| 1. |
- Nystrup, Peter, et al.
(author)
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Detecting change points in VIX and S&P 500: A new approach to dynamic asset allocation
- 2016
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In: Journal of Asset Management. - : Springer Science and Business Media LLC. - 1470-8272 .- 1479-179X. ; 17:5, s. 361-374
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Journal article (peer-reviewed)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.
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| 2. |
- Nystrup, Peter, et al.
(author)
-
Dynamic Portfolio Optimization Across Hidden Market Regimes
- 2016
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Conference paper (peer-reviewed)abstract
- Regime-based asset allocation has been shown to add value over rebalancing to static weights and, in particular, reduce potential drawdowns by reacting to changes in market conditions. The predominant approach in previous studies has been to specify in advance a static decision rule for changing the allocation based on the state of financial markets or the economy. This talk proposes the use of model predictive control to dynamically optimize a portfolio based on forecasts of the mean and variance of financial returns from a hidden Markov model with time-varying parameters. There are computational advantages to using model predictive control when estimates of future returns are updated repeatedly, since the optimal control actions are reconsidered anyway every time a new observation becomes available. Results from testing the approach on market data are presented and compared with previous, rule-based approaches. Further, imposing a trading penalty that reduces the number of trades is discussed as a way to increase the robustness of the approach.
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| 3. |
- Peter, Nystrup, et al.
(author)
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Practical Applications Summary: Regime-Based versus Static Asset Allocation: Letting the Data Speak
- 2016
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In: Journal of Portfolio Management. - : Pageant Media US. - 0095-4918. ; 42:1, s. 103-109
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Journal article (peer-reviewed)abstract
- Regime shifts present a big challenge to traditional strategic asset allocation. This article investigates whether regimebased asset allocation can effectively respond to changes in financial regimes at the portfolio level, in an effort to provide better long-term results than more static approaches can offer. The authors center their regime-based approach around a regime-switching model with time-varying parameters that can match financial markets’ tendency to change behavior abruptly and the fact that the new behavior often persists for several periods after a change. In an asset universe consisting of a global stock index and a global government bond index, they show that, even without any level of forecasting skill, holding a static portfolio may not be optimal.
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