Multi-period portfolio selection with drawdown control
Nystrup, Peter ; Boyd, Stephen ; Lindström, Erik LU
and Madsen, Henrik
(2019)
In Annals of Operations Research
282(1-2).
p.245-271
- Abstract
In this article, model predictive control is used to dynamically optimize an investment portfolio and control drawdowns. The 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. There are computational advantages to using model predictive control when estimates of future returns are updated every time new observations become available, because the optimal control actions are reconsidered anyway. Transaction and holding costs are discussed as a means to address estimation error and regularize the optimization problem. The proposed approach to multi-period portfolio selection is tested out of sample over two decades based on... (More)
In this article, model predictive control is used to dynamically optimize an investment portfolio and control drawdowns. The 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. There are computational advantages to using model predictive control when estimates of future returns are updated every time new observations become available, because the optimal control actions are reconsidered anyway. Transaction and holding costs are discussed as a means to address estimation error and regularize the optimization problem. The proposed approach to multi-period portfolio selection is tested out of sample over two decades based on available market indices chosen to mimic the major liquid asset classes typically considered by institutional investors. By adjusting the risk aversion based on realized drawdown, it successfully controls drawdowns with little or no sacrifice of mean–variance efficiency. Using leverage it is possible to further increase the return without increasing the maximum drawdown.
(Less)
- author
- Nystrup, Peter
; Boyd, Stephen
; Lindström, Erik
LU
and Madsen, Henrik
- organization
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Dynamic asset allocation, Forecasting, Maximum drawdown, Model predictive control, Regime switching, Risk management
- in
- Annals of Operations Research
- volume
- 282
- issue
- 1-2
- pages
- 245 - 271
- publisher
- Springer
- external identifiers
-
- scopus:85048775051
- ISSN
- 0254-5330
- DOI
- 10.1007/s10479-018-2947-3
- language
- English
- LU publication?
- yes
- id
- 052378df-0788-4e4b-9ba0-4ffe561a69ed
- date added to LUP
- 2018-07-05 11:57:28
- date last changed
- 2023-09-14 15:10:38
@article{052378df-0788-4e4b-9ba0-4ffe561a69ed,
abstract = {{<p>In this article, model predictive control is used to dynamically optimize an investment portfolio and control drawdowns. The 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. There are computational advantages to using model predictive control when estimates of future returns are updated every time new observations become available, because the optimal control actions are reconsidered anyway. Transaction and holding costs are discussed as a means to address estimation error and regularize the optimization problem. The proposed approach to multi-period portfolio selection is tested out of sample over two decades based on available market indices chosen to mimic the major liquid asset classes typically considered by institutional investors. By adjusting the risk aversion based on realized drawdown, it successfully controls drawdowns with little or no sacrifice of mean–variance efficiency. Using leverage it is possible to further increase the return without increasing the maximum drawdown.</p>}},
author = {{Nystrup, Peter and Boyd, Stephen and Lindström, Erik and Madsen, Henrik}},
issn = {{0254-5330}},
keywords = {{Dynamic asset allocation; Forecasting; Maximum drawdown; Model predictive control; Regime switching; Risk management}},
language = {{eng}},
number = {{1-2}},
pages = {{245--271}},
publisher = {{Springer}},
series = {{Annals of Operations Research}},
title = {{Multi-period portfolio selection with drawdown control}},
url = {{http://dx.doi.org/10.1007/s10479-018-2947-3}},
doi = {{10.1007/s10479-018-2947-3}},
volume = {{282}},
year = {{2019}},
}