Testing and Valuing Dynamic Correlations for Asset Allocation

Engle, Robert F.; Colacito, Riccardo

doi:10.1198/073500106000000017

Cited by 265 publications

(183 citation statements)

References 16 publications

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“…This is an example of the methodology introduced by Engle and Colacito(2006). The optimal portfolio of two stocks with equal expected return, is to choose the minimum variance combination.…”

Section: Hedging Experimentsmentioning

confidence: 99%

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High Dimension Dynamic Correlations

Engle¹

2009

The Methodology and Practice of Econometrics

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This paper develops time series methods for forecasting correlations in high dimensional problems. The Dynamic Conditional Correlation model is given a new convenient estimation approach called the MacGyver method. It is compared with the FACTOR ARCH model and a new model called the FACTOR DOUBLE ARCH model. Finally the latter model is blended with the DCC to give a FACTOR DCC model. This family of models is estimated with daily returns from 18 US large cap stocks. Economic loss functions designed to form optimal portfolios and optimal hedges are used to compare the performance of the methods. The best approach invariably is the FACTOR DCC and the next best is the FACTOR DOUBLE ARCH.

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Section: Hedging Experimentsmentioning

confidence: 99%

“…Equation (8) only has two parameters no matter how big the system is. The assumption (7) is called "correlation targeting" and is an estimator of the omega parameters that is different from maximum likelihood.…”

Section: Dynamic Conditional Correlationmentioning

confidence: 99%

High Dimension Dynamic Correlations

Engle¹

2009

The Methodology and Practice of Econometrics

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“…We forecast H t and rebalance the portfolio at daily, weekly (5 days) two-weekly (10 days) and monthly (20 days) frequencies, using the A-DCC models described above, testing to see if the impact of volatility spillover tapers off over longer rebalancing horizons. Engle and Colacito (2004) propose a solution to the problem of forecasting expected returns. Expected return estimation errors are not only usually large, but also amplified in the mean-variance optimization process, causing poor out-of-sample portfolio performance.…”

Section: H T W Tmentioning

confidence: 99%

“…Engle and Colacito (2004) show that, for a given required rate of return µ 0 , the portfolio with the smallest realized standard deviation will be a portfolio constructed from the true covariance matrix. We infer that a covariance forecasting model that is closer to the underlying data generating process (DGP) will predict better than other models, and generate lower portfolio risk.…”

Section: Performance Measurementmentioning

confidence: 99%

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Valuing volatility spillovers

Milunovich

Thorp

2006

Global Finance Journal

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We show that volatility spillovers are large enough to matter to investors. We demonstrate that standard deviations of returns to mean-variance portfolios of European equities fall by 1-1.5% at daily, weekly, and monthly rebalancing horizons when volatility spillovers are included in covariance forecasts. We estimate the conditional second moment matrix of (synchronized) daily index returns for the London, Frankfurt and Paris stock markets via two asymmetric dynamic conditional correlation models (A-DCC): the unrestricted model includes volatility spillovers and the restricted model does not. We combine covariance forecasts from the restricted and unrestricted models with a wide range of assumed returns relatives via a polar coordinates method, and compute out-of-sample realized portfolio returns and variances for testing. Diebold-Mariano tests confirm that most risk reductions are statistically significant. Stochastic dominance tests indicate that portfolios accounting for volatility spillover would be preferred by risk averse agents.

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Multivariate Volatility Estimation with High Frequency Data Using Fourier Method

Mancino

Sanfelici

2011

Handbook of Modeling High‐Frequency Data in Finance

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Testing and Valuing Dynamic Correlations for Asset Allocation

Cited by 265 publications

References 16 publications

High Dimension Dynamic Correlations

High Dimension Dynamic Correlations

Valuing volatility spillovers

Multivariate Volatility Estimation with High Frequency Data Using Fourier Method

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