Download or read book Portfolio Optimization with Tail Risk Measures and Non normal Returns written by Minfeng Zhu and published by . This book was released on 2010 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Modern Portfolio Optimization with NuOPTTM S PLUS and S BayesTM written by Bernd Scherer and published by Springer Science & Business Media. This book was released on 2007-09-05 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years portfolio optimization and construction methodologies have become an increasingly critical ingredient of asset and fund management, while at the same time portfolio risk assessment has become an essential ingredient in risk management. This trend will only accelerate in the coming years. This practical handbook fills the gap between current university instruction and current industry practice. It provides a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods using the powerful NUOPT for S-PLUS optimizer.

Download or read book Portfolio Risk Optimization by Fuzzy Approaches written by Thanh Thi Nguyen and published by . This book was released on 2013 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the complexity and uncertainty in real world portfolio management, investors might be reluctant and sometimes unable to provide precise judgements regarding stock performance. In this context, analysts have long advocated use of fuzzy mathematics so that uncertainties and lack of precision can be acknowledged. This research therefore explores the applications of fuzzy sets in particular, or fuzzy logic in general for representing vague and imprecise financial data for portfolio risk optimization. Asset returns are uncertain and changeable over time so we model asset returns as fuzzy random variables and propose portfolio optimization models. Using fuzzy random variables, we introduce a new concept of financial risk, and the fuzzy Sharpe ratio contributing an important advancement in portfolio selection in the fuzzy environment. Two solution methods using a fuzzy approach and a genetic algorithm are applied to the proposed models. The proposed approach exhibits advantages over the so-called standard mean-variance optimization (MVO), throughout experimental results. The non-Gaussian distribution of asset returns has long been recognized, and the conventional MVO has been criticized as inadequate. Hence utilizing higher moments than variance, i.e. skewness, kurtosis soon emerged in portfolio selection. This research investigates the importance of higher moments in portfolio optimization through deploying fuzzy approaches. Marginal impacts of stocks on portfolio return and higher moment risks, are modelled by fuzzy numbers. The fuzzy models are constructed to optimize not only portfolio return and normal variance risk but also the portfolio higher moment risks. From the stock marginal impact modelling, two fuzzy approaches are used to derive optimal portfolio allocations. The first approach applies the constrained fuzzy analytic hierarchy process, whereas the second approach uses the fuzzy linear programming method. The efficiency of both approaches shows advantages of the proposed fuzzy models in portfolio selection. Going beyond the normal variance and higher moment risks, investors also should take into account downside risk measures. The downside risks are inspired by the principle of safety first in portfolio selection. The principle states that an investor would prefer the investment with the smallest probability of going below the target return. A fuzzy integrated framework is proposed accounting for portfolio return and six risk criteria including normal risk (volatility), asymmetric risk (skewness), "fat-tail" risk (kurtosis) and downside risks, i.e. semi-variance, modified Value-at-Risk, and modified Expected Shortfall. Fuzzy goals of portfolio's return and risks are constructed by bootstrapping, and kernel smoothing density estimate. A preselection process dealing with large datasets is also adopted to eliminate low diversification potential stocks before running the optimization model. Various investors' risk preference schemes are implemented with both national and international experimental datasets. Results reported demonstrate the advantages of the proposed fuzzy framework compared to a conventional higher moment portfolio optimization model. The conclusion is that fuzzy modelling is efficient and competent in various portfolio selection formulations when uncertainty and vagueness are deemed present. When appropriately utilized, fuzzy approaches can bring superior investment outcomes compared to conventional non-fuzzy models prevalent in the literature.

Download or read book Modern Portfolio Optimization with NuOPT S PLUS and S Bayes written by Bernd Scherer and published by Springer Science & Business Media. This book was released on 2005-05-03 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: Portfolio optimization and construction methodologies have become an critical ingredient of asset and fund management, while at same time portfolio risk assesment has become an essential ingredient in risk management.

Download or read book Portfolio Theory and Management written by H. Kent Baker and published by Oxford University Press. This book was released on 2013-01-07 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: Portfolio management is an ongoing process of constructing portfolios that balances an investor's objectives with the portfolio manager's expectations about the future. This dynamic process provides the payoff for investors. Portfolio management evaluates individual assets or investments by their contribution to the risk and return of an investor's portfolio rather than in isolation. This is called the portfolio perspective. Thus, by constructing a diversified portfolio, a portfolio manager can reduce risk for a given level of expected return, compared to investing in an individual asset or security. According to modern portfolio theory (MPT), investors who do not follow a portfolio perspective bear risk that is not rewarded with greater expected return. Portfolio diversification works best when financial markets are operating normally compared to periods of market turmoil such as the 2007-2008 financial crisis. During periods of turmoil, correlations tend to increase thus reducing the benefits of diversification. Portfolio management today emerges as a dynamic process, which continues to evolve at a rapid pace. The purpose of Portfolio Theory and Management is to take readers from the foundations of portfolio management with the contributions of financial pioneers up to the latest trends emerging within the context of special topics. The book includes discussions of portfolio theory and management both before and after the 2007-2008 financial crisis. This volume provides a critical reflection of what worked and what did not work viewed from the perspective of the recent financial crisis. Further, the book is not restricted to the U.S. market but takes a more global focus by highlighting cross-country differences and practices. This 30-chapter book consists of seven sections. These chapters are: (1) portfolio theory and asset pricing, (2) the investment policy statement and fiduciary duties, (3) asset allocation and portfolio construction, (4) risk management, (V) portfolio execution, monitoring, and rebalancing, (6) evaluating and reporting portfolio performance, and (7) special topics.

Download or read book Portfolio Optimization with Alternative Risk Premia written by Philipp Müller and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis adds to the literature on portfolio optimisation by analysing how to optimise a portfolio investing solely in equity alternative risk premia. Alternative risk premia feature attractive diversification properties across all market environments. Yet, some of the premia exhibit severe tail risk. In an attempt to reduce the negative impact of extreme events on portfolio performance, portfolio optimisation methods incorporating tail risk are examined. Empirical analysis over a period of close to 50 years reveals that tail risk based top-down optimisation methods do not deliver significantly improved risk and return properties compared to top-down optimisation methods focusing on the first two moments only. In contrast, traditional optimisation approaches like risk parity and inverse volatility weighting proof to be of high relevance. Further, bottom-up optimisation in the form of parametric portfolio policies with predictor variables on the market environment yield portfolios with highly improved downside risk measures compared to all topdown optimisation methods considered.

Download or read book Asset Allocation Performance Measurement and Downside Risk written by Alexandra Elisabeth Janovsky and published by diplom.de. This book was released on 2001-03-26 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inhaltsangabe:Abstract: Investors should not and in fact do not hold a single asset, they hold groups or portfolios of assets. An important aspect in portfolio theory is that the risk of a portfolio is more complex than the risk of its components. It depends on how much the assets represented in the portfolio move together, that is, on the correlation between the single assets. In portfolio theory, there are several definitions of risk: First of all, the Capital Asset Pricing Model (CAPM) relies on the beta factor of an asset relative to the market as a measure for the asset s risk. On the other hand, also downside risk can be used in order to determine a portfolio s risk. The kind of risk in question is market risk, which is the risk of losses arising from adverse movements in market prices or rates. Market risk can be subdivided into interest rate risk, equity price risk, exchange rate risk and commodity price risk. For many investment decisions, there is a minimum return that has to be reached in order to meet different criteria. Returns above this minimum acceptable return ensure that these goals are reached and thus are not considered risky. Standard deviation captures the risk associated with achieving the mean, while downside risk assumes that only those returns that fall below the minimal acceptable return incur risk. One has to distinguish between good and bad volatility. Good volatility is dispersion above the minimal acceptable return, the farther above the minimal acceptable return, the better it is. One way of measuring downside risk is to consider the shortfall probability or chances of falling below the minimal acceptable return. Another possibility is measuring downside variance, i.e. variance of the returns falling below the minimal acceptable return. As a consequence, downside variance is very sensitive to the estimate of the mean of the return function, while standard deviation does not suffer from this problem. Thus the calculation of downside deviation is more difficult than the calculation of standard deviation. The quality of the calculation also depends on the choice of differencing interval of the time series. The calculation of downside risk assumes that financial time series follow either a normal or lognormal distribution. Finally, there is no universal risk measure for the many broad categories of risk. For example, standard deviation captures the risk of not achieving the mean, beta captures the risk of investing [...]

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.

Download or read book Dynamic Portfolio Construction and Portfolio Risk Measurement written by Murat Mazibas and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book ETG ETL Portfolio Optimization written by Yun Zhang and published by . This book was released on 2012 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Portfolio Theory dates back to 1950s, when Markowitz proposed mean-variance portfolio optimization to construct portfolios. It provided a systematic approach to determine portfolio allocation when one is facing complicated risk structure that not only exists for individual assets but also across different assets. Since then there has been much research exploring better ways to quantify risk. In particular, asymmetric risk measures including the more recent downside risk measures. Here we use expected tail loss (ETL) as the risk measure which is a coherent risk measure, and define a reward measure, expected tail gain (ETG), to measure the upside return. We formulate the portfolio optimization problem using these two measures and developed an iterative algorithm to find its optimal solution.

Download or read book Portfolio Optimization with R Rmetrics written by and published by Rmetrics. This book was released on with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advanced Stochastic Models Risk Assessment and Portfolio Optimization written by Svetlozar T. Rachev and published by Wiley. This book was released on 2008-05-16 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Specifying and Managing Tail Risk in Portfolios A Practical Approach written by Pranay Gupta and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Tail risk arises at multiple stages in the investment management process - from the high level asset allocation decision down to the individual portfolio manager's process for selecting securities. We believe that conventional practices followed in these investment decision processes, largely ignore intra-horizon risk. We believe this leads to sub-optimal assessment of risk of assets, particularly in the context of potential tail risk, and leads to the construction of portfolios, which are not in sync with the risk aversion of the client.In the present paper we propose a composite risk measure which simultaneously captures the risk of breaching a specified maximum intra-horizon drawdown threshold, as well as the risk that the performance is not met at the end of the investment horizon. We believe this captures the 'true' risk of a portfolio, much better than traditional end of horizon risk measures.We find that intra-horizon risk can represent a substantial part of the total risk, and thus needs to be managed explicitly which constructing a portfolio of assets, strategies or asset classes. We propose that varying the investment horizon and implementing a customized stop loss for each asset can help construct a portfolio where portfolio risk is kept within bounds of tolerance, and can improve performance over time.

Download or read book Comparing Return Risk and Direct Utility Maximization Portfolio Optimization Methods by Certainty Equivalence Curves written by Hien Q. Vu and published by . This book was released on 2012 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mean-Risk portfolio optimization method proposes an efficient frontier that consists of portfolios not dominated by any portfolio. Consequently, this method reduces the choice set by excluding inefficient portfolios. Different risk measures offer different efficient frontiers, which can be interpreted as different optimal choice sets. The question is whether these different risk measures lead to significantly different efficient frontiers for the investors, and which risk measure should be used. My purpose is to present a method to assess the effect of the choice set reduction from different Return-Risk models and to answer the question presented earlier. The most important contribution of the paper is the creation of a two-dimensional space ldquo;Risk-Aversion ndash; Certainty Equivalence (CE)rdquo; as a platform for comparisons. The curves, representing different risk-averse investors and different models, on this space are called ldquo;Certainty Equivalence Curves (CEC)rdquo;. The empirical analysis shows that the Mean-Variance method is very effective in ranking portfolios for exponential utility investors. Therefore, it is not recommended to use more complicated methods such as Mean-CVaR.

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.

Download or read book Portfolio Optimization in a Downside Risk Framework written by Lars Huelin and published by LAP Lambert Academic Publishing. This book was released on 2011-04 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present study examines how downside risk measures perform in an investment management context compared to variance or standard deviation. To our knowledge, this paper is the first to include several acknowledged downside risk measures in a thorough analysis where their different properties are compared with those of variance Risk is an essential factor to consider when investing in the capital markets. The question of how one should define and manage risk is one that has gained a lot of attention and remains a popular topic in both the academic and professional world. This study considers six different downside risk measures and tests their relationship with the cross-section of returns as well as their performance in portfolio optimization compared to variance. The first part of the analysis suggests that the conditional drawdown-at-risk explains the cross-section of returns the best across methodologies and data frequency. Conditional valueat- risk explains the daily returns the best but the worst in monthly returns. Variance, together with semivariance, perform average in both data frequencies. The second part of the analysis concludes that conditional value-at-risk and conditional drawdown-at-risk are the two superior risk measures whereas semivariance is the worst performing risk measure - mainly caused by the poor performance during bull markets. Again, variance performs average compared to the downside risk measures in most aspects of this analysis. Overall, this thesis shows that the choice of risk measure has a significant effect on the portfolio optimization process. The analysis suggests that some downside risk measures outperform variance while others fail to do so. This suggest that downside risk can be a better tool in investment management than variance.