Parameter Uncertainty in Asset Allocation

Campbell R. Harvey, John C. Liechty, Merrill W. Liechty

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

This article reviews the simulation competition and the set of utility functions with the equivalent resampled efficient frontier approach and also discusses the modification of the specification of the Bayesian investor. The Bayes investor finds the weights, which maximize the expected utility with respect to the predictive moments for each history, while the Michaud investor finds the weights using the resampling scheme. The Bayes investor uses a utility function based on predictive returns and the Michaud investor uses a utility function based on parameter estimates. The Markov Chain Monte Carlo (MCMC) algorithm generates samples from the predictive density and uses the draws to approximate the expected utility integral. The Importance Sampling scheme generates draws from an alternative density and reweights these draws in order to approximate the integral with respect to the predictive density. An important difference between the implementation of the MCMC algorithm and the implementation of the Importance Sampler has to do with the number of samples that are used. The resampling approach maximizes and then averages instead of maximizing an average. There are three components that are considered for both the approaches that include the generation of random parameters, the optimization framework used to determine an optimal set of investment weights, and the investment scenario used to determine how well the resulting weights perform.

Original languageEnglish (US)
Title of host publicationThe Oxford Handbook of Quantitative Asset Management
PublisherOxford University Press
ISBN (Electronic)9780191744082
ISBN (Print)9780199553433
DOIs
StatePublished - Dec 15 2011

Fingerprint

Asset allocation
Parameter uncertainty
Investors
Utility function
Resampling
Integral
Predictive density
Expected utility
Markov chain Monte Carlo
Scenarios
Random parameters
Simulation
Importance sampling
Efficient frontier

All Science Journal Classification (ASJC) codes

  • Economics, Econometrics and Finance(all)

Cite this

Harvey, C. R., Liechty, J. C., & Liechty, M. W. (2011). Parameter Uncertainty in Asset Allocation. In The Oxford Handbook of Quantitative Asset Management Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199553433.013.0013
Harvey, Campbell R. ; Liechty, John C. ; Liechty, Merrill W. / Parameter Uncertainty in Asset Allocation. The Oxford Handbook of Quantitative Asset Management. Oxford University Press, 2011.
@inbook{4004a27ab52844efa62c91eb5d1238db,
title = "Parameter Uncertainty in Asset Allocation",
abstract = "This article reviews the simulation competition and the set of utility functions with the equivalent resampled efficient frontier approach and also discusses the modification of the specification of the Bayesian investor. The Bayes investor finds the weights, which maximize the expected utility with respect to the predictive moments for each history, while the Michaud investor finds the weights using the resampling scheme. The Bayes investor uses a utility function based on predictive returns and the Michaud investor uses a utility function based on parameter estimates. The Markov Chain Monte Carlo (MCMC) algorithm generates samples from the predictive density and uses the draws to approximate the expected utility integral. The Importance Sampling scheme generates draws from an alternative density and reweights these draws in order to approximate the integral with respect to the predictive density. An important difference between the implementation of the MCMC algorithm and the implementation of the Importance Sampler has to do with the number of samples that are used. The resampling approach maximizes and then averages instead of maximizing an average. There are three components that are considered for both the approaches that include the generation of random parameters, the optimization framework used to determine an optimal set of investment weights, and the investment scenario used to determine how well the resulting weights perform.",
author = "Harvey, {Campbell R.} and Liechty, {John C.} and Liechty, {Merrill W.}",
year = "2011",
month = "12",
day = "15",
doi = "10.1093/oxfordhb/9780199553433.013.0013",
language = "English (US)",
isbn = "9780199553433",
booktitle = "The Oxford Handbook of Quantitative Asset Management",
publisher = "Oxford University Press",
address = "United Kingdom",

}

Harvey, CR, Liechty, JC & Liechty, MW 2011, Parameter Uncertainty in Asset Allocation. in The Oxford Handbook of Quantitative Asset Management. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199553433.013.0013

Parameter Uncertainty in Asset Allocation. / Harvey, Campbell R.; Liechty, John C.; Liechty, Merrill W.

The Oxford Handbook of Quantitative Asset Management. Oxford University Press, 2011.

Research output: Chapter in Book/Report/Conference proceedingChapter

TY - CHAP

T1 - Parameter Uncertainty in Asset Allocation

AU - Harvey, Campbell R.

AU - Liechty, John C.

AU - Liechty, Merrill W.

PY - 2011/12/15

Y1 - 2011/12/15

N2 - This article reviews the simulation competition and the set of utility functions with the equivalent resampled efficient frontier approach and also discusses the modification of the specification of the Bayesian investor. The Bayes investor finds the weights, which maximize the expected utility with respect to the predictive moments for each history, while the Michaud investor finds the weights using the resampling scheme. The Bayes investor uses a utility function based on predictive returns and the Michaud investor uses a utility function based on parameter estimates. The Markov Chain Monte Carlo (MCMC) algorithm generates samples from the predictive density and uses the draws to approximate the expected utility integral. The Importance Sampling scheme generates draws from an alternative density and reweights these draws in order to approximate the integral with respect to the predictive density. An important difference between the implementation of the MCMC algorithm and the implementation of the Importance Sampler has to do with the number of samples that are used. The resampling approach maximizes and then averages instead of maximizing an average. There are three components that are considered for both the approaches that include the generation of random parameters, the optimization framework used to determine an optimal set of investment weights, and the investment scenario used to determine how well the resulting weights perform.

AB - This article reviews the simulation competition and the set of utility functions with the equivalent resampled efficient frontier approach and also discusses the modification of the specification of the Bayesian investor. The Bayes investor finds the weights, which maximize the expected utility with respect to the predictive moments for each history, while the Michaud investor finds the weights using the resampling scheme. The Bayes investor uses a utility function based on predictive returns and the Michaud investor uses a utility function based on parameter estimates. The Markov Chain Monte Carlo (MCMC) algorithm generates samples from the predictive density and uses the draws to approximate the expected utility integral. The Importance Sampling scheme generates draws from an alternative density and reweights these draws in order to approximate the integral with respect to the predictive density. An important difference between the implementation of the MCMC algorithm and the implementation of the Importance Sampler has to do with the number of samples that are used. The resampling approach maximizes and then averages instead of maximizing an average. There are three components that are considered for both the approaches that include the generation of random parameters, the optimization framework used to determine an optimal set of investment weights, and the investment scenario used to determine how well the resulting weights perform.

UR - http://www.scopus.com/inward/record.url?scp=84924308213&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84924308213&partnerID=8YFLogxK

U2 - 10.1093/oxfordhb/9780199553433.013.0013

DO - 10.1093/oxfordhb/9780199553433.013.0013

M3 - Chapter

AN - SCOPUS:84924308213

SN - 9780199553433

BT - The Oxford Handbook of Quantitative Asset Management

PB - Oxford University Press

ER -

Harvey CR, Liechty JC, Liechty MW. Parameter Uncertainty in Asset Allocation. In The Oxford Handbook of Quantitative Asset Management. Oxford University Press. 2011 https://doi.org/10.1093/oxfordhb/9780199553433.013.0013