication and Extension of Core Truth-Default Theory Principles by David M. Markowitz and Jeffrey T. Hancock in Journal of Language and Social Psychology</p
Bayesian Portfolio Selection with Gaussian Mixture Returns
Get PDFMarkowitz portfolio selection is challenged by huge implementation barriers. This paper addresses the parameter uncertainty and deviation from normality in a Bayesian framework. The non-normal asset returns are modeled as finite Gaussian mixtures. Gibbs sampler is employed to obtain draws from the posterior predictive distribution of asset returns. Optimal portfolio weights are then constructed so as to maximize agents’ expected utility. Simple experiment suggests that our Bayesian portfolio selection procedure performs exceedingly well.portfolio selection; Gaussian mixtures; Bayesian
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