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Books on the topic 'Mean-Variance Portfolio'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Agarwal, Megha. Developments in Mean-Variance Efficient Portfolio Selection. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137359926.

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2

Developments in mean-variance efficient portfolio selection. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan, 2015.

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3

Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. Oxford, OX, UK: B. Blackwell, 1987.

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Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. Oxford: Basil Blackwell, 1987.

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Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. Oxford: Blackwell, 1990.

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6

Markowitz, H. Mean-variance analysis in portfolio choice and capital markets. New Hope: Frank J. Fabozzi Associates, 1987.

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7

O'Gorman, Aongus J. Mean-risk analysis: An examination of semivariance as an alternative to the traditional risk measure of variance. Dublin: University College Dublin, 1994.

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8

Back, Kerry E. Mean-Variance Analysis. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0005.

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The mean‐variance frontier is characterized with and without a risk‐free asset. The global minimum variance portfolio and tangency portfolio are defined, and two‐fund spanning is explained. The frontier is characterized in terms of the return defined from the SDF that is in the span of the assets. This is related to the Hansen‐Jagannathan bound. There is an SDF that is an affine function of a return if and only if the return is on the mean‐variance frontier. Separating distributions are defined and shown to imply two‐fund separation and mean‐variance efficiency of the market portfolio.

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9

Agarwal, M. Developments in Mean-Variance Efficient Portfolio Selection. Palgrave Macmillan Limited, 2015.

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10

Agarwal, M. Developments in Mean-Variance Efficient Portfolio Selection. Palgrave Macmillan Limited, 2014.

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11

Markowitz, H. Mean-Variance Analysis in Portfolio Choice and Capital Markets. Blackwell Pub, 1991.

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12

Markowitz, H. Mean-Variance Analysis in Portfolio Choice and Capital Markets. Wiley & Sons, Incorporated, John, 2008.

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13

Markowitz, H. Mean-Variance Analysis in Portfolio Choice and Capital Markets. Wiley, 2000.

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14

Back, Kerry E. Portfolio Choice. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0002.

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The portfolio choice model is introduced, and the first‐order condition is derived. Properties of the demand for a single risky asset are derived from second‐order risk aversion and decreasing absolute risk aversion. Optimal investments are independent of initial wealth for investors with constant absolute risk aversion. Optimal investments are affine functions of initial wealth for investors iwth linear risk tolerance. The optimal portfolio for an investor with constant absolute risk aversion is derived when asset returns are normally distributed. Investors with quadratic utility have mean‐variance preferences, and investors have mean‐variance preferences when returns are elliptically distributed.

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15

Milliken, Christopher, Ehsan Nikbakht, and Andrew Spieler. Traditional Asset Allocation Securities. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190269999.003.0020.

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Asset allocation models have evolved in complexity with the development of modern portfolio theory, but they continue to operate under the assumption of investor rationality and other assumptions that do not hold in the real world. For this reason, academics and industry professionals make efforts to understand the behavioral biases of decision makers and the implications these biases have on asset allocation strategies. This chapter reviews the building blocks of asset allocation, involving stocks, bonds, real estate, and cash. It also examines the history and theory behind two of the most popular portfolio management strategies: mean-variance optimization and the Black-Litterman Model. Finally, the chapter examines five common behavioral biases that have direct implications for asset allocation: familiarity, status quo, framing, mental accounting, and overconfidence. Each behavioral bias discussion contains examples, warning signs, and steps to correct the emotional or cognitive errors in decision making.

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16

Fernández-Villaverde, Jesús, Pablo Guerrón-Quintana, and Juan Rubio-Ramírez. Futures markets, Bayesian forecasting and risk modelling. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.14.

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This article demonstrates the utility of the Bayesian approach in forecasting and risk modelling regarding speculative trading strategies in financial futures markets. It first provides an overview of subjective expectations that are motivated as fair prices of futures contracts before discussing the futures markets and a portfolio mean-variance efficiency generalization. In particular, it considers the critical role of hedging to ensue attractive risk-adjusted performance. It also describes general Bayesian dynamic models and specific Bayesian dynamic linear models for assessing risk models in terms of their hedging effectiveness in the context of the risk-adjusted performance of trading strategies. The article showcases applied Bayesian thinking in the context of financial investment management, highlighting the corresponding concepts of betting and investing, prices and expectations, and coherence and arbitrage-free pricing in futures markets over the period 1990–2008.

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17

Back, Kerry E. Factor Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0006.

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The CAPM and factor models in general are explained. Factors can be replaced by the returns or excess returns that are maximally correlated (the projections of the factors). A factor model is equivalent to an affine representation of an SDF and to spanning a return on the mean‐variance frontier. The use of alphas for performance evaluation is explained. Statistical factor models are defined as models in which factors explain the covariance matrix of returns. A proof is given of the Arbitrage Pricing Theory, which states that statistical factors are approximate pricing factors. The CAPM and the Fama‐French‐Carhart model are evaluated relative to portfolios based on sorts on size, book‐to‐market, and momentum.

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