Optimal portfolios

Chapter 5  Portfolio risk and return (Part I). In this chapter, we will explore the process of examining the risk and return characteristics of individual assets, creating all possible portfolios, selecting the most efficient portfolios, and ultimately choosing the optimal portfolio tailored to the individual in question.
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In Chapter 7 we discussed the capital allocation decision. That decision governs how an investor chooses between riskfree assets and “the” optimal portfolio of risky assets. This chapter explains how to construct that optimal risky portfolio. We begin with a discussion of how diversification can reduce the variability of portfolio returns. After establishing this basic point, we examine efficient diversification strategies at the asset allocation and security selection levels.
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Single period market models are the most elementary market models. Only a single period is considered. The beginning of the period is usually denoted by the time t = 0 and the end of the period by time t = 1. At time t = 0 stock prices, bond prices,possibly prices of other financial assets or specific financial values are recorded and the financial agent can choose his investment, often a portfolio of stocks and bond. At time t = 1 prices are recorded again and the financial agent obtains a payoff corresponding to the value of his portfolio at time t = 1....
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Chapter 6  Portfolio risk and return (Part II). The topics discussed in this chapter are: Portfolio risk and return, optimal risky portfolio and the capital market line (CML), returngenerating models and the market model, systematic and nonsystematic risk, capital asset pricing model (CAPM) and the security market line (SML), performance measures, arbitrage pricing theory (APT) and factor models.
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Strategic Corporate Finance provides a ‘‘realworld’’ application of the principles of modern corporate finance, with a practical, investment banking advisory perspective. Building on 15 years of corporate finance advisory experience, this book serves to bridge the chronic gap between corporate finance theory and practice. Topics range from weighted average cost of capital, valuebased management and M&A, to optimal capital structure, risk management and dividend/buyback policy.
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The favorable reception of Portfolio Management Formulas exceeded even the greatest expectation I ever had for the book. I had written it to promote the concept of optimal f and begin to immerse readers in portfolio theory and its missing relationship with optimal f. Besides finding friends out there, Portfolio Management Formulas was surprisingly met by quite an appetite for the math concerning money management. Hence this book. I am indebted to Karl Weber, Wendy Grau, and others at John Wiley & Sons who allowed me the necessary latitude this book required....
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Strategic Corporate Finance provides a ‘‘realworld’’ application of the principles of modern corporate finance, with a practical, investment banking advisory perspective. Building on 15 years of corporate finance advisory experience, this book serves to bridge the chronic gap between corporate finance theory and practice. Topics range from weighted average cost of capital, valuebased management and M&A, to optimal capital structure, risk management and dividend/buyback policy.
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This book presents and develops major numerical methods currently used for solving problems arising in quantitative finance. Our presentation splits into two parts. Part I is methodological, and offers a comprehensive toolkit on numerical methods and algorithms. This includes Monte Carlo simulation, numerical schemes for partial differential equations, stochastic optimization in discrete time, copula functions, transformbased methods and quadrature techniques. Part II is practical, and features a number of selfcontained cases.
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In everyday life we are often forced to make decisions involving risks and perceived opportunities. The consequences of our decisions are affected by the outcomes of random variables that are to various degrees beyond our control. Such decision problems arise, for instance, in financial and insurance markets.
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Chris Adcock is Professor of Financial Econometrics in the University of Sheffield. His career includes several years working in quantitative investment management in the City and, prior to that, a decade in management science consultancy. His research interests are in the development of robust and nonstandard methods for modelling expected returns, portfolio selection methods and the properties of optimized portfolios. He has acted as an advisor to a number of asset management firms. He is the founding editor of the European Journal of Finance. George A.
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If successful, this book will change your idea about what an optimal investment portfolio is. It is intended to be a guide both to understanding irrational investor behavior and to creating individual investors’ portfolios that account for these irrational behaviors. In this book, an optimal portfolio lies on the efficient frontier, but it may move up or down that frontier depending on the individual needs and preferences of each investor.
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Evaluating mutual fund performance is a topic of longstanding interest in the academic literature, but few if any studies have addressed the selection of an optimal portfolio of funds. Instead of using the historical data to estimate performance measures or produce fund rank ings, this study uses the data to explore the mutualfund investment decision.
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Asset allocation investigates the optimal division of a portfolio among different asset classes. Standard theory involves the optimal mix of risky stocks, bonds, and cash together with various subdivisions of these asset classes. Underlying this is the insight that diversification allows for achieving a balance between risk and return: by using different types of investment, losses may be limited and returns are made less volatile without losing too much potential gain.
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Interest income is the most important source of revenue for most of the banks. The aim of this paper is to assess the impact of dierent interest rate scenarios on the banks' interest income. As we do not know the interest rate sensitivity of real banks, we construct for each bank a portfolio with a similar composition of its assets and liabilities, called 'tracking bank'. We evaluate the eect of 260 historical interest rate shocks on the tracking banks of German savings banks and cooperative banks.
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In recent years, the credit derivatives market has become extremely active. Especially credit default swaps (CDSs) and collateralized debt obligations (CDOs) have contributed to what has been an amazing development. The most important benefit of credit derivatives is their ability to transfer the credit risk of an arbitrary number of obligors in a simple, efficient, and standardized way, giving rise to a liquid market for credit risk that can be easily accessed by many market participants.
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Though some pension funds – mostly larger, more sophisticated investors  are able to invest at the riskier end of the spectrum (i.e. in startup, venture capital type projects focusing on clean tech and other innovations), this will only ever constitute a small percentage of their portfolios. The broad mass of pension funds will be more interested in lower risk investments (i.e. in deployable renewables etc.
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Next, we examine predictability in both benchmark returns and fund risk loadings. Consider the dogmatist who believes in such a predictability structure (PD2). This investor would experience a nontrivial utility loss of 15.1 basis points per month (1.8%/ year) in December 2002 if forced to hold the optimal portfolio of the ND. The utility loss is even larger over the course of all 276 monthly investments. This loss averages 21.1 (39) basis points per month over expansions (recessions).
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Indeed, in the presence of predictability in fund risk loadings and benchmark returns, optimal portfolios consist entirely of actively managed funds even when the possibility of manager skills in stock selection and benchmark timing is ruled out. That is, actively managed funds allow the investor to capitalize on predictability in benchmarks and fund risk loadings in a way that cannot be achieved through longonly index fund positions. We now turn to analyze predictability in manager skills.
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We also demonstrate that optimal portfolios of mutual funds are in°uenced substantially by prior beliefs about both managerial skill and pricing models. For example, consider two investors who both rule out managerial skill but believe strongly in di®erent models: one believes in the CAPM while the other embraces a fourfactor model. If either investor is forced to hold the portfolio chosen by the other, the resulting ex ante loss is about 60 basis points per month in certainty equivalent return.
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We construct optimal portfolios of equity funds by combining historical returns on funds and passive indexes with prior views about asset pricing and skill. By including both benchmark and nonbenchmark indexes, we distinguish pricingmodel inaccuracy from managerial skill. Even modest con¯dence in a pricing model helps construct portfolios with high Sharpe ratios. Investing in active mutual funds can be optimal even for investors who believe active managers cannot outperform passive indexes. Optimal portfolios exclude hothand funds even for investors who believe momentum is priced.
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