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Books on the topic 'Optimal portfolio model'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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1

Cao, Bing-Yuan. Optimal Models and Methods with Fuzzy Quantities. Springer-Verlag Berlin Heidelberg, 2010.

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2

Boyle, Phelim P. Optimal portfolio selection with transaction costs. University of Toronto, Dept. of Statistics, 1994.

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3

Jurek, Jakub W. Optimal value and growth tilts in long-horizon portfolios. National Bureau of Economic Research, 2006.

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4

Elsinger, Helmut. Arbitrage and optimal portfolio choice with financial constraints. Oesterreichische Nationalbank, 2001.

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5

Guidolin, Massimo. Optimal portfolio choice under regime switching, skew and kurtosis preferences. Federal Reserve Bank of St. Louis, 2005.

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6

Aiyer, Ajay Subramanian. Optimal portfolio selection with fixed transaction costs in the presence of jumps and random drift. Cornell Theory Center, Cornell University, 1996.

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7

Sercu, Piet. The optimal number of contracts in cross- or delta-hedges. City University of Hong Kong, Department of Economics and Finance, 1997.

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8

Viciera, Luis M. Optimal portfolio choice for long-horizon investors with nontradable labor income. National Bureau of Economic Research, 1999.

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9

McDonnell, Philip J. Optimal portfolio modeling: Models to maximize return and control risk in Excel and R + CD-ROM. Wiley, 2008.

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10

Rüschendorf, Ludger. Mathematical Risk Analysis: Dependence, Risk Bounds, Optimal Allocations and Portfolios. Springer Berlin Heidelberg, 2013.

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11

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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12

Back, Kerry E. Dynamic Portfolio Choice. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0009.

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The first‐order condition for optimal portfolio choice is called the Euler equation. Optimal consumption can be computed by a static approach in a dynamic complete market and by orthogonal projection for a quadratic utility investor. Dynamic programming and the Bellman equation are explained. The envelope condition and hedging demands are explained. Investors with CRRA utility have CRRA value functions. Whether the marginal value of wealth is higher for a CRRA investor in good states or in bad states depends on whether risk aversion is less than or greater than 1. With IID returns, the optimal portfolio for a CRRA investor is the same as the optimal portfolio in a single‐period model.

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13

Cao, Bing-Yuan. Optimal Models and Methods with Fuzzy Quantities. Springer Berlin / Heidelberg, 2012.

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14

Cao, Bing-Yuan. Optimal Models and Methods with Fuzzy Quantities. Springer, 2010.

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15

Back, Kerry E. Continuous-Time Topics. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0015.

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The fundamental PDE for valuing cash flows or cash flow streams is explained. In a complete market, an investor’s optimal wealth satisfies the fundamental PDE, and this provides a means of calculating the optimal portfolio. Risk neutral probabilities and Girsanov’s theorem are explained. Jump processes, including Poisson processes, are introduced. The risk premium of an asset with jump risks depends on covariation of its continuous part with the continuous part of an SDF and the covariation of its discontinuous part with the discontinuous part of an SDF. Portfolio choice with internal habits is characterized. The ability of a representative investor model with an internal habit to explain the equity premium puzzle is discussed.

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16

Optimal Portfolios: Stochastic Models for Optimal Investment and Risk Management in Continuous Time. World Scientific Publishing Co Pte Ltd, 1997.

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17

Optimal portfolios: Stochastic models for optimal investment and risk management in continuous time. World Scientific, 1997.

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18

Kraft, Holger. Optimal Portfolios with Stochastic Interest Rates and Defaultable Assets. Springer London, Limited, 2012.

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19

Optimal Portfolios with Stochastic Interest Rates and Defaultable Assets. Island Press, 2004.

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20

Optimal Portfolios with Stochastic Interest Rates and Defaultable Assets. Springer, 2004.

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21

McDonnell, Philip. Optimal Portfolio Modeling: Models to Maximize Returns and Control Risk in Excel and R. Wiley & Sons, Limited, John, 2015.

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22

McDonnell, Philip. Optimal Portfolio Modeling: Models to Maximize Returns and Control Risk in Excel and R. Wiley & Sons, Incorporated, John, 2008.

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23

McDonnell, Philip. Optimal Portfolio Modeling: Models to Maximize Returns and Control Risk in Excel and R. Wiley & Sons, Incorporated, John, 2010.

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24

Optimal Risk-Return Trade-Offs of Commercial Banks: And the Suitability of Profitability Measures for Loan Portfolios (Lecture Notes in Economics and Mathematical Systems). Springer, 2006.

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25

Rüschendorf, Ludger. Mathematical Risk Analysis: Dependence, Risk Bounds, Optimal Allocations and Portfolios. Springer Berlin / Heidelberg, 2015.

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26

Rüschendorf, Ludger. Mathematical Risk Analysis: Dependence, Risk Bounds, Optimal Allocations and Portfolios. Springer, 2013.

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27

Rüschendorf, Ludger. Mathematical Risk Analysis: Dependence, Risk Bounds, Optimal Allocations and Portfolios. Springer, 2013.

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28

Optimal Portfolio Modeling: Models to Maximize Returns and Control Risk in Excel and R + CD (Wiley Trading). Wiley, 2008.

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29

Golan, Amos. Foundations of Info-Metrics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.001.0001.

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This book provides a framework for info-metrics—the science of modeling, inference, and reasoning under conditions of noisy and insufficient information. Info-metrics is an inherently interdisciplinary framework that emerged from the intersection of information theory, statistical inference, and decision-making under uncertainty. It allows us to process the available information with minimal reliance on assumptions that cannot be validated. This book focuses on unifying all information processing and model building within a single constrained optimization framework. It provides a complete framework for modeling and inference, rather than a problem-specific model. The framework evolves from the simple premise that our available information is often insufficient to provide a unique answer for decisions we wish to make. Each decision, or solution, is derived from the available input information along with a choice of inferential procedure. The book contains many multidisciplinary applications that demonstrate the simplicity and generality of the framework in real-world settings: These include initial diagnosis at an emergency room, optimal dose decisions, election forecasting, network and information aggregation, weather pattern analyses, portfolio allocation, inference of strategic behavior, incorporation of prior information, option pricing, and modeling an interacting social system. This book presents simple derivations of the key results that are necessary to understand and apply the fundamental concepts to a variety of problems. Derivations are often supported by graphical illustrations. The book is designed to be accessible for graduate students, researchers, and practitioners across the disciplines, requiring only basic quantitative skills and a little persistence.

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