Asset Allocation under Multivariate Regime Switching

Guidolin, Massimo; Timmerman, Allan

doi:10.20955/wp.2005.002

Cited by 83 publications

(124 citation statements)

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“…Yet the model has limitations and risk estimates may increase by extending the class of models. For example, Guidolin and Timmermann (2007) and Pettenuzzo and Timmermann (2011) breaks and the risk of future structural breaks, while Pastor and Stambaugh (2012) warn against misspecification of the conditional mean. Avramov (2002) and Cremers (2002) suggest alternative additional prediction variables.…”

Section: Discussionmentioning

confidence: 99%

“…Again, one would need a form of informative priors to arrive at plausible estimates of the long-run volatility. 14 See Figure 6 in Guidolin and Timmermann (2007). We studied the impact of parameter uncertainty on long-term risk and asset allocation of long-term investors who can invest in stocks, bonds and T-bills.…”

Section: Discussionmentioning

confidence: 99%

“…Xia, 2001;Dangl and Halling, 2012;Johannes et al, 2012), structural breaks and regimes shifts (e.g. Pesaran and Timmermann, 2002;Guidolin and Timmermann, 2007), alternative predictors (e.g. Cremers, 2002) and dynamic misspecification (Pastor and Stambaugh, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Strategic Asset Allocation for Long-Term Investors: Parameter Uncertainty and Prior Information

Hoevenaars¹,

Molenaar²,

Schotman

et al. 2011

SSRN Journal

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SUMMARYWe study the effect of parameter uncertainty on the long-run risk for three asset classes: stocks, bills and bonds. Using a Bayesian vector autoregression with an uninformative prior we find that parameter uncertainty raises the annualized long-run volatilities of all three asset classes proportionally with the same factor relative to volatilities that are conditional on maximum likelihood parameter estimates. As a result, the horizon effect in optimal asset allocations is much weaker compared to models in which only equity returns are subject to parameter uncertainty. Results are sensitive to alternative informative priors, but generally the term structure of risk for stocks and bonds is relatively flat for investment horizons up to 15 years.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Strategic Asset Allocation for Long-Term Investors: Parameter Uncertainty and Prior Information

Hoevenaars¹,

Molenaar²,

Schotman

et al. 2011

SSRN Journal

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“…We therefore decide that a 2‐state HMM is sufficient to capture the characteristics of our data set and flexible enough to switch between economic regimes with a reasonably low number of parameters. Guidolin and Timmermann 13 make an econometric argument for using four regimes. They investigate the evolution of asset prices and optimal asset allocations and conclude that there are four separate regimes, namely the crash state, slow growth state, bull state and recovery state.…”

Section: Forecast Of Indicesmentioning

confidence: 99%

An examination of HMM-based investment strategies for asset allocation

Erlwein

Mamon

Davison

2009

Appl. Stochastic Models Bus. Ind.

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We develop and analyse investment strategies relying on hidden Markov model approaches. In particular, we use filtering techniques to aid an investor in his decision to allocate all of his investment fund to either growth or value stocks at a given time. As this allows the investor to switch between growth and value stocks, we call this first strategy a switching investment strategy. This switching strategy is compared with the strategies of purely investing in growth or value stocks by tracking the quarterly terminal wealth of a hypothetical portfolio for each strategy. Using the data sets on Russell 3000 growth index and Russell 3000 value index compiled by Russell Investment Services for the period 1995-2008, we find that the overall risk-adjusted performance of the switching strategy is better than that of solely investing in either one of the indices. We also consider a second strategy referred to as a mixed investment strategy which enables the investor to allocate an optimal proportion of his investment between growth and value stocks given a level of risk aversion. Numerical demonstrations are provided using the same data sets on Russell 3000 growth and value indices. The switching investment strategy yields the best or second best Sharpe ratio as compared with those obtained from the pure index strategies and mixed strategy in 14 intervals. The performance of the mixed investment strategy under the HMM setting is also compared with that of the classical mean-variance approach. To make the comparison valid, we choose the same level of risk aversion for each set-up. Our findings show that the mixed investment strategy within the HMM framework gives higher Sharpe ratios in 5 intervals of the time series than that given by the standard mean-variance approach. The calculated weights through time from the strategy incorporating the HMM set-up are more stable. A simulation analysis further shows a higher performance stability of the HMM strategies compared with the pure strategies and the mean-variance strategy

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“…For example, Engle and Manganelli (2004) analyze the conditional value-at-risk by quantile regression, Chuang et al (2009) investigate the causal relations between stock return and trading volume based on quantile regressions, Chiang and Li (2012) use quantile regression to analyze the risk-return relation over the entire stock return distribution, Baur et al (2012) and Cai et al (2013) use quantile autoregression to study the dependence pattern of stock returns and De Rezende and Ferreira (2013) use quantile regression in yield curve forecasting. E .r t C1 j x t / Dˇx t 4 The need to conduct multivariate modeling has spurred the use of copulas (see, for example, Patton, 2004) and regime-switching models using mixtures of multivariate normal distributions (see, for example, Ang and Bekaert, 2002;Guidolin and Timmermann, 2007). This paper complements the literature on multivariate modeling by introducing a nonparametric alternative to the existing methods.…”

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confidence: 95%

Predictable Return Distributions

Pedersen

2015

Journal of Forecasting

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Using quantile regression this paper explores the predictability of the stock and bond return distributions as a function of economic state variables. The use of quantile regression allows us to examine specific parts of the return distribution such as the tails and the center, and for a sufficiently fine grid of quantiles we can trace out the entire distribution. A univariate quantile regression model is used to examine the marginal stock and bond return distributions, while a multivariate model is used to capture their joint distribution. An empirical analysis on US data shows that economic state variables predict the stock and bond return distributions in quite different ways in terms of, for example, location shifts, volatility and skewness. Comparing the different economic state variables in terms of their out-of-sample forecasting performance, the empirical analysis also shows that the relative accuracy of the state variables varies across the return distribution. Density forecasts based on an assumed normal distribution with forecasted mean and variance is compared to forecasts based on quantile estimates and, in general, the latter yields the best performance. Copyright

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Asset Allocation under Multivariate Regime Switching

Cited by 83 publications

References 1 publication

Strategic Asset Allocation for Long-Term Investors: Parameter Uncertainty and Prior Information

Strategic Asset Allocation for Long-Term Investors: Parameter Uncertainty and Prior Information

An examination of HMM-based investment strategies for asset allocation

Predictable Return Distributions

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