Multi-Period Portfolio Selection with Drawdown Control
Peter, Nystrup ; Henrik, Madsen ; Boyd, Stephen and Lindström, Erik LU
(2017)
International Symposium on Forecasting, 2017
- Abstract
- In this talk, model predictive control (MPC) is used to dynamically optimize an investment portfolio. The
predictive control is based on multi-period forecasts of the mean and covariance of financial returns from a
multivariate hidden Markov model with time-varying parameters. Estimation and forecasting are done using an
online expectation--maximization algorithm. There are computational advantages to using MPC when estimates
of future returns are updated every time new observations become available, since the optimal control actions are
reconsidered anyway. Transaction and holding costs are important and are discussed as a means to address
estimation error and regularize the optimization problem. A complete... (More) - In this talk, model predictive control (MPC) is used to dynamically optimize an investment portfolio. The
predictive control is based on multi-period forecasts of the mean and covariance of financial returns from a
multivariate hidden Markov model with time-varying parameters. Estimation and forecasting are done using an
online expectation--maximization algorithm. There are computational advantages to using MPC when estimates
of future returns are updated every time new observations become available, since the optimal control actions are
reconsidered anyway. Transaction and holding costs are important and are discussed as a means to address
estimation error and regularize the optimization problem. A complete practical implementation is presented
based on available market indices chosen to mimic the major liquid asset classes typically considered by an
institutional investor. In an out-of-sample test spanning two decades, the proposed approach to multi-period
portfolio selection successfully controls drawdowns with little or no sacrifice of mean--variance efficiency. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1592abe6-9a91-44b6-81da-56bedeeae7f8
- author
- Peter, Nystrup
; Henrik, Madsen
; Boyd, Stephen
and Lindström, Erik
LU
- organization
- publishing date
- 2017-06-28
- type
- Contribution to conference
- publication status
- published
- subject
- conference name
- International Symposium on Forecasting, 2017
- conference location
- Cairns, Australia
- conference dates
- 2017-06-25 - 2017-06-28
- language
- Swedish
- LU publication?
- yes
- id
- 1592abe6-9a91-44b6-81da-56bedeeae7f8
- date added to LUP
- 2017-10-26 16:25:02
- date last changed
- 2021-03-22 17:25:28
@misc{1592abe6-9a91-44b6-81da-56bedeeae7f8,
abstract = {{In this talk, model predictive control (MPC) is used to dynamically optimize an investment portfolio. The<br/>predictive control is based on multi-period forecasts of the mean and covariance of financial returns from a<br/>multivariate hidden Markov model with time-varying parameters. Estimation and forecasting are done using an<br/>online expectation--maximization algorithm. There are computational advantages to using MPC when estimates<br/>of future returns are updated every time new observations become available, since the optimal control actions are<br/>reconsidered anyway. Transaction and holding costs are important and are discussed as a means to address<br/>estimation error and regularize the optimization problem. A complete practical implementation is presented<br/>based on available market indices chosen to mimic the major liquid asset classes typically considered by an<br/>institutional investor. In an out-of-sample test spanning two decades, the proposed approach to multi-period<br/>portfolio selection successfully controls drawdowns with little or no sacrifice of mean--variance efficiency.}},
author = {{Peter, Nystrup and Henrik, Madsen and Boyd, Stephen and Lindström, Erik}},
language = {{swe}},
month = {{06}},
title = {{Multi-Period Portfolio Selection with Drawdown Control}},
year = {{2017}},
}