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A non-elliptical orthogonal GARCH model for portfolio selection under transaction costs

Author

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  • Paolella, Marc S.
  • Polak, Paweł
  • Walker, Patrick S.
Abstract
Covariance matrix forecasts for portfolio optimization have to balance sensitivity to new data points with stability in order to avoid excessive rebalancing. To achieve this, a new orthogonal GARCH model for a multivariate set of non-Gaussian asset returns is proposed. The conditional return distribution is multivariate generalized hyperbolic and the dispersion matrix dynamics are driven by the leading factors in a principal component decomposition. Each of these leading factors is endowed with a univariate GARCH structure, while the remaining eigenvalues are kept constant over time. Joint maximum likelihood estimation of all model parameters is performed via an expectation maximization algorithm, and is applicable in high dimensions. The new model generates realistic correlation forecasts even for large asset universes and captures rising pairwise correlations in periods of market distress better than numerous competing models. When applied to portfolio optimization, it generates strategies with lower turnover and maximum drawdown, and superior risk-adjusted returns net of transaction costs. Moreover, unlike its competitors, it performs well in the sudden market downturn triggered by the global COVID-19 pandemic.

Suggested Citation

  • Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2021. "A non-elliptical orthogonal GARCH model for portfolio selection under transaction costs," Journal of Banking & Finance, Elsevier, vol. 125(C).
    • Handle: RePEc:eee:jbfina:v:125:y:2021:i:c:s0378426621000042
    DOI: 10.1016/j.jbankfin.2021.106046
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    1. Weichuan Deng & Pawel Polak & Abolfazl Safikhani & Ronakdilip Shah, 2023. "A Unified Framework for Fast Large-Scale Portfolio Optimization," Papers 2303.12751, arXiv.org, revised Nov 2023.

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    More about this item

    Keywords

    COVID-19; Dynamic conditional correlations; Multivariate GARCH; Generalized hyperbolic distribution; Principal component analysis; Portfolio optimization;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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