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Author: Lukas Plachel
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Lukas Plachel
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Thitapon Ousawat
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Emmanouil Platanakis
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Category :
Languages : en
Pages : 0
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Author: Pascal Alphonse
Publisher: Springer Nature
ISBN: 303129050X
Category :
Languages : en
Pages : 344
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Author: Frank J. Fabozzi
Publisher:
ISBN: 9781119202172
Category : Portfolio management
Languages : en
Pages : 495
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Author: Hagen Wittig
Publisher:
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Category :
Languages : en
Pages : 0
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This dissertation comprises three articles which are covering various aspects in the area of dynamic asset allocation, i.e., how to control an investor's portfolio composition over time in light of changing market conditions and various asset classes with differing return and risk profiles. In the first article, we propose the advancement of the traditional value-based rebalancing framework by setting the bandwidth weights in a non-static manner depending on current market characteristics, namely the relative price and volatility levels. The empirical analysis reveals significant excess returns in comparison to a buy and hold strategy, an idealized SAA strategy, as well as a rebalancing strategy with static bandwidths but otherwise comparable characteristics. The proposed approach also proves to be robust in various subsamples. In the second and third article, we break new ground by explicitly incorporating the risk dimension in the dynamic asset allocation process. In the second article, we present a rebalancing approach which applies the various asset classes' risk contributions to control the rebalancing process during the investment period. In strong contrast to traditional value-based rebalancing strategies, the resulting risk contribution strategies are capable of triggering rebalancing procedures based on deviations in an asset class's stand-alone volatility or correlation to the portfolio's remaining asset classes. Thus, this method lets investors closely maintain the asset classes' initial risk contributions. In the third article, we develop another approach which applies the loadings of the investor's portfolio on various key risk factors as indicators for triggering the rebalancing process. We implement the approach by monitoring the loadings on the risk factors interest rate, term spread, credit risk, equity premium, and volatility. We further define bandwidths for every risk factor loading. Once the effective loading.
Author: Frank J. Fabozzi
Publisher: John Wiley & Sons
ISBN: 0470164891
Category : Business & Economics
Languages : en
Pages : 513
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Book Description
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
Author: Söhnke M. Bartram
Publisher: CFA Institute Research Foundation
ISBN: 195292703X
Category : Business & Economics
Languages : en
Pages : 95
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Book Description
Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
Author: Malcolm Kemp
Publisher: John Wiley & Sons
ISBN: 1119962870
Category : Business & Economics
Languages : en
Pages : 337
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Book Description
Taking due account of extreme events when constructing portfolios of assets or liabilities is a key discipline for market professionals. Extreme events are a fact of life in how markets operate. In Extreme Events: Robust Portfolio Construction in the Presence of Fat Tails, leading expert Malcolm Kemp shows readers how to analyse market data to uncover fat-tailed behaviour, how to incorporate expert judgement in the handling of such information, and how to refine portfolio construction methodologies to make portfolios less vulnerable to extreme events or to benefit more from them. This is the only text that combines a comprehensive treatment of modern risk budgeting and portfolio construction techniques with the specific refinements needed for them to handle extreme events. It explains in a logical sequence what constitutes fat-tailed behaviour and why it arises, how we can analyse such behaviour, at aggregate, sector or instrument level, and how we can then take advantage of this analysis. Along the way, it provides a rigorous, comprehensive and clear development of traditional portfolio construction methodologies applicable if fat-tails are absent. It then explains how to refine these methodologies to accommodate real world behaviour. Throughout, the book highlights the importance of expert opinion, showing that even the most data-centric portfolio construction approaches ultimately depend on practitioner assumptions about how the world might behave. The book includes: Key concepts and methods involved in analysing extreme events A comprehensive treatment of mean-variance investing, Bayesian methods, market consistent approaches, risk budgeting, and their application to manager and instrument selection A systematic development of the refinements needed to traditional portfolio construction methodologies to cater for fat-tailed behaviour Latest developments in stress testing and back testing methodologies A strong focus on the practical implementation challenges that can arise at each step in the process and on how to overcome these challenges “Understanding how to model and analyse the risk of extreme events is a crucial part of the risk management process. This book provides a set of techniques that allow practitioners to do this comprehensively.” Paul Sweeting, Professor of Actuarial Science, University of Kent “How can the likeliness of crises affect the construction of portfolios? This question is highly topical in times where we still have to digest the last financial collapse. Malcolm Kemp gives the answer. His book is highly recommended to experts as well as to students in the financial field.” Christoph Krischanitz, President Actuarial Association of Austria, Chairman WG “Market Consistency” of Groupe Consultatif
Author: John B. Guerard, Jr.
Publisher: Springer Science & Business Media
ISBN: 0387774394
Category : Business & Economics
Languages : en
Pages : 796
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Book Description
Portfolio construction is fundamental to the investment management process. In the 1950s, Harry Markowitz demonstrated the benefits of efficient diversification by formulating a mathematical program for generating the "efficient frontier" to summarize optimal trade-offs between expected return and risk. The Markowitz framework continues to be used as a basis for both practical portfolio construction and emerging research in financial economics. Such concepts as the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT), for example, provide the foundation for setting benchmarks, for predicting returns and risk, and for performance measurement. This volume showcases original essays by some of today’s most prominent academics and practitioners in the field on the contemporary application of Markowitz techniques. Covering a wide spectrum of topics, including portfolio selection, data mining tests, and multi-factor risk models, the book presents a comprehensive approach to portfolio construction tools, models, frameworks, and analyses, with both practical and theoretical implications.
