Regime-Based Asset Allocation - Do Profitable Strategies Exist? | Peter Nystrup
| | Abstract | Regime shifts present a big challenge to traditional strategic asset allocation, demanding a more adaptive approach. In the presence of time-varying investment opportunities, portfolio weights should be adjusted as new information arrives. Regime-switching models can match the tendency of financial markets to change their behavior abruptly and the phenomenon that the new behavior often persists for several periods after a change. They are well suited to capture the stylized behavior of many financial series including skewness, leptokurtosis, volatility persistence, and time-varying correlations.
This thesis builds on this empirical evidence to develop a quantitative framework for regime-based asset allocation. It investigates whether regime-based investing can effectively respond to changes in financial regimes at the portfolio level in an effort to provide better long-term results when compared to more static approaches. The thesis extends previous work by considering both discrete-time and continuous-time models, models with different numbers of states, different univariate and multivariate state-dependent distributions, and different sojourn time distributions. Out-of-sample success depends on developing a way to model the non-linear and non-stationary behavior of asset returns.
Dynamic asset allocation strategies are shown to add value over strategies based on rebalancing to static weights with rebalancing in itself adding value compared to buy-and-hold strategies in an asset universe consisting of a global stock index, a global government bond index, and a commodity index. The tested strategies based on an adaptively estimated two-state Gaussian hidden Markov model outperform a rebalancing strategy out of sample after accounting for transaction costs, assuming no knowledge of future returns, and with a realistic delay between the identification of a regime change and the portfolio adjustment. | | Type | Master's thesis [Academic thesis] | | Year | 2014 | | Publisher | Technical University of Denmark, Department of Applied Mathematics and Computer Science | | Address | Richard Petersens Plads, Building 324, DK-2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk | | Series | DTU Compute M.Sc.-2014 | | Note | DTU supervisor: Henrik Madsen, hmad@dtu.dk, DTU Compute | | Electronic version(s) | [pdf] | | Publication link | http://www.compute.dtu.dk/English.aspx | | BibTeX data | [bibtex] | | IMM Group(s) | Mathematical Statistics |
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