Asset Allocation Under Multivariate Regime Switching

By Massimo Guidolin, Allan G. Timmermann. 2005

439 citations recorded by the source corpus.

This paper studies asset allocation decisions in the presence of regime switching in asset returns. We find evidence that four separate regimes - characterized as crash, slow growth, bull and recovery states - are required to capture the joint distribution of stock and bond returns. Optimal asset allocations vary considerably across these states and change over time as investors revise their estimates of the state probabilities. In the crash state, buy-and-hold investors allocate more of their portfolio to stocks the longer their investment horizon, while the optimal allocation to stocks declines as a function of the investment horizon in bull markets. The joint effects of learning about state probabilities and predictability of asset returns from the dividend yield give rise to a non-monotonic relationship between the investment horizon and the demand for stocks. Welfare costs from ignoring regime switching can be substantial even after accounting for parameter uncertainty. Out-of-sample forecasting experiments confirm the economic importance of accounting for the presence of regimes in asset returns. ; Earlier title: Strategic asset allocation and consumption decisions under multivariate regime switching

Subfields: Accounting, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/c3dcf0a7bc0a48942364c614502cb65b09fb5cdd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

In Economics and Econometrics

In Finance

Citations in the corpus

Regime Changes and Financial Markets (2012, background): “Guidolin and Timmermann (2007) show that these can have signiﬁcant effects on long-term hedging demands.”
Advances in Forecasting Under Instability (2012, positive): “Finally, Guidolin and Timmermann (2007) use Markov-switching models to account for the presence of regimes in asset returns and show that they forecast well out-of-sample.”
Learning in Financial Markets (2009, background): “Guidolin and Timmermann (2007) study asset allocation when regime-shifts affect not only the mean but the whole return distribution.”
Ambiguity in asset pricing and portfolio choice: a review of the literature (2010, neutral): “̂ = 1  £ (2) ( − 1) + ̂ ( − 1) + ̂ √  ¤  (103)”
Likelihood-based scoring rules for comparing density forecasts in tails (2011, background): “…the probability integral transform (PIT) of 1 In addition, density forecasts are starting to be used in other financial decision problems, such as derivative pricing (Campbell and Diebold, 2005; Taylor and Buizza, 2006; Härdle and Hlávka, 2009) and asset allocation (Guidolin and Timmermann, 2007).”
Real-Time Price Discovery in Global Stock, Bond and Foreign Exchange Markets (2006, positive): “The VIX may in turn be interpreted as a state variable related to the level of economic uncertainty and the stage of the business cycle in the sense of David and Veronesi (2004) and Ribeiro and Veronesi (2002). See also the recent related empirical results in Guidolin and Timmermann (2005, 2006).”
Time-varying short-horizon predictability ☆ (2008, neutral): “while Guidolin and Timmermann (2007) and Perez-Quiros and Timmermann (2000) rely on a maximum likelihood framework implemented through the expectation-maximization (EM) algorithm, we adopt a Bayesian approach.”