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Investing in illiquid assets poses a challenge to investors, as the low-frequency data makes it difficult to quantify the risks across portfolios and make asset allocation decisions. This work reviews several principal methods to infer missing data and tests their implications for asset allocation. It compares these methods by applying them to hypothetical portfolios in a realistic simulation environment, helping allocators decide which methodology to use and when. Proxy-based methods, which utilize a related series of higher-frequency observations, outperform non proxy-based inference techniques when the correlation of the available proxy is above 0.3. If data autocorrelation is high, models such as Kalman filters, which are capable of explicitly modeling the autocorrelation outperform other proxy-based methods. In normal market conditions, the CL Method gives the best overall performance of methods tested, indicated by low RMSEs and reliable forecasts for mean return, volatility, Sharpe Ratio, and drawdown. Keywords: Illiquid Investments, Low-Frequency Data, Missing Data.
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.
Since the formalization of asset allocation in 1952 with the publication of Portfolio Selection by Harry Markowitz, there have been great strides made to enhance the application of this groundbreaking theory. However, progress has been uneven. It has been punctuated with instances of misleading research, which has contributed to the stubborn persistence of certain fallacies about asset allocation. A Practitioner's Guide to Asset Allocation fills a void in the literature by offering a hands-on resource that describes the many important innovations that address key challenges to asset allocation and dispels common fallacies about asset allocation. The authors cover the fundamentals of asset allocation, including a discussion of the attributes that qualify a group of securities as an asset class and a detailed description of the conventional application of mean-variance analysis to asset allocation.. The authors review a number of common fallacies about asset allocation and dispel these misconceptions with logic or hard evidence. The fallacies debunked include such notions as: asset allocation determines more than 90% of investment performance; time diversifies risk; optimization is hypersensitive to estimation error; factors provide greater diversification than assets and are more effective at reducing noise; and that equally weighted portfolios perform more reliably out of sample than optimized portfolios. A Practitioner's Guide to Asset Allocation also explores the innovations that address key challenges to asset allocation and presents an alternative optimization procedure to address the idea that some investors have complex preferences and returns may not be elliptically distributed. Among the challenges highlighted, the authors explain how to overcome inefficiencies that result from constraints by expanding the optimization objective function to incorporate absolute and relative goals simultaneously. The text also explores the challenge of currency risk, describes how to use shadow assets and liabilities to unify liquidity with expected return and risk, and shows how to evaluate alternative asset mixes by assessing exposure to loss throughout the investment horizon based on regime-dependent risk. This practical text contains an illustrative example of asset allocation which is used to demonstrate the impact of the innovations described throughout the book. In addition, the book includes supplemental material that summarizes the key takeaways and includes information on relevant statistical and theoretical concepts, as well as a comprehensive glossary of terms.
A highly readable, logically presented, unique guide to asset allocation strategies and technical analysis, this work covers numerous investment alternatives including mutual funds and fixed income securities. Aby and Vaughn provide a comprehensive examination of point and figure charting and vertical bar analysis, combined with an approach that both improves timing and emphasizes the minimization of errors in data interpretation and investment decision making. The authors discuss ways to estimate price targets and provide unique forecasting methods for fixed-income and aggregate equity markets, using an intermarket perspective. This is an important and useful resource for professionals and other knowledgeable investors. Throughout the book, Aby and Vaughn challenge conventional and acceptable academic thinking. Through emphasis on smaller, more obscure capitalization issues, they reduce complex concepts to a highly readable framework pervaded by comprehensive coverages of a large number of investment options. Major topics featured include the illustration and application of critical concepts underlying vertical bar chart analysis; extensive coverage on contemporary strategies that improve timing and challenge past criticisms of point and figure charting; a unique approach utilizing the point and figure charts to reveal how mutual fund selection can be improved; and intermarket technical analysis, a method through which movements in bond prices and yields are predicted.
Alternative Investments: A Primer for Investment Professionals provides an overview of alternative investments for institutional asset allocators and other overseers of portfolios containing both traditional and alternative assets. It is designed for those with substantial experience regarding traditional investments in stocks and bonds but limited familiarity regarding alternative assets, alternative strategies, and alternative portfolio management. The primer categorizes alternative assets into four groups: hedge funds, real assets, private equity, and structured products/derivatives. Real assets include vacant land, farmland, timber, infrastructure, intellectual property, commodities, and private real estate. For each group, the primer provides essential information about the characteristics, challenges, and purposes of these institutional-quality alternative assets in the context of a well-diversified institutional portfolio. Other topics addressed by this primer include tail risk, due diligence of the investment process and operations, measurement and management of risks and returns, setting return expectations, and portfolio construction. The primer concludes with a chapter on the case for investing in alternatives.
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
How to prevent runs on open-end mutual funds? In recent years, markets have observed an innovation that changed the way open-end funds are priced. Alternative pricing rules (known as swing pricing) adjust funds’ net asset values to pass on funds’ trading costs to transacting shareholders. Using unique data on investor transactions in U.K. corporate bond funds, we show that swing pricing eliminates the first-mover advantage arising from the traditional pricing rule and significantly reduces redemptions during stress periods. The positive impact of alternative pricing rules on fund flows reverses in calm periods when costs associated with higher tracking error dominate the pricing effect.
This monograph collects in one place the basic definitions, a careful description of the model, and discussion of how convex optimization can be used in multi-period trading, all in a common notation and framework.
Financial Risk Measurement is a challenging task, because both the types of risk and the techniques evolve very quickly. This book collects a number of novel contributions to the measurement of financial risk, which address either non-fully explored risks or risk takers, and does so in a wide variety of empirical contexts.