Portfolio Construction and Risk Budgeting
First published:
ISBN: 9781782721000
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Bayesian Analysis and Portfolio Choice
Introduction
A Primer on Portfolio Theory
Application in Mean–Variance Investing
Diversification
Frictional Costs of Diversification
Risk Parity
Incorporating Deviations from Normality: Lower Partial Moments
Portfolio Resampling and Estimation Error
Robust Portfolio Optimisation and Estimation Error
Bayesian Analysis and Portfolio Choice
Testing Portfolio Construction Methodologies Out-of-Sample
Portfolio Construction with Transaction Costs
Portfolio Optimisation with Options: From the Static Replication of CPPI Strategies to a More General Framework
Scenario Optimisation
Core–Satellite Investing: Budgeting Active Manager Risk
Benchmark-Relative Optimisation
Removing Long-Only Constraints: 120/20 Investing
Performance-Based Fees, Incentives and Dynamic Tracking Error Choice
Long-Term Portfolio Choice
Risk Management for Asset-Management Companies
Valuation of Asset Management Firms
Tail Risk Hedging
9.1 AN INTRODUCTION TO BAYESIAN ANALYSIS
9.1.1 Theoretical foundations
We have seen in the previous chapter that confining ourselves solely to the information available within a sample will not allow us to tackle the effect of parameter uncertainty on optimal portfolio choice. Not only do we need non-sample information (eg, additional data) to overcome this problem, but it would also be irrational of us to ignore other information based on the experience or insights – also called priors or preknowledge – of investors, statisticians and financial economists. The optimal combination of sample and non-sample information is found in Bayesian statistics. As Nobel laureate Harry Markowitz put it, “the rational investor is a Bayesian” (Markowitz 1987, p. 57).
To appreciate the implications of Bayesian statistics for portfolio choice we first need to understand the main differences between the Bayesian approach to statistics and the traditional, or “frequentist”, approach. The traditional approach creates point estimates for distributional parameters. Estimates are either significant and believed to be 100% true, or insignificant and not believed at all, depending on the researcher’s
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