Download or read book Essays on Portfolio Optimization and Estimation Risk written by Illia Kovalenko and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Asset Pricing and Portfolio Optimization written by Christian Koeppel and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: WThis doctoral thesis focuses on the effects of investor sentiment on asset pricing and the challenges of portfolio optimization under parameter uncertainty. The first essay "Sentiment risk premia in the cross-section of global equity" applies a recently developed sentiment proxy to the construction of a new risk factor and provides a comprehensive understanding of its role in sentiment-augmented asset pricing models for international equity indices. We empirically demonstrate the existence of a statistically significant and economically relevant sentiment premium. Differentiating between developed and emerging markets we reveal different patterns of return reversals / persistence. Our results contribute to the explanation of global cross-sectional average excess returns, demonstrating superiority in terms of predictive power when compared to competing definitions of sentiment. The second essay "Does social media sentiment matter in the pricing of U.S. stocks?" finds that the inclusion of micro-grounded, social media-based sentiment significantly improves the performance of the five-factor model from Fama and French (2015, 2017). This holds for different industry and style portfolios such as size, value, profitability, and investment. Applying a robust GMM estimator, the sentiment risk premium provides the missing component in the behavioral asset pricing theory of Shefrin and Belotti (2008) and (partially) resolves the pricing puzzles of small extreme growth, small extreme investment stocks and small stocks that invest heavily despite low profitability. The third essay "Diversifying estimation errors: An efficient averaging rule for portfolio optimization" proposes a combination of established minimum-variance strategies to minimize the expected out-of-sample variance. The proposed averaging rule overcomes the strategy selection problem and diversifies estimation errors of the strategies included in our rule. Extensive simulations show that the contributions of estimation errors to the out-of-sample variances are uncorrelated between the considered strategies. We therefore conclude that averaging over multiple strategies offers sizable diversification benefits.

Download or read book Essays on Portfolio Optimization and ESG Ratings under Risk Constraints and Incomplete Information written by Janke, Oliver and published by Lehmanns Media. This book was released on with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we analyze various problems of dynamic portfolio optimization as well as green capital requirements under risk constraints and incomplete information. First, we examine the problem of optimal expected utility under the constraint of a utility-based shortfall risk measure in an incomplete market. The existence and uniqueness of an optimal solution to the problem are shown using a Lagrange multiplier and duality methods. Second, we consider the optimization problem under various levels of the investor’s information. By using martingale representation theorems, we demonstrate the existence and uniqueness of optimal solutions, which differ in their market dynamics. Third, we analyze the effects of green- and brownwashing on banks’ lending to firms, on the regulator’s deposit insurance subsidy, and on carbon emissions under different green capital requirement functions. Furthermore, we show that green capital requirements may compromise financial stability.

Download or read book Essays on Portfolio Optimization and ESG Ratings Under Risk Constraints and Incomplete Information written by Oliver Janke and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Continuous time Portfolio Optimization and Credit Risk written by Björn Bick and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Portfolio Optimization Volatility Modelling and Risk Measurement written by Liyuan Chen and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Portfolio Theory 25 Years After written by Harry Markowitz and published by North-Holland. This book was released on 1979 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book On the Value of Portfolio Optimization in the Presence of Estimation Risk written by Raymond Kan and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computational Methods in Financial Engineering written by Erricos Kontoghiorghes and published by Springer Science & Business Media. This book was released on 2008-02-26 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational models and methods are central to the analysis of economic and financial decisions. Simulation and optimisation are widely used as tools of analysis, modelling and testing. The focus of this book is the development of computational methods and analytical models in financial engineering that rely on computation. The book contains eighteen chapters written by leading researchers in the area on portfolio optimization and option pricing; estimation and classification; banking; risk and macroeconomic modelling. It explores and brings together current research tools and will be of interest to researchers, analysts and practitioners in policy and investment decisions in economics and finance.

Download or read book Essays on Applications of the Factor Model written by Xiaolin Sun and published by . This book was released on 2013 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimating the volatilities and correlations of asset returns plays an important role in portfolio and risk management. As of late, interest in the estimation of the covariance matrix of large dimensional portfolios has increased. Estimating large dimensional covariance poses a challenge in that the cross-sectional dimension is often similar to or bigger than the number of observations available. Simple estimators are often poorly conditioned with some small eigenvalues, and so are unsuitable for many real world applications, including portfolio optimization and tracking error minimization. The first chapter introduces our two large dimensional covariance matrix estimators. We estimate the large dimensional realized covariance matrix by using the methods of asymptotic principal components analysis based factor modeling and singular value decomposition. In the second chapter, we show though simulation that our proposed estimators are closer to the true covariance matrix than the current popular shrinkage estimator. We also simulate conducting the out sample portfolio performance tests and find that the portfolios constructed based on our proposed estimators have lower risk than portfolios constructed using the shrinkage matrix. Using S&P 500 stocks from 1926 to 2011, we back test our proposed covariance matrix. In addition, the portfolios constructed based on our proposed estimators exhibit lower risk than portfolios constructed using the shrinkage matrix. The third chapter proposes a new volatility index--a cross-sectional volatility index of residuals using factor model. The cross-sectional volatility index moves closely with the VIX for the S&P 500 stock universe. It is a non-parametric, model-free volatility index, which could be estimated at any frequency for any region, sector, and style of world equity market and also does not depend on any option pricing. We provide some interpretation of the cross-sectional volatility index of residuals as a proxy for aggregate economic uncertainty, and show a high correlation between the VIX index and the corresponding cross-sectional volatility index of residuals based on the S&P 500 universe. Our results show that the portfolio hedged based on the cross-sectional volatility index of residuals has a much higher Sharpe ratio than the portfolio without hedge. Overall, these findings suggest that the cross-sectional volatility index of residuals is intimately related to other volatility measures where and when such measures are available, and that it can be used as a reliable proxy for volatility when such measures are not available.

Download or read book Robust Portfolio Optimization and Management written by Frank J. Fabozzi and published by John Wiley & Sons. This book was released on 2007-08-10 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Robust Portfolio Optimization and Management "In the half century since Harry Markowitz introduced his elegant theory for selecting portfolios, investors and scholars have extended and refined its application to a wide range of real-world problems, culminating in the contents of this masterful book. Fabozzi, Kolm, Pachamanova, and Focardi deserve high praise for producing a technically rigorous yet remarkably accessible guide to the latest advances in portfolio construction." --Mark Kritzman, President and CEO, Windham Capital Management, LLC "The topic of robust optimization (RO) has become 'hot' over the past several years, especially in real-world financial applications. This interest has been sparked, in part, by practitioners who implemented classical portfolio models for asset allocation without considering estimation and model robustness a part of their overall allocation methodology, and experienced poor performance. Anyone interested in these developments ought to own a copy of this book. The authors cover the recent developments of the RO area in an intuitive, easy-to-read manner, provide numerous examples, and discuss practical considerations. I highly recommend this book to finance professionals and students alike." --John M. Mulvey, Professor of Operations Research and Financial Engineering, Princeton University

Download or read book Modern Portfolio Optimization with NuOPTTM S PLUS and S BayesTM written by Bernd Scherer and published by Springer. This book was released on 2010-10-05 with total page 0 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 Essays in Portfolio Optimization written by Fabian Trübenbach and published by . This book was released on 2012 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Robust Portfolio Management written by Lukas Plachel and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Portfolio Theory (MPT) provides an elegant mathematical framework for the efficient portfolio allocation problem. Despite its exceptional popularity, MPT poses a number of well-documented problems in practical applications. Especially the fact that it generates notoriously extreme and non-robust allocations which may seriously impair the out-of-sample performance. This thesis introduces three methods with the common objective to remedy those shortcomings. Chapter 1 addresses the problems of traditional mean-variance optimization originating from model- and estimation errors. In order to simultaneously tackle both error sources, a joint method for covariance regularization and robust optimization is proposed which exploits the inherent complementarity between the two concepts. An application of the method to equity markets reveals similarly attractive behaviour as pure covariance regularization during normal times and improved performance as measured by out-of-sample volatility if a jump in systematic risk occurs. Chapter 2 introduces a covariance estimation approach which is based solely on characteristic company information. In contrast to traditional, time series based estimation procedures which typically lead to extreme and unreliable estimates, the proposed method produces stable covariance matrices which can be used if no time series data is available, or complementary to traditional methods. We derive characteristics-based covariance matrices for a US stock universe and use them as shrinkage targets in a minimum variance optimization example. The resulting strategies clearly dominate the benchmark case of identity shrinkage in terms of out-of-sample volatility. Chapter 3 bridges the gap between MPT and one of the most vivid fields of contemporary research: Artificial Intelligence. A model is introduced which uses a Neural Network to learn the relation between portfolio weights and arbitrary measures of portfolio.

Download or read book Essays on Financial Market written by Pushpak S. Sarkar and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the first chapter I explore the ”tail dependence” of commodities with other financial assets such as equities, foreign exchange and bonds and use copula to model the time-varying ”tail dependence” between commodities and those assets. I show a clear evidence that using copula-based approach we can produce better forecasts of tail-based risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES) for portfolios consisting of commodities and other financial assets. The results of the statistical tests for evaluating the VaR and ES show clear evidence in favor of time-varying Student’s t copula model when compared against the benchmark Dynamic Conditional Correlation (Engel [2002]) and RiskMetrics[trademark] (Morgan et al. [1996]) models. I also look at the effect of tail dependence on optimal portfolio construction by minimizing portfolio expected shortfall and then compare the performance of expected shortfall strategy vs. global minimum variance strategy. For dynamic optimization, the expected shortfall strategy generates a higher portfolio cumulative return than that generated by the global minimum variance strategy. The second chapter describes a study which is a joint work with Professor R. Douglas Martin.1 In this study we explore the question of portfolio optimization based on downside risk estimates. It is well known that the mean-variance portfolio optimization does not take into account the skewness and kurtosis of returns distribution. But asset returns data exhibit non-normality in terms of skewness and fat tails. Also, variance is a symmetric measure as it penalizes both negative and positive returns equally. To penalize only the negative returns, investors may profit by using portfolio optimization based on downside risk measures. One of the most popular downside risk measures is Expected Shortfall (ES) or Conditional Value at-Risk (CVaR). ES is defined as the average of loss beyond the Value at-Risk (VaR) and captures the tail characteristics. Expected shortfall gained popularity as the objective risk measure in portfolio optimization with the publication of the seminal paper of Rockafellar et al. [2000] which proposed an optimization algorithm which can be solved by standard linear programming. Krokhmal [2007] further extended the idea and proposed Higher Moment Coherent Risk (HMCR) measures and showed how the HMCR measures can be implemented by reducing it to a p-order conic programming and approximating via linear programming. Krokhmal [2007] discussed the special case where p = 2 defined it as the Second Moment Coherent Risk Measure (SMCR). The SMCR has similar properties as CVaR but it measures risk in terms of the second moments of loss distributions. In this paper, we call the Second Moment Coherent Risk as Expected Quadratic Shortfall (EQS), which is a natural variant of ES. We construct the following three types of global minimum risk optimal portfolio using daily returns of 30 small cap stocks (which has the largest market capitalization within the small cap category) - a) Global Minimum Variance (GMV), b) Global Minimum Expected Shortfall (GMES), and c) Global Minimum Expected Quadratic Shortfall (GMEQS). We conduct both the static as well as dynamic (i.e. with rebalancing) portfolio optimization and analyze the portfolio performance metrics such as Sharpe Ratio (SR), Downside Sharpe Ratio (DSR), Expected Shortfall Ratio (ES Ratio), cumulative return, cumulative return relative to a benchmark and drawdown. The third chapter describes a study which is a joint work with Professor Yu-Chin Chen 2 and Zihao Chen.3 It uses machine learning techniques to re-examine the long-standing difficulty in predicting currency returns with macroeconomic indicators by focusing on three possible causes: the general lack of information in the macro predictors, mis-specifications in the forecasting equations, and inherent instabilities in the relationship between the exchange rate and its macro determinants. Using a large international dataset that captures current macroeconomic conditions as well as forward-looking market expectations and perceived un certainties, we forecast monthly returns from 1995 onward of four major currencies (AUS, CAD, GBP, and JPY) against the USD. In in-sample regressions, we see that while market expectations embedded in derivatives markets may help predict subsequent exchange rate returns, there is little evidence that they contain predictive content above and beyond what is in the macro indicators themselves. Moreover, both types of predictors perform better in non-linear specifications than under the linear specifications which often deliver adjusted R2 around zero. We take these findings as indicative that the exchange rate is not disconnected to indicators of the macroeconomy - be their current values or expectations, though their functional relation may be more nuanced than simple linear specifications can capture. Moving the analyses to pseudo out-of-sample (OOS) forecasts, we find that a Multilayer Perceptron Neural Network can generate improvements over the long-standing Random Walk benchmark, some of which are over 10% and statistically significant. More prominently, we see that the majority of the ML methods considered do not outperform a RW forecast given our small sample context. In fact, unlike results for other asset returns, ML does not appear to help resolve the FX forecasting puzzle. Nevertheless, our ML explorations unveil signif icant empirical instabilities, especially around the GFC period. These findings support the views that pseudo-OOS exchange rate forecasting in finite samples can be overwhelmed by inherent statistical issues such as parameter and model instabilities, and that the exchange rate dynamics are inherently difficult to distinguish from a RW process statistically (e.g. Engel and West [2005]).

Download or read book Essays in Portfolio Optimization written by David J. Disatnik and published by . This book was released on 2007 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Portfolio Optimization written by Felix Miebs and published by . This book was released on 2012 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: