Comparing Forecasts of Extremely Large Conditional Covariance Matrices

Guilherme Moura (), Andre Santos () and Esther Ruiz ()

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: Modelling and forecasting high dimensional covariance matrices is a key challenge in data-richenvironments involving even thousands of time series since most of the available models sufferfrom the curse of dimensionality. In this paper, we challenge some popular multivariate GARCH(MGARCH) and Stochastic Volatility (MSV) models by fitting them to forecast the conditionalcovariance matrices of financial portfolios with dimension up to 1000 assets observed daily over a30-year time span. The time evolution of the conditional variances and covariances estimated bythe different models is compared and evaluated in the context of a portfolio selection exercise. Weconclude that, in a realistic context in which transaction costs are taken into account, modelling thecovariance matrices as latent Wishart processes delivers more stable optimal portfolio compositionsand, consequently, higher Sharpe ratios.

Keywords: Covariance; Forecasting; Garch; Minimum-Variance; Portfolio; Portfolio; Turnover; Risk-Adjusted; Return; Stochastic; Volatility (search for similar items in EconPapers)
JEL-codes: C53 G17 (search for similar items in EconPapers)
Date: 2019-11-30
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-ore and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:29291

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