Abstract
The purpose of dynamic asset allocation (DAA) is to overcome the challenge that changing market conditions present to traditional strategic asset allocation by adjusting portfolio weights to take advantage of favorable conditions and reduce potential drawdowns. This article proposes a new approach to DAA that is based on detection of change points without fitting a model with a fixed number of regimes to the data, without estimating any parameters and without assuming a specific distribution of the data. It is examined whether DAA is most profitable when based on changes in the Chicago Board Options Exchange Volatility Index or change points detected in daily returns of the S&P 500 index. In an asset universe consisting of the S&P 500 index and cash, it is shown that a dynamic strategy based on detected change points significantly improves the Sharpe ratio and reduces the drawdown risk when compared with a static, fixed-weight benchmark.
Original language | English |
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Pages (from-to) | 361-374 |
Journal | Journal of Asset Management |
Volume | 17 |
Issue number | 5 |
Early online date | 2016 Apr 21 |
DOIs | |
Publication status | Published - 2016 Sept |
Subject classification (UKÄ)
- Probability Theory and Statistics
Free keywords
- regime changes
- change point detection
- dynamic asset allocation
- volatility regimes
- daily returns
- non-parametric statistics