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 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.
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.
Original language | Swedish |
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Publication status | Published - 2017 Jun 28 |
Event | International Symposium on Forecasting, 2017 - Cairns, Australia Duration: 2017 Jun 25 → 2017 Jun 28 Conference number: 37 https://isf.forecasters.org |
Conference
Conference | International Symposium on Forecasting, 2017 |
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Country/Territory | Australia |
City | Cairns |
Period | 2017/06/25 → 2017/06/28 |
Internet address |
Subject classification (UKÄ)
- Probability Theory and Statistics