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Portfolio Construction and Risk Budgeting (5th Edition)- Product Image
Portfolio Construction and Risk Budgeting (5th Edition)- Product Image

Portfolio Construction and Risk Budgeting (5th Edition)

  • ID: 3163800
  • Book
  • April 2015
  • 581 Pages
  • Risk Books
Completely updated and extended to cover the rapid expansion of the literature since the financial crises, this new edition of Portfolio Construction and Risk Budgeting provides the reader with a clear overview of the subject. The author presents quantitative methods and comprehensive and up-to-date coverage of alternative portfolio construction techniques, ranging from traditional methods based on mean– variance and lower-partial moments approaches, through Bayesian techniques, to more recent developments such as portfolio re-sampling and stochastic programming solutions using scenario optimisation.

Chapters feature:

- Application in Mean–Variance Investing
- Incorporating Deviations from Normality
- Portfolio Resampling and Estimation Error
- Robust Portfolio Optimisation and Estimation Error
- Bayesian Analysis and Portfolio Choice

This new edition is highly recommended for practitioners including portfolio managers, consultants, strategists, marketers and quantitative analysts.
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Introduction Acknowledgements
1 A Primer on Portfolio Theory
1.1 Mean–variance-based portfolio construction
1.2 How well does mean–variance investing work?
1.3 Analysis of implied risk and return
1.4 Risk budgeting versus portfolio optimisation
1.5 The unconditional covariance matrix and its properties

Appendix A: relative magnitude of estimation errors
Appendix B: introducing constraints
Appendix C: factor risk contributions Exercises

2 Application in Mean–Variance Investing
2.1 Clustering techniques and the investment universe
2.2 Illiquid assets: correcting for autocorrelation
2.3 Covariance in good and bad times
2.4 The conditional covariance matrix
2.5 Case study: introducing outside wealth
Appendix A: one-step versus two-step optimisation Exercise

3 Diversification
3.1 Diversification revisited
3.2 Equal weighting
3.3 Inverse risk scaling (naive risk parity)
3.4 Minimum variance
3.5 Full risk parity (equal risk contribution)
3.6 Frictional diversification costs
3.7 Most diversified portfolio
3.8 Cap-weighted portfolio
3.9 The Achilles heel of diversification-based portfolio construction: universe selection Exercises

4 Frictional Costs of Diversification
4.1 Hedge funds and frictional diversification costs
4.2 Optimal diversi?cation revisited
4.3 Case study 1: the optimal number of commodity trading advisors in a mean–variance framework

4.4 Case study 2: the optimal number of commodity trading advisors in a contingent claims framework
4.5 Conclusion Appendix A: state price deflators for performance measurement

Appendix B: calibration of a state price deflator
Appendix C: frictional diversi?cation costs and optimal number of assets with return information and mean–variance preferences


5 Risk Parity
5.1 A practical review of risk parity
5.2 Factor risk parity
5.3 Tail risk parity
5.4 The bond problem
5.5 Risk parity and portfolio construction
5.6 Risk parity and asset pricing
5.7 Empirical evidence
5.8 Conclusion Exercises

6 Incorporating Deviations from Normality: Lower Partial Moments
6.1 Non-normality in return data
6.2 Lower partial moments
6.3 A comparison of lower-partial-moments-based and variance-based methods in portfolio choice
6.4 Summary Appendix A: integration techniques for calculating lower partial moments Exercises

7 Portfolio Resampling and Estimation Error
7.1 Visualising estimation error: portfolio resampling
7.2 Errors in means and covariances
7.3 Resampled efficiency
7.4 Distance measures
7.5 Portfolio resampling and linear regression
7.6 Pitfalls of portfolio resampling
7.7 Constrained portfolio optimisation
7.8 Conclusion

Appendix A: linear regression and characteristic portfolios
Appendix B: rank-associated versus lambda-associated portfolios Exercises

8 Robust Portfolio Optimisation and Estimation Error
8.1 Introduction
8.2 The Tüntücü and König (2004) approach
8.3 Box constraints
8.4 A more general objective function
8.5 Deriving optimal portfolio weights
8.6 How well is uncertainty aversion rooted in decision theory?
8.7 How different is robust optimisation relative to existing methods?
8.8 Regularisation constraints
8.9 Conclusions Exercises

9 Bayesian Analysis and Portfolio Choice
9.1 An introduction to Bayesian analysis
9.2 A simple univariate case
9.3 A general multivariate case
9.4 Special case: Black–Litterman
9.5 Uncertainty about the Capital Asset Pricing Model as an equilibrium model
9.6 Introducing factor and alpha bets in the B–L model
9.7 The use of hierarchical priors in manager allocation
9.8 Time series of different lengths

Appendix A: derivation of univariate example
Appendix B: estimation error and volatility forecasts Exercises

10 Testing Portfolio Construction Methodologies Out-of-Sample
10.1 Introduction
10.2 Resampled efficiency
10.3 Competing methodologies and model set-up
10.4 Out-of-sample results: unconstrained optimisation
10.5 Out-of-sample results: constrained optimisation
10.6 Summary Exercise

11 Portfolio Construction with Transaction Costs
11.1 Transaction costs
11.2 Turnover constraints
11.3 Trading constraints
11.4 Proportional transaction costs
11.5 Piecewise linear transaction costs
11.6 Fixed transaction costs
11.7 Multiple accounts with piecewise linear transaction
11.8 Rebalancing problem
11.9 Trade scheduling
11.10 Capacity and trading costs Exercises

12 Portfolio Optimisation with Options: From the Static Replication of CPPI Strategies to a More General Framework
12.1 Introduction
12.2 The tracking problem
12.3 Deriving the Q and P measures
12.4 Numerical application
12.5 Extensions to utility optimisation
12.6 Summary Exercise

13 Scenario Optimisation
13.1 Utility-based scenario optimisation
13.2 Scenario generation
13.3 Mean–absolute deviation
13.4 Minimum regret
13.5 Conditional value-at-risk
13.6 Parametric portfolio choice
13.7 Conclusion

Appendix A: data set for scenario optimisation
Appendix B: scenario optimisation with S-plus Exercises

14 Core–Satellite Investing: Budgeting Active Manager Risk
14.1 Mathematics of multiple manager allocation: two-manager case
14.2 Why multiple managers?
14.3 Why core–satellite?
14.4 Core–satellite: how much should be active?
14.5 Where to be active?
14.6 Risk allocation versus manager allocation
14.7 Limitations of core–satellite investing
14.8 Input generation
14.9 Allocating between alpha and beta bets
14.10 Conclusions

Appendix A: multiple manager mathematics
Appendix B: multiple manager allocation and correlation structure Exercises

15 Benchmark-Relative Optimisation
15.1 Tracking error: selected issues
15.2 Tracking error efficiency versus mean–variance efficiency
15.3 Benchmark-relative portfolio construction: practical issues
15.4 Dual-benchmark optimisation
15.5 Tracking error and its funding assumptions
15.6 Trading bands for tactical asset allocation

Appendix A: global Fixed-income policy model Exercise

16 Removing Long-Only Constraints: 120/20 Investing
16.1 Investment constraints
16.2 Measuring the impact of constraints: the transfer coef?cient
16.3 Some constraints make sense
16.4 A more mathematical treatment of constraints
16.5 Constraints, alpha and value added
16.6 The mechanics of 120/20 investing
16.7 Conclusion

17 Performance-Based Fees, Incentives and Dynamic Tracking Error Choice
17.1 Review of the single-stage incentive-fee model
17.2 Multi-period setting and the portfolio manager’s optimisation problem
17.3 Scenario tree generation and the optimisation algorithm
17.4 Dynamic decision-making under various fee schedules
17.5 Conclusions

18 Long-Term Portfolio Choice
18.1 Long-term portfolio choice under independent and identically distributed return assumptions: two fallacies
18.2 Predictability and the term structure of risk
18.3 Bayesian estimates of the return-generating process
18.4 Multi-period portfolio choice
18.5 Developments in dynamic portfolio choice

19 Risk Management for Asset-Management Companies
19.1 Fees-at-risk
19.2 Current orthodoxy: why did asset managers fail to hedge their P&L?
19.3 New orthodoxy: why should asset managers hedge their P&L?
19.4 Case study: revenue sensitivities of T. Rowe Price
19.5 What to hedge?
19.6 How to hedge?
19.7 An empirical analysis of the asset-management industry
19.8 How to hedge the risk of fund outflows?

Appendix A: approximate distribution of asset-based management fees
Appendix B: simulation study
Appendix C: market beta for asset-based fees

20 Valuation of Asset Management Firms
20.1 Economics of asset management: performance and capital formation
20.2 Discounted cashflow models
20.3 Explicit and implicit options
20.4 Monte Carlo valuation models
20.5 Comparable transactions

21 Tail Risk Hedging
21.1 Portfolio insurance
21.2 Target volatility
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Bernd Scherer

Bernd Scherer is Chief Scientific Officer for First Private Asset Management. During his 21 years career he worked in senior positions for various hedge funds, asset management companies and banks in Frankfurt, London, New York and Vienna as well as Professor of Finance for EDHEC business school. His academic work has been published in Journals like the Journal of Banking and Finance, Journal of Financial Markets, Journal of Economics and Statistics, Quantitative Finance, Journal of Derivatives, Journal of Portfolio Management, Financial Analysts Journal, Journal of Investment Management, Risk, Financial Markets and Portfolio Management, Journal of Asset Management etc.. Bernd is author/editor of 8 books on quantitative asset management. He holds MBA and MSc degrees from the University of Augsburg and the University of London, as well as a PhD in finance from the University of Giessen.
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